Phasor Diagram

image-20251105222421572

"I" is the in-phase or real axis and "Q" is the quadrature or imaginary axis

  • phasor rotating counter-clockwise represents the upper sideband (USB) \(e^{j(\omega_o+\omega_m )t} = e^{j\omega_o t}\color{red}e^{j\omega_m t}\)

  • phasor rotating clockwise represents the lower sideband (LSB) \(e^{j(\omega_o-\omega_m )t} = e^{j\omega_o t}\color{red}e^{-j\omega_m t}\)

AM modulation

image-20251105224322467 \[\begin{align} x(t)&= (1+2k\cos(\omega_m t)) \cos(\omega_0 t) \\ & = \cos(\omega_0 t) + 2k \cos(\omega_m t) \cos(\omega_0 t) \\ &=\mathcal{Re}\{e^{j\omega_0t}+k(e^{j\omega_0t}e^{j\omega_mt}+e^{j\omega_0t}e^{-j\omega_mt})\}\\ &=\mathcal{Re}\{e^{j\omega_0t}(\color{red}1+k(e^{j\omega_mt}+e^{-j\omega_mt})\color{black})\} \end{align}\]

image-20251105230141244

PM modulation with incidental AM

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A. A. Abidi and D. Murphy, "How to Design a Differential CMOS LC Oscillator," in IEEE Open Journal of the Solid-State Circuits Society, vol. 5, pp. 45-59, 2025, doi: 10.1109/OJSSCS.2024 [pdf]

image-20251011000448718

Real & Complex Modulation

J OHN M. C IOFFI. Complex AWGN and Other Channels [https://cioffi-group.stanford.edu/ee379a/Lectures/L4.pdf]

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signal Spectra
\(x_{bb}(t) = x_I(t)+jx_Q(t)\) \(X_{I}(\omega)+jX_Q(\omega)\)
\(x(t)=\color{red}x_I(t)\cdot\cos(\omega_c t)\color{blue}-x_Q(t)\cdot\sin(\omega_c t)\) \(\color{red}\frac{1}{2}X_{I}(\omega+\omega_c)+\color{blue}\frac{1}{2}jX_{Q}(\omega+\omega_c)+\color{red}\frac{1}{2}X_{I}(\omega-\omega_c)+\color{blue}\frac{1}{2}jX_{Q}(\omega-\omega_c)\)
\(x_A=x_{bb}(t)\cdot e^{j\omega_c t}\) \(X_{I}(\omega-\omega_c)+jX_Q(\omega-\omega_c)\)

image-20251123230522862


Matt Guibord. TIPL 4: Real and Complex Modulation [https://www.ti.com/content/dam/videos/external-videos/en-us/2/3816841626001/5576277660001.mp4/subassets/TIPL4708-Real-and-Complex-Modulation.pdf]

image-20251116225706934 \[\begin{align} X_{bb,AM,USB}(t) &= e^{j\omega_m t} = \cos(\omega_m t) + j\sin(\omega_m t)\\ X_{bb,AM,LSB}(t) &= e^{-j\omega_m t} = \cos(\omega_m t) - j\sin(\omega_m t) \end{align}\]

where \(X_{I,AM,USB}(t)=\cos(\omega_m t)\) and \(X_{Q,AM,USB}(t)=\sin(\omega_m t)\); \(X_{I,AM,USB}(t)=\cos(\omega_m t)\) and \(X_{Q,AM,USB}(t)=-\sin(\omega_m t)\);

\(X_{I,AM,USB} = X_{I,AM,LSB}\) and \(X_{Q,AM,USB} = -X_{Q,AM,LSB}\)

\[\begin{align} X_{bb,PM,USB}(t) &= e^{j\omega_m t} = \cos(\omega_m t) + j\sin(\omega_m t)\\ X_{bb,PM,LSB}(t) &= -e^{-j\omega_m t} = -\cos(\omega_m t) + j\sin(\omega_m t) \end{align}\]

where \(X_{I,PM,USB}(t)=\cos(\omega_m t)\) and \(X_{Q,PM,USB}(t)=\sin(\omega_m t)\); \(X_{I,PM,USB}(t)=-\cos(\omega_m t)\) and \(X_{Q,PM,USB}(t)=\sin(\omega_m t)\);

\(X_{I,PM,USB} = -X_{I,PM,LSB}\) and \(X_{Q,PM,USB} = X_{Q,PM,LSB}\)

image-20251116232032760


image-20251116190237879

Amplitude Noise

Deog-Kyoon Jeong. Topics in IC Design: 1.1 Introduction to Jitter [https://ocw.snu.ac.kr/sites/default/files/NOTE/Lec%201%20-%20Jitter%20and%20Phase%20Noise.pdf]

image-20250821202257578

with \(x(t) = A_0\sin (2\pi f_0 t +\phi _0)\), then \(y(t) = x(t) + n_v(t)\)

\[\begin{align} R_y(\tau) &= \mathrm{E}[y(t)y(t+\tau)] \\ &= \mathrm{E}[x(t)x(t+\tau)] + \mathrm{E}[x(t)]\mathrm{E}[n_v(t+\tau)] + \mathrm{E}[x(t+\tau)]\mathrm{E}[n_v(t)] + \mathrm{E}[n_v(t)n_v(t+\tau)]\\ &= \mathrm{E}[x(t)x(t+\tau)] + \mathrm{E}[n_v(t)n_v(t+\tau)] \\ &= R_x(\tau) + R_{n_v}(\tau) \end{align}\]

AM & PM Sidebands

image-20241012001704081

The spectrum of the narrowband FM signal is very similar to that of an amplitude modulation (AM) signal but has the phase reversal for the other sideband component

Assume the modulation frequency of PM and AM are same, \(\omega_m\)

\[\begin{align} x(t) &= (1+A_m\cos{\omega_m t})\cos(\omega_0 t + P_m \sin\omega_m t) \\ &= \cos(\omega_0 t + P_m \sin\omega_m t) + A_m\cos{\omega_m t}\cos(\omega_0 t + P_m \sin\omega_m t) \\ &= X_{pm}(t) + X_{apm}(t) \end{align}\]

\(X_{pm}(t)\), PM Only \[ X_{pm}(t) = \cos\omega_0 t - \frac{P_m}{2}\cos(\omega_0 - \omega_m)t + \frac{P_m}{2}\cos(\omega_0 + \omega_m)t \] \(X_{apm}(t)\), AM & PM \[\begin{align} X_{apm}(t) &= A_m \cos{\omega_m t} (\cos\omega_0 t-P_m\sin\omega_m t\sin\omega_0 t) \\ &= \frac{A_m}{2}[\cos(\omega_0 + \omega_m)t + \cos(\omega_0 -\omega_m)t] - \frac{A_mP_m}{2}\sin(2\omega_m t)\sin(\omega_0 t) \\ &= \frac{A_m}{2}\cos(\omega_0 + \omega_m)t + \frac{A_m}{2}\cos(\omega_0 -\omega_m)t - \frac{A_mP_m}{4}\cos(\omega_0 - 2\omega_m)t + \frac{A_mP_m}{4}\cos(\omega_0 + 2\omega_m)t \end{align}\]

That is \[\begin{align} x(t) &= \cos\omega_0 t + \frac{A_m-P_m}{2}\cos(\omega_0 - \omega_m)t + \frac{A_m+P_m}{2}\cos(\omega_0 + \omega_m)t \\ &\space\space\space\space\space\space\space\space\space\space\space\space\space\space\space\space\space\space - \frac{A_mP_m}{4}\cos(\omega_0 - 2\omega_m)t + \frac{A_mP_m}{4}\cos(\omega_0 + 2\omega_m)t \end{align}\]

For general case, \(x(t) = (1+A_m\cos{\omega_{am} t})\cos(\omega_0 t + P_m \sin\omega_{pm} t)\), i.e., PM is \(\omega_{pm}\), AM is \(\omega_{am}\)

\[\begin{align} x(t) &= \cos\omega_0 t - \frac{P_m}{2}\cos(\omega_0 - \omega_{pm})t + \frac{P_m}{2}\cos(\omega_0 + \omega_{pm})t \\ &\space\space\space\space\space\space\space\space\space\space\space\space\space\space\space\space\space\space + \frac{A_m}{2}\cos(\omega_0 - \omega_{am})t + \frac{A_m}{2}\cos(\omega_0 + \omega_{am})t \\ &\space\space\space\space\space\space\space\space\space\space\space\space\space\space\space\space\space\space - \frac{A_mP_m}{4}\cos(\omega_0 - \omega_{pm}-\omega_{am})t + \frac{A_mP_m}{4}\cos(\omega_0 + \omega_{pm}+\omega_{am})t \\ &\space\space\space\space\space\space\space\space\space\space\space\space\space\space\space\space\space\space + \frac{A_mP_m}{4}\cos(\omega_0 + \omega_{pm}-\omega_{am})t - \frac{A_mP_m}{4}\cos(\omega_0 - \omega_{pm}+\omega_{am})t \end{align}\]

Therefore, sideband is asymmetric if \(\omega_{pm} = \omega_{am}\) same


Hegazi, Emad, Asad Abidi, and Jacob Rael. The Designer's Guide to High-purity Oscillators. [New York]: Kluwer Academic Publishers, 2005. [pdf]

image-20251105231301581

PSD of Narrowband FM Signal

Chembiyan T. Jitter and Phase Noise in Phase Locked Loops [link]

\[ y(t) = A\cos(2\pi f_0t+\phi_n(t)) \approx A \cos(2\pi f_0 t) - A \phi_n (t)\sin(2\pi f_0 t) \]

image-20241228020953646 \[ R_x(\tau) = \frac{A^2}{2}\cos(2\pi f_0\tau) + \frac{A^2}{2}R_\phi(\tau)\cos(2\pi f_0\tau) \] The PSD of the signal \(x(t)\) is given by \[ S_x(f) = \mathcal{F}\{R_x(\tau)\} = \frac{P_c}{2}\left[\delta(f+f_0)+\delta(f-f_0)\right]+\frac{P_c}{2}\left[S_\phi(f+f_0)+S_\phi(f-f_0)\right] \] where \(P_c = A^2/2\) is the carrier power of the signal

PM & FM

Dan Boschen What is the difference between phase noise and frequency noise? [https://dsp.stackexchange.com/a/38230/59253]

Phase Noise and Frequency Noise are not two different noise sources, they are artifacts of the same noise, it is just a matter of what units you want to use

image-20251106205014601


image-20251106001007275

Equipartition theorem

[https://www.ieeetoronto.ca/wp-content/uploads/2020/06/DL-VCO-short.pdf]

Enrico Rubiola. The Measurement of AM-PM Noise, and the Origin of Noise in Oscillators [https://rubiola.org/pdf-slides/2010T-ANL-Noise-and-oscillators.pdf]

image-20251106214759580

image-20251104233701576

Stationary noise can also be decomposed into AM and PM components, but there will always be equal amounts of both.

Narrowband Noise

image-20251116151034742

in-phase & quadrature component

image-20251116151212165

image-20251116150946685

\[\begin{align} n(t)\cdot 2\cos(\omega_c t) &= n_I(t) + n_I(t)\cos(2\omega_c t) - n_Q(t)\sin(2\omega_c t) \\ n(t)\cdot -2\sin(\omega_c t) &= n_Q(t) - n_I(t)\sin(2\omega_c t) - n_Q(t)\cos(2\omega_c t) \end{align}\]

image-20251116152134926

nI_nQ.drawio


[https://arnabiitk.wordpress.com/wp-content/uploads/2013/02/solution-manual-for-communication-systems-haykin.pdf]

image-20251116154806955

image-20251116161009110

If the narrowband noise \(n(t)\) is Gaussian and its power spectral density \(S_N (t )\) is symmetric about the mid-band frequency \(f_c\), then the in-phase component \(n_I(t)\) and quadrature component \(n_Q(t)\) are statistically independent

image-20251116170549277

image-20251116171255430

envelope and phase components

image-20251116174252304

timeaverage Pnoise simulation result

Ken Kundert. Re: Question about phase noise simulation result [https://designers-guide.org/forum/YaBB.pl?num=1309258199/15#15]

Spectre Circuit Simulator RF Analysis Theory — Measuring AM, PM and FM Conversion

image-20251104004721935


noise profile sidebands contribution
stationary uncorrelated \(S_{AM} = S_{PM}\)
cyclostationary correlated \(S_{AM} \gt S_{PM}\) or \(S_{AM} \lt S_{PM}\)

sine wave + white noise

image-20251111231609124

equal amounts of AM and PM noise in both USB and LSB

sine wave + AM

image-20251112002405912


肥肥牛是只虎. PSS+Pnoise仿真:基本设置 [https://mp.weixin.qq.com/s/etyQ2UkfisPkvbc44XFw4w]

image-20251104004017859


image-20251104010500397

AM & PM Noise Separation

Ken Kundert. Introduction to RF Simulation and its Application [https://designers-guide.org/analysis/rf-sim.pdf]

Separation.drawio

image-20251117235457764

image-20251118000021345

[https://designers-guide.org/analysis/am-pm-conv.pdf]

image-20251117233740882


image-20251118213331072

image-20251118213700865

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Single Sideband Modulation (SSB)

image-20251104003558243

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Asymmetrical Linear System

Golara, S. (2015). Identifying Mechanisms of AM-PM Distortion in Large Signal Amplifiers. UCLA [https://escholarship.org/uc/item/4jp786z8]

image-20251010234110656


Bob Nelson. Phase Noise 101: Basics, Applications and Measurements [[https://www.qsl.net/ab4oj/test/docs/20180720_KEE7_PhaseNoise.pdf])https://www.qsl.net/ab4oj/test/docs/20180720_KEE7_PhaseNoise.pdf]

image-20251104003200956

AN-PN Conversion

G. Giust, Influence of Noise Processes on Jitter and Phase Noise Measurements [https://www.signalintegrityjournal.com/articles/800-influence-of-noise-processes-on-jitter-and-phase-noise-measurements]

—. "Methodologies for PCIe5 Refclk Jitter Analysis,", PCI-SIG Electrical Workgroup Meeting (Jan. 19, 2018)

—. How to Identify the Source of Phase Jitter through Phase Noise Plots [https://www.sitime.com/company/newsroom/blog/how-identify-source-phase-jitter-through-phase-noise-plots]

AN10072 Determine the Dominant Source of Phase Noise, by Inspection [https://www.sitime.com/support/resource-library/application-notes/an10072-determine-dominant-source-phase-noise-inspection]

  • AM alone doesn't introduce jitter (e.g., doesn't change zero-crossings) nor impact phase

  • AM changes slew rate, and so influences the conversion of amplitude noise to jitter when amplitude noise (BB) is present

image-20250719122931298

Figure 8 thumb_rev

additive & parametric noise

Enrico Rubiola. The Measurement of AM-PM Noise, and the Origin of Noise in Oscillators [https://rubiola.org/pdf-slides/2010T-ANL-Noise-and-oscillators.pdf]

—, February 7, 2025. Phase Noise - Art, Science and Experimental Methods [https://rubiola.org/pdf-lectures/Scient-Instrum-Files/!-Phase-noise.pdf]

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Phase Noise Measurement

Phase Noise Measurement Solutions [https://www.keysight.com/vn/en/assets/7018-02528/technical-overviews/5990-5729.pdf]

Greg Bonaguide. Advances in Phase Noise Measurement Techniques [https://ieee.li/pdf/viewgraphs/advances_in_phase_noise_measurement_techniques.pdf]

The three most widely adopted techniques are direct spectrum, phase detector, and two-channel cross-correlation.

While the direct spectrum technique measures phase noise with the existence of the carrier signal, the other two remove the carrier (demodulation) before phase noise is measured.

Though direct spectrum technique method may not be useful for measuring very close-in phase noise to a drifting carrier, it is convenient for qualitative quick evaluation on sources with relatively high noise

image-20251105003812138

1
2
3
4
5
6
7
8
9
import numpy as np

# at 5KHz
n_pm = -140 # dBc
n_am = -142 # dBc

# calculate the total noise by PM + AM
n_tot = 10*np.log10(10**(n_pm / 10) + 10**(n_am / 10))
print(n_tot) # -137.8755739720566

image-20251105233926270

Modulation index in Virtuoso

image-20251111225036664

reference

Ken Kundert, Measuring AM, PM & FM Conversion with SpectreRF [https://designers-guide.org/analysis/am-pm-conv.pdf]


Dan Boschen. Creating uneven sidebands with AM + PM modulation? [https://dsp.stackexchange.com/a/61670/59253]

—. Creating uneven sidebands with AM + PM modulation? [https://dsp.stackexchange.com/a/61670/59253]

—. Qualitative Explanation of Fourier Transform [https://dsp.stackexchange.com/a/78911/59253]

Timing 201 #1: The Case of the Phase Noise That Wasn't - Part 1 [https://community.silabs.com/s/share/a5U1M000000knpiUAA/timing-201-1-the-case-of-the-phase-noise-that-wasnt-part-1?]


Noise in mixers, oscillators, samplers, and logic: an introduction to cyclostationary noise [https://designers-guide.org/theory/cyclo-preso.pdf], [https://designers-guide.org/theory/cyclo-paper.pdf]

Haykin, Simon S., and Michael Moher. Communication Systems. 5th ed. John Wiley & Sons, 2009.

Cycle Slips and Hangup

Qasim Chaudhari. What are Cycle Slips and Hangup in Phase Locked Loops? [https://wirelesspi.com/what-are-cycle-slips-and-hangup-in-phase-locked-loops/]

TODO 📅

OSPLL (Reference Oversampling PLL)

J. -H. Seol, K. Choo, D. Blaauw, D. Sylvester and T. Jang, "Reference Oversampling PLL Achieving −256-dB FoM and −78-dBc Reference Spur," in IEEE Journal of Solid-State Circuits, vol. 56, no. 10, pp. 2993-3007, Oct. 2021 [https://sci-hub.se/10.1109/JSSC.2021.3089930]

Taekwang Jang SSCS DL talk : Ultra-low noise Phase-locked Loop Technique

image-20251008190134196

K. J. Wang, A. Swaminathan and I. Galton, "Spurious Tone Suppression Techniques Applied to a Wide-Bandwidth 2.4 GHz Fractional-N PLL," in IEEE Journal of Solid-State Circuits, vol. 43, no. 12, pp. 2787-2797, Dec. 2008 [https://sci-hub.se/10.1109/JSSC.2008.2005716]

image-20251008190318146

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Frequency Divider

Gunnman, Kiran, and Mohammad Vahidfar. Selected Topics in RF, Analog and Mixed Signal Circuits and Systems. Aalborg: River Publishers, 2017

image-20240803225130324

Large values of N lowers the loop BW which is bad for jitter


MMD (Multimodulus Divider)

TODO 📅

Noise in dividers (jitter generation)

S. Levantino, L. Romano, S. Pellerano, C. Samori and A. L. Lacaita, "Phase noise in digital frequency dividers," in IEEE Journal of Solid-State Circuits, vol. 39, no. 5, pp. 775-784, May 2004 [https://sci-hub.se/10.1109/JSSC.2004.826338]

Lacaita, Andrea Leonardo, Salvatore Levantino, and Carlo Samori. Integrated frequency synthesizers for wireless systems. Cambridge University Press, 2007.

TODO 📅


Enrico Rubiola. Phase Noise and Jitter in Digital Electronics [https://rubiola.org/pdf-slides/2016T-EFTF--Noise-in-digital-electronics.pdf]

image-20250724072155205


W. F. Egan, "Modeling phase noise in frequency dividers," in IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 37, no. 4, pp. 307-315, July 1990 [https://sci-hub.se/10.1109/58.56498]

PLL + PSS + PNOISE convergence [https://community.cadence.com/cadence_technology_forums/f/custom-ic-design/48474/pll-pss-pnoise-convergence/1376833]


Signal Source Analyzer: measurement is based on time-average(or frequency-domain) method

real-time digital oscilloscope: measure sampled jitter directly

[https://community.cadence.com/cadence_technology_forums/f/custom-ic-design/41797/inconsistent-phase-noise-results-of-divide-by-2-phase-using-different-pnoise-method/1360890]

phase margin impact

type-I PLLs

image-20241222152826102

image-20241222152916367

frequency divider weakens the feedback and increases the phase margin


type-II PLLs

image-20241222153430163

frequency divider weakens the feedback and decrease the phase margin

DIV 1.5

Xu, Haojie & Luo, Bao & Jin, Gaofeng & Feng, Fei & Guo, Huanan & Gao, Xiang & Deo, Anupama. (2022). A Flexible 0.73-15.5 GHz Single LC VCO Clock Generator in 12 nm CMOS. IEEE Transactions on Circuits and Systems II: Express Briefs. 69. 4238 - 4242. [https://www.researchgate.net/publication/382240520_A_Flexible_073-155_GHz_Single_LC_VCO_Clock_Generator_in_12_nm_CMOS]

TODO 📅

phase noise & jitter

[Timing 101: The Case of the Jitterier Divided-Down Clock, Silicon Labs]

[How division impacts spurs, phase noise, and phase]

[Phase Noise Theory: Ideal Frequency Multipliers and Dividers]

image-20241013212542173

  • Multiplying the frequency of a signal by a factor of N using an ideal frequency multiplier increases the phase noise of the multiplied signal by \(20\log(N)\) dB.
  • Similarly dividing a signal frequency by \(N\) reduces the phase noise of the output signal by \(20\log(N)\) dB

The sideband offset from the carrier in the frequency multiplied/divided signal is the same as for the original signal.

\(20\log (N)\) Rule

If the carrier frequency of a clock is divided down by a factor of \(N\) then we expect the phase noise to decrease by \(20\log(N)\).The primary assumption here is a noiseless conventional digital divider.

