ASU August 17, 2011
CMOS Switched-Capacitor Circuits: Recent Advances in Bio-Medical and
RF Applications
David J. Allstot
Univ. of WashingtonDept. of Electrical Engineering
Seattle, WA 98195-2500
ASU August 17, 2011
2010: 4.6 B subscribers
2012: 1 B WiFi US mobile phones: Use yearly
energy of 638,000 US Homes
Emit 6K tons CO2
Demand increases with newer data phones
PA is dominant energy hog
Motivation
PAMetropolitan Seattle Area
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ASU August 17, 2011
CMOS PA Trends: Pout
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J.S. Walling, S.S. Taylor and D.J. Allstot, “A class-G supply modulator and class-E PA in 130 nm CMOS,” IEEE JSSC, pp. 2339-2347, Sept. 2009. S.-M. Yoo, J.S. Walling, E.C. Woo and D.J. Allstot, “A switched-capacitor power amplifier for EER/Polar transmitters,” IEEE ISSCC Dig. Tech. Papers, pp. 428-429, 2011.
ASU August 17, 2011
CMOS PA Trends: PAE
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ASU August 17, 2011
Outline
Challenges in CMOS RF PA Design Switched-Capacitor PA Solution Analog-domain Compressed Sensing for
Bio-signal Acquisition
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2
=2
V Vout
DD SAT
L
PR
VDD
VDD Scaling
Impedance Transformation
Challenge: Max Power Out
RL=
50
Vout1 : n
Ropt=
RL/n2
45 nm CMOS 1W, VDD = 1.0 V VSAT = 0.2 V Ropt 0.3 Parasitic R Limit)
Linear PAs
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ASU August 17, 2011
Linear Power Amplifiers– AM Signals (i.e., non-
Constant Envelope)
– Class-A:
– Class-B:– Class-AB– Class-C: Peak = 100%
@ Pout = 0 (Attractive for Body Area Networks)
Challenge: Efficiency
= L
DC
PP
2V= 0.5 out
DDV
2
=4
out
DD
VV
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ASU August 17, 2011
ON
OFF
Switching Power Amplifiers– PM and FM Signals
(i.e., Constant Envelope)
Class D, E, F, etc. Zero-V Switching
– Rise in vD delayed until switch OFF
– vD = 0 @ switch ON dvD/dt = 0 @ switch
OFF
Ideal = 100%
Impedance Transformer & Wave-Shaping Network
= = 0DC D DP v i
Challenge: EfficiencyClass-E PA
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ASU August 17, 2011
0 0.2 0.4 0.6 0.8 10
20
40
60
80
100
Normalized Envelope (V)
Ocu
rren
ces
(%)
0 0.2 0.4 0.6 0.8 10
5
10
15
20
25
Normalized Envelope (V)
Ocu
rren
ces
(%)
0 0.2 0.4 0.6 0.8 10
5
10
15
20
25
Normalized Envelope (V)
Ocu
rren
ces
(%)
FM QAM 64-QAMSpectral vs. Energy Efficiency
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ASU August 17, 2011
Linearization Techniques Feedforward Feedback LINC – Linear Amp with Nonlinear Components EER – Envelope Elimination and Restoration
– Can use highly-efficient switching PA; e.g., Class-E
– Pout VDD for Switching PA– Split signal into envelope (A) & phase () paths– Improved overall efficiency– Distortion from delay mismatches in A & paths
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ASU August 17, 2011
A
Kahn EER Technique (1952)
Polar conversion in DSP using CORDIC Algorithm DAC and supply modulator needed
Original Kahn
Modern Kahn
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ASU August 17, 2011
LDO = Low-dropout Reg.
iout
LDO Modulator & Efficiency
LDO
Overall efficiency is product of supply modulator and PA efficiencies
Increased over Linear PA
LDO Characteristics– Vout ≈ ENVin
–––
out out outP v iDD outDCP v i
/ /out DC out DDP P v V
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ASU August 17, 2011
Dual-Supply Modulator
Class-G: Spectral vs. Energy Efficiency Small envelope:
Use Vdd/x Large envelope:
Use Vdd
Extend to more than two power supplies? Class-H?
