Extensive work on DFEs has produced a multitude of architectures,
which can be broadly categorized as "direct"" or
"unrolled" (speculative) DFEs with
"full-rate" or "half-rate"
clocking
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Technologies to Accelerate AI
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the resistance of MOS is not highly controlled -> \(R_T + Z_N\)
Peak power constraint of TX
FIR
Due to circuit limitation, circuit cannot have arbitrarily large
voltage on the output, i.e. a limited maximum swing. In order
to create the high frequency shape, the best we can do is lower DC
gain (low frequency gain < 1)
FIR is not increasing the amplitude on the edges
FIR is reducing the inner eye diagram
The maximum swing stays the same, \(\sum_i
|c_i|=1\)
Sharing termination keep a constant current through leg, which
improve TX speed in this way. On the other hand, the sharing termination
facilitate drain/source sharing technique in layout.
pull-up and pull-down
resistor
Original stacked structure
Pro's:
smaller static current when both pull up and pull down path is
on
Con's:
slowly switching due to parasitic capacitance behind pull-up and
pull-down resistor
with single shared linearization resistor
Pro's:
The parasitic capacitance behind the resistor still exists but is
now always driven high or low actively
Con's:
more static current
VM
Driver Equalization - differential ended termination
\[
V_o = D_{n+1}C_{-1}+D_nC_0+D_{n-1}C_{+1}
\]
where \(D_n \in \{-1, 1\}\)
\[
V_{\text{rx}} = V_{\text{dd}} \frac{(R_2-R_1)R_T}{R_1R_T+R_2R_T+R_1R_2}
\] With \(R_u=(L+M+N)R_T\)
\[\begin{align}
V_{\text{rxp}} &= \frac{1}{2} \cdot \frac{N}{L+M+N} \\
V_{\text{rxm}} &= \frac{1}{2} \cdot \frac{L+M}{L+M+N}
\end{align}\] So \[
V_{L}= \frac{1}{2}\cdot\frac{N-(L+M)}{L+M+N}
\] which is same with differential ended termination
Equation-2
\[\begin{align}
V_{\text{rxp}} &= \frac{1}{2} \\
V_{\text{rxm}} &= 0
\end{align}\] So \[
V_{M}= \frac{1}{2}
\] which is same with differential ended termination
Which can be simpified as \[\begin{align}
V_{\text{rx}} &= \frac{1}{2}(V_p - V_m) \\
&= \frac{1}{2}(\frac{2}{3}(2V_{\text{MSB}}+V_{\text{LSB}})-1) \\
&=\frac{1}{3}(2V_{\text{MSB}}+V_{\text{LSB}})-\frac{1}{2}
\end{align}\]
The above eqations demonstrate that the output \(V_{\text{rx}}\) is the linear sum of
MSB and LSB; LSB and
MSB have relative weight, i.e. 1 for LSB and
2 for MSB.
Assume pre cusor has \(L\) legs,
main cursor \(M\) legs and post cursor
\(N\) legs, which is same with the
convention in "Voltage-Mode Driver Equalization"
The number of legs connected with supply can expressed as \[
n_{up} = (1-d_{n+1})L + d_{n}M + (1-d_{n-1})N
\] Where \(d_n \in \{0, 1\}\),
or \[
n_{up} = \frac{1}{2}(-D_{n+1}+1)L + \frac{1}{2}(D_{n}+1)M +
\frac{1}{2}(-D_{n-1}+1)N
\] Where \(D_n \in \{-1,
+1\}\)
Then the number of legs connected with ground is \[
n_{dn}=L+M+N-n_{up}
\] where \(n_{up}+n_{dn}=L+M+N\)
Voltage resistor divider \[\begin{align}
V_o &=
\frac{\frac{R_{U}}{n_{dn}}}{\frac{R_U}{n_{dn}}+\frac{R_U}{n_{up}}} \\
&= \frac{1}{2}- \frac{1}{2}D_{n+1}\frac{L}{L+M+N}+
\frac{1}{2}D_{n}\frac{M}{L+M+N}-\frac{1}{2}D_{n-1}\frac{N}{L+M+N} \\
&= \frac{1}{2}-\frac{1}{2}D_{n+1}\cdot l+ \frac{1}{2}D_{n}\cdot
m-\frac{1}{2}D_{n-1}\cdot n
\end{align}\]
where \(l+m+n=1\)
\(V_{\text{MSB}}\) and \(V_{\text{LSB}}\) can be obtained
\[\begin{align}
V_{\text{MSB}} &= \frac{1}{2}-\frac{1}{2}D^{\text{MSB}}_{n+1}\cdot
l+ \frac{1}{2}D^{\text{MSB}}_{n}\cdot
m-\frac{1}{2}D^{\text{MSB}}_{n-1}\cdot n \\
V_{\text{LSB}} &= \frac{1}{2}-\frac{1}{2}D^{\text{LSB}}_{n+1}\cdot
l+ \frac{1}{2}D^{\text{LSB}}_{n}\cdot
m-\frac{1}{2}D^{\text{LSB}}_{n-1}\cdot n
\end{align}\]
Substitute the above equation into \(V_{\text{rx}}\), we obtain the relationship
