development of a cmos pixel sensor for embedded space ...single-particle pulse analysis; good...

Development of a CMOS pixel sensor for embedded space dosimeter with low weight

and minimal power dissipation

Yang ZHOU

Ph.D. thesis defense

23th of September 2014

Outline

1

 Challenges in space radiation counters   Space radiation environment   Past generation devices & the main challenges   How CMOS pixel sensors work and potentially cope with the challenges

 New concept realization   Requirements translation from measurement to sensor design   Sensor design

 Concept validation   Tests of the prototype   Embedded smart digital process performances

 Conclusion   Thesis contributions

Yang ZHOU Ph. D. thesis defense 23/09/2014

Earth orbit radiation environment (radiation belt region)

2

Various particle species: electrons, protons,

X rays, various heavy ion species (~1%);

Why monitoring this radiation environment?   Recommendations for spacecraft design

(eg. surface damage, solar cell damage)   Improvement of mission planning

(avoid the high radiation region)

  Safety concerns for human space habitation and exploration (< 25 Rems per mission)

Part 1: challenges in space radiation counters


What should be measured?

electrons & protons

  High flux density:

  E-: 104 → several 107 particles/cm2/s

  P+: 103 → 104 particles/cm2/s

  Large energy range:

  E-: 100 keV– 10 MeV;

  P+: 100 keV– 400 MeV;


3

  Monitors in current use:

  Scientific payloads:

  Good particle measurement capability

  Large mass (>> 1kg)

and power requirements (>> 1W)



Why small scale monitors? Spatial resoultion of this region requires a lot of monitors working on different orbits

 Small support instruments:

  Limited functionality (dose rate)

and offer little or no particle identification

Particles species, energy & intensity vary with different orbits


4

Radiation effects on biology and spacecraft devices strongly depend on particle species and energy

A small & accurate instrument: suitable for widespread use on satellites in Earth orbit could open new prospects for radiation monitoring in space



Why particle identification is important?

Particle Energy Typical Effects Proton 100 keV – 1 MeV

1 – 10 MeV 10 – 100 MeV

> 50 MeV

Surface damage; Surface material & solar cell damage; Radiation damage (both ionizing and nonionizing); Background in sensors; Single-event effects; Nuclear interaction-caused background;

electron 10 – 100 keV >100 keV

>1 MeV

Surface electrostatic charging; Deep-dielectric charging Background in sensors Solar cell damage; Radiation damage (ionizing);

Past generation small-scale monitors in space

5

  Geiger counter: (gaseous detector) on the Explorer-1 satellite in 1958   First radiation detector used in space;   No particle species identification   Very low count rate

  The Scintillating Fibre Detector (SFD): on the EQUATOR-S satellite in 1997   Compactness (397 g, 332 cm3 and 105 mW)   Dose rate measurement   No single-particle pulse measurement

  The Standard Radiation Environment Monitor (SREM):   Reliable operation: 7 flight missions

(STRV-1c, PROBA 1, Integral, GIOVE B, etc….) since 2000 to 2010   Single-particle pulse analysis; good spatial resolution of the radiation belts   Flux limitation: 4×105/cm2/s << the highest flux rate in the radiation belt;   The particle species and energy identification is probabilistic only

2.6 kg, 2400 cm3 and 2.5 W



Main challenges

6

  Main challenges:   High flux measurement: ~ 107 Part./cm2/s

  Single particle species and energy identification;

  Small-scale device: mass, power;

  Why pixelated detectors?   Higher flux cope capability;

  Lower operation frequency required (less power);

  May provide additional method for particle identification (cluster analysis)


Non-segmented Segmented

Individual impacts Merging impacts


Pixel sensors

7

  Charge Coupled Devices (CCD):   Uneconomical   Complexity of using   No processing inside the

sensor possible

  Hybrid pixel sensors:   Excellent radiation

tolerance   High complex and labor-

intensive chip bonding process

  ~ 300 µm-thick sensing volume

  CMOS Pixel Sensors (CPS):   Good radiation tolerance

(> 100 kRad)   Low fabrication cost   Highly integrated:

on-chip signal processing   ~10 – 15 µm-thick

sensing volume: less electron scattering, better energy resolution



CMOS pixel sensor working principles and advantages

8

Why CPS?   High flux cope capability: high granularity (10×10 µm2 possible); fast readout speed (~10 k frames/s)

