what does spad afterpulsing actually tell us about defects in inp?

What Does SPAD Afterpulsing Actually

Tell Us About Defects in InP?

Mark Itzler, Mark Entwistle, and Xudong Jiang

SPW2011 – June 2011

Princeton Lightwave Inc.SPW2011 – June 2011

Presentation Outline

50 MHz photon counting with RF matched delay line scheme Afterpulse probability (APP) dependence on hold-off time Fitting of APP data

inadequacy of legacy approach assuming one or few traps new fitting based on broad trap distribution

Implications of APP modeling for trap distributions Summary

2


i-InGaAs absorption

n+-InP buffer

n-InGaAsP grading n-InP charge

i-InP cap

p+-InP diffused region

multiplication region

SiNx passivation p-contact metallization

n+-InP substrate anti-reflection coating n-contact metallization

optical input

Afterpulsing: increased DCR at high rate Single photon detection by avalanche multiplication in SPADs Avalanche carriers trapped at defects in InP multiplication region Carrier de-trapping at later times initiates “afterpulse” avalanches Serious drawback of afterpulsing → limitation on counting rate

Long hold-off time

# of trapped carriers

primary avalanche

afterpulsesshort hold-off

time

# of trapped carriers

trap sites located in multiplication region

Ec

Ev

3


New results for RF delay line circuit

Enhance matched delay line circuit to operate at higher repetition rate Inverted and non-inverted RF reflections cancel transients Based on existing PLI product platform

Bethune and Risk, JQE 36, 340 (2000)

Cancel transient response synchronous with photon arrival

Temporally gate out leading and trailing transients

Set threshold for remaining avalanche signal

4


1E-4

1E-3

1E-2

1E-1

0% 10% 20% 30% 40%

After

puls

e Pr

obab

ility

Photon Detection Efficiency

50 MHz33 MHz10 MHz1 MHz

PER DETECTED PHOTON

Matched delay line solution to 50 MHz Extension of cancellation scheme to higher frequencies

More precise cancellation for reduced detection threshold → detect smaller avalanches Higher speed components to enable 50 MHz board-level operation

Measure cumulative afterpulsing using odd gates “lit”, even gates “dark” Take all counts in even gates above dark count background to be afterpulses

OLD Performance (1 ns gate duration) NEW Performance (1 ns gate duration)

1E-6

1E-5

1E-4

1E-3

1E-2

1E-1

1E+0

5% 10% 15% 20% 25% 30%Photon Detection Efficiency

Afte

rpul

se P

roba

bilit

y

( per

1 n

s ga

te p

ulse

)

10 MHz5 MHz2 MHz1 MHz0.5 MHz

• Absence of afterpulsing “runaway” indicates higher frequencies can be achieved5

Princeton Lightwave Inc.SPW2011 – June 2011 6

“Double-pulse” afterpulse measurement Use “time-correlated carrier counting” technique to measure afterpulses Trigger single-photon avalanches in 1st gate Measure probability of afterpulse in 2nd gate at Tn

Use range of Tn to determine dependence of afterpulse probability on time following primary avalanche

Double-pulse (“pump-probe”) methodT1

≈

Cova, Lacaita, Ripamonti, EDL 12, 685 (1991)

T2≈

Afte

rpul

se

prob

abili

ty

TimeT1 T2


FPGA-based data acquisition Use FPGA circuitry to control gating and data collection Generalize double-pulse method to many gates

Capture afterpulse counts in up to 128 gates following primary avalanche Temporal spacing of gates determined by gate repetition rate

Allows capture of afterpulse count in any gate after avalance No need to step gate position as in double-pulse method

1 ns gates

7

1 2 3 4 5 6 126 127 128 1 2≈50 MHz:

20 ns

25 MHz:

1 2 3 128 1

≈40 ns


1E-5

1E-4

1E-3

1E-2

1E-1

10 100 1000

After

pusl

e pr

obab

ility

per

de

tect

ed p

hoto

n p

er g

ate

Time following primary avalanche (ns)

50 MHz40 MHz33 MHz25 MHz10 MHz

PDE = 20%1 ns gates

FPGA-based afterpulse measurements Obtain afterpulsing probability data at 5 frequencies for 128 gates