between driver legs and FFE coefficients
After scaling, we obtain \[
V_{\text{rx}} = -l\cdot(2 \cdot
D^{\text{MSB}}_{n+1}+D^{\text{LSB}}_{n+1})+ m\cdot(2\cdot
D^{\text{MSB}}_{n}+D^{\text{LSB}}_{n}) - n \cdot(2\cdot
D^{\text{MSB}}_{n-1}+D^{\text{LSB}}_{n-1})
\] Where \(C_{-1} = l\), \(C_0 = m\) and \(C_{1}=n\), which is same with that of
NRZ
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Negative Capacitance Circuit
Negative Miller Capacitance
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\[
C_{d1} = C_{dd1} + (1+\frac{1}{|A_{gd}|})C_{gd1}
\] where \(A_{gd}\lt 0\)
For differential mode input, effective
input capacitance\[
C_{in} = C_{gs} +(1+A_{dm}) C_{gd}+\color{red}(1-A_{dm})C_n
\] and effective output capacitance\[
C_{out} = C_{dd} + (1+\frac{1}{A_{dm}})C_{gd}+\color{red}
(1-\frac{1}{A_{dm}})C_n
\] That is \(C_n\) deteriorate
the effective output capacitance
For common mode input, effective input
capacitance\[
C_{in} = C_{gs} + (1+A_{cm}) C_{gd}+ \color{red}(1+A_{cm})C_n
\] and effective output capacitance\[
C_{d1} = C_{dd} + (1+\frac{1}{A_{cm}})C_{gd}+\color{red}
(1+\frac{1}{A_{cm}})C_n
\] i.e., \(C_n\) deteriorate
both effective input capacitance and effective output capacitance,
unfortunately
effective input capacitance \(\Pi\)
model, which is appropriate for both differential input and common mode
input
Suppose \(C_n=C_{gd}\), effective
differential input capacitance is same with effective
common-mode input capacitance (\(C_n=\frac{A_{dm}-A_{cm}}{A_{dm}+A_{cm}}C_{gd}\))
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A resonant circuit refers to an electrical
circuit using circuit elements such as an inductor (L) and a capacitor
(C) to cause resonance at a specific frequency.
There are two types of resonant circuits:
series resonant circuits
parallel resonant circuits
In a series resonant circuit, the impedance of the circuit reaches
its minimum value at resonance, whereas in a parallel resonant circuit,
the impedance reaches its maximum value
\[
f_\text{SRF} = \frac{1}{2\pi \sqrt{LC}}
\] The SRF of an inductor is the frequency at which the parasitic
capacitance of the inductor resonates with the ideal inductance of the
inductor, resulting in an extremely high impedance. The inductance only
acts like an inductor below its SRF
For choking applications, chose an inductor
whose SRF is at or near the frequency to be attenuated
For other applications, the SRF should be at least
10 times higher than the operating frequency
it is more important to have a relatively flat inductance
curve (constant inductance vs. frequency) near the required
frequency
J. Nako, G. Tsirimokou, C. Psychalinos and A. S. Elwakil,
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subplot(2,1,2) plot(t, x1+x2+x3, 'r-', LineWidth=2); hold on; plot(t, x1/2+x2+x3, 'g-', LineWidth=2); plot(t, x1+x2+x3/2, 'b-', LineWidth=2); grid on; xlim([01.25]) legend('ref', 'Low frequency attenuated', 'high frequency attenuated')
phase delay
Phase delay directly measures the device or system time delay of
individual sinusoidal frequency components in the
steady-state conditions
group delay
Phase Delay & Group Delay
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The periodic signal on VCTRL modulates the
VCO, giving rise to deterministic jitter
Timing Offsets Between Up and Dn Pulses
Mismatch Between Charge-Pump Current Sources
Incomplete Settling of Charge-Pump Currents
Finite Output Resistance of the Charge Pump
Up/Dn Timing Offset
If Dn pulse arrives \(\Delta T\)
after the Up pulse, the steady-state VCTRL will be slightly
lower than it would be without the \(\Delta T\) mismatch so as to return the
VCO's phase to match the reference clocks.