The \(20\log(N)\) rule only applies to phase noise and not integrated phase noise or phase jitter. Phase jitter should generally measure about the same.

20log(N).png

What About Phase Jitter?

We integrate SSB phase noise L(f) [dBc/Hz] to obtain rms phase jitter in seconds as follows for “brick wall” integration from f1 to f2 offset frequencies in Hz and where f0 is the carrier or clock frequency.

phase jitter.png

Note that the rms phase jitter in seconds is inversely proportional to f0. When frequency is divided down, the phase noise, L(f), goes down by a factor of 20log(N). However, since the frequency goes down by N also, the phase jitter expressed in units of time is constant.

Therefore, phase noise curves, related by 20log(N), with the same phase noise shape over the jitter bandwidth, are expected to yield the same phase jitter in seconds.

excess phase around n-th harmonic

image-20250523222041505

\(\Delta t\) is same for any n-th harmonic

Spurious Tones

Spur-to-Carrier Ratio (SCR)

image-20250523222846691

Nicola Da Dalt, ISSCC 2012: Jitter Basic and Advanced Concepts, Statistics and Applications [https://www.nishanchettri.com/isscc-slides/2012%20ISSCC/TUTORIALS/ISSCC2012Visuals-T5.pdf]


image-20250529220609357

P.E. Allen - 2003 ECE 6440 - Frequency Synthesizers: Lecture 150 – Phase Noise-I [https://pallen.ece.gatech.edu/Academic/ECE_6440/Summer_2003/L150-PhaseNoise-I(2UP).pdf]

Reference Spur

spurs are carrier or clock frequency spectral imperfections measured in the frequency domain just like phase noise. However, unlike phase noise they are discrete frequency components.

  • Spurs are deterministic

  • Spur power is independent of bandwidth

  • Spurs contribute bounded peak jitter in the time domain

Sources of Spurs:

  • External (coupling from other noisy block) Supply, substrate, bond wires, etc.
  • Internal (int-N/fractional-N operation)
    • Frac spur: Fractional divider (multi-modulus and frequency accumulation)
    • Ref. spur: PFD/charge pump/analog loop filter non-idealities, clock coupling

Fractional Spur

TODO 📅

Integer-N PLL

integer-N PLL frequency synthesizers

  • the frequency resolution, is equal to the reference frequency, meaning that only integer multiples of the reference frequency can be synthesized

  • Stability requirements limit the loop bandwidth to about one tenth of the reference frequency; therefore

    • decreasing the reference frequency increases the settling time as the loop bandwidth also has to be decreased
    • a reduced loop bandwidth allows less suppression of the VCO’s inherent phase noise
  • Another drawback of the integer-N PLL is the trade-off between phase noise and settling time when the divider ratio becomes large (The contributions to the output phase noise of almost all PLL building blocks, except the VCO, are multiplied by the division ratio)

    [https://people.engr.tamu.edu/spalermo/ecen620/lecture03_ee620_pll_system.pdf]

    image-20250602100424369

  • if a small reference frequency is chosen, the reference spur in the output phase noise is located at a smaller offset frequency

Fractional-N

  1. Dither Feedback Divider Ratio by a delta-sigma modulator

image-20241003105023092

  1. Frequency Accumulation

image-20241003105059989

Switched Capacitor Banks

Q: why \(R_b\) ?

A: TODO 📅

image-20240901105919333

Hu, Yizhe. "Flicker noise upconversion and reduction mechanisms in RF/millimeter-wave oscillators for 5G communications." PhD diss., 2019.

S. D. Toso, A. Bevilacqua, A. Gerosa and A. Neviani, "A thorough analysis of the tank quality factor in LC oscillators with switched capacitor banks," Proceedings of 2010 IEEE International Symposium on Circuits and Systems, Paris, France, 2010, pp. 1903-1906

False locking

TODO 📅

  • divider failure
  • even-stage ring oscillator ( multipath ring oscillators)
  • DLL: harmonic locking, stuck locking

clock edge impact

clock2clock.drawio

ck1 is div2 of ck0

  • edge of ck0 is affected differently by ck1

  • edge of ck1 is affected equally by ck0

Tri-gate Clock MUX vs Pass-gate Clock MUX

TODO 📅

clk_mux.drawio

Why Type 2 PLL ?

Type: # of integrators within the loop

Order: # of poles in the closed-loop transfer function

Type \(\leq\) Order

  1. That is, to have a wide bandwidth, a high loop gain is required
  2. More importantly, the type 1 PLL has the problem of a static phase error for the change of an input frequency

Type 1 PLL with input phase step \(\Delta \phi \cdot u(t)\) \[\begin{align} \Delta \phi\cdot u(t) - K\int_0^{t}\phi _e (\tau)d\tau &= \phi _e (t) \\ \phi _e (0) &= \Delta \phi \end{align}\]

we obtain \(\phi _e (t) = \Delta \phi \cdot e^{-Kt}\cdot u(t)\)

and \(\phi _e(\infty) = 0\)


Daniel Boschen. GRCon24 - Quick Start on Control Loops with Python Workshop [https://events.gnuradio.org/event/24/contributions/599/attachments/187/480/Boschen%20Control%20Presentation.pdf]

image-20250831230607689

image-20250831230637667

image-20250831230937353

PLL bandwidth test

A step response test is an easy way to determine the bandwidth.

Sum a small step into the control voltage of your oscillator (VCO or NCO), and measure the 90% to 10% fall time of the corrected response at the output of the loop filter as shown in this block diagram

PLL Step Response Test

a first order loop \[ BW = \frac{0.35}{t} \space\space\space\space \text{(first order system)} \] Where \(BW\) is the 3 dB bandwidth in Hz and \(𝑡\)​ is the 10%/90% rise or fall time.

For second order loops with a typical damping factor of 0.7 this relationship is closer to: \[ BW = \frac{0.33}{t}\space\space\space\space \text{(second order system, damping factor = 0.7)} \]

[How can I experimentally find the bandwidth of my PLL?, https://dsp.stackexchange.com/a/73654/59253]

reference

Dennis Fischette, Frequently Asked PLL Questions [https://www.delroy.com/PLL_dir/FAQ/FAQ.htm]

Ian Galton, ISSCC 2010 SC3: Fractional-N PLLs [https://www.nishanchettri.com/isscc-slides/2010%20ISSCC/Short%20Course/SC3.pdf]

Mike Shuo-Wei Chen, ISSCC 2020 T6: Digital Fractional-N Phase Locked Loop Design [https://www.nishanchettri.com/isscc-slides/2020%20ISSCC/TUTORIALS/T6Visuals.pdf]

Additive White Gaussian Noise (AWGN)

Qasim Chaudhari. Additive White Gaussian Noise (AWGN) [https://wirelesspi.com/additive-white-gaussian-noise-awgn/]

TODO 📅

Pulse Amplitude Modulation (PAM)

Qasim Chaudhari. Pulse Amplitude Modulation (PAM) [https://wirelesspi.com/pulse-amplitude-modulation-pam/]

TODO 📅

Nyquist Stability Criterion

Michael H. Perrott, High Speed Communication Circuits and Systems, Lecture 15 Integer-N Frequency Synthesizers[https://www.cppsim.com/CommCircuitLectures/lec15.pdf]

TODO 📅

image-20251122095944300

image-20251122095709631

pulse averaging

The average value of output pulse are same because of same DC gain (0dB)

image-20251122195302536

image-20251122195751984

fft vs. ifft

Jason Sachs, Ten Little Algorithms, Part 2: The Single-Pole Low-Pass Filter [https://www.embeddedrelated.com/showarticle/779.php]

image-20250907005625071

\[ \mathcal{ifft} = \frac{\mathcal{fft}}{N} \] image-20250907005201992

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import matplotlib.pyplot as plt
import numpy as np
np.random.seed(123456789) # repeatable results

f0 = 4
t = np.arange(0,1.0,1.0/65536)
mysignal = (np.mod(f0*t,1) < 0.5)*2.0-1
mynoise = 1.0*np.random.randn(*mysignal.shape)

plt.figure(figsize=(8,6))
plt.plot(t, mysignal+mynoise, 'gray',
t, mysignal, 'black');


def spectrum_fftovN(x):
return np.abs(np.fft.fft(x))/len(x)

def spectrum_ifft(x):
return np.abs(np.fft.ifft(x))


mysignal_spectrum_fftovN = spectrum_fftovN(mysignal)
mynoise_spectrum_fftovN = spectrum_fftovN(mynoise)
mysignal_spectrum_ifft= spectrum_ifft(mysignal)
mynoise_spectrum_ifft = spectrum_ifft(mynoise)

N1 = 500
f = np.arange(N1)
plt.figure(figsize=(16,12))
plt.subplot(2,1,1)
plt.plot(f,mysignal_spectrum_fftovN[:N1], 'b-',
f,mysignal_spectrum_ifft[:N1], 'r--', linewidth=3)
plt.legend(('fftovN','ifft'), fontsize=16, loc='upper right')
plt.title('signal', fontsize=24); plt.xlim(0,N1); plt.xlabel('frequency'); plt.ylabel('amplitude')

plt.subplot(2,1,2)
plt.plot(f,mynoise_spectrum_fftovN[:N1], 'b-',
f,mynoise_spectrum_ifft[:N1], 'r--', linewidth=3)
plt.legend(('fftovN','ifft'), fontsize=16, loc='upper right')
plt.title('noise', fontsize=24); plt.xlim(0,N1); plt.xlabel('frequency'); plt.ylabel('amplitude')

plt.show()

Pulse Code Modulation (PCM)

John M Pauly. Lecture 13: Pulse Code Modulation [https://web.stanford.edu/class/ee179/lectures/notes13.pdf]

Pulse Code Modulation (PCM) is a method for digitally representing analog signals by sampling their amplitude at regular intervals and then encoding these samples into binary numbers

image-20250820222558595

Energy/bit (pJ/b)

1mW/Gbps = 1pJ/bit

Joules are a unit of work or energy. Watts are a unit of power which is the rate at which energy is generated or consumed.

modulation depth

The modulation index (or modulation depth) of a modulation scheme describes by how much the modulated variable of the carrier signal varies around its unmodulated level

TODO 📅

Image frequency

Antonio Liscidini, ESSCIRC 2019 Tutorials: Ultra Low Power Receivers [https://youtu.be/OJRB8g4vUZw]

TODO 📅

white noise

white noise doesn't mean it has a Gaussian/normal distribution

The only criteria for a (discrete) signal to be "white" is for each sample to be independently taken from the same probability distribution

img

By understanding input signal's statistical nature, we can gather more insights about certain requirements for our circuits than just from frequency domain

img

Kevin Zheng. The Frequency Domain Trap – Beware of Your AC Analysis [https://circuit-artists.com/the-frequency-domain-trap-beware-of-your-ac-analysis/]


image-20250727111432313

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fs = 1;
N = 2^20;
Nhist = 100;
segmentLength = 512;

x_norm = randn(1, N,1); % Generate random data (e.g., Gaussian white noise)
[pxx_norm, f] = pwelch(x_norm, segmentLength, [], [], fs);

x_uniform = 2*rand(N,1) - 1; % uniform random -1 ~ 1;
[pxx_uniform, ~] = pwelch(x_uniform, segmentLength, [], [], fs);

x_sin = sin(2*pi*(rand(N,1) - 0.5)); % sinusoidal-like
[pxx_sin, ~] = pwelch(x_sin, segmentLength, [], [], fs);

x_bin = 2*randi(2,N,1) -3; % binomial distribution, -1, 1
[pxx_bin, ~] = pwelch(x_bin, segmentLength, [], [], fs);

subplot(2, 4, 1)
histogram(x_norm, Nhist);
title('normal distribution')

subplot(2, 4, 5)
plot(f, 10*log10(pxx_norm));
xlabel('Frequency (Hz)');
ylabel('dB');
title('PSD of normal distribution')

%%
subplot(2, 4, 2)
histogram(x_uniform, Nhist);
title('uniform distribution')

subplot(2, 4, 6)
plot(f, 10*log10(pxx_uniform));
xlabel('Frequency (Hz)');
ylabel('dB');
title('PSD of uniform distribution')

%%
subplot(2, 4, 3)
histogram(x_sin, Nhist);
title('sinusoidal-like distribution')

subplot(2, 4, 7)
plot(f, 10*log10(pxx_sin));
xlabel('Frequency (Hz)');
ylabel('dB');
title('PSD of sinusoidal-like distribution')

%%
subplot(2, 4, 4)
histogram(x_bin, Nhist);
title('binomial distribution')

subplot(2, 4, 8)
plot(f, 10*log10(pxx_bin));
xlabel('Frequency (Hz)');
ylabel('dB');
title('PSD of binomial distribution')

image-20250922225811644

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val = randn(1, 2^20,1);
val_abs = abs(val);
val_abs_avg = mean(val_abs);
subplot(2,2,1)
histogram(val);
title('x_{norm} distribution'); grid on

subplot(2,2,2)
histogram(val_abs);
title('|x_{norm}| distribution')

subplot(2,2,[3 4])
[pxx, f] = pwelch(val_abs - val_abs_avg, 512, [], [], 1);
plot(f, 10*log10(pxx), LineWidth=4); grid on
title('psd (dB)')

RMS for non-sinusoidal periodic function

image-20241220214234185

Nyquist rate & Nyquist frequency

  • Nyquist rate

    The Nyquist rate is the minimum sample rate required to accurately measure a signal's highest frequency. It's equal to twice the highest frequency of the signal

  • Nyquist frequency

    The Nyquist frequency is the highest frequency that can be represented without aliasing in a discrete signal. It's equal to half the sampling frequency

https://upload.wikimedia.org/wikipedia/commons/d/d8/Nyquist_frequency_%26_rate.svg

Oversampling Ratio (OSR) is defined as the ratio of the Nyquist frequency \(f_s/2\) to the signal bandwidth \(B\) given by \(\text{OSR}=f_s/2B\)

Summation & Integration

impulse response Transform ROC
Summation \(u(t)\) \(\frac{1}{s}\) \(\mathfrak{Re}\{s\}\gt 0\)
Integration \(u[n]\) \(\frac{1}{1-z^{-1}}\) \(|z| \gt 1\)

both are NOT stable

sinc function

image-20241002143413907

image-20250628181534951

where \(W\) is sampling frequency in Hz

sinc.drawio

image-20241002143219224


sinc function is square integrable but not absolutely integrable

Zero-order hold (ZOH)

image-20240928101832121 \[ h_{ZOH}(t) = \text{rect}(\frac{t}{T} - \frac{1}{2}) = \left\{ \begin{array}{cl} 1 & : \ 0 \leq t \lt T \\ 0 & : \ \text{otherwise} \end{array} \right. \] The effective frequency response is the continuous Fourier transform of the impulse response \[ H_{ZOH}(f) = \mathcal{F}\{h_{ZOH}(t)\} = T\frac{1-e^{j2\pi fT}}{j2\pi fT}=Te^{-j\pi fT}\text{sinc}(fT) \] where \(\text{sinc}(x)\) is the normalized sinc function \(\frac{\sin(\pi x)}{\pi x}\)

The Laplace transform transfer function of the ZOH is found by substituting \(s=j2\pi f\) \[ H_{ZOH}(s) = \mathcal{L}\{h_{ZOH}(t)\}=\frac{1-e^{-sT}}{s} \]

image-20240928103227690

frequency convention

  • radian frequency \(\omega_0\) in rad/s
  • cyclic frequency \(f_0\) in Hz

Energy signals vs Power signal

Topic 5 Energy & Power Signals, Correlation & Spectral Density [https://www.robots.ox.ac.uk/~dwm/Courses/2TF_2021/N5.pdf]


image-20240427155046131

image-20240719203550628



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modulation & demodulation

Hossein Hashemi, RF Circuits, [https://youtu.be/0f3yZMvD2Jg]

image-20240826221237312

image-20240826221251379

Convolution of probability distributions

The probability distribution of the sum of two or more independent random variables is the convolution of their individual distributions.

image-20240804104528903

Thermal noise

Thermal noise in an ideal resistor is approximately white, meaning that its power spectral density is nearly constant throughout the frequency spectrum.

When limited to a finite bandwidth and viewed in the time domain, thermal noise has a nearly Gaussian amplitude distribution

image-20240804102454281

Barkhausen criteria

Barkhausen criteria are necessary but not sufficient conditions for sustainable oscillations

image-20240720090654883

it simply "latches up" rather than oscillates

System Type

Control of Steady-State Error to Polynomial Inputs: System Type

image-20240502232125317

control systems are assigned a type number according to the maximum degree of the input polynominal for which the steady-state error is a finite constant. i.e.

  • Type 0: Finite error to a step (position error)
  • Type 1: Finite error to a ramp (velocity error)
  • Type 2: Finite error to a parabola (acceleration error)

The open-loop transfer function can be expressed as \[ T(s) = \frac{K_n(s)}{s^n} \]

where we collect all the terms except the pole (\(s\)) at eh origin into \(K_n(s)\),

The polynomial inputs, \(r(t)=\frac{t^k}{k!} u(t)\), whose transform is \[ R(s) = \frac{1}{s^{k+1}} \]

Then the equation for the error is simply \[ E(s) = \frac{1}{1+T(s)}R(s) \]

Application of the Final Value Theorem to the error formula gives the result

\[\begin{align} \lim _{t\to \infty} e(t) &= e_{ss} = \lim _{s\to 0} sE(s) \\ &= \lim _{s\to 0} s\frac{1}{1+\frac{K_n(s)}{s^n}}\frac{1}{s^{k+1}} \\ &= \lim _{s\to 0} \frac{s^n}{s^n + K_n}\frac{1}{s^k} \end{align}\]

  • if \(n > k\), \(e=0\)
  • if \(n < k\), \(e\to \infty\)
  • if \(n=k\)
    • \(e_{ss} = \frac{1}{1+K_n}\) if \(n=k=0\)
    • \(e_{ss} = \frac{1}{K_n}\) if \(n=k \neq 0\)

where we define \(K_n(0) = K_n\)

sinusoidal steady-state and frequency response

image-20231104104933781

image-20231104104946203

image-20231104105056345

image-20231104105139814

image-20231104105223549

Due to KCL and \(u(t)=e^{j\omega t}\) and \(y(t)=H(j\omega)e^{j\omega t}\), we have ODE:

\[\begin{align} \frac{u(t) - y(t)}{R} = C \frac{dy(t)}{dt} \\ e^{j\omega t} - H(j\omega) e^{j\omega t} = H(j\omega)\cdot j\omega e^{j\omega t} \\ \end{align}\]

\(H(j\omega)\) is obtained as below \[ H(j\omega) = \frac{1}{1+j\omega} \]

image-20231104135855739

Different Variants of the PSD Definition

In the practice of engineering, it has become customary to use slightly different variants of the PSD definition, depending on the particular application or research field.

  • Two-Sided PSD, \(S_x(f)\)

    this is a synonym of the PSD defined as the Fourier Transform of the autocorrelation.

  • One-Sided PSD, \(S'_x(f)\)

    this is a variant derived from the two-sided PSD by considering only the positive frequency semi-axis.

    To conserve the total power, the value of the one-sided PSD is twice that of the two-sided PSD \[ S'_x(f) = \left\{ \begin{array}{cl} 0 & : \ f \geq 0 \\ S_x(f) & : \ f = 0 \\ 2S_x(f) & : \ f \gt 0 \end{array} \right. \]

image-20230603185546658

Note that the one-sided PSD definition makes sense only if the two-sided is an even function of \(f\)

If \(S'_x(f)\) is even symmetrical around a positive frequency \(f_0\), then two additional definitions can be adopted:

  • Single-Sideband PSD, \(S_{SSB,x}(f)\)

    This is obtained from \(S'_x(f)\) by moving the origin of the frequency axis to \(f_0\) \[ S_{SSB,x}(f) =S'_x(f+f_0) \] This concept is particularly useful for describing phase or amplitude modulation schemes in wireless communications, where \(f_0\) is the carrier frequency.

    Note that there is no difference in the values of the one-sided versus the SSB PSD; it is just a pure translation on the frequency axis.

  • Double-Sideband PSD, \(S_{DSB,x}(f)\)

    this is a variant of the SSB PSD obtained by considering only the positive frequency semi-axis.

    As in the case of the one-sided PSD, to conserve total power, the value of the DSB PSD is twice that of the SSB \[ S_{DSB,x}(f) = \left\{ \begin{array}{cl} 0 & : \ f \geq 0 \\ S_{SSB,x}(f) & : \ f = 0 \\ 2S_{SSB,x}(f) & : \ f \gt 0 \end{array} \right. \]

image-20230603222054506

Note that the DSB definition makes sense only if the SSB PSD is even symmetrical around zero

Poles and Zeros of transfer function

poles

\[ H(s) = \frac{1}{1+s/\omega_0} \]

magnitude and phase at \(\omega_0\) and \(-\omega_0\) \[\begin{align} H(j\omega_0) &= \frac{1}{1+j} = \frac{1}{\sqrt{2}}e^{-j\pi/4} \\ H(-j\omega_0) &= \frac{1}{1-j} = \frac{1}{\sqrt{2}}e^{j\pi/4} \end{align}\]

system response \(y(t)\) of input \(\cos(\omega_0 t)\), note \(\cos(\omega_0t) = \frac{1}{2}(e^{j\omega_0 t} + e^{-j\omega_0 t})\) \[\begin{align} y(t) &= H(j\omega_0)\cdot \frac{1}{2}e^{j\omega_0 t} + H(-j\omega_0)\cdot \frac{1}{2}e^{-j\omega_0 t} \\ &= \frac{1}{\sqrt{2}}\cos(\omega_0t-\pi/4) \end{align}\]

\(\cos(\omega_0 t)\), with frequency same with pole DON'T have infinite response

That is, pole indicate decrease trending

zeros

similar with poles, \(\cos(\omega_0 t)\), with frequency same with zero DON'T have zero response

\[ H(s) = 1+s/\omega_0 \]

magnitude and phase at \(\omega_0\) and \(-\omega_0\) \[\begin{align} H(j\omega_0) &= 1+j = \sqrt{2}e^{j\pi/4} \\ H(-j\omega_0) &= 1-j = \sqrt{2}e^{-j\pi/4} \end{align}\]

system response \(y(t)\) of input \(\cos(\omega_0 t)\), note \(\cos(\omega_0t) = \frac{1}{2}(e^{j\omega_0 t} + e^{-j\omega_0 t})\) \[\begin{align} y(t) &= H(j\omega_0)\cdot \frac{1}{2}e^{j\omega_0 t} + H(-j\omega_0)\cdot \frac{1}{2}e^{-j\omega_0 t} \\ &= \sqrt{2}\cos(\omega_0t+\pi/4) \end{align}\]

baud rate

symbol rate, modulation rate or baud rate is the number of symbol changes per unit of time.