All frequencies

8

1E-5

1E-4

1E-3

1E-2

1E-1

10 100 1000

After

pusl

e pr

obab

ility


50 MHz

1E-5

1E-4

1E-3

1E-2

1E-1

10 100 1000

After

pusl

e pr

obab

ility


40 MHz

1E-5

1E-4

1E-3

1E-2

1E-1

10 100 1000

After

pusl

e pr

obab

ility


33 MHz

1E-5

1E-4

1E-3

1E-2

1E-1

10 100 1000

After

pusl

e pr

obab

ility


25 MHz

1E-5

1E-4

1E-3

1E-2

1E-1

10 100 1000

After

pusl

e pr

obab

ility


10 MHz

50 MHz

40 MHz

33 MHz 25 MHz 10 MHz

APP ~ 1% at 30 ns


Legacy approach to afterpulse fitting Try to fit afterpulse probability (APP) data with exponential fit

Physically motivated by assumption of single dominant trap

Single exponential curve generally fits range of ~5X in time

1E-5

1E-4

1E-3

1E-2

1E-1

10 100 1000

After

pusl

e pr

obab

ility

per

de

tect

ed p

hoto

n p

er g

ate



PDE = 20%1 ns gates

APP1 exp(-t/τ1)

9


Legacy approach to afterpulse fitting Try to fit afterpulse probability (APP) data with exponentials

Physically motivated by assumption of single dominant trap Single exponential not sufficient; assume second trap


1E-5

1E-4

1E-3

1E-2

1E-1

10 100 1000

After

pusl

e pr

obab

ility

per

de

tect

ed p

hoto

n p

er g

ate



PDE = 20%1 ns gates

APP2 exp(-t/τ2)

10


1E-5

1E-4

1E-3

1E-2

1E-1

10 100 1000

After

pusl

e pr

obab

ility

per

de

tect

ed p

hoto

n p

er g

ate



PDE = 20%1 ns gates

Legacy approach to afterpulse fitting Try to fit afterpulse probability (APP) data with exponentials

Physically motivated by assumption of single dominant trap Single exponential not sufficient; assume second trap Still need third exponential to fit full data set


APP3 exp(-t/τ3)

11


Legacy approach to afterpulse fitting Can achieve reasonable fit with several exponentials …but choice of time constants is completely arbitrary!

→ depends on range of times used in data set Our assertion: No physical significance to time constants in fitting

→ simply minimum set of values to fit the data set in question

APP = C1exp(-t/τ1) + C2exp(-t/τ2) + C3exp(-t/τ3)

1E-5

1E-4

1E-3

1E-2

1E-1

10 100 1000

After

pusl

e pr

obab

ility

per

de

tect

ed p

hoto

n p

er g

ate



PDE = 20%1 ns gates

τ1 = 30 ns

τ2 = 120 ns

τ3 = 600 ns

12


What other functions describe APP?

Good fit for simple power law T-α with α ≈ -1→ Is power law behavior found for other afterpulsing measurements?→ Is the power law functional form physically significant?

APP = C T-α

y = 0.52x-1.07

1E-5

1E-4

1E-3

1E-2

1E-1

10 100 1000

After

pusl

e pr

obab

ility

per

de

tect

ed p

hoto

n p

er g

ate



PDE ~ 20%1 ns gates

13


y = 3.44x-1.03

y = 2.20x-1.05

y = 0.74x-1.09

1E-4

1E-3

1E-2

1E-1

1E+0

10 100 1000

After

puls

e pr

obab

ility


3 ns gate2 ns gate1 ns gate

UVA data~30% PDE

Afterpulsing data from Univ. Virginia

Good fit for power law T-α with α ≈ -1.0 to -1.1

data from Joe Campbell, UVA

Double-pulse method

PLI SPADs

14


y = 2.92x-1.16

y = 0.49x-1.21

y = 0.13x-1.24

y = 0.06x-1.25

1E-6

1E-5

1E-4

1E-3

1E-2

1E-1

1E+0

1 10 100 1000

After

puls

e Pr

obab

ility


1.50 ns gate1.00 ns gate0.63 ns gate0.50 ns gate

NIST data~15% PDE

Afterpulsing data from NIST

Good fit for power law T-α with α ≈ -1.15 to -1.25

data from Alessandro Restelli and Josh Bienfang, NIST

Double-pulse method

PLI SPADs

15


y = 237.66x-1.38

0.001

0.01

0.1

1

10 100 1000

Nor

mal

ized

After

puls

e Pr

obab

ility


Nihon U. data213 K

Afterpulsing data from Nihon Univ.

Good fit for power law T-α with α = -1.38

data from Naota Namekata, Nihon U.