Vice versa, if If Up pulse arrives \(\Delta
T\) after the Dn pulse, the steady-state VCTRL will be slightly
higher than without \(\Delta
T\) mismatch
Current Sources Mismatch
Incomplete Settling
TODO 📅
W. Rhee, "Design of high-performance CMOS charge pumps in
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2nd loop filter
PI (proportional - integral) Loop Filter
PFD Deadzone
Dead zone induced by incomplete settling of charge-pump
currents
This situation can be avoided by adding additional delay to the
AND gate in the PFD
For the sake of simplicity, \(V_{ctr}\) looks like a rectangular pulse
with an amplitude of \(I_{CP}R_1\) and
a duty ratio of (\(I_{leak}/I_{CP}\)),
whose first coefficient of Fourier series is
where \(I_\text{leak} \ll I_{CP}\)
is assumed
Then, the peak frequency deviation \(\Delta f\)\[
\Delta f = a_1 \cdot K_v = 2I_\text{leak}R_1 K_v
\] using narrowband FM approximation, we have \[
P_\text{spur} = 20\log\left(\frac{\Delta f}{2f_\text{ref}}\right) =
20\log\left(\frac{I_\text{leak}R_1 K_v}{f_\text{ref}}\right)
\]
W. Rhee, "Design of high-performance CMOS charge pumps in
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Circuit Design Aspects. John Wiley & Sons
Lacaita, Andrea Leonardo, Salvatore Levantino, and Carlo Samori.
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Cambridge University Press, 2007.
Hunting jitter is often referred to as
dithering jitter, the periodic time
error between data clock and input data, which exhibits a
limit-cycle behavior
BB PD
Youngdon Choi, Deog-Kyoon Jeong and W. Kim, "Jitter transfer analysis
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[slides]
It's ternary, because early, late
and no transition
notice the transition density = 1 in
digital PLL
Linearization
The effective PD gain is a function of the input jitter
pdf, it enables one to anticipate the effects of input jitter
on loop characteristics
BB Gain is the slope of average BB output \(\mu\), versus phase offset \(\phi\), i.e. \(\frac {\partial \mu}{\partial \phi}\),
BB only produces output for a transition and this de-rates the gain.
Transition density = 0.5 for
random data
Input referred jitter from BB PD is
proportional to incoming jitter
gain simulation
L. Avallone, M. Mercandelli, A. Santiccioli, M. P. Kennedy, S.
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# polyfit coef_fit = np.polyfit(dt, et, 1) print(f'coef_fit: {coef_fit}')
x = np.linspace(-3.5, 3.5, 1000) y = coef_fit[0]*x + coef_fit[1]
plt.figure(figsize=(12,6)) plt.plot(dt, et, 'o') plt.plot(x, y, linewidth=2, linestyle='--')
# Calculate histogram counts and bin edges counts, bin_edges = np.histogram(dt, bins=100) # Find the maximum count max_count = counts.max() # Create weights to normalize the maximum height to 1 weights = np.ones_like(dt) / max_count plt.hist(dt, bins=100, weights=weights)
That is \[
P_{x_s x_s} (f)= \frac{1}{T_s}P_{xx}(f)
\] In going from discrete time to continuous
time, we must add a scale factor \(1/T\), the sample period
Y. Hu, T. Siriburanon and R. B. Staszewski, "Multirate Timestamp
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Walker, Richard. (2003). Designing Bang-Bang PLLs for Clock and Data
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hunting jitter
S. Jang, S. Kim, S. -H. Chu, G. -S. Jeong, Y. Kim and D. -K. Jeong,
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[phd
thesis]
Carlo Samori ISSCC2016 T1: Understanding Phase Noise in LC VCOs
LTV Models
PPV (Perturbation Projection
Vector)
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Pulse-Injection Driver Achieving −255.2dB FoMJ Including the XO Power
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(ISSCC), San Francisco, CA, USA, 2025
If \(m=0\)\[
\phi(t) \approx \frac{I_0C_0}{2q_\text{max}\Delta
\omega}\sin(\Delta\omega t)
\] If \(m\neq 0\) and \(m=n\)\[
\phi(t) \approx \frac{I_mC_m}{2q_\text{max}\Delta
\omega}\sin(\Delta\omega t)
\]
\(m\omega_0 +\Delta \omega \ge
0\)
A. Hajimiri and T. H. Lee, "A general theory of phase noise in
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Electrical Oscillators"
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