  • Bit rate refers to the number of bits transmitted between two devices per unit of time
  • The baud or symbol rate refers to the number of symbols that can be sent in the same amount of time

reference

Stephen P. Boyd. EE102 Lecture 10 Sinusoidal steady-state and frequency response [https://web.stanford.edu/~boyd/ee102/freq.pdf]

Gene F. Franklin, J. David Powell, and Abbas Emami-Naeini. 2018. Feedback Control of Dynamic Systems (8th Edition) (8th. ed.). Pearson.

Inter-Symbol Interference (or Leaky Bits) [http://blog.teledynelecroy.com/2018/06/inter-symbol-interference-or-leaky-bits.html]

[AN001] Designing from zero an IIR filter in Verilog using biquad structure and bilinear discretization. URL:[https://www.controlpaths.com/articles/an001_designing_iir_biquad_filter_bilinear/]

Frequency warping using the bilinear transform. URL:[https://www.controlpaths.com/2022/05/09/frequency-warping-using-the-bilinear-transform/]

Digital control loops. Theoretical approach. URL:[https://www.controlpaths.com/2022/02/28/digital-control-loops-theoretical-approach/]

Simulation of DSP algorithms in Verilog. URL:[https://www.controlpaths.com/2023/05/20/simulation-of-dsp-algorithms-in-verilog/]

Implementing a digital biquad filter in Verilog. URL:[https://www.controlpaths.com/2021/04/19/implementing-a-digital-biquad-filter-in-verilog/]

Implementing a FIR filter using folding. URL:[https://www.controlpaths.com/2021/05/17/implementing-a-fir-filter-using-folding/]

Oppenheim, Alan V. and Cram. “Discrete-time signal processing : Alan V. Oppenheim, 3rd edition.” (2011).

Extras: PID Compensator with Bilinear Approximation URL:[https://ctms.engin.umich.edu/CTMS/index.php?aux=Extras_PIDbilin]

image-20241208103218870


noise power at filter output

Chembian Thambidurai, "Comparison Of Noise Power At Lowpass Filter Output" [link]

—, "On Noise Power At The Bandpass Filter Output" [link]

—, "Integrated Power of Thermal and Flicker Noise" [link]

TODO 📅

Sampling Noise

Chembian Thambidurai, "Noise, Sampling and Zeta Functions" [link]

A random signal \(v_n(t)\) is sampled using an ideal impulse sampler

image-20241201165157743

TODO 📅

1/f Noise - Fourier Transform

Steve Smith. An Interesting Fourier Transform - 1/f Noise [https://www.dsprelated.com/showarticle/40.php]

TODO 📅

Pulsed Noise Signals

Chembian Thambidurai, "Power Spectral Density of Pulsed Noise Signals" [link]

image-20241208075822212

Above, the output of the multiplier be \(y(t)\) is passed through a ideal brick wall low pass filter with a bandwidth of \(f_0/2\)

When a random signal is multiplied by a pulse function, the resulting signal becomes a cyclo-stationary random process.

As rule of thumb, the spectrum of such a pulsed noise signal

  • thermal noise is multiplied by \(D\)

  • flicker noise is multiplied by \(D^2\),

where \(D\) is the duty cycle of the pulse signal

image-20241208111744647

banlimited input

image-20241208113904927

wideband white noise input

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flicker noise input

with \(S_x(f)=\frac{K_f}{f}\)

image-20241208121027250

image-20241208121402724

Assuming \(\Delta f \ll f_0\)

image-20241208121645637


image-20241208111506517

ADC SNR & clock jitter

cyclostationary random process

image-20250809170358612


image-20250525134220901

image-20250525135503671

\[\begin{align} \text{SNR}_\text{ADC}[\text{dB}] &= -20\cdot \log \sqrt{\left(10^{-\frac{\text{SNR}_\text{Quantization Noise}}{20}}\right)^2 + \left(10^{-\frac{\text{SNR}_\text{Jitter}}{20}}\right)^2} \\ &= -10\cdot \log \left(\left(10^{-\frac{\text{SNR}_\text{Quantization Noise}}{20}}\right)^2 + \left(10^{-\frac{\text{SNR}_\text{Jitter}}{20}}\right)^2\right) \\ &= -10\cdot \log \left(\left(10^{-\frac{10\log(\frac{3\times2^{2N}}{2})}{20}}\right)^2 + \left(10^{-\frac{-20\log{(2\pi f_\text{in}\sigma_\text{jitter})}}{20}}\right)^2\right) \\ &= -10\cdot \log \left( \frac{2}{3\times 2^{2N}} + (2\pi f_\text{in}\sigma_\text{jitter})^2 \right) \end{align}\]

Ayça Akkaya, "High-Speed ADC Design and Optimization for Wireline Links" [https://infoscience.epfl.ch/server/api/core/bitstreams/96216029-c2ff-48e5-a675-609c1e26289c/content]

CC Chen, Why Absolute Jitter Matters for ADCs & DACs? [https://youtu.be/jBgDDFFDq30?si=XFyTEfApN86Ef-RG]

Thomas Neu, TIPL 4704. Jitter vs SNR for ADCs [https://www.ti.com/content/dam/videos/external-videos/en-us/2/3816841626001/5529003238001.mp4/subassets/TIPL-4704-Jitter-vs-SNR.pdf]

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import numpy as np
import matplotlib.pyplot as plt

N = 12 # ADC bit
fin = 100e6 # the frequency of the sinusoidal input signal
jrms = np.linspace(0, 10, 1000)*1e-12 #ps

# ADC (SNR) with Quantization Noise & Jitter degradation
SNR_ADC = -10 * np.log10(10**(-np.log10(3*2**(2*N)/2)) + (2*np.pi*fin*jrms)**2)
ENOB = (SNR_ADC - 1.76) / 6.02

plt.plot(jrms*1e12, ENOB, label='100 MHZ input')
plt.plot([0, 10], [12, 12], '--', label='12-bit limit')
plt.plot([0, 10], [6, 6], '--', label='6-bit limit')

plt.xscale('linear')
plt.xlim([0, 10])
plt.ylim([5, 15])
plt.xlabel('RMS Jitter (ps)')
plt.ylabel('Effective Number of Bits (ENOB')
plt.grid(which='both')
plt.title('ENOB vs. RMS Clock Jitter (100 MHz)')
plt.legend()
plt.show()

Chun-Hsien Su (蘇純賢). Design of Oversampled Sigma-Delta Data Converters. July, 2006 [pdf]

image-20250809182751263


Chembian Thambidurai, "SNR of an ADC in the presence of clock jitter" [https://www.linkedin.com/posts/chembiyan-t-0b34b910_adcsnrjitter-activity-7171178121021304833-f2Wd/]

Unlike the quantization noise and the thermal noise, the impact of the clock jitter on the ADC performance depends on the input signal properties like its PSD

image-20241123205352661

The error between the ideal sampled signal and the sampling with clock jitter can be treated as noise and it results in the degradation of the SNR of the ADC

image-20241124004634365

For sinusoid input:

image-20241210235817281

image-20241222140258960

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import numpy as np
import matplotlib.pyplot as plt

ENOB = 8
fin = np.logspace(8, 11, 60)

# quantization noise: SNR = 6.02*ENOB + 1.76 dB
Ps_PnQ = 10**((6.02*ENOB + 1.76)/10)
PnQ = 1/Ps_PnQ

# jitter noise: SNR = 6 - 20log10(2*pi*fin*Jrms) dB @ref. Chembiyan T
Jrms_list = [25e-15, 50e-15, 100e-15, 250e-15, 500e-15, 1000e-15]
for Jrms in Jrms_list:
# Ps_PnJ_lcl = 10**((6-20*np.log10(2*np.pi*fin*Jrms))/10) # ref. Chembiyan T
Ps_PnJ_lcl = 10**((0 - 20 * np.log10(2 * np.pi * fin * Jrms)) / 10) # ref. Nicola Da Dalt
PnJ_lcl = 1/Ps_PnJ_lcl
SNR_lcl = 10*np.log10(1/(PnQ+PnJ_lcl))
plt.plot(fin, SNR_lcl, label=r'$\sigma_{jitter}$'+'='+str(int(Jrms*1e15))+'fs')

plt.xscale('log')
plt.ylim([0, 55])
plt.xlabel(r'$f_{in}$ [Hz]')
plt.ylabel(r'SNR [dB]')
plt.grid(which='both')
# plt.title(r'ref. Chembiyan T')
plt.title(r'ref. Nicola Da Dalt')
plt.legend()
plt.show()

K. Tyagi and B. Razavi, "Performance Bounds of ADC-Based Receivers Due to Clock Jitter," in IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 70, no. 5, pp. 1749-1753, May 2023 [https://www.seas.ucla.edu/brweb/papers/Journals/KT_TCAS_2023.pdf]

N. Da Dalt, M. Harteneck, C. Sandner and A. Wiesbauer, "On the jitter requirements of the sampling clock for analog-to-digital converters," in IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, vol. 49, no. 9, pp. 1354-1360, Sept. 2002 [https://sci-hub.se/10.1109/TCSI.2002.802353]

M. Shinagawa, Y. Akazawa and T. Wakimoto, "Jitter analysis of high-speed sampling systems," in IEEE Journal of Solid-State Circuits, vol. 25, no. 1, pp. 220-224, Feb. 1990 [https://sci-hub.se/10.1109/4.50307]

image-20241210232716862

Ayça Akkaya, "High-Speed ADC Design and Optimization for Wireline Links" [https://infoscience.epfl.ch/server/api/core/bitstreams/96216029-c2ff-48e5-a675-609c1e26289c/content]


待学芯. ADC量化结果反推采样时钟抖动(Jitter) [https://mp.weixin.qq.com/s/55xfVQMe_N8zUGpI8ZvmsQ]

—. 关于时钟抖动(Jitter)与ADC的一些讨论 [https://mp.weixin.qq.com/s/GW1keHhfq7zrd036lyG0CQ]

image-20250811210300829

DAC SNR & clock jitter

ampling Jitter Effects for ADC/DAC

  • In both DAC or ADC cases, doubling the timing jitter doubles the noise level
  • Also, doubling the frequency or amplitude doubles the jitter induced noise - SNR is not improved

image-20250810213544751

image-20250810213615814

Boris Murmann ISSCC 2022 SC1: Introduction to ADCs/DACs: Metrics, Topologies, Trade Space, and Applications [pdf]

S. Kim, K. -Y. Lee and M. Lee, "Modeling Random Clock Jitter Effect of High-Speed Current-Steering NRZ and RZ DAC," in IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 65, no. 9, pp. 2832-2841, Sept. 2018 [https://sci-hub.se/10.1109/TCSI.2018.2821198]

Martin Clara. High-Performance D/A-Converters - Application to Digital Transceivers, 2013 [pdf]

Chun-Hsien Su (蘇純賢). Design of Oversampled Sigma-Delta Data Converters. July, 2006 [pdf]

Sampled Thermal Noise

The aliasing of the noise, or noise folding, plays an important role in switched-capacitor as it does in all switched-capacitor filters

image-20240425215938141

Assume for the moment that the switch is always closed (that there is no hold phase), the single-sided noise density would be

image-20240428182816109


image-20240428180635082

\(v_s[n]\) is the sampled version of \(v_{RC}(t)\), i.e. \(v_s[n]= v_{RC}(nT_C)\) \[ S_s(e^{j\omega}) = \frac{1}{T_C} \sum_{k=-\infty}^{\infty}S_{RC}(j(\frac{\omega}{T_C}-\frac{2\pi k}{T_C})) \cdot d\omega \] where \(\omega \in [-\pi, \pi]\), furthermore \(\frac{d\omega}{T_C}= d\Omega\) \[ S_s(j\Omega) = \sum_{k=-\infty}^{\infty}S_{RC}(j(\Omega-k\Omega_s)) \cdot d\Omega \]

image-20240428215559780

image-20240425220033340

The noise in \(S_{RC}\) is a stationary process and so is uncorrelated over \(f\) allowing the \(N\) rectangles to be combined by simply summing their noise powers

image-20240428225949327

image-20240425220400924

where \(m\) is the duty cycle

Kundert, Ken. (2006). Simulating Switched-Capacitor Filters with SpectreRF [https://designers-guide.org/analysis/sc-filters.pdf]

Pavan, Schreier and Temes, "Understanding Delta-Sigma Data Converters, Second Edition" ISBN 978-1-119-25827-8

Tania Khanna, ESE568 Fall 2019, Mixed Signal Circuit Design and Modeling URL: https://www.seas.upenn.edu/~ese568/fall2019/

Matt Pharr, Wenzel Jakob, and Greg Humphreys. 2016. Physically Based Rendering: From Theory to Implementation (3rd. ed.). Morgan Kaufmann Publishers Inc., San Francisco, CA, USA.

R. Gregorian and G. C. Temes. Analog MOS Integrated Circuits for Signal Processing. Wiley-Interscience, 1986

Trevor Caldwell, Lecture 9 Noise in Switched-Capacitor Circuits [http://individual.utoronto.ca/trevorcaldwell/course/NoiseSC.pdf]

Christian-Charles Enz. High precision CMOS micropower amplifiers [pdf]


Below analysis focusing on sampled noise

Boris Murmann. Noise Analysis in Switched-Capacitor Circuits, ISSCC 2011 / tutorials [slides, transcript]

—. EE315A VLSI Signal Conditioning Circuits [pdf]

—. EE315B VLSI Data Conversion Circuits, Autumn 2013 [pdf]

image-20250810085721440

  • Calculate autocorrelation function of noise at the output of the RC filter
  • Calculate the spectrum by taking the discrete time Fourier transform of the autocorrelation function

Bernhard E. Boser . Advanced Analog Integrated Circuits Switched Capacitor Gain Stages [https://people.eecs.berkeley.edu/~boser/courses/240B/lectures/M05%20SC%20Gain%20Stages.pdf]

image-20240427183700971

Cyclostationary Noise (Modulated Noise)

[https://ece-research.unm.edu/bsanthan/ece541/cyclo.pdf]

Chembian Thambidurai, "Power Spectral Density of Pulsed Noise Signals" [link]

image-20241123230025107

image-20241123230049341

image-20241123215631264

White Noise Modulation

Noisy Resistor & Clocked Switch

\[ v_t (t) = v_i(t)\cdot m_t(t) \]

where \(v_i(t)\) is input white noise, whose autocorrelation is \(A\delta(\tau)\), and \(m_t(t)\) is periodically operating switch, then autocorrelation of \(v_t(t)\) \[\begin{align} R_t (t_1, t_2) &= E[v_t(t_1)\cdot v_t(t_2)] \\ &= R_i(t_1, t_2)\cdot m_t(t_1)m_t(t_2) \end{align}\]

Then \[\begin{align} R_t(t, t-\tau) &= R_i(\tau)\cdot m_t(t)m_t(t-\tau) \\ & = A\delta(\tau) \cdot m_t(t)m_t(t-\tau) \\ & = A\delta(\tau) \cdot m_t(t) \end{align}\] Because \(m_t(t)=m_t(t+T)\), \(R_t(t, t-\tau)\) is is periodic in the variable \(t\) with period \(T\)

The time-averaged ACF is denoted as \(\tilde{R_t}(\tau)\)

\[ \tilde{R}_{t}(\tau) = m\cdot A\delta(\tau) \] That is, \[ S_t(f) = m\cdot S_{A}(f) \]


image-20241118212505205

image-20241118212242823

image-20241116170450589

Colored Noise Modulation

tavg_factor.drawio \[ \tilde{R_t}(\tau) = R_i(\tau)\cdot m_{tac}(\tau) \]

where \(m_t(t)m_t(t-\tau)\) averaged on \(t\) is denoted as \(m_{tac}(\tau)\) or \(\overline{m_t(t)m_t(t-\tau)}\)

The DC value of \(m_{tac}(\tau)\) can be calculated as below

  1. for \(m\le 0.5\), the DC value of \(m_{tac}(\tau)\) \[ \frac{m\cdot mT}{T} = m^2 \]

  2. for \(m\gt 0.5\), the DC value of \(m_{tac}(\tau)\) \[ \frac{(m+2m-1)(1-m)T + (2m-1)\{mT -(1-m)T\}}{T} = m^2 \]

Therefore, time-average power spectral density and total power are scaled by \(m^2\) in fundamental frequency sideband


image-20241118213007400

image-20241118215846751

image-20241117205422217


Switched-Capacitor Track signal

image-20241118213830893

image-20241116165632847

track signal pnoise (sc)

image-20241118220145885

image-20241118215956843

zoom in first harmonic by linear step of pnoise

image-20241118220904802

decreasing the rising/falling time of clock, the harmonics still retain

equivalent circuit for pnoise (eq)

  1. thermal noise of R is modulated at first
  2. then filtered by ideal filter

image-20241118214320950

image-20241118220027598


sc vs eq

image-20241118222730383

  • sc: harmonic distortion
  • eq: no harmonic distortion

Non-Stationary Processes (Comparator)

T. Sepke, P. Holloway, C. G. Sodini and H. -S. Lee, "Noise Analysis for Comparator-Based Circuits," in IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 56, no. 3, pp. 541-553, March 2009 [https://dspace.mit.edu/bitstream/handle/1721.1/61660/Speke-2009-Noise%20Analysis%20for%20Comparator-Based%20Circuits.pdf]

Sepke, Todd. "Comparator design and analysis for comparator-based switched-capacitor circuits." (2006). [https://dspace.mit.edu/handle/1721.1/38925]

image-20251020204611858

WSS white noise input image-20251022084714201 image-20251022084805639
white noise step input image-20251022084648664 image-20251022084948197

Wide-Sense-Stationary Noise

Much like sinusoidal-steady-state signal analysis, steady-state noise analysis methods assume an input \(x(t)\) of infinite duration, which is a Wide-Sense Stationary (WSS) random process

Frequency-domain Analysis

image-20241122233117654


image-20251020212455123

Time-domain Analysis

The output \(y(t)\) of a linear time-invariant (LTI) system \(h(t)\) \[\begin{align} R_{yy}(\tau) &= R_{xx}(\tau)*[h(\tau)*h(-\tau)] \\ &= S_{xx}(0)\delta(\tau) * [h(\tau)*h(-\tau)] \\ &= S_{xx}(0)[h(\tau)*h(-\tau)] \\ &= S_{xx}(0) \int_\alpha h(\alpha)h(\alpha-\tau)d\alpha \end{align}\]

with WSS white noise input \(x(t)\), \(R_{xx}(\tau)=S_{xx}(0)\delta(\tau)\), therefore

image-20251020210923501


image-20251020211524360

\[ V_o(s) = \frac{1}{C}\frac{RC}{1+sRC}\cdot I_n(s) \overset{\mathcal{L}} {\longrightarrow} \frac{1}{C}e^{-t/\tau_0} \]

where \(\tau_0 = RC\)

Non-stationary Noise

Assuming the noise applied duration is much less than the time constant, the output voltage does not reach steady-state and WSS noise analysis does not apply

image-20251020212837224

Time-domain Analysis

image-20251020213929095

The step noise input \(x(t) = \nu(t)u(t)\) where an underlying WSS process \(\nu(t)\) \[ R_{xx}(t_1,t_2) = E[x(t_1)x(t_2)] = R_{\nu\nu}(t_1, t_2)u(t_1)u(t_2)=R_{\nu\nu}(t_1, t_2) \tag{3.28} \] image-20251020213729837

image-20241123005644828

\[ R_{xy}(t_1, t_2) = E[x(t_1)y(t_2)] = E[x(t_1)(x(t_2)*h(t_2))] = E(x(t_1)x(t_2))*h(t_2) = R_{xx}(t_1,t_2)*h(t_2) \]

\[ R_{yy}(t_1,t_2) = E[y(t_1)y(t_2)] = E[(x(t_1)*h(t_1))y(t_2)] = E[x(t_1)y(t_2)]*h(t_1)=R_{xy}(t_1,t_2)*h(t_1) \]

image-20241123011304449

the absolute value of each time index is important for a non-stationary signal, and only the time difference was important for WSS signals

\[\begin{align} R_{yy}(t_1,t_2) &= h(t_1)*R_{\nu\nu}(t_1, t_2)*h(t_2) \\ &= h(t_1)*S_{xx}(0)\delta(t_2-t_1)*h(t_2) \\ &=S_{xx}(0) h(t_1)*(\delta(t_2-t_1)*h(t_2)) \\ &= S_{xx}(0)h(t_1)*h(t_2-t_1) \\ &= S_{xx}(0)\int_\tau h(\tau)h(t_2-t_1+\tau))d\tau \end{align}\]

That is \[ \sigma^2_y (t)= R_{yy}(t_1,t_2)|_{t_1=t_2=t}=S_{xx}(0)\int_{-\infty}^t |h(\tau)|^2d\tau \tag{3.33} \]