Autocorrelation test of time-tagged data

PLI SPADs

16


Literature on InP trap defects

Literature on defects in InP describes dense spectrum of levels

Instead of assuming one or a few dominant trap levels: → consider implications of a broad distribution for τ

i-InGaAs absorption

n+-InP buffer

n-InGaAsP grading n-InP charge

i-InP cap




n+-InP substrate anti-reflection coating n-contact metallization

optical input

Deep-level traps in multiplication region

Ec – 0.24 eVEc – 0.30 eVEc – 0.37 eVEc – 0.40 eV

Ec – 0.55 eV

W. A. Anderson and K. L. Jiao, in “Indium Phosphide and Related Materials: Processing, Technology, and Devices”, A. Katz (ed.) (Artech House, Boston, 1992)

Early work

Later work

Radiation effects

17


Implications of trap distribution on APP Develop model for APP with distribution of detrap rates R ≡ 1/τ

– APP related to change in trap occupation: dN/dt ~ R exp(-t R)– Integrate over detrapping rate distribution D(R)

→ APP ~ ∫ dR D(R) R exp(-t R)

18

D(R)

RR0

D(R)

RR0

D(R)

R

D(R)

R

δ(R – R0)single trap

“Uniform”

Normal “Inverse”D(R) α 1/R

narrowest distribution

widest distribution


Implications of trap distribution on APP

“Single trap” leads to exponential behavior– Fitting requires multiple exponentials and is arbitrary

Normal distribution is similar to single trap– Gaussian broadening of δ(R – R0) doesn’t change exponential behavior

“Uniform” and “inverse” distributions can be solved analytically– Require assumptions for a few model parameters

Minimum detrapping time: τmin = 10 ns Maximum detrapping time: τmax = 10 µsNumber of trapped carriers: n = 5Detection efficiency: 20%

19

just sample values;can be generalized


y = 200.93x-2.00y = 25.23x-1.18

1E-6

1E-5

1E-4

1E-3

1E-2

1E-1

1E+0

10 100 1000 10000

Nor

mal

ized

after

puls

ing

prob

abili

ty


Inverse

Uniform

Modeling results for APP APP results for Uniform and Inverse detrap rate distributions D(R) APP behavior fit well by T-α for 10 ns to 10 µs

– Value of α depends on model parameter values, but α is well-bounded

20

Inverse D(R): T-α with 1.05 < α < 1.30

Uniform D(R): T-α with 1.9 < α < 2.1


Insights from modeling of APP Inverse distribution provides correct power law behavior

– More traps with slower release rates D(R) α 1/R– Other distributions considered do not agree with data

Inverse distribution not necessarily a unique solution– But it provides more accurate description than single trap or uniform

Slower falloff of APP with hold-off time for Inverse distribution– Need longer hold-off times to achieve same relative decrease in total AP

Other possible explanations for power law behavior to explore– Role of field-assisted detrapping, especially in non-uniform E-field– Model in literature cites power law behavior for “correlated” detrapping

21

D(R)

R

A. K. Jonscher,Sol. St. Elec. 33, 139 (1990)


Afterpulsing data on Silicon SPADs

Neither power law nor exponential provide particularly good fit! Nature of defects in Si SPADs may be categorically different than for InP

data from Massimo Ghioni, Politecnico di Milano

Double-pulse method

22

y = 0.0002e-0.005x

y = 0.21x-1.54

1.E-07

1.E-06

1.E-05

1.E-04

1.E-03

1.E-02

10 100 1000

Afte

rpul

sing

Pro

babi

lity

Den

sity

(ns

-1)

Time (ns)

10

10

10

10

10

10

-7

-6

-5

-4

-3

-2

Silicon SPADsT = -25 C, Pap = 6%

Power law

Exponential


Summary

Reached 50 MHz photon counting with RF matched delay line scheme Significant further repetition rate increases should be feasible

Fitting of APP data with multiple exponentials not physically meaningful Extracted detrapping times are arbitrary, depend on hold-off times used Literature on defects in InP suggests possibility of broad distribution of defects

Consistent power law behavior of APP data found by various groups APP vs. time T described by T-α with α ~ 1.2 ± 0.2

Assumption of “inverse” distribution D(R) α 1/R for detrapping rate R provides best description of data among distributions considered so far Not unique, but establishes general behavior May be other models that predict power law APP behavior for dominant trap Further modeling can predict behavior for different operating conditions

23


BACK-UP SLIDES

24


Vertical structure to realize SAGCM structure for well-designed APD Multiplication gain: high field for impact ionization Carrier drift in absorber: low but finite absorber field Avoid of tunneling in all layers Eliminate interface carrier pile-up

Control of 3-D electric field distribution to avoid edge breakdown

Electric field engineering in APDs

i-

n+-InP buffer

n -InGaAsP gradingn- InP charge

i-InP cap


n+-InP substrate

anti-reflection coating n-contact metallization

optical input

E

InGaAs absorption



Schematic design for InGaAs/InP SPAD for 1.5 μm photon counting

25

what does spad afterpulsing actually tell us about defects in inp?

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