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ASU August 17, 2011
0 0.2 0.4 0.6 0.8 10
20
40
60
80
100
Vout (V)
Dra
in E
ffici
ency
(%)
Class-G
Class-B
OFDM PDF
0 0.2 0.4 0.6 0.8 10
2
4
6
8
10
Prob
abili
ty (%
)
Avg. Class-B
Class-G: Spectral vs. Energy Efficiency
Avg. Class-G
Overall efficiency is product of class-G modulator and class-E PA efficiencies
Ideally 5X higher average than linear PA for this probability density function
Class-E
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ASU August 17, 2011
Class-E PA and Driver
Interstage tuning inductors reduce driver powerDriver taper of 2 – custom stages
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130nm Class-G PA
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Class-G Static Measurements
0 0.2 0.4 0.6 0.8 10
200
400
600
800
1000
Input Envelope2 (V2)
Out
put P
ower
(mW
)
0 0.2 0.4 0.6 0.8 10
16
32
48
64
80
PAE
(%)
0 0.2 0.4 0.6 0.8 10
20
40
60
80
Normalized Envelope (V)Ef
ficie
ncy
(%)
0 0.2 0.4 0.6 0.8 10
2
4
6
8
Prob
abili
ty (%
)
Class G PAE64QAM OFDM PDFTheory Avg PAEMeas. Avg PAE
64 QAM OFDM Symbol Period = 4 s
Theoretical avg. PAE = 24% Measured avg. PAE = 22%
Freq = 2 GHz
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ASU August 17, 2011
Class-G Dynamic Measurement
-80 -60 -40 -20 0 20 40 60 80-80
-60
-40
-20
0
Frequency Offset (MHz)N
orm
. Out
put P
ower
(dB
)
rms EVM = 2.5% Freq = 2 GHz
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ASU August 17, 2011
• DAC, supply modulator functions combined – No supply modulator: Higher efficiency and
smaller area• Multiple unit current-cell-based PAs as DAC
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PA based on digital modulation Unit current cells[Kavousian, et al., ISSCC 2007 ] [Presti, et al., JSSC 2009]
Digitally-Modulated PA
ASU August 17, 2011
• Accuracy / Efficiency Tradeoff• Accurate current cell requires high rout
– Cascode more headroom: Lower efficiency• Extra resolution required for predistortion• Efficiency:
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OUTOUT
OUT
DC
OUTIdeal P
VP
PP
Nonlinear VOUT
Input Code
V OU
T
Linear
Saturated
Current-Cell-Based PA
ASU August 17, 2011
Switched-Capacitor Basics
• Energy is lost w/ precharge and reset• No energy lost in charge redistribution w/o precharge
(b) Charge Redistribution w/o precharge
(a) Precharge and Reset
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SCPA in Polar Transmitter
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Basic SCPA Concept
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• SC technique can be used for voltage generation• Easy to split into capacitor bank (small area & loss)
– Resonant frequency maintained (Constant C)Constant envelope
Good efficiency
ASU August 17, 2011
Switched-Capacitor PA
• Capacitor can be arrayed– Single capacitor can be split into many– Each capacitor is switched to VDD or GND– Constant resonant frequency– RF Switched-Capacitor DAC
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Constant Capacitance
ASU August 17, 2011
Thevenin Equivalent Circuit
• Digitally-controlled output voltage• Constant top-plate capacitance vs. the
number of switched capacitors
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CU=C1=C2=Cn=CN= NC
Constant Capacitance = C
ASU August 17, 2011
Output Power
Pout delivered to ROUT
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• VOUT n/N
• POUT (n/N)2
• 4/ for 1st harmonic component
RV
Nn DD
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2
2
POUT =DDV
Nn
4
21
VOUT =
ASU August 17, 2011
Power Dissipated in SC
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• Charging & discharging with switch→ CV2f dynamic power
• Assume fast tr,tf with constant current through L
• Effective switched capacitance varies with envelope code
ASU August 17, 2011
Ideal Efficiency
• Higher efficiency with higher QLoaded
• Higher QLoaded:- Smaller Capacitance- Less CV2f dynamic power- Efficiency tradeoff due to L & switch
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fCRRfLQ Loaded
2
12SCOUT
OUT
PPP
LoadedQnNnn
n)(4
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2
ASU August 17, 2011
Ideal vs. Practical
Normalized POUT (dBm)
Practical Efficiency