\(t\), the upper limit of integration is just intuitive, which lacks strict derivation

image-20241123074316370


Because stable systems have impulse responses that decay to zero as time goes to infinity, the output noise variance approaches the WSS result as time approaches infinity

image-20251020221312637


Richard Schreier. ECE1371 Advanced Analog Circuits Lecture 8 - COMPARATOR & FLASH ADC DESIGN [http://individual.utoronto.ca/schreier/lectures/2015/8-6.pdf]

image-20250710221018596

\[ R_{yy}(0) = \frac{1}{2\pi}\int_{-\infty}^{\infty}|H(\omega)|^2S_{xx}(\omega)d\omega = S \cdot \frac{1}{2\pi}\int_{-\infty}^{\infty}|H(\omega)|^2d\omega \overset{\text{Parseval's Relation}}{=} S\cdot \int_{-\infty}^{\infty}|h(t)|^2dt \]

Frequency-domain Analysis

Because the definition of the PSD assumes that the variance of the noise process is independent of time, the PSD of a non-stationary process is not very meaningful

image-20251020223516242

image-20251020223535752

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Input Referred Noise

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Noise Voltage to Timing Jitter Conversion & noise gain

image-20241123100031499

image-20251020235112340

\[\begin{align} \overline{v_n^2}(t_i) &= \frac{\overline{v_{on}^2}}{|A_N|^2} \\ &= \frac{G_n}{G_m}\frac{kT}{C}\frac{1}{A_0}\frac{1+e^{-t_i/\tau_o}}{1-e^{-t_i/\tau_o}} \\ &=4kT\frac{G_n}{G_m^2}\frac{1}{4R_oC} \coth(\frac{t_i}{2\tau_o}) \\ &= 4kTR_n\frac{1}{4\tau_o} \coth(\frac{t_i}{2\tau_o}) \end{align}\]

where \(R_n = \frac{G_n}{G_m^2}\), the equivalent thermal noise resistance

image-20251021000657960

image-20241123111642852


Suppose \(t_i \ll \tau_0\) \[ \overline{v_n^2}(t_i) = 4kTR_n\frac{1}{4\tau_o} \coth(\frac{t_i}{2\tau_o}) \approx 4kTR_n\frac{1}{4\tau_o} \frac{1 + (1-\frac{t_i}{\tau_0})}{1 - (1-\frac{t_i}{\tau_0})} =4kTR_n \cdot \frac{1}{2t_i} \]

image-20251021004235631

Suppose \(t_i \gg \tau_0\) \[ \overline{v_n^2}(t_i) = 4kTR_n\frac{1}{4\tau_o} \coth(\frac{t_i}{2\tau_o}) \approx 4kTR_n\frac{1}{4\tau_o} = \frac{G_n}{G_m}\frac{kT}{C}\frac{1}{A_0} \] As expected, the input referred noise voltage is \(kT/C\) noise

reference

David Herres, The difference between signal under-sampling, aliasing, and folding URL: https://www.testandmeasurementtips.com/the-difference-between-signal-under-sampling-aliasing-and-folding-faq/

Pharr, Matt; Humphreys, Greg. (28 June 2010). Physically Based Rendering: From Theory to Implementation. Morgan Kaufmann. ISBN 978-0-12-375079-2. Chapter 7 (Sampling and reconstruction)

Alan V Oppenheim, Ronald W. Schafer. Discrete-Time Signal Processing, 3rd edition

Miller's approximation

miller-approx.drawio

Right-Half-Plane Zero

\[ \left[(v_i - v_o)sC_c - g_m v_i\right]R_o = v_o \] Then \[ \frac{v_o}{v_i} = -g_mR_o\frac{1-s\frac{C_c}{g_m}}{1+sR_oC_c} \] right-half-plane Zero \(\omega _z = \frac{g_m}{C_c}\)


image-20251024211342562

Equivalent cap

The amplifier gain magnitude \(A_v = g_m R_o\) \[ I_\text{c,in} = (v_i - v_o)sC_c \] Then \[\begin{align} I_\text{c,in} &= (v_i + A_v v_i)sC_c \\ & = v_i s (1+A_v)C_c \end{align}\]

we get \(C_\text{in,eq}= (1+A_v)C_c\simeq A_vC_c\)

Similarly \[\begin{align} I_\text{c,out} &= (v_o - v_i)sC_c \\ & = v_o s (1+\frac{1}{A_v})C_c \end{align}\]

we get \(C_\text{out,eq}= (1+\frac{1}{A_v})C_c\simeq C_c\)

Pole Splitting

Generic circuit in textbook

In addition to lowering the required capacitor value, Miller compensation entails a very important property: it moves the output pole away from the origin. This effect is called pole splitting

image-20230103223438823

The 1st stage is replaced with Thevenin equivalent circuit , \(V_i \cong V_i \cdot g_{m1}R_{o1}\)

\[\begin{align} \frac{V_i-V_{o1}}{R_{o1}} &= V_{o1}\cdot sC_{o1}+(V_{o1}-V_o)\cdot sC_c \\ V_{o1} &= \frac{V_i+sR_{o1}C_cV_o}{1+sR_{o1}(C_{o1}+C_c)} \end{align}\] \[ (V_{o1}-V_o)sC_c=g_{m2}V_{o1}+V_o(\frac{1}{R_{o2}+sC_L}) \] substitute \(V_{o1}\), we get

\[\begin{align} \frac{V_o}{V_i} &= \frac{(sC_c-g_{m2})R_{o2}}{s^2R_{o1}R_{o2}(C_cC_{o1}+C_LC_{o1}+C_LC_c)+s\left\{ R_{o1}C_c\cdot g_{m2}R_{o2}+R_{o2}(C_c+C_L)+R_{o1}(C_{o1}+C_c) \right\} +1} \\ &= \frac{g_{m2}R_{o2}(s\frac{C_c}{g_{m2}}-1)}{s^2R_{o1}R_{o2}(C_cC_{o1}+C_LC_{o1}+C_LC_c)+s\left\{ R_{o1}C_c\cdot g_{m2}R_{o2}+R_{o2}(C_c+C_L)+R_{o1}(C_{o1}+C_c) \right\} +1} \end{align}\]

left hand plane poles

\[\begin{align} \omega_1 &= \frac{1}{R_{o1}C_c\cdot g_{m2}R_{o2}+R_{o2}(C_c+C_L)+R_{o1}(C_{o1}+C_c)} \\ \omega_2 &= \frac{R_{o1}C_c\cdot g_{m2}R_{o2}+R_{o2}(C_c+C_L)+R_{o1}(C_{o1}+C_c)}{R_{o1}R_{o2}(C_cC_{o1}+C_LC_{o1}+C_LC_c)} \end{align}\]

and RHP (right-hand plane) zero \[ \omega_z=\frac{g_{m2}}{C_c} \]

with series switch

image-20230103230122637

replace \(sC_L\) with \(1/(R_{sw}+\frac{1}{sC_L})\) \[\begin{align} \frac{V_{o2}}{V_i} &= \frac{g_{m2}R_{o2}(s\frac{C_c}{g_{m2}}-1)(1+sR_{sw}C_L)}{s^3R_{o1}R_{o2}R_{sw}C_{o1}C_cC_L+s^2\left\{R_{o1}R_{o2}(C_cC_{o1}+C_LC_{o1}+C_LC_c)+ \left[ R_{o1}C_c\cdot g_{m2}R_{o2}+R_{o2}(C_c+0)+R_{o1}(C_{o1}+C_c)\right]R_{sw}C_L \right\}+s\left\{ R_{o1}C_c\cdot g_{m2}R_{o2}+R_{o2}(C_c+C_L)+R_{o1}(C_{o1}+C_c) +R_{sw}C_L\right\} +1} \end{align}\] Due to \[ \frac{V_o}{V_{o2}} = \frac{\frac{1}{sC_L}}{R_{sw}+\frac{1}{sC_L}}=\frac{1}{1+sR_{sw}C_L} \] Then \[\begin{align} \frac{V_o}{V_i} &= \frac{V_{o2}}{V_i} \cdot \frac{V_o}{V_{o2}} \\ &= \frac{g_{m2}R_{o2}(s\frac{C_c}{g_{m2}}-1)}{s^3R_{o1}R_{o2}R_{sw}C_{o1}C_cC_L+s^2\left\{R_{o1}R_{o2}(C_cC_{o1}+C_LC_{o1}+C_LC_c)+ \left[ R_{o1}C_c\cdot g_{m2}R_{o2}+R_{o2}(C_c+0)+R_{o1}(C_{o1}+C_c)\right]R_{sw}C_L \right\}+s\left\{ R_{o1}C_c\cdot g_{m2}R_{o2}+R_{o2}(C_c+C_L)+R_{o1}(C_{o1}+C_c) +R_{sw}C_L\right\} +1} \end{align}\]

\(R_{sw}\) & \(R_c\)

image-20230103232837318

\[\begin{align} \frac{V_{o2}}{V_i} &=-g_{m2}R_{o2}\frac{sC_c(R_c-1/g_{m2})+1}{(1+sR_{o1}C_{o1})sR_{o2}C_c+sR_{o1}\cdot g_{m2}R_{o2}C_c+\frac{s(R_{o2}+R_{sw})C_L+1}{sR_{sw}C_L+1}\left[(1+sR_{o1}C_{o1})(1+sR_cC_c)+sR_{o1}C_c \right]} \\ &=-g_{m2}R_{o2}\frac{sC_c(R_c-1/g_{m2})+1}{s^2R_{o1}R_{o2}C_{o1}C_c+sR_{o2}C_c+sR_{o1}\cdot g_{m2}R_{o2}C_c+\frac{s(R_{o2}+R_{sw})C_L+1}{sR_{sw}C_L+1}\left[(1+sR_{o1}C_{o1})(1+sR_cC_c)+sR_{o1}C_c \right]} \\ &=-g_{m2}R_{o2}\frac{\left[ sC_c(R_c-1/g_{m2})+1 \right](sR_{sw}C_L+1)}{s^2R_{o1}R_{o2}C_{o1}C_c(sR_{sw}C_L+1)+sR_{o2}C_c(sR_{sw}C_L+1)+sR_{o1}\cdot g_{m2}R_{o2}C_c(sR_{sw}C_L+1)+\left[s(R_{o2}+R_{sw})C_L+1\right]\left[(1+sR_{o1}C_{o1})(1+sR_cC_c)+sR_{o1}C_c \right]} \end{align}\]

\(s^3\) terms in denominator \[ H_3 = s^3\cdot(R_{o1}R_{o2}R_c+R_{o1}R_{o2}R_{sw} +R_{o1}R_cR_{sw})\cdot C_{o1}C_cC_L \] \(s^2\) terms in denominator \[\begin{align} H_2 &=s^2\cdot(R_{o1}R_{o2}C_{o1}C_c+R_{o1}R_{o2}C_{o1}C_L+R_{o2}R_cC_cC_L+R_{o1}R_{o2}C_cC_L+R_{o1}R_cC_{o1}C_c\\ &+R_{o2}R_{sw}C_cC_L+R_{o1}R_{sw}C_cC_L\cdot g_{m2}R_{o2}+R_{o1}R_{sw}C_{o1}C_L+R_{sw}R_cC_cC_L+R_{o1}R_{sw}C_cC_L) \end{align}\]

\(s^1\) term in denominator \[ H_1=s(R_{o1}\cdot g_{m2}R_{o2}C_c+R_{o1}C_{o1}+R_cC_c+R_{o1}C_c+R_{o2}C_c+R_{o2}C_L+R_{sw}C_L) \] \(s^0\) term in denominator \[ H_0=1 \] set \(R_c=0\) and \(R_{sw}=0\), the \(H_*\) reduced to \[\begin{align} H_3 &= 0 \\ H_2 &=s^2R_{o1}R_{o2}(C_{o1}C_c+C_{o1}C_L+C_cC_L) \\ H_1&=s(R_{o1}\cdot g_{m2}R_{o2}C_c+R_{o1}C_{o1}+R_{o1}C_c+R_{o2}C_c+R_{o2}C_L) \\ H_0&=1 \end{align}\] That is \[ H=s^2R_{o1}R_{o2}(C_{o1}C_c+C_{o1}C_L+C_cC_L)+s(R_{o1}\cdot g_{m2}R_{o2}C_c+R_{o1}C_{o1}+R_{o1}C_c+R_{o2}C_c+R_{o2}C_L)+1 \]

which is same with our previous analysis of Generic circuit in textbook

And we know \[ \frac{V_o}{V_{o2}}=\frac{1}{1+sR_{sw}C_L} \] Finally, we get \(\frac{V_o}{V_i}\) \[\begin{align} \frac{V_o}{V_i} &= \frac{V_{o2}}{V_i} \cdot \frac{V_o}{V_{o2}} \\ &= -g_{m2}R_{o2}\frac{\left[ sC_c(R_c-1/g_{m2})+1 \right](sR_{sw}C_L+1)}{H_3+H_2+H_1+1} \cdot \frac{1}{1+sR_{sw}C_L} \\ &= -g_{m2}R_{o2}\frac{ sC_c(R_c-1/g_{m2})+1}{H_3+H_2+H_1+1} \end{align}\]

The loop transfer function is \[ \frac{V_o}{V_i} =-g_{m1}R_{o1}g_{m2}R_{o2}\frac{ sC_c(R_c-1/g_{m2})+1}{H_3+H_2+H_1+1} \]


vccs model

image-20230106214027323


simplify the transfer function

  1. omit \(C_{o1}\)

We omit \(C_{o1}\) in frequency range of interest

\[\begin{align} H_3 &= 0 \\ H_2 &=s^2(R_{o2}R_c+R_{o1}R_{o2}+R_{o2}R_{sw}+R_{o1}R_{sw}\cdot g_{m2}R_{o2}+R_{sw}R_c+R_{o1}R_{sw})\cdot C_cC_L \\ H_1 &=s(R_{o1}\cdot g_{m2}R_{o2}C_c+R_cC_c+R_{o1}C_c+R_{o2}C_c+R_{o2}C_L+R_{sw}C_L) \\ H_0 &= 1 \end{align}\]

two poles and 1 zero

  1. more simplification

Then, some terms can be omitted

\[\begin{align} H_2 &=s^2R_{o1}R_{o2}(1+g_{m2}R_{sw})\cdot C_cC_L \\ H_1 &=sR_{o1}\cdot g_{m2}R_{o2}C_c \\ H_0 &= 1 \end{align}\]

The poles can be deduced \[\begin{align} \omega_1 &= \frac{1}{R_{o1}\cdot g_{m2}R_{o2}C_c} \\ \omega_2 &= \frac{1}{1+g_{m2}R_{sw}}\cdot \frac{g_{m2}}{C_L} \\ &= \frac{1}{(gm_2^{-1}+R_{sw})C_L} \end{align}\]

The pole \(\omega_2=\frac{1}{gm_2^{-1}C_L}\) is changed to \(\omega_2=\frac{1}{(gm_2^{-1}+R_{sw})C_L}\)

In order to cancell \(\omega_2\) with \(\omega_z\), \(R_c\) shall be increased

\[ R_{eq}=g_{m2}^{-1}+R_{sw} \]

fndRsw.drawio.svg


image-20251024233028351

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gm1 = 400e-6;
gm2 = 3.2e-3;
Ro1 = 800e3;
Ro2 = 100e3;
Co1 = 100e-15;
CL = 7e-12;
Rc = 15e3;
Cc = 2e-12;
Rsw = 6e3;


s = tf('s');

%%%%%%%%%%%%%%%%%%%%%%%%%%
% org equation
H3 = s^3*(Ro1*Ro2*Rc + Ro1*Ro2*Rsw+Ro1*Rc*Rsw)*Co1*Cc*CL;
H2 = s^2*(Ro1*Ro2*Co1*Cc+Ro1*Ro2*Co1*CL+Ro2*Rc*Cc*CL+Ro1*Ro2*Cc*CL+Ro1*Rc*Co1*Cc+ ...
Ro2*Rsw*Cc*CL+Ro1*Rsw*Cc*CL*gm2*Ro2+Ro1*Rsw*Co1*CL+Rsw*Rc*Cc*CL+Ro1*Rsw*Cc*CL);
H1 = s*(Ro1*gm2*Ro2*Cc+Ro1*Co1+Rc*Cc+Ro1*Cc+Ro2*Cc+Ro2*CL+Rsw*CL);
Nmt = s*Cc*(Rc-1/gm2)+1;
TF = -gm1*Ro1*gm2*Ro2*Nmt/(H3+H2+H1+1);
[mag, phase, wout] = bode(TF, {100*2*pi, 100*1e9*2*pi});


%%%%%%%%%%%%%%%%%%%%%%%%%%
% omit Co1
H3 = 0;
H2 = s^2*(Ro2*Rc+Ro1*Ro2+Ro2*Rsw+Ro1*Rsw*gm2*Ro2+Rsw*Rc+Ro1*Rsw)*Cc*CL;
H1 = s*(Ro1*gm2*Ro2*Cc+Rc*Cc+Ro1*Cc+Ro2*Cc+Ro2*CL+Rsw*CL);
Nmt = s*Cc*(Rc-1/gm2)+1;
TF = -gm1*Ro1*gm2*Ro2*Nmt/(H3+H2+H1+1);
[mag2, phase2, wout2] = bode(TF, {100*2*pi, 100e9*2*pi});


%%%%%%%%%%%%%%%%%%%%%%%%%%
% more simplification
H3 = 0;
H2 = s^2*Ro1*Ro2*(1+gm2*Rsw)*Cc*CL;
H1 = s*Ro1*gm2*Ro2*Cc;
Nmt = s*Cc*(Rc-1/gm2)+1;
TF = -gm1*Ro1*gm2*Ro2*Nmt/(H3+H2+H1+1);
[mag3, phase3, wout3] = bode(TF, {100*2*pi, 100e9*2*pi});

subplot(2,1,1)
semilogx(wout(:)/2/pi, 20*log10(mag(:)), 'k', 'linewidth', 2);
hold on;
semilogx(wout2(:)/2/pi, 20*log10(mag2(:)), 'ro--', 'linewidth', 2)
semilogx(wout3(:)/2/pi, 20*log10(mag3(:)), 'bs--', 'linewidth', 2)
grid on; title("loop gain (dB)"); xlabel("frequency (Hz)"); legend("original", "Omit C_{o1}", "more simp");

subplot(2,1,2)
semilogx(wout(:)/2/pi, phase(:), 'k', 'linewidth', 2);
hold on;
semilogx(wout2(:)/2/pi, phase2(:), 'ro--', 'linewidth', 2);
semilogx(wout3(:)/2/pi, phase3(:), 'bs--', 'linewidth', 2);
grid on; title("loop phase (Ao)"); xlabel("frequency (Hz)"); legend("original", "Omit C_{o1}", "more simp");

non-dominant pole in Sansen's book

image-20230103231300609

Following demonstrate how derive \(f_{nd}\) from Razavi's equation. We copy \(\omega_2\) here \[ \omega_2 = \frac{R_{o1}C_c\cdot g_{m2}R_{o2}+R_{o2}(C_c+C_L)+R_{o1}(C_{o1}+C_c)}{R_{o1}R_{o2}(C_cC_{o1}+C_LC_{o1}+C_LC_c)} \] which can be reduced as below

\[\begin{align} \omega_2 &= \frac{R_{o1}C_c\cdot g_{m2}R_{o2}+R_{o2}(C_c+C_L)+R_{o1}(C_{o1}+C_c)}{R_{o1}R_{o2}(C_cC_{o1}+C_LC_{o1}+C_LC_c)} \\ &= \frac{R_{o1}C_c\cdot g_{m2}R_{o2}}{R_{o1}R_{o2}(C_cC_{o1}+C_LC_{o1}+C_LC_c)} \\ &= \frac{C_c\cdot g_{m2}}{C_cC_{o1}+C_LC_{o1}+C_LC_c} \\ &= \frac{g_{m2}}{C_{o1}+C_L\frac{C_{o1}}{C_c}+C_L} \\ &= \frac{g_{m2}}{C_L\frac{C_{o1}}{C_c}+C_L} \\ &= \frac{g_{m2}}{C_L} \cdot \frac{1}{1+\frac{C_{o1}}{C_c}} \end{align}\]

image-20230129221424763

Exercise of 2-stage opamp

image-20230105001747174

image-20230105001758050

image-20230105001812304

image-20230105001849554

\(R_{o1}\) and \(R_{o2}\) don't affect stability, if \(f_{nd}>3\text{GBW}\)

DC gain: \(g_{m1}g_{m2}R_{o1}R_{o2}\)

dominant pole: \(\omega_d=\frac{1}{R_{o1}\cdot g_{m2}R_{o2}C_c}\)

image-20230105001923985

image-20230105002653735

\(20log_{10}(1.414^2)=6\text{dB}\)

Cascode Compensation

This cascode compensation topology is popularly known as ahuja compensation

The cause of the positive zero is the feedforward current through \(C_m\).

To abolish this zero, we have to cut the feedforward path and create a unidirectional feedback through \(C_m\).

