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

David J. Allstot

Mackay Professor of EECS

University of California

Berkeley, CA 94720

Origin of Switched-Capacitors?James C. Maxwell, A Treatise on Electricity and Magnetism

Oxford: Clarendon Press, 1873, vol. 2, pp. 374-375.

2

Paul R. Gray

David A. Hodges

Robert W. Brodersen

• 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.

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

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.

Future Research Topics

Golden Age for RF-CMOS Design!*Courtesy of Prof. James Buckwalter, UC Santa Barbara

Switched Capacitor:

High-efficiency, high-power

transmitters;Converters

Switched Capacitor:

High-efficiency, high-power

transmitters;Converters

N-Path Filters: Blocker-tolerant

front ends

N-Path Filters: Blocker-tolerant

front ends

Time-to-Digital Converter:

Ring-oscillator amplifiers;

Analog-to-digital converters

Time-to-Digital Converter:

Ring-oscillator amplifiers;

Analog-to-digital converters

4

Outline

Challenges in CMOS Radio Design

Switched-Capacitor N-path Filters

Analog-domain Compressed Sensing for

Bio-signal Acquisition

5

Emerging IT platforms fundamentally change the way we interact with and live in the information-rich world

Ubiquitous Wireless

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

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

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.

Sensors

Mobile Access

Core

6

• 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

State-of-the-Art N-pathRF Transceiver Coexistence

7*Courtesy of Prof. James Buckwalter, UC Santa Barbara

“Brain Radio” Coexistence

8

LNAPA

Neural Recording

Neural Stimulation

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

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

N-path filter basics

• Scaled transistors are good

switches with low Ron on Coff

500.0M 1.0G 1.5G 2.0G-25.0

-20.0

-15.0

-10.0

-5.0

0.0

Frequency (Hz)

S21 S11

S21

S11

(dB

)

Shunt RLC filter that is tuned with local oscillator

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

• Large switches reduce insertion loss but limit tunability* Luo and Buckwalter, MWCL 2014

Translational Filter à la Smith

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• Shunt filter: Bandpass response

• Series filter: Bandreject response

• compatible with digital CMOS

• Benefits from faster switches (e.g., CMOS SOI process)

Shunt vs. Series N-path Filters

* Luo and Buckwalter, MWCL 2014 11

How Many Paths?

• Number depends on the tunability of the filter

• Require each path to be switched with 1/N duty cycle

2 3 4 5 6 7 8 9 10 11 12 13 14 15-100-90-80-70-60-50-40-30-20-10

Har

mon

ic a

liasi

ng (

dBc)

Harmonic

simulation

measurement

• 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

* Luo and Buckwalter, MWCL 2014 12

N-path filter basics

Can We Filter at the Antenna?

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

*Courtesy of Prof. Behzad Razavi, UCLA, 2015 ISCAS Keynote Presentation13

Miller Resistance

14*Courtesy of Prof. Behzad Razavi, UCLA, 2015 ISCAS Keynote Presentation

Miller Bandpass FilterCtot=2 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 15

*Courtesy of Prof. Behzad Razavi, UCLA, 2015 ISCAS Keynote Presentation

Miller Multiplication / Harmonic Rejection

Fundamental Third Harmonic

50

100 pF

16*Razavi, 2014 CICC; Weldon, et al., Dec. 2001 JSSC

Outline for Compressed Sensing

Motivation for Compressive Sampling

Intuition and Key Ideas

Reconstruction

Experimental Results

17

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

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CS Sensor System

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

LNA ADCPower

Amplifier

Antenna

CS AFEElectrode

Compressed Sampling Bio-Signal Acquisition System

Sensor

x(t) [Y]

Compressed Data RateFeedback

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Conventional Sampling

1 2 3 4 5 6

7 8 9 11 1210

12 Ball Problem: 11 Light Balls (1 g); 1 Heavy Ball (100g)

Goal: Identify Heavy Ball in Fewest Measurements

Conventional Sampling requires 12 measurements

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Intuition for CS

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.

1 2 3 4 5 6

7 8 9 11 1210

1g1g1g1g1g1g1g1g1g

100g1g1g

1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 00 0 1 0 0 0 0 0 0 0 0 00 0 0 1 0 0 0 0 0 0 0 00 0 0 0 1 0 0 0 0 0 0 00 0 0 0 0 1 0 0 0 0 0 00 0 0 0 0 0 1 0 0 0 0 00 0 0 0 0 0 0 1 0 0 0 00 0 0 0 0 0 0 0 1 0 0 00 0 0 0 0 0 0 0 0 1 0 00 0 0 0 0 0 0 0 0 0 1 00 0 0 0 0 0 0 0 0 0 0 1

1g1g1g1g1g1g1g1g1g

100g1g1g

=

Y = X

(Measurement Vector)

(Measurementmatrix)

(Signal Vector)

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Random Sampling – 1

7 116

1 5

2 811

8

10

3 4

129

1

10

102g 0 0 0 0 0 0 0 1 0 1 1 0 1g1g1g1g1g1g1g1g1g

100g1g1g

=

Random Sample to Find Y11

Use 1-b Random Numbers (e.g., Bernoulli, Toeplitz, Circulant, etc.) Incoherent Between Rows

