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 23rd, 2011 – EE380

Peter Seitz, Ph.D. Vice President Nanomedicine, CSEM SA Adjunct Professor, EPFL Institute of Microengineering

Information Content: Photography and X-Ray Imagery

X-Ray Imaging

Stanford EE380 | Peter Seitz | Page 1

First (black-and-white) photograph: 1826 Photograph today Early X-ray image: 1896 X-ray image today

Absorption Properties of X-Rays in Matter

Stanford EE380 | Peter Seitz | Page 2

X-Ray Imaging

x

e I I

 











n i i i x

e I I

1



x α I0 I x1 x2 x3 α1 α2 α3 I0 I

 

 

         

n i i i n i i tot

w

1 1

   

   

α : linear attenuation coefficient (1/cm) μ : mass attenuation coefficient (cm2/g) ρ : density (g/cm3)

Photon Energy Dependent X-Ray Absorption Spectra

Stanford EE380 | Peter Seitz | Page 3

X-Ray Imaging

Source: NIST XCOM Database

Medical X-rays

Functional Dependence of the Photoelectric Effect

Stanford EE380 | Peter Seitz | Page 4

X-Ray Imaging

Textbooks: Bragg-Pierce Law for the photoelectric absorption of a homogeneous piece of elemental matter as a function

• f X-ray photon energy E and atomic number Z :

a b E

Z E Z  ) , ( 

b  4.0 ; a  -3.0

Absorption Properties of X-Rays

Stanford EE380 | Peter Seitz | Page 5

X-Ray Imaging

End of slide show, click to exit.

Photoelectric Absorption Revisited …

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X-Ray Imaging

) ( ) (

) (

Z a E b Z

E Z c E  

NIST Database : “Tables of X-Ray Mass Attenuation Coefficients and Mass- Energy Absorption Coefficients“ http://www.physics.nist.gov/PhysRefData/XrayMassCoef/cover.html

Assume “cross dependence“ b(E) and a(Z) :

E log Z(E)

Absorption due to photo-effect

Photoelectric Absorption : Monotonous Functions !

Stanford EE380 | Peter Seitz | Page 7

X-Ray Imaging

) ( ) (

) (

Z a E b Z

E Z c E  

Color X-Ray Imaging !

Stanford EE380 | Peter Seitz | Page 8

Color X-Ray Imaging

i = 1, 2, … n constituting elements

) ( ) ( 1

) (

i

Z a E b i n i i

E Z c E





  

) ( ) ( 1

i j

Z a j E b i n i i j

E Z c



  

j = 1, 2, … m energy sampling

i j

a j b i i j

E Z c    

,

   

Simple linear problem: Find the elemental composition vector ρi, given the (tabulated/measured) matrix elements Bi,j and the measured attenuation vector data αj

i i j j

x x            

Color X-Ray Imaging In Practice

Stanford EE380 | Peter Seitz | Page 9

Color X-Ray Imaging

• 1. Measure reference absorption

spectra for pure elements (basis)

• r use tabulated data
• 2. Measure absorption spectrum of

unknown sample

• 3. Solve for linear combination of

basis spectra which best fit the measured attenuation results

2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 14 16 18

Al (550m) Si (390m) Ti (46m) NIST Al NIST Si NIST Ti

/ (m

2/kg)

energy (keV)

) ( ) ( 1

i j

Z a j E b i n i i j

E Z c



  

j = 1, 2, … m energy sampling

Experimental Verification

Stanford EE380 | Peter Seitz | Page 10

Color X-Ray Imaging X-ray spectrometer (Amptek X-123) Microfocus X-ray source (Hamamatsu L1010101) Sample

Element-Sensitive X-Ray Imaging Demonstrated !

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Color X-Ray Imaging

Conventional X-ray

Element-sensitive X-ray

E = 11.6 … 13.1 keV

Color X-Ray Imaging Around the Corner ?

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Color X-Ray Imaging

i j

a j b i i j

E Z c    

,

   

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 μm X-ray pixels with ΔE<50 eV

Fundamental Noise Source: Johnson Noise in Resistor

Stanford EE380 | Peter Seitz | Page 13

X-Ray Pixel

B R kT

V

4  

σV : noise voltage; k : Boltzmann„s constant; T : temperature; R : resistance; B : bandwidth

Energy-Selective Single X-Ray-Photon Detector Pixel

Stanford EE380 | Peter Seitz | Page 14

X-Ray Pixel

Cdetector Rr Csensing Cload

load sensing detector sensing noise

C C C kTC q  

Cdetector

Problem: Large X-ray pixels (area of several 1000 μm2) can have capacitances of pF and more

RC B B R kT

V

1 ; 4   

Lateral Drift-Field Pixels !