  1. Adding a resistor(nulling resistor) is one way to mitigate the effect of the feedforward current.

  2. Another approach uses a current buffer cascode to pass the small-signal feedback current but cut the feedforward current

People name this approach after the author Ahuja

The benefits of Ahuja compensation over Miller compensation are severa

  • better PSRR

  • higher unity-gain bandwidth using smaller compensation capacitor

  • ability to cope better with heavy capacitive and resistive loads


image-20240817193513058

image-20240817201727109

Of course, , if the capacitance at the gate of \(M_1\) is taken into account, pole splitting is less pronounced.


including \(r_\text{o2}\)

image-20240819202642809 \[ \frac{V_{out}}{I_{in}} \approx \frac{-g_{m1}R_SR_L(g_{m2}+C_Cs)}{\frac{R_S+r_\text{o2}}{r_\text{o2}}R_LC_LC_Cs^2+g_{m1}g_{m2}R_LR_SC_Cs+g_{m2}} \] The poles as

\[\begin{align} \omega_{p1} &\approx \frac{1}{g_{m1}R_LR_SC_c} \\ \omega_{p2} &\approx \frac{g_{m2}R_Sg_{m1}}{C_L}\frac{r_\text{o2}}{R_S+r_\text{o2}} \end{align}\]

and zero is not affected, which is \(\omega_z =\frac{g_{m2}}{C_C}\)

the above model simulation result is shown below

image-20240819221653262

the zero is located between two poles

take into the capacitance at the gate of \(M_1\) and all other second-order effect

image-20240819222727276

intuitive analysis of zero

miller compensation

  • zero in the right half plane \[ g_\text{m1}V_P = sC_c V_P \]

cascode compensation

  • zero in the left half plane \[ g_\text{m2}V_X = - sC_c V_X \]

zero_loc.drawio

Mitigate Impact of Zero

cascode_compensation

dominant pole \[ \omega_\text{p,d} = \frac {1} {R_\text{eq}g_\text{m9}R_{L}C_{c}} \] first nondominant pole \[ \omega_\text{p,nd} = \frac {g_\text{m4}R_\text{eq}g_\text{m9}} {C_L} \] zero \[ \omega_\text{z} = (g_\text{m4}R_\text{eq})(\frac {g_\text{m9}} {C_c}) \] a much greater magnitude than \(g_\text{m9}/C_C\)

ahuja variations

image-20250609210452373

Pole-Zero Compensation

Pole-Zero Compensation is also known as Lead Compensation, Parallel Compensation

image-20220307234938855

Note: The dominant pole is at output of the first stage, i.e. \(\frac{1}{R_{EQ}C_{EQ}}\).

image-20221006001114562

Pole & Zero in transfer function

Design with operational amplifiers and analog integrated circuits / Sergio Franco, San Francisco State University. – Fourth edition

image-20221005220854411

\[ Y = \frac{1}{R_1} + sC_1+\frac{1}{R_c+1/SC_c} \]

\[\begin{align} Z &= \frac{1}{\frac{1}{R_1} + sC_1+\frac{1}{R_c+1/SC_c}} \\ &= \frac{R_1(1+sR_cC_c)}{s^2R_1C_1R_cC_c+s(R_1C_c+R_1C_1+R_cC_c)+1} \end{align}\] If \(p_{1c} \ll p_{3c}\), two real roots can be found \[\begin{align} p_{1c} &= \frac{1}{R_1C_c+R_1C_1+R_cC_c} \\ p_{3c} &= \frac{R_1C_c+R_1C_1+R_cC_c}{R_1C_1R_cC_c} \end{align}\]

The additional zero is \[ z_c = \frac{1}{R_cC_c} \] Given \(R_c \ll R\) and \(C_c \gg C\) \[\begin{align} p_{1c} &\simeq \frac{1}{R_1(C_c+C_1)} \simeq \frac{1}{R_1C_c}\\ p_{3c} &= \frac{1}{R_cC_1}+\frac{1}{R_cC_c}+\frac{1}{R_1C_1} \simeq \frac{1}{R_cC_1} \end{align}\]

The output pole is unchanged, which is \[ p_2 = \frac{1}{R_LC_L} \] We usually cancel \(p_2\) with \(z_c\), i.e. \[ R_cC_c=R_LC_L \]

Phase margin

unity-gain frequency \(\omega_t\) \[ \omega_t = A_\text{DC}\cdot P_{1c} =\frac{g_{m1}g_{m2}R_L}{C_c} \]

  1. PM=45\(^o\) \[ p_{3c} = \omega_t \] Then, \(C_c\) and \(R_c\) can be obtained

    \[\begin{align} R_c &= \sqrt{\frac{R_1}{C_1\cdot A_{DC}\cdot p_2}}=\sqrt{\frac{R_1\cdot R_LC_L}{C_1\cdot A_{DC}}} \\ C_c &= \sqrt{\frac{A_{DC}\cdot C_1}{R_1\cdot p_2}}=\sqrt{\frac{A_{DC}\cdot C_1 \cdot R_LC_L}{R_1}} \end{align}\]

  2. PM=60\(^o\) \[ p_{3c} = 2\cdot\omega_t \] Then, \(C_c\) and \(R_c\) can be obtained \[\begin{align} R_c &= \sqrt{\frac{R_1}{C_1\cdot 2A_{DC}\cdot p_2}} = \sqrt{\frac{R_1\cdot R_LC_L}{C_1\cdot 2A_{DC}}} \\ &= \sqrt{\frac{C_L}{2g_{m1}g_{m2}C_1}}\\ C_c &= \sqrt{\frac{2A_{DC}\cdot C_1}{R_1\cdot p_2}} = \sqrt{\frac{2A_{DC}\cdot C_1 \cdot R_LC_L}{R_1}} \\ &= R_L\sqrt{2g_{m1}g_{m2}C_1C_L} \end{align}\]

    for the unity-gain frequency \(\omega_t\) we find \[ \omega_t = \sqrt{\frac{1}{2}\cdot \frac{g_{m1}g_{m2}}{C_1C_L}} \] The parallel compensation shows a remarkably good result. The new 0 dB frequency lies only a factor \(\sqrt{2}\) lower than the theoretical maximum

To increase \(\phi_m\), we need to raise \(C_c\) a bit while lowering \(R_c\) in proportion in order to maintain pole-zero cancellation. This causes \(p_{1c}\) and \(p_{3c}\) to split a bit further apart.

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clc;
clear;

fd = 84*1e3; % dominant freq, unit: Hz
fnd = 3.25*1e6; % unit: Hz
C = 478*1e-15;
R = 1/fd/(2*pi)/C;
Adc = 10^(80/20);

ri = 2; % PM=45: 1; PM=60: 2
Rc = (R/C/fnd/2/pi/ri/Adc)^0.5; % compensation resistor
Cc = (ri*Adc*C/fnd/2/pi/R)^0.5; % compensation capacitor

wzc = 1/2/pi/Rc/Cc; % zero frequency

ECEN 457 (ESS). Op-Amps Stability and Frequency Compensation Techniques [https://people.engr.tamu.edu/s-sanchez/457%20Stability%20Final.pdf]

image-20251025011022249

image-20251025011047343

image-20251025011056582

Pole-Zero Doublet

image-20251103210637155


image-20251102164516063

The denominator part of \(H_{closed}(s)\) is \[ D(s) = \frac{s^2}{(A_0+1)\omega_{p1}\omega_{p2}}+\frac{\frac{1}{\omega_{p1}} + \frac{1}{\omega_{p2}}+\frac{A_0}{\omega_{z}}}{A_0+1}s+1 \]

image-20251102165416360

suppose \(\omega_{pA} \ll\omega_{pB}\), then \[ D(s) = \left( 1+ \frac{s}{\omega_{pA}}\right)\left( 1+ \frac{s}{\omega_{pB}}\right)\approx \frac{s^2}{\omega_{pA}\omega_{pB}}+\frac{s}{\omega_{pA}} + 1 \]

Thus, the two poles of the closed-loop transfer function of system are \[\begin{align} \omega_{pA} &= \frac{A_0+1}{\frac{1}{\omega_{p1}} + \frac{1}{\omega_{p2}}+\frac{A_0}{\omega_{z}}} = \frac{(A_0+1)\omega_{p1} \omega_{p2}}{\omega_{p1} + \omega_{p2} + \frac{A_0}{\omega_z}\omega_{p1} \omega_{p2}}\\ \omega_{pB} &= \omega_{p1} + \omega_{p2} + \frac{A_0}{\omega_z}\omega_{p1} \omega_{p2} \end{align}\]

image-20251102170244359

non-dominant pole \(\omega_{p2}\) and zero \(\omega_z\) are in UGB

\[\begin{align} \omega_{pA} &\approx \omega_{p2}\\ \omega_{pB} &\approx (1+A_0)\omega_{p1} \end{align}\] Then, closed-loop transfer function is \[ H_{closed}(s) \approx \frac{\frac{A_0}{A_0+1}\left(1+\frac{s}{\omega_z}\right)}{\left(1+\frac{s}{(1+A_0)\omega_{p1}}\right)\left( 1+\frac{s}{\omega_{p2}} \right)} \]

Consider the Laplace transform function of step response, \(X(s)=\frac{1}{s}\) \[ Y(s)=\frac{1}{s}\times H_{closed}(s) \] Thus, the small-signal step response of the closed-loop amplifier is \[ y(t)=\frac{A_0}{A_0+1}\left[1-e^{-(A_0+1)\omega_{p1}t}-\left(1-\frac{\omega_{p2}}{\omega_z}\right)e^{-\omega_{p2}t} \right]u(t) \] Since, \(\omega_{p2}\ll (1+A_0)\omega_{p1}\). rewrite the \(y(t)\) \[ y(t)\approx \frac{A_0}{A_0+1}\left[1-\left(1-\frac{\omega_{p2}}{\omega_z}\right)e^{-\omega_{p2}t} \right]u(t) \] image-20251102173521194

perfect pole-zero cancellation with \(\omega_{p2}=\omega_z\) \[ y(t) \approx \frac{A_0}{A_0+1}\left[1-e^{-(A_0+1)\omega_{p1}t}\right]u(t) \approx \frac{A_0}{A_0+1}u(t) \]

image-20251102194445318

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clear
clc

A0 = 1e6;
wp1= 2*pi*1; % 1Hz
wpz = 2*pi*2e4; % 20KHz

s=tf('s');

wp2 =0.5*wpz;
wz =2*wpz;
Ho = A0*(1+s/wz)/(1+s/wp1)/(1+s/wp2);
Hc = Ho/(1+Ho);


wi = logspace(-3, 7, 100000)*2*pi;
ti = linspace(0, 100,100000)*1e-6;

[mag_0p1, phase_0p1, wout_0p1] = bode(Ho, wi);
[vo_0p1, to_0p1] = step(Hc, ti);


wp2 =1*wpz;
wz =1*wpz;
Ho = A0*(1+s/wz)/(1+s/wp1)/(1+s/wp2);
Hc = Ho/(1+Ho);
[mag_1p0, phase_1p0, wout_1p0] = bode(Ho, wi);
[vo_1p0, to_1p0] = step(Hc, ti);


wp2 =2*wpz;
wz =0.5*wpz;
Ho = A0*(1+s/wz)/(1+s/wp1)/(1+s/wp2);
Hc = Ho/(1+Ho);
[mag_10p0, phase_10p0, wout_10p0] = bode(Ho, wi);
[vo_10p0, to_10p0] = step(Hc, ti);

subplot(2,2,1)
semilogx(wout_0p1(:)/2/pi, 20*log10(mag_0p1(:)),'b-',LineWidth=2);
hold on
semilogx(wout_1p0(:)/2/pi, 20*log10(mag_1p0(:)),'r-',LineWidth=2);
semilogx(wout_10p0(:)/2/pi, 20*log10(mag_10p0(:)),'g-',LineWidth=2);
grid on; xlabel('Hz'); ylabel('Mag (dB)');
legend('\omega_{p2}<\omega_{z}', '\omega_{p2}=\omega_{z}', '\omega_{p2}>\omega_{z}')

subplot(2,2,3)
semilogx(wout_0p1(:)/2/pi, phase_0p1(:),'b-',LineWidth=2);
hold on
semilogx(wout_1p0(:)/2/pi, phase_1p0(:),'r-',LineWidth=2);
semilogx(wout_10p0(:)/2/pi, phase_10p0(:),'g-',LineWidth=2);
grid on; xlabel('Hz'); ylabel('Phase')
legend('\omega_{p2}<\omega_{z}', '\omega_{p2}=\omega_{z}', '\omega_{p2}>\omega_{z}')

subplot(2,2,[2 4])
plot(to_0p1(:), vo_0p1(:),'b-',LineWidth=2);
hold on
plot(to_1p0(:), vo_1p0(:),'r-',LineWidth=2);
plot(to_10p0(:), vo_10p0(:),'g-',LineWidth=2);
grid on; xlabel('time'); ylabel('V')
legend('\omega_{p2}<\omega_{z}', '\omega_{p2}=\omega_{z}', '\omega_{p2}>\omega_{z}')

image-20230108233523345

image-20230108234123707

The zero comes from the mirror node

Thanks to unity gain buffer, zero is alleviated for \(C_c\)

image-20251103220259992

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A0 = 1e6;
wp1= 2*pi*1; % 1Hz
wpz = 2*pi*2e4; % 20KHz
wp2 = 5*A0*wp1;

s=tf('s');

rio = 0.8;
wpx =rio*wpz;
wzx =wpz/rio;
wp1x = 1.5*wp1; % keep UGB constant by changing dominant pole wp1x
Ho = A0*(1+s/wzx)/(1+s/wp1x)/(1+s/wp2)/(1+s/wpx); % 2 poles + pole-zero doublet pair
Hc = Ho/(1+Ho);


wi = logspace(-3, 7, 100000)*2*pi;
ti = linspace(0, 100,100000)*1e-6;

[mag_0p1, phase_0p1, wout_0p1] = bode(Ho, wi);
[vo_0p1, to_0p1] = step(Hc, ti);


rio = 1;
wpx =rio*wpz;
wzx =wpz/rio;
wp1x = 1*wp1;
Ho = A0*(1+s/wzx)/(1+s/wp1x)/(1+s/wp2)/(1+s/wpx);
Hc = Ho/(1+Ho);
[mag_1p0, phase_1p0, wout_1p0] = bode(Ho, wi);
[vo_1p0, to_1p0] = step(Hc, ti);


rio = 1.25;
wpx =rio*wpz;
wzx =wpz/rio;
wp1x = 0.65*wp1;
Ho = A0*(1+s/wzx)/(1+s/wp1x)/(1+s/wp2)/(1+s/wpx);
Hc = Ho/(1+Ho);
[mag_10p0, phase_10p0, wout_10p0] = bode(Ho, wi);
[vo_10p0, to_10p0] = step(Hc, ti);

subplot(2,2,1)
semilogx(wout_0p1(:)/2/pi, 20*log10(mag_0p1(:)),'b-',LineWidth=2);
hold on
semilogx(wout_1p0(:)/2/pi, 20*log10(mag_1p0(:)),'r-',LineWidth=2);
semilogx(wout_10p0(:)/2/pi, 20*log10(mag_10p0(:)),'g-',LineWidth=2);
grid on; xlabel('Hz'); ylabel('Mag (dB)');
legend('\omega_{p2}<\omega_{z}', '\omega_{p2}=\omega_{z}', '\omega_{p2}>\omega_{z}')

subplot(2,2,3)
semilogx(wout_0p1(:)/2/pi, phase_0p1(:),'b-',LineWidth=2);
hold on
semilogx(wout_1p0(:)/2/pi, phase_1p0(:),'r-',LineWidth=2);
semilogx(wout_10p0(:)/2/pi, phase_10p0(:),'g-',LineWidth=2);
grid on; xlabel('Hz'); ylabel('Phase')
legend('\omega_{p2}<\omega_{z}', '\omega_{p2}=\omega_{z}', '\omega_{p2}>\omega_{z}')

subplot(2,2,[2 4])
plot(to_0p1(:), vo_0p1(:),'b-',LineWidth=2);
hold on
plot(to_1p0(:), vo_1p0(:),'r-',LineWidth=2);
plot(to_10p0(:), vo_10p0(:),'g-',LineWidth=2);
grid on; xlabel('time'); ylabel('V')
legend('\omega_{p2}<\omega_{z}', '\omega_{p2}=\omega_{z}', '\omega_{p2}>\omega_{z}')

reference

Viola Schaffer, ISSCC 2021 Tutorials Designing Amplifiers for Stability [pdf]

J. H. Huijsing, "Operational Amplifiers, Theory and Design, 3rd ed. New York: Springer, 2017"

Razavi, Behzad. Design of Analog CMOS Integrated Circuits. India: McGraw-Hill, 2017. [pdf]

Sansen, Willy M. Analog Design Essentials. Germany: Springer US, 2006.

Gray, P. R., Hurst, P. J., Lewis, S. H., & Meyer, R. G. (2024). Analysis and design of analog integrated circuits (Sixth edition.). John Wiley & Sons, Inc..


Ahuja Compensation

B. K. Ahuja, "An Improved Frequency Compensation Technique for CMOS Operational Amplifiers," IEEE 1. Solid-State Circuits, vol. 18, no. 6, pp. 629-633, Dec. 1983. [https://sci-hub.se/10.1109/JSSC.1983.1052012]

U. Dasgupta, "Issues in "Ahuja" frequency compensation technique", IEEE International Symposium on Radio-Frequency Integration Technology, 2009. [https://sci-hub.se/10.1109/RFIT.2009.5383679]

R. J. Reay and G. T. A. Kovacs, "An unconditionally stable two-stage CMOS amplifier," in IEEE Journal of Solid-State Circuits, vol. 30, no. 5, pp. 591-594, May 1995 [https://sci-hub.se/10.1109/4.384174]

A. Garimella and P. M. Furth, "Frequency compensation techniques for op-amps and LDOs: A tutorial overview," 2011 IEEE 54th International Midwest Symposium on Circuits and Systems (MWSCAS), 2011 [https://sci-hub.se/10.1109/MWSCAS.2011.6026315]

H. Aminzadeh, R. Lotfi and S. Rahimian, "Design Guidelines for Two-Stage Cascode-Compensated Operational Amplifiers," 2006 13th IEEE International Conference on Electronics, Circuits and Systems, 2006 [https://sci-hub.se/10.1109/ICECS.2006.379776]

H. Aminzadeh and K. Mafinezhad, "On the power efficiency of cascode compensation over Miller compensation in two-stage operational amplifiers," Proceeding of the 13th international symposium on Low power electronics and design (ISLPED '08), Bangalore, India, 2008 [https://sci-hub.se/10.1145/1393921.1393995]

Stabilizing a 2-Stage Amplifier URL:https://a2d2ic.wordpress.com/2016/11/10/stabilizing-a-2-stage-amplifier/

EE 240B: Advanced Analog Circuit Design, Prof. Bernhard E. Boser [OTA II, Multi-Stage]


Parallel Compensation

R.Eschauzier "Wide Bandwidth Low Power Operational Amplifiers", Delft University Press, 1994.

Gene F. Franklin, J. David Powell, and Abbas Emami-Naeini. 2018. Feedback Control of Dynamic Systems (8th Edition) (8th. ed.). Pearson. 6.7 Compensation

Application Note AN-1286 Compensation for the LM3478 Boost Controller

ECEN 607 Advanced Analog Circuit Design Techniques Spring 2017 [Lect 1D Op-Amps Stability and Frequency Compensation Techniques]

Sergio Franco, San Francisco State University, Design with Operational Amplifiers and Analog Integrated Circuits, 4/e [pdf]


Pole-Zero Doublet

Elad Alon, Lecture 10: Settling-Limited Amplifier Design Methodology, EE 240B – Spring 2018, Advanced Analog Integrated Circuits [https://inst.eecs.berkeley.edu/~ee240b/sp18/lectures/Lecture10_Settling_Design_2up.pdf]

Eric Chang, Prof. Elad Alon EE240B HW3 [https://inst.eecs.berkeley.edu/~ee240b/sp18/homeworks/hw3.pdf and https://inst.eecs.berkeley.edu/~ee240b/sp18/homeworks/hw3_soln.pdf]

Prof. Tai-Haur Kuo, Analog IC Design ( 類比積體電路設計 ), Operational Amplifiers http://msic.ee.ncku.edu.tw/course/aic/201809/chapter5.pdf

SERGIO FRANCO, Demystifying pole-zero doublets [https://www.edn.com/demystifying-pole-zero-doublets/]

B. Y. T. Kamath, R. G. Meyer and P. R. Gray, "Relationship between frequency response and settling time of operational amplifiers," in IEEE Journal of Solid-State Circuits, vol. 9, no. 6, pp. 347-352, Dec. 1974, [https://sci-hub.se/10.1109/JSSC.1974.1050527]

P. R. Gray and R. G. Meyer, "MOS operational amplifier design-a tutorial overview," in IEEE Journal of Solid-State Circuits, vol. 17, no. 6, pp. 969-982, Dec. 1982, [https://sci-hub.se/10.1109/JSSC.1982.1051851]

image-20251025022548719

show qualitatively how changing pole locations in the s-plane affect impulse responses

image-20251025091424995

image-20251025093453473

\(\omega_d\) called damped natural frequency \[ \omega_n^2 = \sigma^2 + \omega_d^2 \] image-20251025093918419

image-20251025094454542

Resonant Frequency

image-20251025112923346

Phase Margin & Damping Factor

  • Phase Margin is defined for open loop system

  • Damping Factor (\(\zeta\)) is defined for close loop system

image-20251025101546484

image-20251025101113687

We can analyze open loop system in a better perspective because it is simpler. So, we always use the loop gain analysis to find the phase margin and see whether the system is stable or not.

image-20251025101756106


image-20251025215110687

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zta = 0:.001:1;
PM = atan(2*zta ./ sqrt(sqrt(1+4*zta.^4) - 2*zta.^2))/pi*180;