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CLOCKDRSWCOUTSC
OUT
PPPPPP
OUTSC
OUTIdeal PP
P
• Practical implementation:− Lossy inductor: → − SW parasitic R: → − SW parasitic C: − Switch driver:− Clock distribution:
fVCNnP DDSWSWC2)/(
fVCP DDCLOCKCLOCK2
fVCNnP DDDRDR2)/(
Benefit from scaling
Idea
l (%
)
Prac
tical
(%
)
ASU August 17, 2011
CMOS Switch as Voltage Source
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0.250.50.751
n/N
CB
Volta
ge
(CB
)
AM-PM AM-AM
time 1/fs0
0
VDD
• Faster switch improves both AM-AM and AM-PM distortion performance (e.g., better with CMOS scaling)
ASU August 17, 2011
6-bit Switched-Capacitor Array
• Split into 4-bit unary and 2-bit binary arrays• Additional bits possible
– More unary/binary bits or C-2C ladder• Unit-cell switch and switch-driver
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Switch Implementation
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• Cascode More output power with same Rout
• Total supply voltage of 2VDD
• All thin-gate devices
• Separate driver voltage ranges for NMOS & PMOS
ASU August 17, 2011
Switched-Capacitor PA Schematic
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C= 8.2pF
Bandpass Matching Network
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• 90 nm RF LP CMOS process (MIM cap and UTM)
Output Matching Network
Capacitor A
rray
Switch,Drivers,Logic & Bypass Capacitor
1430 m
730 m
Chip Microphotograph
ASU August 17, 2011
PA Measurement: Pout & PAE
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• 6-bit implementation • Fewer Pdriver at backoff• Peak = 45%
ASU August 17, 2011
AM-AM & AM-PM / Pout vs. Freq.
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• Different impedance seen from source depending on input code
• Scaling friendly
• Peak Pout ≥ 24dBm• Peak ≥ 45%
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Constellation / Spectral Mask
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• 64 QAM/OFDM• EVM = 2.9%
• Pout = 17.7 dBm
ASU August 17, 2011
Reference Degani, et. al.ISSCC 2008
Presti, et. al.JSSC 2009
Xu, et. al. ESSCIRC 2010
Walling, et. al.JSSC 2009 This work
Architecture Class-AB DPACurrent Cell Outphasing Class-G Switched-
CapacitorProcess 90nm 0.13um 32nm 0.13um 90nm
Power Supply 3.3V 1.2V/2.1V 2V 3.3V 1.5V/3VPeak Power 25 dBm 25 dBm 25.1 dBm 29.3 dBm 25 dBm
Peak Efficiency 50% 47% 40.6% 69% 45%
Avg. Power(OFDM) 15.5 dBm 15.3 dBm 18.6 dBm 19.6 dBm 17.7 dBm
Avg. Efficiency(OFDM) 19% 22% 18.1% 22.6% 27%
Output Matching NW N/A Ext.
MatchingOn-Chip
BalunOn-Chip Matching
On-Chip Matching
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Performance Comparison
• What’s next? Class-G SCPA in package – high PAE.
ASU August 17, 2011
Outline
Motivation for Compressed Sampling (CS) Compressed Sampling and three key ideas CS reconstruction Experimental Procedures and Results Conclusions
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ASU August 17, 2011
Body Area Network Many wireless sensors linked to personal Smartphone, etc. Personal mobile units linked to Dr. via internet/cellular network Dr. feedback for real-time control of detail vs. energy efficiency
Reduce data rates to increase sensor lifetime and energy efficiency40
Motivation for Compressed Sampling
ASU August 17, 2011
Compressed Sampling Sensor System
Ultra-low power CS analog front-end (AFE) RF power amplifier is energy hog; ADC is energy piglet CS reduces data rates with commensurate energy
savings for PA, ADC, etc; i.e., only [Y] is digitized and transmitted
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LNA ADCPower
Amplifier
Antenna
CS AFEElectrode
Compressed Sampling Bio-Signal Acquisition System
Sensor
x(t) [Y]
Compressed Data RateFeedback
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Intuition for CS – Conventional
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Intuition for CS – Group Sampling
• R. Dorfman, “The detection of defective members of large populations,” The Annals of Mathematical Statistics, vol. 14, no. 4, pp. 436-440, Dec. 1943.
• M. Sobel and P.A. Groll, “Group testing to eliminate efficiently all defectives in a binomial sample,” Bell System Technical Journal, vol. 38, no. 5, pp. 1179-1252, Sept. 1959.