  Good particle identification potential: very thin less e- scattering less deposited energy fluctuation Higher measurable precision

  Small-scale device: on-chip signal processing


Basic advantages for particle detection:

 Very sensitive:

excellent noise performance

(could be ~10 e-, SNR > 20 for MIP)

 High detection efficiency: sensitive;

100% fill-factor; almost “dead” time free;


Radiation tolerance: >100k Rad good enough based on the ESA project requirement

On-going project with CMOS pixel sensor

9

  Highly Miniaturized Radiation Monitor (HMRM): A CMOS pixel Sensor based detector

  Identify particle fluxes: up to 108 particles/cm2/s (expected pile-up probability < 5%);

  Particle identification principle: based on the particles' different penetration capability;

  Highly miniaturized: 15 cm3, 52 g;

HMRM designed by STFC (Rutherford Appleton Laboratory), ESA & Imperial College London , currently undergoing integration on the TechDemoSat-1 spacecraft


  Sensor main specifications:

  50×51 pixel array;

  20 µm pixel pitch;

  Sensitive area: 1 mm2;

  Frame rate: 10, 000 fps;

  Column-parallel 3-bit ADC;

  Digital output;

  Data processing: in a FPGA


Proposed new concept: cluster analysis

10

This study: further exploiting the full potential of a single CMOS monolithic pixel sensor



HMRM architecture Difficulties:

1.  How to perform the complex treatment without compromising sensitivity? (Sensitivity & saturation; identify strategy & measurement precision; speed & power dissipation … …)

2.  The embedding of a smart digital process

Shield

Part 2: New concept realization

Sensitive area, granularity & operation speed

11

  Granularity & operation speed (drive the hits pile-up): < 0.1% for 107 part./cm2/s; assuming electrons trigger < 3×3 pixels

  20 µm pitch size, 100 µs/frame

  50 µm pitch size, 20 µs/frame

Challenge 1: high flux

  Sensitive area (drives the flux estimation speed and accuracy): 10 mm2 sensitive area

  103 part./cm2/s: 1 s with 10 % relative uncertainty;

  Higher flux: better accuracy or within a shorter time;

? More specifications:

o  Pixel signal range;

o  ADC bits;

o  Embedded algorithm

… …

  4 steps of simulation


Requirements from measurement to sensor design

12

Challenge 2: particle identification

protons �

electrons �

Interval containing: 68%, 95% of the values �

50 MeV�

Monte Carlo simulation results (obtained using GEANT4)

  Step 1: signal generated:   Step 2&3: pixel matrix response

  Signal over pixels;

  Digitization: 3-bit;

Limit the particle incident angle Reduce the deposited energy fluctuation

  Step 4: cluster analysis

 Particle classification: by cluster ADC counts


ClusterADC counts

Particle species

>71 p+ < 2 MeV

[21,70] p+ : [2 MeV, 30 MeV]

[9,20] p+ : [30 MeV, 50 MeV]

[1,8] e- & p+ > 50 MeV 8 20

70

Specifications for each block of the sensor design

13

Proposed CMOS Pixel Sensor architecture developed through MIMOSA series @ IPHC

  Pixel:   Speed: Sensing diode fast reset and recovery;

  Low noise: sensitive to MIP;

  linear response: for downstream digitization

  Signal range: up to ~ 4400 e-

  ADC:   Column-level;   3-bit resolution   Tunable threshold: for detection efficiency

  Intelligent digital processing: Cluster separation and counting

Challenge 3: highly integrated and smart detection system


Specifications for each block of the sensor design

14

  The sensor:

  Rolling-shutter mode: <315 ns/row   Functionalities:

particle identification and counting   Output: Low data rate


  General principles:

  No temperature sensitive blocks

(eg. Logarithm amplifier/ADC)