[~, idx45] = min(abs(PM - 45));
[~, idx60] = min(abs(PM - 60));
[~, idx70] = min(abs(PM - 70));

plot(PM, zta, 'b', LineWidth=3);
hold on
plot(PM(idx45), zta(idx45), 'ro', MarkerFaceColor='r', LineWidth=3, MarkerSize=8)
text(PM(idx45)+2, zta(idx45), ['\zeta=' num2str(zta(idx45)) ' @PM=45^o'])
plot(PM(idx60), zta(idx60), 'ro', MarkerFaceColor='r', LineWidth=3, MarkerSize=8)
text(PM(idx60)+2, zta(idx60), ['\zeta=' num2str(zta(idx60)) ' @PM=60^o'])
plot(PM(idx70), zta(idx70), 'ro', MarkerFaceColor='r', LineWidth=3, MarkerSize=8)
text(PM(idx70)-10, zta(idx70), ['\zeta=' num2str(zta(idx70)) ' @PM=70^o'])
grid on; xlabel('Phase Margin, ^o'); ylabel('Damping ratio, \zeta')

Zeros & Non-Minimum Phase Zeros

Influence of Zeros and Non-Minimum Phase Zeros of Transfer Functions on Dynamical System Behavior [https://aleksandarhaber.com/effect-of-zeros-of-transfer-functions-on-dynamical-system-behavior/]

The zeros in the right half of the complex plane are called nonminimum phase zeros. Systems with nonminimum phase zeros are called nonminimum phase systems

Zero close to the real pole attenuates the effect of that pole on the system response


Zeros Tend to Increase the Overshoot of the System

image-20251025161530090

image-20251025162936453

image-20251025163805090

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zeta=0.4
omega_n=3
k=1
s=tf('s')

W1=1/(s^2+2*zeta*s+1)
W2=(1/(k*zeta))*s*W1
W3=W1+W2

figure(1)
hold on
step(W1,'r')
step(W2,'k')
step(W3,'b')
grid

Nonminimum Phase Zeros – Effect on the Transient Response

image-20251025164001079

image-20251025164027378

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zeta=0.4
omega_n=3
k=1
s=tf('s')
S1=1/(s^2+2*zeta*s+1)
S2=(1/(k*zeta))*s*S1
S=S1-S2
figure(1)
hold on
step(S1,'r')
step(-S2,'k')
step(S,'b')
grid

\[\begin{align} TF &= \frac{s +\omega_z}{s^2+2\zeta \omega_ns+\omega_n^2} \\ &= \frac{\omega _z}{\omega _n^2}\cdot \frac{1+s/\omega _z}{1+s^2/\omega_n^2+2\zeta s/\omega_n} \end{align}\]

Let \(s=j\omega\) and omit factor, \[ A_\text{dB}(\omega) = 10\log[1+(\frac{\omega}{\omega _z})^2] - 10\log[1+\frac{\omega^4}{\omega_n^4}+\frac{2\omega^2(2\zeta ^2 -1)}{\omega_n^2}] \] peaking frequency \(\omega_\text{peak}\) can be obtained via \(\frac{d A_\text{dB}(\omega)}{d\omega} = 0\) \[ \omega_\text{peak} = \omega_z \sqrt{\sqrt{(\frac{\omega_n}{\omega_z})^4 - 2(\frac{\omega_n}{\omega_z})^2(2\zeta ^2-1)+1} - 1} \]

Settling Time

One Pole

image-20251025110452834

we have \[ \tau \approx \left(1 + \frac{R_1}{R_2}\right)\frac{1}{A_0\omega_0}= \frac{1}{\beta \omega_\text{ugb}} \]

image-20251025110745442

Two Poles

with open-loop transfer function \(A_{OL}=\frac{A_0}{(1+s/\omega_1)(1+s/\omega_2)}\) and assuming \(\omega_1\) is dominant pole, then yield closed-loop transfer function

\[\begin{align} A_{CL}(s) &= \frac{\frac{A_0}{(1+s/\omega_1)(1+s/\omega_2)}}{1+\beta \frac{A_0} {(1+s/\omega_1)(1+s/\omega_2)}} \\ &= \frac{A_0}{1+A_0 \beta}\frac{1}{\frac{s^2}{\omega_1\omega_2(1+A_0\beta)}+\frac{1/\omega_1+1/\omega_2}{1+A_0\beta}s+1} \\ &\approx \frac{A_0}{1+A_0 \beta}\frac{1}{\frac{s^2}{\omega_u\omega_2}+\frac{1}{\omega_u}s+1} \\ &= \frac{A_0}{1+A_0 \beta}\frac{\omega_u\omega_2}{s^2+\omega_2s+\omega_u\omega_2} \end{align}\]

That is \(\omega_n = \sqrt{\omega_u\omega_2}\), \(\zeta = \frac{1}{2}\sqrt{\frac{\omega_2}{\omega_u}}\) , where \(\omega_u\approx \beta A_0 \omega_1\) is the unity gain bandwidth

image-20251025120830411


image-20251025122148288

Rise Time (0% to 100% )

image-20251025131719553

\[ t_r = \frac{\pi - \beta}{\omega_d}=\frac{\pi - \arctan\frac{\omega_n\sqrt{1-\zeta^2}}{\zeta\omega_n}}{\omega_n\sqrt{1-\zeta^2}}\approx\frac{\pi - \arctan\frac{\sqrt{1-\zeta^2}}{\zeta}}{\sqrt{\omega_u\omega_2}\sqrt{1-\zeta^2}}=\frac{\pi - \arctan\frac{\sqrt{1-\zeta^2}}{\zeta}}{\omega_u\sqrt{k(1-\zeta^2)}} \] where \(k = \frac{\omega_2}{\omega_u}\), is the function of PM

PM \(45^o\) \(60^o\) \(70^o\)
\(k\) 1 1.73 2.75
\(\zeta\) 0.42 0.61 0.80
\(t_r\) \(\frac{2.2}{\omega_u}\) \(\frac{2.14}{\omega_u}\) \(\frac{2.53}{\omega_u}\)
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PM = 70;
ztap = 0.80;
k = 1/tan((90-PM)/180*pi)
tr = (pi - atan(sqrt(1-ztap^2)/ztap))/sqrt(k*(1-ztap^2))

Settling Time

Gene F. Franklin, Feedback Control of Dynamic Systems, 8th Edition

image-20251025150948717

As we know \[ \zeta \omega_n=\frac{1}{2}\sqrt{\frac{\omega_2}{\omega_u}}\cdot \sqrt{\omega_u\omega_2}=\frac{1}{2}\omega_2 \]

Then \[ t_s = \frac{9.2}{\omega_2} \]

For \(\text{PM}=70^o\), \(\omega_2 = 2.75\omega_u\), that is \[ t_s \approx \frac{3.35}{\omega_u} \]

For \(\text{PM}=45^o\), \(\omega_2 = \omega_u\), that is \[ t_s \approx \frac{9.2}{\omega_u} \]

Above equation is valid only for underdamped, \(\zeta=\frac{1}{2}\sqrt{\frac{\omega_2}{\omega_u}}\lt 1\), that is \(\omega_2\lt 4\omega_u\)

2 Stage RC filter

High Pass Filter

image-20251025021242772

Since \(1/sC_1+R_1 \gg R_0\) \[ \frac{V_m}{V_i}(s) \approx \frac{R_0}{R_0 + 1/sC_0} = \frac{sR_0C_0}{1+sR_0C_0} \] step response of \(V_m\) \[ V_m(t) = e^{-t/R_0C_0} \] where \(\tau = R_0C_0\)

And \(V_o(s)\) can be expressed as \[\begin{align} \frac{V_o}{V_i}(s) & \approx \frac{sR_0C_0}{1+sR_0C_0} \cdot \frac{sR_1C_1}{1+sR_1C_1} \\ &= \frac{sR_0C_0R_1C_1}{R_0C_0-R_1C_1}\left(\frac{1}{1+sR_1C_1} - \frac{1}{1+sR_0C_0}\right) \end{align}\]

Then step response of \(Vo\) \[\begin{align} Vo(t) &= \frac{R_0C_0R_1C_1}{R_0C_0-R_1C_1} \left(\frac{1}{R_1C_1}e^{-t/R_1C_1} - \frac{1}{R_0C_0}e^{-t/R_0C_0}\right) \\ &= \frac{1}{R_0C_0-R_1C_1}\left(R_0C_0e^{-t/R_1C_1} - R_1C_1e^{-t/R_0C_0}\right) \\ &\approx \frac{1}{R_0C_0-R_1C_1}\left(R_0C_0e^{-t/R_1C_1} - R_1C_1\right) \end{align}\]

where \(\tau=R_1C_1\)


Partial-fraction Expansion

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syms C0
syms R0
syms C1
syms R1
syms s

Z0 = 1/s/C1 + R1;
Z1 = R0*Z0/(R0+Z0);
vm = Z1 / (Z1 + 1/s/C0);
vo = R1/Z0 * vm;
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>> partfrac(vm, s)

ans =

1 - (s*(C1*R0 + C1*R1) + 1)/(C0*C1*R0*R1*s^2 + (C0*R0 + C1*R0 + C1*R1)*s + 1)

>> partfrac(vo, s)

ans =

1 - (s*(C0*R0 + C1*R0 + C1*R1) + 1)/(C0*C1*R0*R1*s^2 + (C0*R0 + C1*R0 + C1*R1)*s + 1)

\[\begin{align} V_m(s) &= 1 - \frac{s(C_1R_0+C_1R_1)+1}{C_0C_1R_0R_1s^2+(C_0R_0+C_1R_0+C_1R_1)s+1} \\ V_o(s) &= 1 - \frac{s(C_0R_0+C_1R_0+C_1R_1)+1}{C_0C_1R_0R_1s^2+(C_0R_0+C_1R_0+C_1R_1)s+1} \end{align}\]


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C0 = 200e-9;
R0 = 50;
C1 = 400e-15;
R1 = 200e3;

s = tf("s");

Z0 = 1/s/C1 + R1;
Z1 = R0*Z0/(R0+Z0);
vm = Z1 / (Z1 + 1/s/C0);
vo = R1/Z0 * vm;

vm_exp = 1 - (s*(C1*R0 + C1*R1) + 1)/(C0*C1*R0*R1*s^2 + (C0*R0 + C1*R0 + C1*R1)*s + 1);
vo_exp = 1 - (s*(C0*R0 + C1*R0 + C1*R1) + 1)/(C0*C1*R0*R1*s^2 + (C0*R0 + C1*R0 + C1*R1)*s + 1);

figure(1)
subplot(1,2,1)
step(vm, 500e-9, 'k-o');
hold on;
step(vm_exp, 500e-9, 'r-^')
title('vm step response')
grid on;
legend()


subplot(1,2,2)
step(vo, 500e-9, 'k-o');
hold on;
step(vo_exp, 500e-9, 'r-^')
title('vo step response')
grid on;
legend()


% with approximation
figure(2)
vm_exp2 = s*R0*C0/(1+s*R0*C0);
vo_exp2 = s*R0*C0/(1+s*R0*C0) * s*R1*C1/(1+s*R1*C1);

subplot(1,2,1)
step(vm, 500e-9, 'k-o');
hold on;
step(vm_exp2, 500e-9, 'r-^')
title('vm step response')
grid on;
legend()

subplot(1,2,2)
step(vo, 500e-9, 'k-o');
hold on;
step(vo_exp2, 500e-9, 'r-^')
title('vo step response')
grid on;
legend()

image-20251025021442878

image-20251025021458532


spectre vs matlab

image-20251025020526197

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C0 = 200e-9;
R0 = 50;
C1 = 400e-15;
R1 = 200e3;

s = tf("s");

Z0 = 1/s/C1 + R1;
Z1 = R0*Z0/(R0+Z0);
vm = Z1 / (Z1 + 1/s/C0);
vo = R1/Z0 * vm;

step(vm, 500e-9);
hold on;
step(vo, 500e-9);

grid on
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>> vm

vm =

1.024e-44 s^5 + 2.56e-37 s^4 + 1.6e-30 s^3
-----------------------------------------------------------
1.024e-44 s^5 + 2.571e-37 s^4 + 1.626e-30 s^3 + 1.6e-25 s^2

Continuous-time transfer function.

>> vo

vo =

8.194e-52 s^6 + 2.048e-44 s^5 + 1.28e-37 s^4
---------------------------------------------------------------------------
8.194e-52 s^6 + 3.081e-44 s^5 + 3.871e-37 s^4 + 1.638e-30 s^3 + 1.6e-25 s^2

Continuous-time transfer function.

Low Pass Filter

image-20251025014308419

\[ \left\{ \begin{array}{cl} \frac{V_i - V_m}{R_0} &= C_0\frac{dV_m}{dt} + C_1\frac{dV_o}{dt} \\ \frac{V_m - V_o}{R_1} &= C_1\frac{dV_o}{dt} \\ V_m(t=0) &= 1 \\ V_o(t=0) &= 0 \end{array} \right. \]

Take into initial condition account \[ \frac{dV_m}{dt} \overset{\mathcal{L}}{\longrightarrow} sV_M-V_{m0} \] where \(V_{m0} = 1\)

\[\begin{align} V_O(s) &= \frac{V_I}{s^2R_0C_0R_1C_1+s(R_0C_0+R_1C_1+R_0C_1)+1} + \frac{V_{m0}R_0C_0}{s^2R_0C_0R_1C_1+s(R_0C_0+R_1C_1+R_0C_1)+1} \\ &\approx \frac{V_I}{s^2R_0C_0R_1C_1+sR_0(C_0+C_1)+1} + \frac{V_{m0}R_0C_0}{s^2R_0C_0R_1C_1+sR_0(C_0+C_1)+1} \\ &= \frac{V_I}{R_0(C_0+C_1)}\left(\frac{R_0(C_0+C_1)}{sR_0(C_0+C_1)+1} - \frac{R_1\frac{C_0C_1}{C_0+C_1}}{sR_1\frac{C_0C_1}{C_0+C_1}+1}\right) \\ &+ \frac{C_0}{C_0+C_1}\left(\frac{R_0(C_0+C_1)}{sR_0(C_0+C_1)+1} - \frac{R_1\frac{C_0C_1}{C_0+C_1}}{sR_1\frac{C_0C_1}{C_0+C_1}+1}\right) \end{align}\]

with \(V_I = \frac{1}{s}\), using inverse Laplace transform \[\begin{align} V_o(t) &= 1 - \frac{C_1}{C_0+C_1}e^{-t/\tau_0}-\frac{C_0}{C_0+C_1}e^{-t/\tau_1} - \frac{R_1C_0C_1}{R_0(C_0+C_1)^2} \tag{Eq.0} \\ &= 1 - \frac{C_1}{C_0+C_1}e^{-t/\tau_0}-\frac{C_0}{C_0+C_1}e^{-t/\tau_1} \tag{Eq.1} \end{align}\]

where

\[\begin{align} \tau_0 &= R_0(C_0+C_1) \\ \tau_1 &= R_1C_1\cdot \frac{C_0}{C_0+C_1} \end{align}\]

and \(\tau_0 \gg \tau_1\)


image-20251025022237093

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R0 = 100e3;
C0 = 200e-15;
R1 = 10e3;
C1 = 100e-15;

s = tf('s');

VI = 1/s;
Vm0 = 1;

deno = (s^2*R0*C0*R1*C1 + s*(R0*C0 + R1*C1 + R0*C1) + 1);
VO = VI/deno + (Vm0*R0*C0)/deno;
VM = (1+s*R1*C1)*VO;

t = linspace(0,40,1e3); % ns
[vot, ~] = impulse(VO, t*1e-9);
[vmt, ~] = impulse(VM, t*1e-9);

tau0 = R0*(C0+C1)*1e9; %ns
tau1 = R1*C0*C1/(C0+C1)*1e9; %ns
y0 = 1 - C1/(C0+C1)*exp(-t/tau0) - C0/(C0+C1)*exp(-t/tau1) - R1*C0*C1/R0/(C0+C1)^2; % Eq.0
y1 = 1 - C1/(C0+C1)*exp(-t/tau0) - C0/(C0+C1)*exp(-t/tau1); % Eq.1

plot(t, vot, t, vmt,LineWidth=2);
hold on
plot(t,y0, t,y1, LineWidth=2, LineStyle="--" );
xlim([-10, 40])
legend('V_o(t)', 'V_m(t)','y_0(t)', 'y_1(t)', fontsize=12)
xlabel('t (ns)')
ylabel('mag (V)')
grid on;

reference

Gene F. Franklin, J. David Powell, and Abbas Emami-Naeini. 2018. Feedback Control of Dynamic Systems (8th Edition) (8th. ed.). Pearson. [pdf]

Katsuhiko Ogata, Modern Control Engineering, 5th edition [pdf]

C. T. Chuang, "Analysis of the settling behavior of an operational amplifier," in IEEE Journal of Solid-State Circuits, vol. 17, no. 1, pp. 74-80, Feb. 1982 [https://sci-hub.se/10.1109/JSSC.1982.1051689]

Are AC-Driven Circuits Linear?

\[ f(x_1 + x_2)= f(x_1)+ f(x_2) \]

Often, AC-driven circuits can be mistaken as non-linear as the basis that determines the linearity of a circuit is the relationship between the voltage and current.

While an AC signal varies with time, it still exhibits a linear relationship across elements like resistors, capacitors, and inductors. Therefore, AC driven circuits are linear.

Phasor

Phasor concept has no real physical significance. It is just a convenient mathematical tool.

Phasor analysis determines the steady-state response to a linear circuit driven by sinusoidal sources with frequency \(f\)

If your circuit includes transistors or other nonlinear components, all is not lost. There is an extension of phasor analysis to nonlinear circuits called small-signal analysis in which you linearize the components before performing phasor analysis - AC analyses of SPICE

A sinusoid is characterized by 3 numbers, its amplitude, its phase, and its frequency. For example \[ v(t) = A\cos(\omega t + \phi) \tag{1} \] In a circuit there will be many signals but in the case of phasor analysis they will all have the same frequency. For this reason, the signals are characterized using only their amplitude and phase.

The combination of an amplitude and phase to describe a signal is the phasor for that signal.

Thus, the phasor for the signal in \((1)\) is \(A\angle \phi\)

In general, phasors are functions of frequency

Often it is preferable to represent a phasor using complex numbers rather than using amplitude and phase. In this case we represent the signal as: \[ v(t) = \Re\{Ve^{j\omega t} \} \tag{2} \] where \(V=Ae^{j\phi}\) is the phasor.

\((1)\) and \((2)\) are the same

Phasor Model of a Resistor

A linear resistor is defined by the equation \(v = Ri\)

Now, assume that the resistor current is described with the phasor \(I\). Then \[ i(t) = \Re\{Ie^{j\omega t}\} \] \(R\) is a real constant, and so the voltage can be computed to be \[ v(t) = R\Re\{Ie^{j\omega t}\} = \Re\{RIe^{j\omega t}\} = \Re\{Ve^{j\omega t}\} \] where \(V\) is the phasor representation for \(v\), i.e. \[ V = RI \]

  1. Thus, given the phasor for the current we can directly compute the phasor for the voltage across the resistor.

  2. Similarly, given the phasor for the voltage across a resistor we can compute the phasor for the current through the resistor using \(I = \frac{V}{R}\)

Phasor Model of a Capacitor

A linear capacitor is defined by the equation \(i=C\frac{dv}{dt}\)

Now, assume that the voltage across the capacitor is described with the phasor \(V\). Then \[ v(t) = \Re\{ V e^{j\omega t}\} \] \(C\) is a real constant \[ i(t) = C\Re\{\frac{d}{dt}V e^{j\omega t}\} = \Re\{j\omega C V e^{j\omega t}\} \] The phasor representation for \(i\) is \(i(t) = \Re\{Ie^{j\omega t}\}\), that is \(I = j\omega C V\)

  1. Thus, given the phasor for the voltage across a capacitor we can directly compute the phasor for the current through the capacitor.

  2. Similarly, given the phasor for the current through a capacitor we can compute the phasor for the voltage across the capacitor using \(V=\frac{I}{j\omega C}\)

Phasor Model of an Inductor

A linear inductor is defined by the equation \(v=L\frac{di}{dt}\)

Now, assume that the inductor current is described with the phasor \(I\). Then \[ i(t) = \Re\{ I e^{j\omega t}\} \] \(L\) is a real constant, and so the voltage can be computed to be \[ v(t) = L\Re\{\frac{d}{dt}I e^{j\omega t}\} = \Re\{j\omega L I e^{j\omega t}\} \] The phasor representation for \(v\) is \(v(t) = \Re\{Ve^{j\omega t}\}\), that is \(V = j\omega L I\)

  1. Thus, given the phasor for the current we can directly compute the phasor for the voltage across the inductor.

  2. Similarly, given the phasor for the voltage across an inductor we can compute the phasor for the current through the inductor using \(I=\frac{V}{j\omega L}\)

Impedance and Admittance

Impedance and admittance are generalizations of resistance and conductance.

They differ from resistance and conductance in that they are complex and they vary with frequency.

Impedance is defined to be the ratio of the phasor for the voltage across the component and the current through the component: \[ Z = \frac{V}{I} \]

Impedance is a complex value. The real part of the impedance is referred to as the resistance and the imaginary part is referred to as the reactance

For a linear component, admittance is defined to be the ratio of the phasor for the current through the component and the voltage across the component: \[ Y = \frac{I}{V} \]

Admittance is a complex value. The real part of the admittance is referred to as the conductance and the imaginary part is referred to as the susceptance.

