Advanced signal processing hardware for high throughput ... · 3/21/2013 · NSLS Presentation, March 21, 2013 Advanced signal processing hardware for high throughput energy dispersive

NSLS Presentation, March 21, 2013

Advanced signal processing hardware for high

throughput energy dispersive spectroscopy

Paul Scoullar†, David Scoullar†, Mick Brown†, Peter Grudberg§ & Chris Cox §

†Southern Innovation, Melbourne, Australia §XIA LLC, Hayward CA

Company Overview

Page 2 of 35

Southern Innovation

Incorporated: 2004

Headquarters: Melbourne, Australia

Size: 11 employees

Key Facts:

Electrical Engineering & Digital Signal

Processing (DSP) specialists

Commercializing “SITORO®” an award winning

DSP technology from Melbourne University

SITORO® significantly accelerates radiation

based analysis applications

SITORO® is proven, market-ready and has been

licensed in the X-ray instrumentation market

Southern Innovation

Presentation Overview

1. Problem Introduction

2. Traditional Solutions

3. Model Based Signal Processing

4. Technology Implementation

5. Performance Results

6. Conclusions

Southern Innovation Page 3 of 35

Problem Introduction Pulse Pile-Up

• A burst of radiation ‘arrives’ within the detector response time

• Reduces energy resolution, increase dead-time and extends the

time required for accurate classification

• Current solution to the problem is reject piled-up pulses

Page 4 of 35 Southern Innovation

Traditional Approaches Trapezoidal Filtering

• Filter the step reset preamplifier signal to produce a decay pulse

• Digitise the signal and convolved with a digital filter

• But how to design the digital filter

Shaping times

• Long shaping times produce good resolution – low throughput

• Shorter shaping times reduce pulse duration – high throughput

• Shorter shaping times reduce signal power & amplifies noise

resulting in lower SNR and degraded FWHM energy resolution


Trapezoidal Filtering



• But how to design the digital filter?

Shaping times


• Shorter shaping time reduce pulse duration – high throughput


resulting in lower SNR and reduced FWHM energy resolution.

Traditional Approaches


Frequency

En

erg

y Filters with optimal SNR do not shorten pulse

length, however, filters that shorten pulse

length increase noise power and attenuate the

signal power leading to reduced SNR.

Noise Signal

Trapezoidal Filtering



• But how to design the digital filter?

Shaping times


• Shorter shaping time reduce pulse duration – high throughput


resulting in lower SNR and reduced FWHM energy resolution.

Traditional Approaches


Resolution Vs Shaping Time

Re

so

luti

on

at

5.9

ke

V (

eV

)

Shaping Time (us)

Model Based Data Processing


X-ray Events From SDD Detectors

• Data mathematically modeled as the difference of two exponents

• The difference of two exponential values, α = 0.0102 and β = 0.1075

Model Based Pulse Processing

X-ray Data from SDD Detectors


This Data Modeled Digitally As:



We Need to Determine The Parameters of the Model

1) The characteristic produced by an SDD to an event h[n]

2) The number of radiation events in the digital data (N)

3) The relative time of arrival of each of these events (δ)

4) the energy of each radiation event (α)

So How To Do This

Southern Innovation


Maximum Likelihood Estimation

Page 11 of 35

Impulse Response


Maximum Likelihood Estimation


Solve for individual

Energies (α)

Technology Implementation The Pulse Pile-up Recovery Algorithm




System Characterisation


Determining The Expected Pulse Shape

• Data mathematically modeled as the difference of two exponents

• The average of 100 events used as the pulse shape model



Pulse Localisation


Exponent Curve Fitting

• Fit an exponential model across a fixed ‘window’ i.e. 16 samples

• The detection metric is the sum of the square of the ‘error’ in the fit

Digitised Detector Data

Fixed length Window

Detection Metric

ADC Samples

Vo

lta

ge

(vo

lts

)

Pulse Localisation


Exponential Curve Fitting

• Detect events which arrive very close to each other within ≈ 50ns

• Resolution of event time of arrival to 16 sub sample positions ≈ 1 ns

ADC Samples (x104)

Vo

lta

ge

(vo

lts

)

Digitised Detector Data

Detection Metric

Pulse Localisation Input Count Rate Performance

• Evaluated with a range of ‘known’ count rate data – Poisson Distribution

• Digital waveform generator ICR used and rates from 10 – 1,900 kc/s


0

250

500

750

1,000

1,250

1,500

1,750

2,000

0 250 500 750 1,000 1,250 1,500 1,750 2,000

Perfect Linearity

EstimatedICR

Input Count Rate (000s)

Ou

tpu

t C

ou

nt

Ra

te (

00

0s

)

Pulse Localisation Input Count Rate Performance

• 10 experiments at a range of input count rates from 10 – 1,900 kc/s

• Error remains less than 1%, most of the error remains below 0.5%


Input Count Rate (000s)

Inp

ut

Co

un

t R

ate

Err

or

(%)

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

0 250 500 750 1,000 1,250 1,500 1,750 2,000



Pulse Identification

• Create the System matrix from pulse shape h(n) and time of arrival

• Solve for all the event energies α1, α2, . . . , αN

• α = inv(AT A) AT y gives the energies of all events

• The detector data model may be written in matrix notation as:

y = A α + w

Where the output data y is represented via the multiplication of a

system matrix A by a vector of event energies ‘α’ and additive noise

• where A is an m x n matrix with entries given by:

