discovered single-photon CMOS imaging Stanford University EE - PowerPoint PPT Presentation

How we wanted to revolutionize X-ray radiography, and how we then "accidentally" discovered single-photon CMOS imaging Stanford University EE Computer Systems Colloquium February 23 rd , 2011 – EE380 Peter Seitz, Ph.D. Vice President Nanomedicine, CSEM SA Adjunct Professor, EPFL Institute of Microengineering

X-Ray Imaging Information Content: Photography and X-Ray Imagery First (black-and-white) Early X-ray photograph: 1826 image: 1896 Photograph X-ray image today today Stanford EE380 | Peter Seitz | Page 1

X-Ray Imaging Absorption Properties of X-Rays in Matter    x α I 0 I I I e 0 x n    i x i α 1 α 2 α 3  I 0 I  I I e i 1 0 x 1 x 2 x 3 α : linear attenuation coefficient (1/cm)    μ : mass attenuation coefficient (cm 2 /g)  ρ : density (g/cm 3 )   n n          w   tot i i      i i 1 i 1 Stanford EE380 | Peter Seitz | Page 2

X-Ray Imaging Photon Energy Dependent X-Ray Absorption Spectra Medical X-rays Source: NIST XCOM Database Stanford EE380 | Peter Seitz | Page 3

X-Ray Imaging Functional Dependence of the Photoelectric Effect Textbooks: Bragg-Pierce Law for the photoelectric absorption of a homogeneous piece of elemental matter as a function of X-ray photon energy E and atomic number Z : b E   a ( Z , E ) Z b  4.0 ; a  -3.0 Stanford EE380 | Peter Seitz | Page 4

End of slide show, click to exit. X-Ray Imaging Absorption Properties of X-Rays Stanford EE380 | Peter Seitz | Page 5

X-Ray Imaging Photoelectric Absorption Revisited … Assume “cross dependence“ b(E) and a(Z) :  E  b ( E ) a ( Z ) ( ) c Z E Z NIST Database : “Tables of X -Ray Mass Attenuation Coefficients and Mass- Energy Absorption Coefficients“ http://www.physics.nist.gov/PhysRefData/XrayMassCoef/cover.html log  Z (E) Absorption due to photo-effect E Stanford EE380 | Peter Seitz | Page 6

X-Ray Imaging Photoelectric Absorption : Monotonous Functions !  E  b ( E ) a ( Z ) ( ) c Z E Z Stanford EE380 | Peter Seitz | Page 7

Color X-Ray Imaging Color X-Ray Imaging ! n  i = 1, 2, … n constituting elements    a ( Z ) b ( E ) ( E ) c Z E i i i  i 1 n c     b E ( ) j = 1, 2, … m energy sampling a ( Z ) Z j E i j i i j  i 1     b                 a j x x c Z E i j j i i j , i i j Simple linear problem: Find the elemental composition vector ρ i , given the (tabulated/measured) matrix elements B i,j and the measured attenuation vector data α j Stanford EE380 | Peter Seitz | Page 8

Color X-Ray Imaging Color X-Ray Imaging In Practice n c     b ( E ) j = 1, 2, … m energy sampling a ( Z ) Z j E i j i i j  i 1 18 1. Measure reference absorption Al (550  m) 16 Si (390  m) Ti (46  m) spectra for pure elements (basis) 14 NIST Al NIST Si 12 or use tabulated data NIST Ti 2 /kg) 10 2. Measure absorption spectrum of 8  /  (m 6 unknown sample 4 2 3. Solve for linear combination of 0 0 2 4 6 8 10 12 14 16 18 20 basis spectra which best fit the energy (keV) measured attenuation results Stanford EE380 | Peter Seitz | Page 9

Color X-Ray Imaging Experimental Verification X-ray spectrometer Microfocus X-ray source (Amptek X-123) (Hamamatsu L1010101) Sample Stanford EE380 | Peter Seitz | Page 10

Color X-Ray Imaging Element-Sensitive X-Ray Imaging Demonstrated ! Element-sensitive X-ray Conventional X-ray E = 11.6 … 13.1 keV Stanford EE380 | Peter Seitz | Page 11

Color X-Ray Imaging Color X-Ray Imaging Around the Corner ?   b       a j c Z E i j i i j , Ill-conditioned inversion problem ! Example: cond(B) ~ 200…600 for the combination Al and Si with the limited single energy interval of around 11 – 14 keV Possible way out: Multiple energy intervals for reduced cond(B) Large, expensive, power-hungry, high- resolution X-ray spectrometer Wanted: Affordable Megapixel 2D array of <100 × 100 μ m X-ray pixels with Δ E<50 eV Stanford EE380 | Peter Seitz | Page 12

X-Ray Pixel Fundamental Noise Source: Johnson Noise in Resistor   4 kT R B V σ V : noise voltage; k : Boltzmann„s constant; T : temperature; R : resistance; B : bandwidth Stanford EE380 | Peter Seitz | Page 13