Response to Complex Exponentials

The response of an LTI system to a complex exponential input is the same complex exponential with only a change in amplitude

\[\begin{align} y(t) &= H(s)e^{st} \\ H(s) &= \int_{-\infty}^{+\infty}h(\tau)e^{-s\tau}d\tau \end{align}\]

where \(h(t)\) is the impulse response of a continuous-time LTI system

convolution integral is used here

\[\begin{align} y[n] &= H(z)z^n \\ H(z) &= \sum_{k=-\infty}^{+\infty}h[k]z^{-k} \end{align}\]

where \(h(n)\) is the impulse response of a discrete-time LTI system

convolution sum is used here

The signals of the form \(e^{st}\) in continuous time and \(z^{n}\) in discrete time, where \(s\) and \(z\) are complex numbers are referred to as an eigenfunction of the system, and the amplitude factor \(H(s)\), \(H(z)\) is referred to as the system's eigenvalue

Laplace transform

One of the important applications of the Laplace transform is in the analysis and characterization of LTI systems, which stems directly from the convolution property \[ Y(s) = H(s)X(s) \] where \(X(s)\), \(Y(s)\), and \(H(s)\) are the Laplace transforms of the input, output, and impulse response of the system, respectively

From the response of LTI systems to complex exponentials, if the input to an LTI system is \(x(t) = e^{st}\), with \(s\) the ROC of \(H(s)\), then the output will be \(y(t)=H(s)e^{st}\); i.e., \(e^{st}\) is an eigenfunction of the system with eigenvalue equal to the Laplace transform of the impulse response.

s-Domain Element Models

image-20231223225541693

image-20231223225609893

Sinusoidal Steady-State Analysis

Here Sinusoidal means that source excitations have the form \(V_s\cos(\omega t +\theta)\) or \(V_s\sin(\omega t+\theta)\)

Steady state mean that all transient behavior of the stable circuit has died out, i.e., decayed to zero

image-20231223212820547

image-20231223212846596

image-20231223213016508

\(s\)-domain and phasor-domain

Phasor analysis is a technique to find the steady-state response when the system input is a sinusoid. That is, phasor analysis is sinusoidal analysis.

  • Phasor analysis is a powerful technique with which to find the steady-state portion of the complete response.
  • Phasor analysis does not find the transient response.
  • Phasor analysis does not find the complete response.

The beauty of the phasor-domain circuit is that it is described by algebraic KVL and KCL equations with time-invariant sources, not differential equations of time

image-20231224001422189

image-20231223230739219

The difference here is that Laplace analysis can also give us the transient response

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General Response Classifications

img

  • zero-input response, zero-state response & complete response

    image-20231223235252850

    The zero-state response is given by \(\mathscr{L^1}[H(s)F(s)]\), for the arbitrary \(s\)-domain input \(F(s)\)

    where \(Z_L(s) = sL\), the inductor with zero initial current \(i_L(0)=0\) and \(Z_C(s)=1/sC\) with zero initial voltage \(v_C(0)=0\)

  • transient response & steady-state response

    image-20231224000454014

  • natural response & forced response

    image-20231224000817438


image-20240118212304219

Transfer Functions and Frequency Response

transfer function

The transfer function \(H(s)\) is the ratio of the Laplace transform of the output of the system to its input assuming all zero initial conditions.

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image-20240106185937270

frequency response

An immediate consequence of convolution is that an input of the form \(e^{st}\) results in an output \[ y(t) = H(s)e^{st} \] where the specific constant \(s\) may be complex, expressed as \(s = \sigma + j\omega\)

A very common way to use the exponential response of LTIs is in finding the frequency response i.e. response to a sinusoid

First, we express the sinusoid as a sum of two exponential expressions (Euler’s relation): \[ \cos(\omega t) = \frac{1}{2}(e^{j\omega t}+e^{-j\omega t}) \] If we let \(s=j\omega\), then \(H(-j\omega)=H^*(j\omega)\), in polar form \(H(j\omega)=Me^{j\phi}\) and \(H(-j\omega)=Me^{-j\phi}\). \[\begin{align} y_+(t) & = H(s)e^{st}|_{s=j\omega} = H(j\omega)e^{j\omega t} = M e^{j(\omega t + \phi)} \\ y_-(t) & = H(s)e^{st}|_{s=-j\omega} = H(-j\omega)e^{-j\omega t} = M e^{-j(\omega t + \phi)} \end{align}\]

By superposition, the response to the sum of these two exponentials, which make up the cosine signal, is the sum of the responses \[\begin{align} y(t) &= \frac{1}{2}[H(j\omega)e^{j\omega t} + H(-j\omega)e^{-j\omega t}] \\ &= \frac{M}{2}[e^{j(\omega t + \phi)} + e^{-j(\omega t + \phi)}] \\ &= M\cos(\omega t + \phi) \end{align}\]

where \(M = |H(j\omega|\) and \(\phi = \angle H(j\omega)\)

This means if a system represented by the transfer function \(H(s)\) has a sinusoidal input, the output will be sinusoidal at the same frequency with magnitude \(M\) and will be shifted in phase by the angle \(\phi\)

Laplace transform vs. Fourier transform

  • Laplace transforms such as \(Y(s)=H(s)U(s)\) can be used to study the complete response characteristics of systems, including the transient response—that is, the time response to an initial condition or suddenly applied signal
  • This is in contrast to the use of Fourier transforms, which only take into account the steady-state response

Given a general linear system with transfer function \(H(s)\) and an input signal \(u(t)\), the procedure for determining \(y(t)\) using the Laplace transform is given by the following steps:

image-20240106224403401

Laplace derivative formula at \(t = 0\)

S. Boyd EE102 Table of Laplace Transforms. [https://web.stanford.edu/~boyd/ee102/laplace-table.pdf]

One-Sided (unilateral) and Two-Sided (bilateral) Laplace Transforms

[https://sps.ewi.tudelft.nl/Education/courses/ee2s11/slides/3_laplace_P.pdf]

reference

Ken Kundert. Introduction to Phasors. Designer’s Guide Community. September 2011.

How to Perform Linearity Circuit Analysis [https://resources.pcb.cadence.com/blog/2021-how-to-perform-linearity-circuit-analysis]

Stephen P. Boyd. EE102 Lecture 7 Circuit analysis via Laplace transform [https://web.stanford.edu/~boyd/ee102/laplace_ckts.pdf]

Cheng-Kok Koh, EE695K VLSI Interconnect, S-Domain Analysis [https://engineering.purdue.edu/~chengkok/ee695K/lec3c.pdf]

Kenneth R. Demarest, Circuit Analysis using Phasors, Laplace Transforms, and Network Functions [https://people.eecs.ku.edu/~demarest/212/Phasor%20and%20Laplace%20review.pdf]

DeCarlo, R. A., & Lin, P.-M. (2009). Linear circuit analysis : time domain, phasor, and Laplace transform approaches (3rd ed).

Davis, Artice M.. "Linear Circuit Analysis." The Electrical Engineering Handbook - Six Volume Set (1998)

Duane Marcy, Fundamentals of Linear Systems [http://lcs-vc-marcy.syr.edu:8080/Chapter22.html]

Gene F. Franklin, J. David Powell, and Abbas Emami-Naeini. 2018. Feedback Control of Dynamic Systems (8th Edition) (8th. ed.). Pearson.

Data Register, DR:

  • Bypass Register, BR
  • Boundary Scan Register, BSR

Instruction Register, IR

TAP Controller

image-20240113191225838

  • FSM and Shift Register of DR and IR works at the posedge of the clock
  • TMS, TDI, TDO and Hold Register of DR and IR changes value at the negedge of the clock

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capture IR 01, the fixed is for easier fault detection

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After power-up, they may not be in sync, but there is a trick. Look at the state machine and notice that no matter what state you are, if TMS stays at "1" for five clocks, a TAP controller goes back to the state "Test-Logic Reset". That's used to synchronize the TAP controllers.

It is important to note that in a typical Boundary-Scan test, the time between launching a signal from driver (at the falling edge of test clock (TCK) in the Update-DR or Update-IR TAP Controller state) and capturing that signal (at the rising edge of TCK in the Caputre-DR TAP Controller state) is no less tha 2.5 TCK cycles

Further, the time between successive launches on a driver is governed - not only by the TCk rate - but by the amount of serial data shifting needed to load the next pattern data in the concatenated Boundary-Scan Registers of the Boundary-Scan chain

Thus the effective test data rate of a driver could be thousands of the times lower than the TCK rate

  1. For DC-coupled interconnect, this time is of no concern
  2. For AC-coupled interconnect, the signal may easily decay partially or completely before it can be captured
  3. If only partial decay occurs before capture, that decay will very likely be completed before the driver produces the next edge

AC-coupling

In general, AC-coupling can distort a signal transmitted across a channel depending on its frequency.

Figure 5

  • The high frequency signal is relatively unaffected by the coupling
  • The low frequency signal is severely impacted
    1. it decays to \(V_T\) after a few time constants
    2. its amplitude is double the input amplitude > transient response, before AC-coupling capacitor: \(-A_p \to A_p\); after AC-coupling capacitor \(V_T \to V_T+2A_p\) > A key item to note is that the transitions in the original signal are preserved, although their start and end points are offset > > compared to where they were in the high frequency

Test signal implementation

The test data is either the content of the Boundary-Scan Register Update latch (U) when executing the (DC) EXTEST instruction, or an "AC Signal" when an AC testing instruction is loaded into the device.

The AC signal is a test waveform suited for transmission through AC-coupling

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Test signal reception

  • When an AC testing instruction is loaded, a specialized test receiver detects transitoins of the AC signal seen at the input and determines if this represents a logic '0' or '1'
  • When EXTEST is loaded, the input signal level is detected and sent to the output of the test receiver to the Boundary-Scan Register cell

When testing for a shorted capacitor, the test software must ensure that enough time has passed for the signal to decay before entering Capture-DR, either by stopping TCk or by spending additional TCK cycles in the Run-Test/Idle TAP Controller state

EXTEST_PULSE & EXTEST_TRAIN

The two new AC-test instructions provided by this standard differ primarily in the number and timing of transitions to provide flexibility in dealing with the specific dynamic behavior of the channels being tested

AC Test Signal essentially modulates test data so that it will propagate through AC-coupled channels, for devices that contatin AC pins

Tools should use the EXTEST_PULSE instruction unless there is a specific requirement for the EXTEST_TRAIN instruction

EXTEST_PULSE

Generate two additional driver transitions and allows a tester to vary the time between them dependent on how many TCK cycles the TAP is left in the Run-Test/Idle TAP Controller state.

This is intended to allow any undesired transient condition to decay to a DC steady-state value when that will make the final transition more reliably detectable

The duration in the Run-Test/Idle TAP Controller state should be at least three times the high-pass coupling time constant. This allows the first additional transition to decay away to the DC steady-state value for the channel, and ensures that the full amplitude of the final transition is added to or subtracted from that steady-state value

This establishes a known initial condition for the final transition and permits reliable specification of the detection threshold of the test receiver

image-20240113190314947

EXTEST_TRAIN

Generate multiple additional transitions, the number dependent on how long the TAP is left in the Run-Test/Idle TAP Controller state

This is intended to allow any undesired transient condition to decay to an AC steady-state value when that will make the final transition more reliably detectable

image-20240113190345323

IEEE Std 1149.6-2003

This standard is built on top of IEEE Std 1149.1 using the same Test Access Port structure and Boundary-Scan architecture.

  • It adds the concept of a "test receiver" to input pins that are expected to handle differential and/or AC-coupling
  • It adds two new instructions that cause drivers to emit AC waveforms that are processed by test receivers.

JTAG Instruction

Implementation

  • AC mode hysteresis, detect transistion
  • DC mode threshold is determined by jtag initial value

reference

IEEE Std 1149.1-2001, IEEE Standard Test Access Port and Boundary-Scan Architecture, IEEE, 2001

IEEE Std 1149.6-2003, IEEE Standard for BoundaryScan Testing of Advanced Digital Networks, IEEE, 2003

IEEE 1149.6 Tutorial | Testing AC-coupled and Differential High-speed Nets [https://www.asset-intertech.com/resources/eresources/ieee-11496-tutorial-testing-ac-coupled-and-differential-high-speed-nets/]

Prof. James Chien-Mo Li, Lab of Dependable Systems, National Taiwan University. VLSI Testing [http://cc.ee.ntu.edu.tw/~cmli/VLSItesting/]

K.P. Parker, The Boundary Scan Handbook, 3rd ed., Kluwer Academic, 2003.

B. Eklow, K. P. Parker and C. F. Barnhart, "IEEE 1149.6: a boundary-scan standard for advanced digital networks," in IEEE Design & Test of Computers, vol. 20, no. 5, pp. 76-83, Sept.-Oct. 2003, doi: 10.1109/MDT.2003.1232259.

image-20241106231114717


autocorrelation

image-20241116112504606

The expectation returns the probability-weighted average of the specific function at that specific time over all possible realizations of the process

Ergodicity

ensemble autocorrelation and temporal autocorrelation (time autocorrelation)

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image-20240719210621021


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ECE438 - Laboratory 7: Discrete-Time Random Processes (Week 2) October 6, 2010 [https://engineering.purdue.edu/VISE/ee438L/lab7/pdf/lab7b.pdf]

Ensemble Averages and Time Averages

[https://ece-research.unm.edu/bsanthan/ece541/stat.pdf]

[https://www.nii.ac.jp/qis/first-quantum/e/forStudents/lecture/pdf/noise/chapter1.pdf]

  • time average: time-averaged quantities for the \(i\)-th member of the ensemble
  • ensemble average: ensemble-averaged quantities for all members of the ensemble at a certain time

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image-20241116215140298

where \(\theta\) is one member of the ensemble; \(p(x)dx\) is the probability that \(x\) is found among \([x, x + dx]\)

sample autocorrelation

[https://engineering.purdue.edu/VISE/ee438L/lab7/pdf/lab7b.pdf]

image-20250912203526260


[http://www.signal.uu.se/Courses/CourseDirs/SignalbehandlingIT/forelas02.pdf]

image-20250912205534933

ergodic vs. stationary

[https://bookdown.org/kevin_davisross/stat350-handouts/stationary.html]

image-20250912211619068


image-20250912211353183

Strict Sense Stationary (SSS) & Wide Sense Stationary (WSS)

[https://ece-research.unm.edu/bsanthan/ece541/station.pdf]

image-20250912210738010


image-20241123221623537

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LTI Filtering of WSS process

mean

image-20240917104857284

image-20240917104916086


image-20240827221945277

autocorrelation

deterministic autocorrelation function

image-20240427170024123

\[ R_{yy}(\tau) = h(\tau)*R_{xx}(\tau)*h(-\tau) =R_{xx}(\tau)*h(\tau)*h(-\tau) \]

image-20240907211343832

why \(\overline{R}_{hh}(\tau) \overset{\Delta}{=} h(\tau)*h(-\tau)\) is autocorrelation ? the proof is as follows:

\[\begin{align} \overline{R}_{hh}(\tau) &= h(\tau)*h(-\tau) \\ &= \int_{-\infty}^{\infty}h(x)h(-(\tau - x))dx \\ &= \int_{-\infty}^{\infty}h(x)h(-\tau + x)dx \\ &=\int_{-\infty}^{\infty}h(x+\tau)h(x)dx \end{align}\]


PSD

image-20240827222224395

image-20240827222235906

Topic 6 Random Processes and Signals [https://www.robots.ox.ac.uk/~dwm/Courses/2TF_2021/N6.pdf]

Alan V. Oppenheim, Introduction To Communication, Control, And Signal Processing [https://ocw.mit.edu/courses/6-011-introduction-to-communication-control-and-signal-processing-spring-2010/a6bddaee5966f6e73450e6fe79ab0566_MIT6_011S10_notes.pdf]

Balu Santhanam, Probability Theory & Stochastic Process 2020: LTI Systems and Random Signals [https://ece-research.unm.edu/bsanthan/ece541/LTI.pdf]


Time Reversal \[ x(-t) \overset{FT}{\longrightarrow} X(-j\omega) \]

if \(x(t)\) is real, then \(X(j\omega)\)​ has conjugate symmetry \[ X(-j\omega) = X^*(j\omega) \]

Derivatives of Random Processes

since \(x(t)\) is stationary process, and \(y(t) = \frac{dx(t)}{dt}\)

Using \(R_{yy}(\tau) = h(\tau)*R_{xx}(\tau)*h(-\tau)\)

\[\begin{align} R_{yy}(\tau) &= \mathcal{F}^{-1}[H(j\omega)\Phi_{xx}(j\omega)H(-j\omega)] \\ &= \mathcal{F}^{-1}[-(j\omega)^2\Phi_{xx}(j\omega)] \end{align}\]

we obtain the autocorrelation function of the output process as \[ R_{yy}(\tau) = -\frac{d^2}{d\tau^2}R_{xx}(\tau) \]

Liu Congfeng, Xidian University. Random Signal Processing: Chapter 5 Linear System: Random Process [https://web.xidian.edu.cn/cfliu/files/20121125_153218.pdf]

[https://sharif.ir/~bahram/sp4cl/PapoulisLectureSlides/lectr14.pdf]

Periodogram

The periodogram is in fact the Fourier transform of the aperiodic correlation of the windowed data sequence

image-20240907215822425

image-20240907215957865

image-20240907230715637

estimating continuous-time stationary random signal

periodogram.drawio

The sequence \(x[n]\) is typically multiplied by a finite-duration window \(w[n]\), since the input to the DFT must be of finite duration. This produces the finite-length sequence \(v[n] = w[n]x[n]\)

image-20240910005608007

image-20240910005927534

image-20240910005723458

\[\begin{align} \hat{P}_{ss}(\Omega) &= \frac{|V(e^{j\omega})|^2}{LU} \\ &= \frac{|V(e^{j\omega})|^2}{\sum_{n=0}^{L-1}(w[n])^2} \tag{1}\\ &= \frac{L|V(e^{j\omega})|^2}{\sum_{k=0}^{L-1}(W[k])^2} \tag{2} \end{align}\]

image-20240910010638376

That is, by \((1)\) \[ \hat{P}_{ss}(\Omega) = T_s\hat{P}_{xx(\omega)} = \frac{T_s|V(e^{j\omega})|^2}{\sum_{n=0}^{L-1}(w[n])^2}=\frac{|V(e^{j\omega})|^2}{f_{res}L\sum_{n=0}^{L-1}(w[n])^2} \]

That is, by \((2)\) \[ \hat{P}_{ss}(\Omega) = T_s\hat{P}_{xx(\omega)} = \frac{T_sL|V(e^{j\omega})|^2}{\sum_{k=0}^{L-1}(W[k])^2} = \frac{|V(e^{j\omega})|^2}{f_{res}\sum_{k=0}^{L-1}(W[k])^2} \]

!! ENBW

Wiener-Khinchin theorem

Norbert Wiener proved this theorem for the case of a deterministic function in 1930; Aleksandr Khinchin later formulated an analogous result for stationary stochastic processes and published that probabilistic analogue in 1934. Albert Einstein explained, without proofs, the idea in a brief two-page memo in 1914

\(x(t)\), Fourier transform over a limited period of time \([-T/2, +T/2]\) , \(X_T(f) = \int_{-T/2}^{T/2}x(t)e^{-j2\pi ft}dt\)

With Parseval's theorem \[ \int_{-T/2}^{T/2}|x(t)|^2dt = \int_{-\infty}^{\infty}|X_T(f)|^2df \] So that \[ \frac{1}{T}\int_{-T/2}^{T/2}|x(t)|^2dt = \int_{-\infty}^{\infty}\frac{1}{T}|X_T(f)|^2df \]

where the quantity, \(\frac{1}{T}|X_T(f)|^2\) can be interpreted as distribution of power in the frequency domain

For each \(f\) this quantity is a random variable, since it is a function of the random process \(x(t)\)

The power spectral density (PSD) \(S_x(f )\) is defined as the limit of the expectation of the expression above, for large \(T\): \[ S_x(f) = \lim _{T\to \infty}\mathrm{E}\left[ \frac{1}{T}|X_T(f)|^2 \right] \]

The Wiener-Khinchin theorem ensures that for well-behaved wide-sense stationary processes the limit exists and is equal to the Fourier transform of the autocorrelation \[\begin{align} S_x(f) &= \int_{-\infty}^{+\infty}R_x(\tau)e^{-j2\pi f \tau}d\tau \\ R_x(\tau) &= \int_{-\infty}^{+\infty}S_x(f)e^{j2\pi f \tau}df \end{align}\]

Note: \(S_x(f)\) in Hz and inverse Fourier Transform in Hz (\(\frac{1}{2\pi}d\omega = df\))

image-20240910003805151

[https://www.robots.ox.ac.uk/~dwm/Courses/2TF_2011/2TF-L5.pdf]

Frank R. Kschischang. The Wiener-Khinchin Theorem [https://www.comm.utoronto.ca/~frank/notes/wk.pdf]


Example

image-20240904203802604

Remember: impulse scaling

image-20240718210137344 \[ \cos(2\pi f_0t) \overset{\mathcal{F}}{\longrightarrow} \frac{1}{2}[\delta(f -f_0)+\delta(f+f_0)] \]


image-20250821200319537


\(x(t)\) \(R(\tau)\)
\(A_0 \sin(\omega_0 t+\phi_0)\) \(\frac{A_0^2}{2}\cos(\omega_0 \tau)\)
\(A_0 \cos(\omega_0 t+\phi_0)\) \(\frac{A_0^2}{2}\cos(\omega_0 \tau)\)

due to \(\cos(\omega_0 t +\phi_0) = \sin(\omega_0 t +\phi_0 + \frac{\pi}{2})\)

Energy Signal

image-20240910004411501

image-20240910004421791

image-20240910004448439

Discrete time

SIMG-713 Noise and Random Processes Spring 2002 . Lecture 15 Power Spectrum Estimation [https://www.cis.rit.edu/class/simg713/Lectures/Lecture713-15-4.pdf]

Properties of the Fourier Transform for Discrete-Time Signals [https://www.comm.utoronto.ca/dkundur/course_info/362/EmanHammadDTFT2.pdf]

image-20250810165917800 \[ \frac{1}{2\pi}F^{-1}\{R_{xx}\}d\omega = \frac{1}{2\pi}F^{-1}\{R_{xx}\}d(2\pi f T)=T\cdot F^{-1}\{R_{xx}\}df = P_{xx}(f)df \] power spectral density of a discrete-time random process \(\{x(n)\}\) is given by \[ P_{xx}(f) =T\cdot F^{-1}\{R_{xx}\} \]