  Low system power dissipation and less complexity

o  Rolling shutter mode: one row at a time;

o  Switch off amplifiers/ADCs when not using

o  Add one extra bit for the ADC output

to simplify the digital processing block


Proposed CMOS Pixel Sensor architecture developed through MIMOSA series @ IPHC

Design of the pixel: active reset & in-pixel offset cancellation

Timing diagram: 240 ns (< 315 ns) for one pixel

15

Pixel schematic

  Signal processing chain in pixel:

  Active reset: speed

  Pre-amplify stage: low noise

  Double sampling: low noise

  Source follower: speed



  Key issues for pixel design:

  Speed

  Low noise

  linear response

  Signal range

Design of the pixel: key points

16


 Key issues for pixel design:   Noise and speed

  Capacitor value: trade-off between noise and operation speed

  Reset transistors: should remain in linear region for a fast reset

  Linear response in the useful signal range

  NMOS transistors only: PMOS competes for charge collection.

  Pre-amplification stage: trade off between gain& linearity in the range

  SF stage: stable gain in the signal range match with CS output;

Signal transmission: CS stage SF stage

To column

Pixel schematic

The Common Source (CS) amplifier simple architecture (low noise)


Design of the ADC: Successive approximation ADC

Proposed ADC architecture: based on the SAR logic

Specifications:

  Tunable threshold: adjust for detection efficiency

  240 ns/sample: match with pixel signal processing

17


Expected transfer function of the ADC


Chosen architecture: SAR ADC

  Low power dissipation: only one comparator with

3 or 4 comparisons are enough for the whole conversion

Design of the ADC: sample & hold block

18


Timing diagram of the sample and hold circuit


4 capacitors, an output buffer & 6 switches

Key design issues:

 Signal subtraction:

obtain the signal amplitude

from pixel double sampling

 Speed: followed by capacitor arrays

 Free from offset

The architecture with 3 steps operation:

 Operation Step 1&2: signal subtraction

 Operation Step 3: offset cancellation

 Small value CH1: fast sampling (speed)

 Unit gain buffer: fast readout (speed)

Design of the ADC: first comparison & power efficient design

Timing diagram for the first comparison

19


Based on charge redistribution principle


  Key design issues:

  Power efficient design: An additional comparison before typical SAR cycle Turn-off ADC (signal value < threshold)

  Tunable threshold & expected transfer function:

4 external references

Appropriate timing

Design of the ADC: high precision comparator

20


Offset cancellation

Tracking

Latching


  Key design issues:   High precision:

Output Offset Storage (OOS) technique,

One additional mode than typical tracking + latching

  Low kickback noise:

Two buffers are added in the differential inputs of the amplifier

Design of the ADC: amplifier & latch in the comparator

21


Dynamic latch: Amplifier:


  Key design issues:

  Amplifier:

First stage: gain;

second stage: speed & kickback noise reduction

  Dynamic Latch:

good speed, no-static power dissipation

Design of the digital processing stage: targets

22



  Key design issues:   Real-time processing

row by row

  Correct functionalities

0 0 0 0 0 0 0 00 1 1 0 0 0 0 00 1 5 0 0 0 0 00 0 0 0 0 1 0 00 0 0 0 1 5 1 00 0 0 0 1 7 2 00 0 0 0 0 1 1 00 0 0 0 0 0 0 0

0 1 5 0 0 0 020

out

Cluster trimming driven by zeros

Shut

ter

read

out

row

by

row

∑ Sum of rows

  Clusterization

  Sum ADC counts in each cluster

  Separation

  Counting

Design of the digital processing stage: principle

23

Reading row 2: 240 ns


Step 1

Step 2 Step 3



1

2

3


Design of the digital processing stage: schematic

24



Design of the digital processing stage: layout

25


Realization:   Algorithm described using Verilog;

  Size: 1.2×3.2 mm2

  Control timing clock frequency: 25 MHz

  Power dissipation:

56.7 mW (3.3 power supply)


Layout of the digital processing stage (synthesized in a 0.35 µm process)

