Advanced signal processing hardware for high throughput ... · 3/21/2013 · NSLS Presentation,...
Transcript of Advanced signal processing hardware for high throughput ... · 3/21/2013 · NSLS Presentation,...
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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
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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
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Presentation Overview
1. Problem Introduction
2. Traditional Solutions
3. Model Based Signal Processing
4. Technology Implementation
5. Performance Results
6. Conclusions
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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
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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
Southern Innovation Page 5 of 35
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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 time reduce pulse duration – high throughput
• Shorter shaping times reduce signal power & amplifies noise
resulting in lower SNR and reduced FWHM energy resolution.
Traditional Approaches
Southern Innovation Page 6 of 35
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
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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 time reduce pulse duration – high throughput
• Shorter shaping times reduce signal power & amplifies noise
resulting in lower SNR and reduced FWHM energy resolution.
Traditional Approaches
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Resolution Vs Shaping Time
Re
so
luti
on
at
5.9
ke
V (
eV
)
Shaping Time (us)
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Model Based Data Processing
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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
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Model Based Pulse Processing
X-ray Data from SDD Detectors
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This Data Modeled Digitally As:
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Model Based Pulse Processing
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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
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Southern Innovation
Model Based Pulse Processing
Maximum Likelihood Estimation
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Impulse Response
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Model Based Pulse Processing
Maximum Likelihood Estimation
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Solve for individual
Energies (α)
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Technology Implementation The Pulse Pile-up Recovery Algorithm
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Technology Implementation The Pulse Pile-up Recovery Algorithm
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System Characterisation
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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
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Technology Implementation The Pulse Pile-up Recovery Algorithm
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Pulse Localisation
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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
)
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Pulse Localisation
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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
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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
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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
)
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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%
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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
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Technology Implementation The Pulse Pile-up Recovery Algorithm
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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
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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)
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Algorithm Implementation The Pulse Pile-up Recovery Algorithm
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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
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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
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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
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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
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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
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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)
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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
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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)
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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%
Southern Innovation Page 30 of 35
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)
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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
Southern Innovation Page 31 of 35
Pix
el D
we
ll T
ime
(n
s)
Y-a
xis
po
sit
ion
(p
ixe
ls)
X-axis position (pixels) Pixel Number
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X-ray Energy Spectra Low Energy Detection
• Lower limit of detection ≈ 250 eV (recently extended to ≈ 150 eV)
Southern Innovation Page 32 of 35
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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
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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