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CMOS Switched-Capacitor Circuits for Bio-Medical and RF Applications David J. Allstot

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

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CMOS Switched-Capacitor Circuits for Bio-Medical and RF Applications David J. Allstot

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  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 Time-to-Digital Converter: Ring-oscillator amplifiers; Analog-to-digital converters Switched Capacitor: High-efficiency, high-power transmitters; Converters N-Path Filters: Blocker-tolerant front ends 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 N-path State-of-the-Art • 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. LNA 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. BehzadRazavi, UCLA, 2015 ISCAS Keynote Presentation 9

  10. N-path filter basics Translational Filter à la Smith • Scaled transistors are good switches with low Ronon 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 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 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. BehzadRazavi, UCLA, 2015 ISCAS Keynote Presentation 13

  14. Miller Resistance *Courtesy of Prof. BehzadRazavi, UCLA, 2015 ISCAS Keynote Presentation 14

  15. Miller Bandpass Filter Ctot=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  *Courtesy of Prof. BehzadRazavi, 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 Feedback Compressed Data Rate • 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 1g 1g 1g 1g 1g 1g 1g 1g 1g 100g 1g 1g 1g 1g 1g 1g 1g 1g 1g 1g 1g 100g 1g 1g 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 = = F X Y (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 1g 1g 1g 1g 1g 1g 1g 1g 1g 100g 1g 1g 102g 0 0 0 0 0 0 0 1 0 1 1 0 = • Random Sample to Find Y11 • Use 1-b Random Numbers (e.g., Bernoulli, Toeplitz, Circulant, etc.) Incoherent Between Rows 22

  23. Random Sampling – 2 1g 1g 1g 1g 1g 1g 1g 1g 1g 100g 1g 1g 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 = • Random Sample to Find Y21 • Use 1-b Random Numbers (e.g., Bernoulli, Toeplitz, Circulant, etc.) Incoherent Between Rows 23

  24. Random Sampling – 3 1g 1g 1g 1g 1g 1g 1g 1g 1g 100g 1g 1g 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 = • 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 24

  25. 22 18 14 10 6 2 50 60 70 80 90 100 Sparsity (%) Sparsity vs. Compressibility Compression Factor, C = N/M 8-bit ECG • 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. 25

  26. Compressed Sampling - I [F]MXN = [F11, …, F1N ] ] [ … [ ] [Y]MX1 = [Y11, …, YM1] FM1, …, FMN [ ] [X]NX1 = [X11, …, XN1] [Y] = [Φ][X] K = 3 • [X]16X1; [F]8X16; [Y]8X1; C = 2 • [F] is Gaussian, Uniform, Bernoulli, Toeplitz, etc. • Multiply and sum for each Yijis a Random Linear Projection • [Y] is compressed analog signal with global information • K < M < N for sparse signal such as ECG, EMG, etc. 26

  27. Compressed Sampling - II [X] [Y] • [X]1024 X 1: Analog ECG samples • [Y]256 X 1: Compressed analog output • [F]256 X 1024: Measurement Matrix • C = 4X 27

  28. CS Reconstruction Compressed Sensing Bio-Signal Reconstruction System Antenna y(t) Baseband DSP CS Optimization/ Reconstruction DAC LNA Original Nyquist Data Rate • 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 [Y]—Sparsifying 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 [X] [Y] • Accuracy depends on: • Compression Factor, C = N/M • PDF of random coefficients and # bits • Algorithm—Convex Optimization with L1 Norm 29

  30. Switched-capacitor CS CODER Electrode • Structure Matrix operations so that input is pipelined. Eliminates many explicit S/H circuits CSADC CSADC 30

  31. Switched-capacitor CS CODER [Y] = [Φ][X] Compressed Sensing Bio-Signal Acquisition System SC Multiplying Digital-Analog Converter Antenna Ultra-low Power Analog Circuits CS AFE Power Amplifier ADC LNA • 64 Rows Implemented: • C-2C 6-b MDAC/ADC • C-2C 10-b SAR ADC Electrode Sensor 31

  32. Switched-capacitor CS CODER • 64 Rows digitally selectable • N is programmable 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 N=128 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) Amplitude (mV) time (s) Measured reconstruction of an ECG signal sparse in the time domain using 8 frames each of N=128 samples. (thresholded at input.) 34

  35. Switched-capacitor CSADC IBias, Timing 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 10-b C-2C SAR Cap-DAC 8 pad drivers 64 6-b C -2C MIDACs 3 mm 64 SAR logic blocks 64 Comparators 64 Op Amps 64 6-b Word Fibonacci / Galois LFSR Test Structures : MIDAC and SAR 2 mm 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; Time-to-Digital Converter: Ring-oscillator amplifiers; Analog-to-digital converters Switched Capacitor: High-efficiency, high-power transmitters; Converters Analog-to-Digital Converters Open Area of Research for Wireless and Biomedical! *Courtesy of Prof. James Buckwalter, UC Santa Barbara 36

  37. Mulţumesc 37

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