The full size sensor

26

Layout of the propose sensor: designed in a 0.35 µm process, with 14 µm thick, high resistivity (1k ohm-cm ) epitaxial layer, and named as COMETH*

o  Core area: 16.8 mm2

o  Power dissipation: ~100 mW (Vdd = 3.3 V)

o  Operation speed: ~ 65, 000 frames/s

o  Output rate: 80 bps



*: COunter for Monitoring the Energy and Type of charged particles in High flux

Part 3: concept validation

Tests of the prototype

27


Reduced scale prototype with 32×32 pixels & 32 column ADCs

  Monochromatic X-rays: calibration.   CVF (charge to voltage conversion factor);   CCE (charge collection efficiency);

  Laser illumination: validating the pixel design   Linearity;   Range;   Speed;

  Electrons source: validating the simulated sensor response with electrons   Verify the CCE obtained in X-ray tests;

  Pixel matrix: illuminated with 3 types of radiations

  ADCs


  Protons illumination test: lack of appropriate source

Calibration peak: 1640 e-

55Fe radioactive source: 5.9 keV X-rays, 1640 e-

28

  Seed pixel calibration peak: 364 ADC unit; 4.5 e-/ADC unit;   CVF: ~33 µV/e-

  CCE (seed pixel): 115/364 = (31.6±0.5)%

  CCE (3×3 pixels): 323/364 = (88.7±0.5)%

 Satisfactory high CCE for a pixel sensor with 50 µm pitch size

  CCE (5×5 pixels): 343/364 = (94.3±0.5)%



cluster

Threshold

90Sr source: e- energy spectrum end point being at 2.3 MeV

29

 Threshold (determines the detection efficiency): 120 e- for 99.95%

 Seed pixel MPV (Most probably value): 371±2 e-

 Confirmed the simulated sensor response to electrons: small triggered cluster size

 Most of the clusters contain: 2 to 4 pixels > threshold; 9



~80 × 14 × 31%

Estimated from CDS value variation without any source

30

  ENC: 30 e-;

  SNR (for electrons): 13 (MPV)



For the smallest signal in this application: electrons

Eve

nts

Pulsed laser illumination: 1603 nm infrared

31

  Laser illumination test:

  Linear response range: 0 to 4600 e-;

(satisfy the expectation 0 to 4400 e-)

  Sensing diode fast reset and recovery:

confirmed though no remaining signal observed

after large intensity illumination.



ADCs test: External voltage biases replacing the pixel outputs

32

  Noise:   INL and DNL for a single ADC:

< ±0.12 LSB   Temporal noise (rms): 0.02 LSB;   Fix pattern noise between 32 columns (rms):

0.21 LSB (need to be improved in next version)

  Power efficient design:

  With signal: 759 µW/ADC;

  Without signal: 532 µW/ADC (most of the cases);

Single ADC transfer function

  Threshold tunable to meet the detection efficiency

1LSB = 700 e- = 700 e- × CVF = 23.1 mV



Digital processing stage: reconstruction performances

33

 Cluster reconstruction performances:

(checked by the full simulation with the layout):

 Success cases:

  Normal convex clusters;

  Tricky clusters with one “dead” pixel;

  Most of two “dead” pixels cases;

  Failure cases: (marginal)

  Two adjacent “dead” pixels in a cluster center;

  One or both of the two adjacent “dead” pixels

locates in the middle of the last row of a cluster



Rec

onst

ruct

ed e

nerg

y (k

eV) �

Deposited energy (keV) �

Layout of the digital processing stage

  Deposited energy reconstruction performances: Based on the reconstructed cluster ADC counts, with 10% standard deviation

Summary of the concept validation

34

 Performances have been validated to ensure the expected capability:

 High flux capability: (Fully checked)

  Electron triggered clusters size are small

  Designed operation speed; sensing diode fast reset and efficient recovery

 Particle identification: (Mostly checked)

  Tested sensor responses with electrons and protons match with the simulations

( cluster size, cluster ADC counts);

  Circuits function as the design specifications: pixels; ADC; Digital processing stage



Part 4: conclusions

Conclusions COMETH

35

  COMETH:   Handle with high flux: up to 108 particles/cm2/s for electrons (hits pile-up < 5%);

  Single particle identification capability:   Electrons & protons > 50 MeV

  Protons: [30 MeV, 50 MeV]

  Protons: [2 MeV, 30 MeV]

  Protons < 2 MeV

  Highly integrated, miniaturized, low power/mass/data rate, smart detection system: no signal treatment power aside the sensor required

  Sensor area < 20 mm2;

  ~ 100 mW power dissipation;

  80 bps output data rate: no data transmission stress for the satellite;

  High level output information: directly output particles species/energies/flux information;

Part 4: conclusions

Differentiation beyond this energy could achieved by a strategy of exploiting several sensors equipped with various shields.