Periodic and Cyclostationary Processes

image-20250809234136591

image-20250809234318422

Multirate Systems & Random Sequences

Balu Santhanam. ece541 Probability Theory & Stochastic Process: Random Signals and Multirate Systems [http://ece-research.unm.edu/bsanthan/ece541/rand.pdf]

WSS Random Sequences

autocorrelation function of WSS random sequences

image-20250818202413226


psd of WSS of WSS random sequences

image-20250818202138087


Interpretation of the psd

image-20250818205138125

Equation 8.4-4 is permissible for cyclostationary waveforms

decimation

image-20250810194206762

\[ \frac{1}{M}\int_{-Mx_0}^{Mx_0}f(\frac{x}{M})dx = \int_{-Mx_0}^{Mx_0}f(\frac{x}{M}) d\frac{x}{M} = \int_{-x_0}^{x_0} f(\acute{x}) d\acute{x} \]

where \(\acute{x} = \frac{x}{M}\)

randSeq-decimation.drawio


image-20250818195951584

image-20250818205826429

interpolation

image-20250819182525306

The resulting expanded random sequence is clearly nonstationary, because of the zero insertions.

image-20250810194541608

This random sequences and processes is classified as being cyclostationary

psd of \(X_e[n]\), the expansion or the upsampled version of \(X[n]\)

\[ S_{X_eX_e}(\omega) = \frac{1}{L}S_{XX}(L\omega) \]

where \(L\) is upsampling factor

image-20250810204047368

apply \(X_e[n]\) to an ideal lowpass filter with bandwidth \([-\pi/2 , +\pi/2]\) and gain of \(2\) or \(L\)

\[ S_{YY}(\omega) = \left\{ \begin{array}{cl} L \cdot S_{XX}(L\omega), &\ |\omega|\leq \pi/L \\ 0, &\ \pi/L \lt |\omega| \leq \pi \end{array} \right. \]


Stark H, Woods JW. Probability, Statistics, and Random Processes for Engineers, 3rd [pdf]

—, 3th Ed Solution Manual [https://www.scribd.com/document/353335818/Stark-Woods-3th-Ed-Manual#page=212]

image-20250818222910938

General case with upsampling factor \(L\)

\[\begin{align} &\mathrm{E}\{|\sum_{n=-N}^N X_e[n]e^{-j\omega n}|^2\}\\ &= \sum_{n=-N}^N\sum_{l=-N}^NX_e[n]\cdot X_e^*[l]\cdot e^{-j\omega(n-l)}\\ &= \sum_{n=-N}^N\sum_{l=-N}^NX[\frac{n}{L}]\cdot X^*[\frac{l}{L}]\cdot e^{-j\omega(n-l)}\\ &= \sum_{k=-N/L}^{N/L}\sum_{m=-N/L}^{N/L}X[k]\cdot X^*[m]\cdot e^{-jL\omega (k-m)}\\ &= \sum_{k=-\infty}^{\infty}\sum_{m=-\infty}^{\infty}R_{XX}[k-m]\cdot e^{-j\omega L(k-m)}\cdot \mathcal{rect}[\frac{k}{2N/L}]\cdot \mathcal{rect}[\frac{m}{2N/L}]\\ &= \sum_{i=-\infty}^{\infty}\sum_{m=-\infty}^{\infty}R_{XX}[i]\cdot e^{-jL\omega i}\cdot \mathcal{rect}[\frac{i+m}{2N/L}]\cdot \mathcal{rect}[\frac{m}{2N/L}]\\ &= \sum_{i=-\infty}^{\infty}R_{XX}[i]\cdot e^{-jL\omega i} \cdot \frac{2N}{L}\left[1 -\frac{|i|}{2N/L} \right] \end{align}\]

Then \[\begin{align} S_{X_eX_e}(\omega) &= \lim_{N\to \infty}\frac{1}{2N+1}\cdot \mathrm{E}\{|\sum_{n=-N}^N X_e[n]e^{-j\omega n}|^2\} \\ &= \frac{1}{L}\sum_{i=-\infty}^{\infty}R_{XX}[i]\cdot e^{-jL\omega i}\\ &= \frac{1}{L}S_{XX}(L\omega) \end{align}\]


image-20250818211010290

image-20250818205936474

Wiener Process (Brownian Motion)

Dennis Sun, Introduction to Probability: Lesson 49 Brownian Motion [https://dlsun.github.io/probability/brownian-motion.html]

Wiener process (also called Brownian motion)

unnamed-chunk-178-1

image-20241202001449997

NRZ PSD

image-20231111100420675

image-20231111101322771

image-20231110224237933

Lecture 26 Autocorrelation Functions of Random Binary Processes [https://bpb-us-e1.wpmucdn.com/sites.gatech.edu/dist/a/578/files/2003/12/ECE3075A-26.pdf]

Lecture 32 Correlation Functions & Power Density Spectrum, Cross-spectral Density [https://bpb-us-e1.wpmucdn.com/sites.gatech.edu/dist/a/578/files/2003/12/ECE3075A-32.pdf]


Normal Distribution and Input-Referred Noise [https://a2d2ic.wordpress.com/2013/06/05/normal-distribution-and-input-referred-noise/]

Fig.1 PDF for sum of a large number of random variables

Fig.2 The noise signal, its auto correlation function, and spectral density [2]

reference

L.W. Couch, Digital and Analog Communication Systems, 8th Edition, 2013 [pdf]

Alan V Oppenheim, George C. Verghese, Signals, Systems and Inference, 1st edition [pdf]

R. Ziemer and W. Tranter, Principles of Communications, Seventh Edition, 2013 [pdf]

Stark H, Woods JW. Probability, Statistics, and Random Processes for Engineers, 4th ed. 2012 [pdf]

Kuchler, Ryan J. Theory of multirate statistical signal processing and applications. Monterey, California.: Naval Postgraduate School, 2005. [pdf]


Balu Santhanam. Fall 2020 ECE541 Probability Theory & Stochastic Process [https://ece-research.unm.edu/bsanthan/ece541/]

PSS and HB Overview

The steady-state response is the response that results after any transient effects have dissipated.

The large signal solution is the starting point for small-signal analyses, including periodic AC, periodic transfer function, periodic noise, periodic stability, and periodic scattering parameter analyses.

Designers refer periodic steady state analysis in time domain as "PSS" and corresponding frequency domain notation as "HB"

image-20231028163033255

Harmonic Balance Analysis

The idea of harmonic balance is to find a set of port voltage waveforms (or, alternatively, the harmonic voltage components) that give the same currents in both the linear-network equations and nonlinear-network equations

that is, the currents satisfy Kirchoff's current law

Define an error function at each harmonic, \(f_k\), where \[ f_k = I_{\text{LIN}}(k\omega) + I_{\text{NL}}(k\omega) \] where \(k=0, 1, 2,...,K\)

Note that each \(f_k\) is implicitly a function of all voltage components \(V(k\omega)\)

Newton Solution of the Harmonic-Balance Equation

Iterative Process and Jacobian Formulation

image-20231108222451147

The elements of the Jacobian are the derivatives \[ \frac{\partial F_{\text{n,k}}}{\partial _{V_\text{m,l}}} \] where \(n\) and \(m\) are the port indices \((1,N)\), and \(k\) and \(l\) are the harmonic indices \((0,...,K)\)

Number of Harmonics & Time Samples

image-20251013203212011


image-20251014065943176

Initial Estimate

One important property of Newton's method is that its speed and reliability of convergence depend strongly upon the initial estimate of the solution vector.

Conversion Matrix Analysis

Large-signal/small-signal analysis, or conversion matrix analysis, is useful for a large class of problems wherein a nonlinear device is driven, or "pumped" by a single large sinusoidal signal; another signal, much smaller, is applied; and we seek only the linear response to the small signal.

The most common application of this technique is in the design of mixers and in nonlinear noise analysis

  1. First, analyzing the nonlinear device under large-signal excitation only, where the harmonic-balance method can be applied
  2. Then, the nonlinear elements in the device's equivalent circuit are then linearized to create small-signal, linear, time-varying elements
  3. Finally, a small-signal analysis is performed

Element Linearized

Below shows a nonlinear resistive element, which has the \(I/V\) relationship \(I=f(V)\). It is driven by a large-signal voltage

image-20220511203515431

Assuming that \(V\) consists of the sum of a large-signal component \(V_0\) and a small-signal component \(v\), with Taylor series \[ f(V_0+v) = f(V_0)+\frac{d}{dV}f(V)|_{V=V_0}\cdot v+\frac{1}{2}\frac{d^2}{dV^2}f(V)|_{V=V_0}\cdot v^2+... \] The small-signal, incremental current is found by subtracting the large-signal component of the current \[ i(v)=I(V_0+v)-I(V_0) \] If \(v \ll V_0\), \(v^2\), \(v^3\),... are negligible. Then, \[ i(v) = \frac{d}{dV}f(V)|_{V=V_0}\cdot v \]

\(V_0\) need not be a DC quantity; it can be a time-varying large-signal voltage \(V_L(t)\) and that \(v=v(t)\), a function of time. Then \[ i(t)=g(t)v(t) \] where \(g(t)=\frac{d}{dV}f(V)|_{V=V_L(t)}\)

The time-varying conductance \(g(t)\), is the derivative of the element's \(I/V\) characteristic at the large-signal voltage

By an analogous derivation, one could have a current-controlled resistor with the \(V/I\) characteristic \(V = f_R(I)\) and obtain the small-signal \(v/i\) relation \[ v(t) = r(t)i(t) \] where \(r(t) = \frac{d}{dI}f_R(I)|_{I=I_L(t)}\)

A nonlinear element excited by two tones supports currents and voltages at mixing frequencies \(m\omega_1+n\omega_2\), where \(m\) and \(n\) are integers. If one of those tones, \(\omega_1\) has such a low level that it does not generate harmonics and the other is a large-signal sinusoid at \(\omega_p\), then the mixing frequencies are \(\omega=\pm\omega_1+n\omega_p\), which shown in below figure

image-20231108223600922

A more compact representation of the mixing frequencies is \[ \omega_n=\omega_0+n\omega_p \] which includes only half of the mixing frequencies:

  • the negative components of the lower sidebands (LSB)
  • and the positive components of the upper sidebands (USB)

image-20220511211336437

For real signal, positive- and negative-frequency components are complex conjugate pairs

Shooting Newton

TODO 📅

Nonlinearity & Linear Time-Varying Nature

Nonlinearity Nature

The nonlinearity causes the signal to be replicated at multiples of the carrier, an effect referred to as harmonic distortion, and adds a skirt to the signal that increases its bandwidth, an effect referred to as intermodulation distortion

image-20231029093504162

It is possible to eliminate the effect of harmonic distortion with a bandpass filter, however the frequency of the intermodulation distortion products overlaps the frequency of the desired signal, and so cannot be completely removed with filtering.

Time-Varying Linear Nature

image-20231029101042671

linear with respect to \(v_{in}\) and time-varying

Given \(v_{in}(t)=m(t)\cos (\omega_c t)\) and LO signal of \(\cos(\omega_{LO} t)\), then \[ v_{out}(t) = \text{LPF}\{m(t)\cos(\omega_c t)\cdot \cos(\omega_{LO} t)\} \] and \[ v_{out}(t) = m(t)\cos((\omega_c - \omega_{LO})t) \]

A linear periodically-varying transfer function implements frequency translation

Periodic small signal analyses

Analysis in Simulator

  1. LPV analyses start by performing a periodic analysis to compute the periodic operating point with only the large clock signal applied (the LO, the clock, the carrier, etc.).
  2. The circuit is then linearized about this time-varying operating point (expand about the periodic equilibrium point with a Taylor series and discard all but the first-order term)
  3. and the small information signal is applied. The response is calculated using linear time-varying analysis

Versions of this type of small-signal analysis exists for both harmonic balance and shooting methods

PAC is useful for predicting the output sidebands produced by a particular input signal

PXF is best at predicting the input images for a particular output

image-20241109110936909

image-20241109110958641

Linear Time Varying

The response of a relaxed LTV system at a time \(t\) due to an impulse applied at a time \(t − \tau\) is denoted by \(h(t, \tau)\)

The first argument in the impulse response denotes the time of observation.

The second argument indicates that the system was excited by an impulse launched at a time \(\tau\) prior to the time of observation.

Thus, the response of an LTV system not only depends on how long before the observation time it was excited by the impulse but also on the observation instant.

The output \(y(t)\) of an initially relaxed LTV system with impulse response \(h(t, \tau)\) is given by the convolution integral \[ y(t) = \int_0^{\infty}h(t,\tau)x(t-\tau)d\tau \] Assuming \(x(t) = e^{j2\pi f t}\) \[ y(t) = \int_0^{\infty}h(t,\tau)e^{j2\pi f (t-\tau)}d\tau = e^{j2\pi f t}\int_0^{\infty}h(t,\tau)e^{-j2\pi f\tau}d\tau \] The (time-varying) frequency response can be interpreted as \[ H(j2\pi f, t) = \int_0^{\infty}h(t,\tau)e^{-j2\pi f\tau}d\tau \] Linear Periodically Time-Varying (LPTV) Systems, which is a special case of an LTV system whose impulse response satisfies \[ h(t, \tau) = h(t+T_s, \tau) \] In other words, the response to an impulse remains unchanged if the time at which the output is observed (\(t\)) and the time at which the impulse is applied (denoted by \(t_1\)) are both shifted by \(T_s\) \[ H(j2\pi f, t+T_s) = \int_0^{\infty}h(t+T_s,\tau)e^{-j2\pi f\tau}d\tau = \int_0^{\infty}h(t,\tau)e^{-j2\pi f\tau}d\tau = H(j2\pi f, t) \] \(H(j2\pi f, t)\) of an LPTV system is periodic with timeperiod \(T_s\), it can be expanded as a Fourier series in \(t\), resulting in \[ H(j2\pi f, t) = \sum_{k=-\infty}^{\infty} H_k(j2\pi f)e^{j2\pi f_s k t} \] The coefficients of the Fourier series \(H_k(j2\pi f)\) are given by \[ H_k(j2\pi f) = \frac{1}{T_s}\int_0^{T_s} H(j2\pi f, t) e^{-j2\pi k f_s t}dt \]

reference

K. S. Kundert, "Introduction to RF simulation and its application," in IEEE Journal of Solid-State Circuits, vol. 34, no. 9, pp. 1298-1319, Sept. 1999, doi: 10.1109/4.782091. [pdf]

Stephen Maas, Nonlinear Microwave and RF Circuits, Second Edition , Artech, 2003. [pdf]

Karti Mayaram. ECE 521 Fall 2016 Analog Circuit Simulation: Simulation of Radio Frequency Integrated Circuits [pdf1, pdf2]

The Value Of RF Harmonic Balance Analyses For Analog Verification: Frequency domain periodic large and small signal analyses. [https://semiengineering.com/the-value-of-rf-harmonic-balance-analyses-for-analog-verification/]

Shanthi Pavan, "Demystifying Linear Time Varying Circuits"

S. Pavan and G. C. Temes, "Reciprocity and Inter-Reciprocity: A Tutorial— Part I: Linear Time-Invariant Networks," in IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 70, no. 9, pp. 3413-3421, Sept. 2023, doi: 10.1109/TCSI.2023.3276700.

S. Pavan and G. C. Temes, "Reciprocity and Inter-Reciprocity: A Tutorial—Part II: Linear Periodically Time-Varying Networks," in IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 70, no. 9, pp. 3422-3435, Sept. 2023, doi: 10.1109/TCSI.2023.3294298.

S. Pavan and R. S. Rajan, "Interreciprocity in Linear Periodically Time-Varying Networks With Sampled Outputs," in IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 61, no. 9, pp. 686-690, Sept. 2014, doi: 10.1109/TCSII.2014.2335393.

Prof. Shanthi Pavan. Introduction to Time - Varying Electrical Network. [https://youtube.com/playlist?list=PLyqSpQzTE6M8qllAtp9TTODxNfaoxRLp9]

Shanthi Pavan. EE5323: Advanced Electrical Networks (Jan-May. 2015) [https://www.ee.iitm.ac.in/vlsi/courses/ee5323/start]

R. S. Ashwin Kumar. EE698W: Analog circuits for signal processing [https://home.iitk.ac.in/~ashwinrs/2022_EE698W.html]

Piet Vanassche, Georges Gielen, and Willy Sansen. 2009. Systematic Modeling and Analysis of Telecom Frontends and their Building Blocks (1st. ed.). Springer Publishing Company, Incorporated.

Beffa, Federico. (2023). Weakly Nonlinear Systems. 10.1007/978-3-031-40681-2.

Wereley, Norman. (1990). Analysis and control of linear periodically time varying systems.

Hameed, S. (2017). Design and Analysis of Programmable Receiver Front-Ends Based on LPTV Circuits. UCLA. ProQuest ID: Hameed_ucla_0031D_15577. Merritt ID: ark:/13030/m5gb6zcz. Retrieved from https://escholarship.org/uc/item/51q2m7bx

Matt Allen. Introduction to Linear Time Periodic Systems. [https://youtu.be/OCOkEFDQKTI]

Fivel, Oren. "Analysis of Linear Time-Varying & Periodic Systems." arXiv preprint arXiv:2202.00498 (2022).

RF Harmonic Balance Analysis for Nonlinear Circuits [https://resources.pcb.cadence.com/blog/2019-rf-harmonic-balance-analysis-for-nonlinear-circuits]

Steer, Michael. Microwave and RF Design (Third Edition, 2019). NC State University, 2019.

Steer, Michael. Harmonic Balance Analysis of Nonlinear RF Circuits - Case Study Index: CS_AmpHB [link]

Vishal Saxena, "SpectreRF Periodic Analysis" URL:https://www.eecis.udel.edu/~vsaxena/courses/ece614/Handouts/SpectreRF%20Periodic%20Analysis.pdf

Josh Carnes,Peter Kurahashi. "Periodic Analyses of Sampled Systems" URL:https://slideplayer.com/slide/14865977/

Kundert, Ken. (2006). Simulating Switched-Capacitor Filters with SpectreRF. URL:https://designers-guide.org/analysis/sc-filters.pdf

Article (20482538) Title: Why is pnoise sampled(jitter) different than pnoise timeaverage on a driven circuit? URL:https://support.cadence.com/apex/ArticleAttachmentPortal?id=a1O0V000009EStcUAG

Article (20467468) Title: The mathematics behind choosing the upper frequency when simulating pnoise jitter on an oscillator URL:https://support.cadence.com/apex/ArticleAttachmentPortal?id=a1O0V000007MnBUUA0

Andrew Beckett. "Simulating phase noise for PFD-CP". URL:https://groups.google.com/g/comp.cad.cadence/c/NPisXTElx6E/m/XjWxKbbfh2cJ

Dr. Yanghong Huang. MATH44041/64041: Applied Dynamical Systems [https://personalpages.manchester.ac.uk/staff/yanghong.huang/teaching/MATH4041/default.htm]

Jeffrey Wong. Math 563, Spring 2020 Applied computational analysis [https://services.math.duke.edu/~jtwong/math563-2020/main.html]

Jeffrey Wong. Math 353, Fall 2020 Ordinary and Partial Differential Equations [https://services.math.duke.edu/~jtwong/math353-2020/main.html]

Tip of the Week: Please explain in more practical (less theoretical) terms the concept of "oscillator line width." [https://community.cadence.com/cadence_blogs_8/b/rf/posts/please-explain-in-more-practical-less-theoretical-terms-the-concept-of-quot-oscillator-line-width-quot]

Rubiola, E. (2008). Phase Noise and Frequency Stability in Oscillators (The Cambridge RF and Microwave Engineering Series). Cambridge: Cambridge University Press. doi:10.1017/CBO9780511812798

Dr. Ulrich L. Rohde, Noise Analysis, Then and Today [https://www.microwavejournal.com/articles/29151-noise-analysis-then-and-today]

Nicola Da Dalt and Ali Sheikholeslami. 2018. Understanding Jitter and Phase Noise: A Circuits and Systems Perspective (1st. ed.). Cambridge University Press, USA.

Kester, Walt. (2005). Converting Oscillator Phase Noise to Time Jitter. [https://www.analog.com/media/en/training-seminars/tutorials/MT-008.pdf]

Drakhlis, B.. (2001). Calculate oscillator jitter by using phase-noise analysis: Part 2 of two parts. Microwaves and Rf. 40. 109-119.

Explanation for sampled PXF analysis. [https://community.cadence.com/cadence_technology_forums/f/custom-ic-design/45055/explanation-for-sampled-pxf-analysis/1376140#1376140]

模拟放大器的低噪声设计技术2-3实践 아날로그 증폭기의 저잡음 설계 기법2-3실습 [https://youtu.be/vXLDfEWR31k?si=p2gbwaKoTxtfvrTc]

2.2 How POP Really Works [https://www.simplistechnologies.com/documentation/simplis/ast_02/topics/2_2_how_pop_really_works.htm]

Jaeha Kim. Lecture 11. Mismatch Simulation using PNOISE [https://ocw.snu.ac.kr/sites/default/files/NOTE/7037.pdf]

Hueber, G., & Staszewski, R. B. (Eds.) (2010). Multi-Mode/Multi-Band RF Transceivers for Wireless Communications: Advanced Techniques, Architectures, and Trends. John Wiley & Sons. https://doi.org/10.1002/9780470634455

B. Boser,A. Niknejad ,S.Gambin 2011 EECS 240 Topic 6: Noise Analysis [https://mixsignal.files.wordpress.com/2013/06/t06noiseanalysis_simone-1.pdf]

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