A (n,i) =

h(n - δi) δi ≤ n < min (m, δi + T -1 )

0 otherwise


Pulse Identification

• Construct a system of equations using (τ, n, α) and pulse shape

• Solve [ α = inv(AT A) AT y ] to determine the energy of all events

α1

α2

α3

αN

• •

•

•

=

τ1

τ2

τ3

τn

A (M x N) x (N x 1) Y (M x 1)


Algorithm Implementation The Pulse Pile-up Recovery Algorithm


Validation Signal Reconstruction

• Use the estimated parameters to reconstruct ‘noise free’ data model

• Analysis of residuals of the data fit enable to detect poor results


Real Time Performance Hardware Implementation Details

• Implemented, operating in real-time, in a Spartan-6 LX100 FPGA

• Data digitized at 60 MHz with 16-Bit accuracy

Real Time Performance

• Sustained output count rate from SDD detectors > 1,000 kc/s

• 16 sub-sample timing positions, time of arrival accuracy ≈ 1 ns

• Pulse-pair resolution for X-ray events ≈ 50 ns

• Input count rate is accurate to within 1% from 10 kc/s – 1,900 kc/s

• List mode data transfer of individual X-ray events at > 240 Mb/s


Results High Count Rate

• Technology performance evaluated with a 30 mm2 Ketek detector

• 30 second spectra collected using an X-ray tube and Mn foil


Tube Current

(uA)

Input

Count Rate (kc/s)

Output

Count Rate (kc/s)

Dead Time

(%)

Peak

Position (keV)

Resolution

FWHM (eV)

5 72.1 68.8 4.3 5.89 141.3

10 139.6 134.7 3.4 5.89 145.7

15 202.7 195.3 3.6 5.89 150.1

20 258.1 247.7 4.0 5.89 153.5

30 360.2 342.6 4.9 5.89 160.5

40 460.4 433.5 5.8 5.89 166.6

50 557.9 520.2 6.8 5.89 172.8

60 653.9 603.6 7.7 5.89 178.5

70 747.5 683.3 8.6 5.89 183.8

80 841.1 761.4 9.5 5.89 190.2

90 932.3 835.8 10.4 5.89 195.3

100 1,023.3 908.6 11.2 5.90 200.6

120 1,156.5 1,012.3 12.5 5.89 208.1

140 1,305.2 1,122.6 14.0 5.89 216.4

Results Output Count Rate

• Resolution degrades linearly (not exponentially) with increasing output

• Resolution less than 210 eV at an output count rate of 1 million c/s


0

200

400

600

800

1,000

1,200

100 120 140 160 180 200 220 240

FWHM Resolution @ 5.9 keV (eV)

Ou

tpu

t C

ou

nt

Ra

te (

kc

/s)

Results Output Count Rate

• Resolution degrades linearly (not exponentially) with increasing output

• Resolution less than 210 eV at an output count rate of 1 million c/s


0

200

400

600

800

1,000

1,200

100 120 140 160 180 200 220 240

FWHM Resolution @ 5.9 keV (eV)

Ou

tpu

t C

ou

nt

Ra

te (

kc

/s)

Results Efficient Detection at Very High Input Rates

• The SITORO® technology maintains good OCR with increasing ICR

• At an output count rate of 1 million c/s the dead time is < 12.5%


0

200

400

600

800

1,000

1,200

1,400

0 200 400 600 800 1,000 1,200 1,400

Input Count Rate (kc/s)

Ou

tpu

t C

ou

nt

Ra

te (

kc

/s)

List Mode Data Output Scanning/Mapping Applications

• Time of arrival and energy data streamed off board at > 240 Mb/s

• Pixel dwell time demonstrated at ALS XFM Beam line < 50 us

• Up to 8 channels of encoder input in addition to Clk / Gate


Pix

el D

we

ll T

ime

(n

s)

Y-a

xis

po

sit

ion

(p

ixe

ls)

X-axis position (pixels) Pixel Number

X-ray Energy Spectra Low Energy Detection

• Lower limit of detection ≈ 250 eV (recently extended to ≈ 150 eV)


In Conclusion

SITORO® is a real-time model based signal processing

technology for the estimation of key parameters in pulse

processing including: the number; energy;

and time of arrival of pulses.

Key Performance Metrics Include:

• Sustained output count rate > than 1,000 kc/s

• Pulse-pair resolution of ≈ 50 ns seconds

• Super-resolution of event arrival times

• Timing accuracy of ≈ 1ns at 60 MHz

• Input count rate accurate to < 1% at 2 Mc/s

• List mode data transfer > 240 Mb/s

• Adaptable to a wide range of detectors

Page 33 of 35 Southern Innovation

Additional Slides Recent firmware update • Ketek 30 mm2 SDD rated to 128 eV at input rate of 10,000 c/s

• Using an Amptek Mini-X tube, Mn Foil include and an ICR of 112.4 kc/s:

• an output count rate of 100.3 kc/s;

• a dead time of 10.8 %; and

• resolution of 135.9 eV.

Southern Innovation

Documents

Advanced signal processing hardware for high throughput ... · 3/21/2013 · NSLS Presentation, March 21, 2013 Advanced signal processing hardware for high throughput energy dispersive