CMOS Switched-Capacitor Circuits for Bio-Medical and RF Applications

1

• CMOS Switched-Capacitor Circuits for Bio-Medical
  and RF Applications
• David J. Allstot
• Mackay Professor of EECS
• University of California
• Berkeley, CA 94720

2
Origin of Switched-Capacitors?
James C. Maxwell, A Treatise on Electricity and
Magnetism Oxford Clarendon Press, 1873, vol. 2,
pp. 374-375.
2
3
MOS Switched Capacitors - 1972

• David L. Fried, Analog Sample-Data Filters,
  IEEE J. Solid-State Circuits, pp. 302-304, Aug.
  1972. MOS SC resistor concept and SC n-path
  filter

Early MOS data converters and switched-capacitor
filters for the per-channel voice-to-PCM
interface of digital telephony UC Berkeley

• J.L. McCreary and P.R. Gray, All-MOS charge
  redistribution analog-to-digital conversion
  techniques Part I, IEEE JSSC, Dec. 1975.
• R.E. Suarez, P.R. Gray and D.A. Hodges, All-MOS
  charge redistribution analog-to-digital
  conversion techniques Part II, IEEE JSSC, Dec.
  1975.
• Y.P. Tsividis and P.R. Gray, An integrated NMOS
  operational amplifier with internal
  compensation, IEEE JSSC, Dec. 1976.
• I.A. Young, D.A. Hodges and P.R. Gray, Analog
  NMOS sampled-data recursive filter, IEEE ISSCC,
  Feb. 1977.
• D.J. Allstot, R.W. Brodersen and P.R. Gray, MOS
  switched-capacitor ladder filters, IEEE JSSC,
  Dec. 1978.

Paul R. Gray
David A. Hodges
Key Paper on n-path filter analysis

• B.D. Smith, Analysis of commutated networks,
  IRE Trans. on Aerospace and Navigational
  Electronics, pp. 21-26, 1953.

Robert W. Brodersen
3
4
Future Research Topics
Golden Age for RF-CMOS Design!
Courtesy of Prof. James Buckwalter, UC Santa
Barbara
4
5
Outline

• Challenges in CMOS Radio Design
• Switched-Capacitor N-path Filters
• Analog-domain Compressed Sensing for Bio-signal
  Acquisition

5
6
Ubiquitous Wireless
Emerging IT platforms fundamentally change the
way we interact with and live in the
information-rich world
Core
Mobile Access

• Vision potentially doomed by network
  deficiencies
• lack of availability
• lack of reliability/robustness
• lack of security

Sensors
J. M. Rabaey, "A Brand New Wireless Day What
Does It Mean for Design Technology?," Asia and
South Pacific Design Automation Conf., 2008, p.
1.
6
7
RF Transceiver Coexistence
State-of-the-Art
N-path

• Without SAW filter
• TX leakage needs at least 20dB of rejection to
  improve IIP3 so that LNAs can handle input power
• Challenge Reconfigurable, linear duplexer SAW
  replacement

7
Courtesy of Prof. James Buckwalter, UC Santa
Barbara
8
Brain Radio Coexistence
Neural Recording
Neural Stimulation
PA
LNA

• Stimulator leakage needs rejection to increase
  IIP3 so LNAs can handle input power

8
9
Universal Receiver Blocker Rejection

• Low Cost
• - No Inductors
• - No Off-Chip Filters
• Low Noise Figure
• High Linearity
• Low Power Diss.
• High Blocker Tolerance
• Wide Frequency Range

• Low Cost
• - No Inductors ?
• - No Off-Chip Filters
• Low Noise Figure ?
• High Linearity
• Low Power Diss. ?
• High Blocker Tolerance
• Wide Frequency Range ?

GSM Example
Courtesy of Prof. Behzad Razavi, UCLA, 2015
ISCAS Keynote Presentation
9
10
N-path filter basics
Translational Filter à la Smith

• Scaled transistors are good switches with low Ron
  on Coff

• Each path behaves as a passive mixer that
  translates the baseband impedance to an RF
  impedance

Shunt RLC filter that is tuned with local
oscillator

• Large switches reduce insertion loss but limit
  tunability

Luo and Buckwalter, MWCL 2014
10
11
Shunt vs. Series N-path Filters

• Shunt filter Bandpass response
• Series filter Bandreject response
• compatible with digital CMOS
• Benefits from faster switches (e.g., CMOS SOI
  process)

11
Luo and Buckwalter, MWCL 2014
12
How Many Paths?

• Number depends on the tunability of the filter
• Require each path to be switched with 1/N duty
  cycle

• Aliasing is prevented to the N-1 LO harmonic.
• Low OOB rejection is a problem in spite of high
  linearity.