X-Ray Pixel Energy-Selective Single X-Ray-Photon Detector Pixel R r  C C  detector sensing q kTC C sensing noise sensing C load C load 1    4 kT R B ; B C detector V RC C detector Large X-ray pixels (area of several 1000 μ m 2 ) Problem : can have capacitances of pF and more Stanford EE380 | Peter Seitz | Page 14

X-Ray Pixel Lateral Drift-Field Pixels ! K. Hoffmann: “Surface charge transport with an MOS- transmission line“, Solid State Electronics Vol. 20, 177 (1977) Note: Lateral drift-field pixels have recently been adopted by industry (Hamamatsu, Mesa Imaging, Espros Photonics, etc.) Stanford EE380 | Peter Seitz | Page 15

X-Ray Pixel Fundamental Noise Limit : Recharge Resistor ! R r C sensing  q kTC noise sensing C load C detector Note : Johnson (resistor) noise is RC-filtered: Independent of R ! Typical value : C sensing = 50 fF, T = 300 K : q noise = 90 electrons Stanford EE380 | Peter Seitz | Page 16

X-Ray Pixel Energy-Selective Single-Particle (X-Ray) Detection • Integration on (small) capacitance on sensor side • Continuous “reset” on sensor side • Continuous-time high-pass filtering of reset noise • Narrow bandwidth shaping of recharge noise: High R r (G Ω ) implementation difficult when connected as feedback resistor R r sense node C s Stanford EE380 | Peter Seitz | Page 17

X-Ray Pixel Energy-Selective Single-Particle (X-Ray) Detection Parameter Value 150 ns – 1.5 µs detected pulse width 27 µV/e - conversion factor recharge time constant 10 µs high-pass time constant 2 µs pixel area 30 x 20 µm fill factor 56 % Hi-pass filter capacitance 200 fF 13.5 e - Overall noise (r.m.s.) Stanford EE380 | Peter Seitz | Page 18

Low-Noise Sensing Low-Noise Charge Detection : Noise Sources Noise contribution Value 1.6 e - Buffer (first transistor) High-pass filter resistor 3.9 e - 1.4 e - Active low-pass filter Reset resistor ( R r ) 12.7 e - 13.5 e - Overall noise (r.m.s.) • Reduce noise substantially (to less than 5 electrons) by changing from “continuous” (asynchronous) reset to “switched” (synchronous) reset ! • Input stage resembles a CMOS active pixel (APS). Is it possible to employ the same ideas (bandwidth engineering, in-pixel amp, input capacitance reduction, synchronous reset) to ultra-low-noise CMOS image sensing? Stanford EE380 | Peter Seitz | Page 19

Low-Noise Sensing The Holy Grail : Single-Electron/Photon Detection ! Stanford EE380 | Peter Seitz | Page 20

Single electron detection CMOS/APS Image Sensing Conventional CMOS pixel reset transfer V R V sense node V R Reset V select column t line reset reset bias Stanford EE380 | Peter Seitz | Page 21

Single electron detection Noise Sources in CMOS/APS Pixels MOS-FET channel noise reset (input-referred Johnson noise) Reset noise transfer (kTC noise) sense node Solution: Correlated Double select Sampling column (CDS) line  4 kT B   bias C Q S g m Stanford EE380 | Peter Seitz | Page 22

Single electron detection State of the Art: MOS-FET Channel Noise  kT B 4   C Q S g m C S = 10 fF ; T = 300 K ; B = 20 MHz ; α = 1 ; g m = 50 μ S σ Q = 5.1 electrons Stanford EE380 | Peter Seitz | Page 23

Single electron detection The Long Quest for Single-Electron/Photon Detection S G D reset gate output gate out p + p + n summing gate p-well CCD P1 P2 P3 dump gate sensing channel dump drain n-substrate V DD V reset V DD reset V diff select out Stanford EE380 | Peter Seitz | Page 24

Single electron detection Novel CMOS/APS Pixel With In-Pixel Gain • In-pixel amplification for reduced bandwidth and reduced impact of downstream circuit noise  very low readout noise transfer Amplifying pixel sense node (common-source connected p-MOS amplifier pixel) select_n column reset_n line q  CF A pixel v C R l sense   v kT A C n , thermal v column Stanford EE380 | Peter Seitz | Page 25

Single electron detection Gain Pixel : Reset and Amplifying State Stanford EE380 | Peter Seitz | Page 26

Single electron detection Gain Pixel : Column-Level Bandwidth Engineering Stanford EE380 | Peter Seitz | Page 27

Single electron detection Single Electron/Photon Detection With CMOS Imagers ! Parameter Value 11x11 μ m pixel pitch 11 µm CMOS pixel with 50% fill fill factor 50% factor transistor count 4 sense node capacitance 5.3 fF Sample voltage gain (linear) 9.9 picture of 300 µV/e - pixel conversion factor (lin.) 256x256 4 ke - linear range imager. Average: 29 ke - full well capacity 6 photo- 0.86 e - rms readout noise (60 fps, 300K) electrons dynamic range (t exp = 17ms) 90.4 dB per pixel Stanford EE380 | Peter Seitz | Page 28