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Random Sampling – 2

7 116

1 5

2 811

8

10

3 4

129

1

10

7 116

1 5

2 811

8

10

3 4

129

1

10

102g5g

0 0 0 0 0 0 0 1 0 1 1 0 1 0 0 0 1 1 1 0 0 0 1 0

1g1g1g1g1g1g1g1g1g

100g1g1g

=

23

Random Sample to Find Y21

Use 1-b Random Numbers (e.g., Bernoulli, Toeplitz, Circulant, etc.) Incoherent Between Rows

Random Sampling – 3

7 116

1 5

2 811

8

10

3 4

129

1

10

7 116

1 5

2 811

8

10

3 4

129

1

10

102g5g

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

1g1g1g1g1g1g1g1g1g

100g1g1g

=

7 116

1 5

2 811

8

10

3 4

129

1

10

Random Sample to Find Y31

Reconstruction: Two Heavy Measurements—Only #10 Common

Fewer Measurements (e.g., 3)

CS Works for Sparse Signals

Other (unlikely) Possibilities:

Solution in 1 Measurement

No Solution in M Measurements

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Sparsity vs. Compressibility

Limit: M > K log(N/K); K Nonzero Samples; Heuristic: M > 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.

50 60 70 80 90 100

Sparsity (%)

2

6

10

14

18

22 Compression Factor, C = N/M

8-bit ECG

25

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Compressed Sampling - I

[X]NX1 = [X11, …, XN1]

[Y]MX1 = [Y11, …, YM1]

[X]16X1; []8X16; [Y]8X1; C = 2

[] 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 < M < N for sparse signal such as ECG, EMG, etc.

[]MXN = [11, …, N ][[[

]]]M1, …, N

…K = 3[Y] = [Φ][X]

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Compressed Sampling - II

[X]1024 X 1: Analog ECG samples

[Y]256 X 1: Compressed analog output

[]256 X 1024: Measurement Matrix

C = 4X

[X]

[Y]

CS Reconstruction

Reconstruction of Compressed Signal (e.g., Smartphone)

[Φ] is Non-square; Under-determined System with Many Solutions

Optimize; e.g., Convex Optimization with L1-Norm Minimization

“Feature Extraction” in DECODER Using []—Sparsifying Matrix; e.g., Mexican Hat Wavelet to extract

QRS Complex of ECG Waveform

LNA DAC

Antenna

Baseband DSPCS Optimization/ Reconstruction

Compressed Sensing Bio-Signal Reconstruction System

y(t)

Original Nyquist Data Rate

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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.

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CS Reconstruction - II

Accuracy depends on:

Compression Factor, C = N/M

PDF of random coefficients and # bits

Algorithm—Convex Optimization with L1 Norm

[X]

[Y]

Switched-capacitor CS CODER

Structure Matrix operations so that input is

pipelined. Eliminates many explicit S/H circuitsElectrode  

CSADC

CSADC

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Switched-capacitor CS CODER

64 Rows Implemented:

C-2C 6-b MDAC/ADC

C-2C 10-b SAR ADC

[Y] = [Φ][X]LNA ADC Power

Amplifier

Antenna

CS AFEElectrode

Compressed Sensing Bio-Signal Acquisition System

Sensor

Ultra-low Power Analog Circuits

SC Multiplying Digital-Analog

Converter

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Switched-capacitor CS CODER

64 Rows digitally selectable

N is programmable

Fig. 3. CSADC circuits. Counterclockwise from top: Opamp, C-2C MDAC/integrator, C-2C SAR ADC (withpre-amp offset cancellation), and comparator. Device stacking to reduce W/L and dual-gate switchesand logic gates are used to minimize leakage.

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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 N=128 samples. (Not thresholded at input.)

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CSADC Results (ECG Bio-signals)

time (s)

Am

pli

tud

e (m

V)

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 N=128 samples. (thresholded at input.)

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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

3 m

m

2 mm

64

6-b

C -

2C M

IDA

Cs

IBias, Timing

64

10-b

C-2

C

SA

R C

ap-D

AC

8

pad

dri

vers

64

SA

R lo

gic

blo

cks

64 C

om

par

ato

rs

Test Structures : MIDAC and SAR

64

Op

Am

ps

64

6-b

Wo

rd F

ibo

nac

ci /

Gal

ois

LF

SR

35D. 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.

Future Research Topics

Open Area of Research for Wireless and Biomedical!*Courtesy of Prof. James Buckwalter, UC Santa Barbara

Switched Capacitor:

High-efficiency, high-power

transmitters;Converters

Switched Capacitor:

High-efficiency, high-power

transmitters;Converters

Time-to-Digital Converter:

Ring-oscillator amplifiers;

Analog-to-digital converters

Time-to-Digital Converter:

Ring-oscillator amplifiers;

Analog-to-digital converters

36

N-Path Filters: Blocker-

tolerant front ends

Time-to-Digital Converters;

Analog-to-Digital Converters

Mulţumesc

37