ASU August 17, 2011
Intuition – II: Sub-Nyquist Sampling
Intuitive explanation of three key ideas Nyquist sample a sinusoid; i.e., 2 samples/period Only 2 amplitude values (i.e., looks like sawtooth
waveform) How to get enough amplitude values to infer sinusoid?
WW
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WW
r1 r2
Key Idea #1: Randomize Sampling Multiply original analog samples by random weights to obtain
many more analog amplitudes45
Intuition – II: Sub-Nyquist Sampling
ASU August 17, 2011
W
r1 r2
r3
r4
r5
r6
r7 r8
W
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Key Idea #2: Reconstruction (e.g., 8! possible solutions) Key Idea #3: Optimization assuming known class of signal; e.g.,
sinusoid). 8! Solutions—CS finds best with high probability. What about compression?
Intuition – II: Sub-Nyquist Sampling
ASU August 17, 2011
Formal Compressed Sampling
[X]NX1
[Y]MX1
[X]: Analog input sample vector (e.g., N = 16) []: Measurement matrix of (e.g., 6-bit Gaussian or
Uniform) random coefficients (M rows and N columns) [Y]: Compressed analog output vector (e.g., M = 8) Compression Factor C = N/M (e.g., C = 2)
[]MXN
[Y] = [Φ][X]
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ASU August 17, 2011
Compressed Sampling - I
[X]NX1 = [X1, …, XN]
[Y]MX1 = [Y1, …, YM]
[X]16X1; []8X16; [Y]8X1; C = 2 []8X16 is Measurement Matrix;
e.g., Gaussian or Uniform random coefficients each quantized to n = 6 bits
Multiply and sum for each Yi is a Random Linear Projection [Y] is a compressed analog signal with global information Typically K < M < N (i.e., signal is sparse such as ECG)
[]MXN = [11, …, N ][[[
]]]M1, …, N
…
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1 11
Niii
Y X
K = 3
[Y] = [Φ][X]
ASU August 17, 2011
Compressed Sampling - II
[X]1024X1: Analog samples from ECG signal [Y]256X1: Compressed analog output signal []256X1024: Measurement Matrix C = 4X in this example; (C = 2X – 16X possible
for ECG)
[X]
[Y]
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ASU August 17, 2011
CS Reconstruction
Reconstruction/optimization of compressed signal (e.g., Smartphone) [Φ] is non-square and non-invertible; under-determined system with
many solutions Optimize exploiting knowledge of signal; e.g., ECG bio-signals are
time-domain sparse50
LNA DAC
Antenna
Baseband DSPCS Optimization/ Reconstruction
Compressed Sensing Bio-Signal Reconstruction System
y(t)
Original Nyquist Data Rate
ASU August 17, 2011
Accuracy Requirments for ECG
AAMI—American Institute for Advancement of Medical Instrumentation (Standards Vary)
Ambulatory Quality ECG—8-10 bits (48-60 dB) Diagnostic Quality ECG—10-12 bits (60-72 dB)
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ASU August 17, 2011
Accuracy depends on: Compression Factor, C = N/M PDF of random coefficients and # bits Algorithm—Convex Optimization with L1 Norm
CS Reconstruction - II
[X]
[Y]
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ASU August 17, 2011
Sparsity vs. Compressibility
Theoretical Limit: M > K log(N/K) with K nonzero input samples (Heuristic: M > 2K)
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50 60 70 80 90 100
Sparsity (%)
2
6
10
14
18
22 Compression Factor, C = N/M
ASU August 17, 2011
Quantization of Random Coefficients - I
Gaussian []: Choose n = 6 bits for C = 2X – 16X 54
ASU August 17, 2011
Switched-Capacitor CS CODER
For ECG signal: BW = 2 KHz fS = 4 KHz C = 100 fF PDYN ≈ 0.4 nW
C-2C in MDAC/ADC
[Y] = [Φ][X]
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LNA ADC PowerAmplifier
Antenna
CS AFEElectrode
Compressed Sensing Bio-Signal Acquisition System
Sensor
Ultra-low Power Analog Circuits
SC Multiplying Digital-Analog
Converter
ASU August 17, 2011
CS-ADC Chip-photo
IBM8RF 0.13 µm CMOS3 mm x 3 mm
M = 64N=128 to 1024
Testing Underway: Expect ~ 1 uW total power with C = 16
ASU August 17, 2011
Thank you very much!
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