Luo and Buckwalter, MWCL 2014
12
Luo and Buckwalter, MWCL 2014
13
N-path filter basics
Can We Filter at the Antenna?

• For BW 200 kHz Ctot 28 nF
• For 20-dB rejection Rsw 5 W
• Switch linearity with 0-dBm blocker?

Courtesy of Prof. Behzad Razavi, UCLA, 2015
ISCAS Keynote Presentation
13
14
Miller Resistance
Courtesy of Prof. Behzad Razavi, UCLA, 2015
ISCAS Keynote Presentation
14
15
Miller Bandpass Filter
Ctot2 nF
NF 1.6 dB

• Low Cost
• - No Inductors ?
• - No Off-Chip Filters ?
• Low Noise Figure ?
• High Linearity?
• Low Power Diss. ?
• High Blocker Tolerance?
• Wide Frequency Range ?

Courtesy of Prof. Behzad Razavi, UCLA, 2015
ISCAS Keynote Presentation
15
16
Miller Multiplication / Harmonic Rejection
100 pF
50 W
Fundamental
Third Harmonic
Razavi, 2014 CICC Weldon, et al., Dec. 2001
JSSC
16
17
Outline for Compressed Sensing

• Motivation for Compressive Sampling
• Intuition and Key Ideas
• Reconstruction
• Experimental Results

17
18
Motivation for Compressive Sampling

• (Medical) Body Area Networks
• Many wireless sensors linked to Smartphone,
  nearby IPAD, 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 efficiency

18
19
CS Sensor System
Compressed Sampling Bio-Signal
Acquisition System
Antenna
x(t)
Y
Power Amplifier
CS AFE
ADC
LNA
Electrode
Sensor
Compressed Data Rate
Feedback

• Ultra-low-power CS Analog Front-end
• RF PA is Dominant Energy Consumer ADC Next
• CS Compresses Data Rate and PA/ADC Duty Cycles
• Compressed Data Y is Digitized and Transmitted

19
20
Conventional Sampling

• 12 Ball Problem 11 Light Balls (1 g) 1 Heavy
  Ball (100g)
• Goal Identify Heavy Ball in Fewest Measurements
• Conventional Sampling requires 12 measurements

20
21
Intuition for CS
(Measurement Vector)
(Signal Vector)
(Measurement matrix)

• Key Idea Extend Group Sampling Fewer Measurements

• R. Dorfman, The detection of defective members
  of large populations, The Annals of Mathematical
  Statistics, vol. 14, 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, pp. 1179-1252, Sept. 1959.

21
22
Random Sampling 1
102g
0 0 0 0 0 0 0 1 0 1 1 0
1g 1g 1g 1g 1g 1g 1g 1g 1g 100g 1g 1g

• Random Sample to Find Y11
• Use 1-b Random Numbers (e.g., Bernoulli,
  Toeplitz, Circulant, etc.) Incoherent Between
  Rows

22
23
Random Sampling 2
102g 5g
0 0 0 0 0 0 0 1 0 1 1 0 1 0 0 0 1 1 1 0 0 0 1 0
1g 1g 1g 1g 1g 1g 1g 1g 1g 100g 1g 1g

• Random Sample to Find Y21
• Use 1-b Random Numbers (e.g., Bernoulli,
  Toeplitz, Circulant, etc.) Incoherent Between
  Rows

23
24
Random Sampling 3
102g 5g 105g
0 0 0 0 0 0 0 1 0 1 1 0 1 0 0 0 1 1 1 0 0 0 1 0
1 0 1 1 0 0 0 0 1 1 0 1
1g 1g 1g 1g 1g 1g 1g 1g 1g 100g 1g 1g

• Random Sample to Find Y31
• Reconstruction Two Heavy MeasurementsOnly 10
  Common
• Fewer Measurements (e.g., 3)
• CS Works for Sparse Signals