A very compact monitor could be foreseen


Thesis contributions

36

Part 4: conclusions


  Developed & validated a new concept for single particles identification with CPS:

Pulse measurment

Multiple sensors Cluster analysis

Measurable energy range Low High High

Measurable flux rate Low High High

Monitor archiecture Simple complex Simplest

Monitor scale (mass, power) Large medium small

  The embedding of a smart digital process:

Significantly reduced the complexity and scale of a monitor

Contributions of this thesis:

  Developed a CPS architecture from particle tracking to energy measurement: By sacrificing the hit position information

Publications & communications during this study

37

  Communications:   Y. Zhou, J. Baudot, Ch. Hu-Guo, Y. Hu, K. Jaaskelainen and M. Winter. COMETH: a CMOS pixel sensor for a highly

miniaturized high-flux radiation monitor, Oral presentation at the Technology and Instrumentation in Particle Physics

(TIPP) conference 2014, 2-6 June, Amsterdam, Holland.

  Y. Zhou, J. Baudot, C. Duverger, Ch. Hu-Guo, Y. Hu and M. Winter, Development of a CMOS Pixel Sensor for Space

Radiation Monitor, Poster at l’école IN2P3 de microélectronique 2013, 24-27 June 2013. Porquerolles, France.

  Y. Zhou, J. Baudot, C. Duverger, Ch. Hu-Guo, Y. Hu and M. Winter. CMOS Pixel Sensor for a Space Radiation Monitor

with very low cost, power and mass, Oral presentation at the 14TH International Workshop on Radiation Imaging

Detectors (IWORID2012), 1-5 July 2012. Figueira da Foz, Coimbra, Portugal.

  Publications:   Y. Zhou, J. Baudot, Ch. Hu-Guo, Y. Hu, K. Jasskelainen and M. Winter. COMETH: a CMOS pixel sensor for highly

miniaturized High-flux radiation monitor, Proceedings of Science (PoS) 2014.9

  Y. Zhou, J. Baudot, C. Duverger, Ch. Hu-Guo, Y. Hu and M. Winter, CMOS Pixel Sensor for a Space Radiation

Monitor with very low cost, power and msss, 2012 JINST 7 C12003.


Radiation dose in the radiation belts

44

The highest recommended limit for radiation exposures is for astronauts-25,000 millirems per Space Shuttle mission.

85% protons in space <10 MeV

0.7 mm aluminum can shield e- < 0.5 MeV and p < 10 MeV

2 mm aluminum can shield e- < 1.5 MeV and p < 20 MeV

Those effects which do exist but be ignored (1/2)

45

  X ray

GOES X-ray satellite data (35,800 km above the Earth)

100 10 1 wavelength (A)

Only hard X-ray: 0.1 A – 1 A (12 keV – 120 keV) can penetrate the shielding

Considering the efficiency of 62.5 keV X-ray in the sensor is lower than 1%, that means every 100 seconds, X-ray deposit 62.5 keV energy in the sensor. That is marginal.