Stanford EE380 | Peter Seitz | Page 15

X-Ray Pixel

Note: Lateral drift-field pixels have recently been adopted by industry (Hamamatsu, Mesa Imaging, Espros Photonics, etc.)

• K. Hoffmann: “Surface charge transport

with an MOS-transmission line“, Solid State Electronics Vol. 20, 177 (1977)

Fundamental Noise Limit : Recharge Resistor !

Stanford EE380 | Peter Seitz | Page 16

X-Ray Pixel

Cdetector Rr Csensing Cload sensing noise

kTC q 

Note: Johnson (resistor) noise is RC-filtered: Independent of R ! Typical value: Csensing = 50 fF, T = 300 K : qnoise = 90 electrons

Stanford EE380 | Peter Seitz | Page 17

• 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 Rr (GΩ)

implementation difficult when connected as feedback resistor

Cs Rr sense node

Energy-Selective Single-Particle (X-Ray) Detection

X-Ray Pixel

Stanford EE380 | Peter Seitz | Page 18

Energy-Selective Single-Particle (X-Ray) Detection

X-Ray Pixel

Parameter Value

detected pulse width 150 ns – 1.5 µs conversion factor 27 µV/e- 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 Overall noise (r.m.s.) 13.5 e-

Stanford EE380 | Peter Seitz | Page 19

Low-Noise Charge Detection : Noise Sources

Low-Noise Sensing

Noise contribution Value

Buffer (first transistor) 1.6 e- High-pass filter resistor 3.9 e- Active low-pass filter 1.4 e- Reset resistor (Rr) 12.7 e- Overall noise (r.m.s.) 13.5 e-

• 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 20

The Holy Grail : Single-Electron/Photon Detection !

Low-Noise Sensing

CMOS/APS Image Sensing

Single electron detection

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Conventional CMOS pixel

sense node column line reset transfer select bias

Reset VR V V VR t reset reset

Noise Sources in CMOS/APS Pixels

Single electron detection

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sense node column line reset transfer select bias

Reset noise (kTC noise) Solution: Correlated Double Sampling (CDS) MOS-FET channel noise (input-referred Johnson noise)

m S Q

g B kT C   4 

State of the Art: MOS-FET Channel Noise

Single electron detection

Stanford EE380 | Peter Seitz | Page 23

m S Q

g B kT C   4 

CS = 10 fF ; T = 300 K ; B = 20 MHz ; α = 1 ; gm = 50 μS

σQ = 5.1 electrons

The Long Quest for Single-Electron/Photon Detection

Single electron detection

Stanford EE380 | Peter Seitz | Page 24

n-substrate p-well p+ p+ n

S G D

CCD sensing channel

P1 P2 P3 summing gate

• utput gate

reset gate

• ut

dump gate dump drain Vdiff VDD VDD Vreset

reset select

• ut

Novel CMOS/APS Pixel With In-Pixel Gain

Single electron detection

Stanford EE380 | Peter Seitz | Page 25

• In-pixel amplification for reduced bandwidth and reduced impact of

downstream circuit noise  very low readout noise

Amplifying pixel

(common-source connected p-MOS amplifier pixel)

sense v pixel

C q A CF 

sense node reset_n transfer select_n column line

Rl

column v thermal n

C A kT v  

,

Gain Pixel : Reset and Amplifying State

Single electron detection

Stanford EE380 | Peter Seitz | Page 26

Gain Pixel : Column-Level Bandwidth Engineering

Single electron detection

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Single Electron/Photon Detection With CMOS Imagers !

Single electron detection

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Parameter Value pixel pitch 11 µm fill factor 50% transistor count 4 sense node capacitance 5.3 fF voltage gain (linear) 9.9 pixel conversion factor (lin.) 300 µV/e- linear range 4 ke- full well capacity 29 ke- readout noise (60 fps, 300K) 0.86 e- rms dynamic range (texp = 17ms) 90.4 dB

11x11 μm CMOS pixel with 50% fill factor Sample picture of 256x256 imager. Average: 6 photo- electrons per pixel

Summary

Stanford EE380 | Peter Seitz | Page 29

• 105 years after Röntgen‟s discovery, it has been shown that “color” X-ray

imagery (element-sensitive radiography) is possible, in principle.

• Determination of elemental composition from spectral measurements is

an ill-posed problem. Only decomposition into a few components seems

• practical. Relevance of “color radiography”? Too early to tell !
• Spectral measurements require integrated (monolithic) energy-resolved

single-photon X-ray detectors. State of the art (ΔE = 50-100 eV) can be improved by an order of magnitude (ΔE = 5-15 eV), using bandwidth opti- mization, in-pixel amplif., input capacitance reduction, synchronous reset

• The same techniques can be applied to CMOS/APS image sensors,

resulting in sub-electron (photo-) charge detection at room temperature and at video rates. Night vision for everybody is around the corner!

Stanford EE380 | Peter Seitz | Page 30