• Other (unlikely) Possibilities
• Solution in 1 Measurement
• No Solution in M Measurements

24
25
Sparsity vs. Compressibility
8-bit ECG

• Limit M gt K log(N/K) K Nonzero Samples
  Heuristic M gt 2K
• Error Bounds E. Candès, An introduction to
  compressive sampling, IEEE Signal Processing
  Magazine, vol. 25, pp. 21-30, Mar. 2008.
• E. Candès and T. Tao, Near optimal signal
  recovery from random projections Universal
  encoding strategies, IEEE Trans. Info. Theory,
  vol. 52, pp. 5406-5425, Dec. 2006.

25
26
Compressed Sampling - I
YMX1 Y11, , YM1
XNX1 X11, , XN1
Y FX
K 3

• X16X1 F8X16 Y8X1 C 2
  
• F is Gaussian, Uniform, Bernoulli, Toeplitz,
  etc.
• Multiply and sum for each Yij is a Random Linear
  Projection
• Y is compressed analog signal with global
  information
• K lt M lt N for sparse signal such as ECG, EMG, etc.

26
27
Compressed Sampling - II

• X1024 X 1 Analog ECG samples
• Y256 X 1 Compressed analog output
• F256 X 1024 Measurement Matrix
• C 4X

27
28
CS Reconstruction
Original Nyquist Data Rate

• Reconstruction of Compressed Signal (e.g.,
  Smartphone)
• F is Non-square Under-determined System with
  Many Solutions
• Optimize e.g., Convex Optimization with L1-Norm
  Minimization
• Feature Extraction in DECODER Using
  YSparsifying Matrix e.g., Mexican Hat Wavelet
  to extract QRS Complex of ECG Waveform

A.M.R. Dixon, E.G. Allstot, D. Gangopadhyay, and
D.J. Allstot, Compressed sensing system
considerations for ECG and EMG wireless
bio-sensors, IEEE Trans. on Biomedical Circuits
and Systems, vol. 6, pp. 156-166, April 2012.
28
29
CS Reconstruction - II

• Accuracy depends on
• Compression Factor, C N/M
• PDF of random coefficients and bits
• AlgorithmConvex Optimization with L1 Norm

29
30
Switched-capacitor CS CODER

• Structure Matrix operations so that input is
  pipelined. Eliminates many explicit S/H circuits

CSADC
30
31
Switched-capacitor CS CODER

• 64 Rows Implemented
• C-2C 6-b MDAC/ADC
• C-2C 10-b SAR ADC

31
32
Switched-capacitor CS CODER
32
33
CSADC Measured Results (ECG)
Raw ECG Compressed Y values 2X (32 rows
0.9 uW) 4X (16 rows 0.4 uW) 6X (10 rows 250
nW)
Measured reconstruction of an ECG signal sparse
in Daubechies-4 wavelet domain using 8 frames
each of N128 samples. (Not thresholded at input.)
33
34
CSADC Results (ECG Bio-signals)
Raw ECG Compressed Y values 2X (64 rows
0.9 uW) 4X (32 rows 0.45 uW) 8X (16 rows
225 nW) 16X (8 rows 112 nW)
Measured reconstruction of an ECG signal sparse
in the time domain using 8 frames each of N128
samples. (thresholded at input.)
34
35
Switched-capacitor CSADC
IBM8RF 64 6-b C-2C MDAC 64 10-b C-2C SAR ADC
0.13 µm CMOS 2 mm x 3 mm M 1 64
(selectable) N 128, 256, 512, 1024 C N / M
(Comp. Ratio) 28 nW/row
64 6-b Word Fibonacci / Galois LFSR
D. Gangopadhyay, E.G. Allstot, A.M.R. Dixon, S.
Gupta, K. Natarajan and D.J. Allstot, Compressed
sensing analog front-end for wireless
bio-sensors, IEEE JSSC, vol. 49, pp. 426-438,
Feb. 2014.
35
36
Future Research Topics
N-Path Filters Blocker-tolerant front ends
Time-to-Digital Converters
Analog-to-Digital Converters
Open Area of Research for Wireless and Biomedical!
Courtesy of Prof. James Buckwalter, UC Santa
Barbara
36
37
Multumesc
37