Background Introduction Detector options Conclusion

Those effects which do exist but be ignored (2/2)

46

 Proton nuclear reaction (rare) depends on the energy and range. Still do not have exact data to support, but the probability would be less than 1%

Nuclear reaction

  Various heavy ion species (~1%)

*A.B. Rosenfeld, et al,. A New Silicon Detector for Microdosimetry Applications in Proton Therapy, IEEE TRANSACTIONS ON NUCLEAR SCIENCE, VOL. 47, NO. 4, AUGUST 2000

*J. E. Mazur. An Overview of the Space Radiation Environment. Crosslink, Volume 4, Number 2 (Summer 2003)

Background Introduction Detector options Conclusion

Model for signal over pixels

47

 

*PSF: Point Spread Function; was generated from the test results of MIMOSA 5, 17, 18, 22, 24 and LUCY with the same collecting diode size;

  Dead time: 100 ns/ 240 ns/64 = 6.5%

Pixel-level offset cancellation

49

50

Normal pixel noise Noise pixel with RTS noise

51

Pedestal

Sensitive area, granularity & speed

7

  Probability of misjudgment by hits pile-up

Frame time (speed)

Pixel pitch

Cluster size in pixels

P(N≥2)

100 µs 20 µm 3×3 0.07%

5×5 0.47%

50 µm 3×3 2.18%

5×5 13.02%

50 µs 50 µm 3×3 0.59%

5×5 3.98%

20 µs 50 µm 3×3 0.10%

5×5 0.72%

6×6 0.002%

Challenge 1: high flux

103 part./cm2/s 1 s 100 s

107 part./cm2/s 0.1 ms 10 ms

  Flux estimation: 10 mm2 sensitive area

Sensor response simulations: strategy performances �Challenge 2: single particle identification

Rec

onst

ruct

ed e

nerg

y (k

eV) �

Deposited energy (keV) �Number of particles hit the sensor/frame �

Num

ber o

f par

ticle

s rec

onst

ruct

ed�

  Reconstruction capability :   Flux: 0.8 × 106/cm2/s; (not an absolute limit of this architecture)   Energy: a relative value of 10% standard deviation;

Electrons and protons (50/50) with mixed energies (1 MeV to 100 MeV)

  Conclusion of the simulations:   High flux counting capability:

  Small cluster size triggered by electrons: 2 to 3 pixels in average;.   Single particle identification capability: particles could be discriminated by their triggered cluster ADC counts

  Protons triggered cluster size is inversely proportional to its energy. (low energy protons possibly trigger 9×9 pixels)

Reconstruct Energy �Reconstruct flux (particles/frame) �

Step 4: Check both the high-flux cope and particle identification (by the deposited energy reconstruction) capability

9

Based on the various cluster ADC counts

Accumulated charges_COMETH

54

Column FPN

55

channel charge injection:

Solutions to reduce the ADCs column FPN

56

Part 4: conclusions

Idea 1: compensating the offset error by additional references:

Architecture 2: especially attractive to low resolution, column ADCs for small size sensors

Column FPN (rms value) could be reduced to:

  Idea 1: ~ 7.5 e- ;   Idea 2: ~ 15 e-;

Employs a 6-bit trimming DAC, each ADC has its own trimming register

Idea 2: a switch multiplexer and 8 references for all the columns

Column FPN (rms value) ~ 147 e-


Offset cancellation in S/H circuit

57

58

Logarithm amplifier example

Challenge 2: single particle identification

Logarithm amplifier example:

Id = Is (e(Vd/Vt)-1)

Ir = Id

Ir = Vin/R

Vin/R = Id

Vin/R = Is e(Vd/Vt)

Vout = -Vt In(Vin/IsR) Vt: thermal voltage

Digital processing stage: estimated power

59

Block mW

Signal column 0.87252 mW

Memory_temp 0.068

FSM1_N 0.03351

Adder_Column 0.09566

Adder*3 0.1445

SUM_Ctroller 0.4736

FSM2_N 0.05725

System 0.87397 mW

Counter*4 0.801

Accumulator*4 0.07297

Whole System = (0.87252×64) + 0.87397 ≈ 56.7 mW

Timing for the digital processing stage

60

Clock_ADC, Clock1, Clock1_delay1/2/3: pulse width 120 ns, period 240 ns, frequency 4.17M Hz; Clock2: 6 times speed of clock1; pulse width 20 ns, period 40 ns, frequency 25M Hz; Chosen depends on the largest cluster size 9×N (Column × Row); Clock3: Flexible

Not scaled

Cluster reconstruction examples

61

Cluster merges

Failure cases

development of a cmos pixel sensor for embedded space ...single-particle pulse analysis; good...

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