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Silicon Drift Detectors: Understanding the Advantages
for EDS MicroanalysisPatrick Camus, PhDApplications ScientistMarch 18, 2010
2
EDS Detector Requirements
•
Detect whole energy range of x-rays•
50 eV (Li-K) to incident beam energy•
SEM: up to 30 keV•
(S)TEM: up to 300 keV, realistically up to 50 keV•
Lower limit set by intrinsic noise of system•
Good spectral resolution•
Separate closely spaced energy peaks•
Varies with x-ray energy•
Theoretical limit based on sensor material, sensor design and system noise•
High x-ray detection rates•
Reduced collection times•
Fast sensor response to reduce detection overlap•
Physical geometry to maximize x-ray collection•
Shorten the sample-to-detector distance•
Leave enough physical room for other detectors and accessories
3
EDS Sensor Technologies
Si(Li) DiodeSilicon Drift
Different geometries provide different absorption and electrical pulse characteristics
Not to Scale
Entrance window
INPUT X-RAYS INPUT X-RAYS
4
Comparison of SDD to Si(Li) Technology
•
SDD•
Fabrication Technology•
Semiconductor•
Operating Temperature•
230~250 K at the sensor•
Peltier cooled•
Most have convective cooling•
Some have fans (vibration)•
Sensor thickness•
0.5 mm•
Good sensitivity up to 10 keV•
Reduced sensitivity until 20 keV•
Electric Field and Electron Path•
Radial field•
Low capacitance
•
Si(Li)•
Fabrication Technology•
Discrete components •
Operating Temperature•
77~130 K at the sensor•
LN2
•
Peltier+Water or refrigeration
•
Sensor thickness•
3 mm•
Good sensitivity up to 20 keV •
Reduced sensitivity until 50 keV•
Electric Field and Electron Path•
Axial field•
High capacitance
5
Si(Li) Detector Features
•
Sensitive to ~70 eV (Be-K) x-rays•
Good sensitivity above 20 keV•
Parallel-plate contacts•
High capacitance•
Medium throughput rates•
Extreme cooling required•
Reduce electronic noise•
Good spectral resolution: 129 eV @ Mn-Kα
•
Degrades substantially with increased input count rate
•
Limits usable throughput maximum
6
Si(Li) Detector Performance
•
Spectral Resolution•
Shorter shaping times means degraded resolution
•
100µs, 4µs, 2µs, 1µs•
129 eV to >200 eV•
5k cps input to 130k cps input
•
Throughput curve•
Shorter shaping times produces more output at same input
•
Increased dead time % cause peak•
Operation past peak is counter-
productive 0
5000
10000
15000
20000
25000
0 20000 40000 60000 80000 100000Input Count Rate
Out
put C
ount
Rat
e (c
ps)
4 us14 us30 us50 us100 us
7
History of Silicon Drift Detectors (Abridged)•
1983 Emilio Gatti and Pavel Rehak (Brookhaven National Lab) Silicon Drift Chamber
•
1995 Rontec (now Bruker) in cooperation with MPI and Ketek introduce EDS and XRF SDD detectors
•
1997 Photon Imaging (now Seiko) introduce XRF SDD detector•
2000-2007 All major EDS companies introduce SDD technology for electron beam instruments
•
Notable EDS Dates•
2004 Peak and resolution stability from Thermo Fisher Scientific
•
2006 Quad 10mm2
detector from Bruker AXS•
2006 30mm2
SDD from Thermo Fisher Scientific
•
2007 Good low-energy (Be-K) performance from Thermo Fisher Scientific
•
2008 80mm2
SDD from Oxford Instruments
8
Silicon Drift Detector Features
•
High input count rate capability up to 106
cps.•
Comfortable operation –
Peltier cooling @ -10°C to -60°C•
Good energy resolution down to 124 eV @ Mn-Kα•
Maintains good energy resolution as input count rates increase•
Size and shape limited only by fabrication technology
9
Silicon Drift Detector Benefits
•
Small capacitance•
Small electrical contact•
Low noise for better spectral resolution•
High input and output count rates•
Integration of first FET•
Further noise improvement•
No pickup, no microphony
Electron Potential and Trajectories
10
Types of Silicon Drift Detectors
•
Concentric rings•
Allows large areas with good resolution•
Droplet rings•
For small devices hides the pickup and FET under the collimator
•
Discreet FET•
Lower noise potential in FET•
Complex manufacturing and sensitive to microphonics
•
Integrated FET•
Higher count rates due to lower capacitance
11
Silicon Drift Detector Devices
12
TO8 Package for SDD up to 30 mm2
13
Quantum Efficiency Comparison
SDD
Si(Li)
0 2 4 6 8 10 12 14 16 18 20keV
•
High energy x-rays:•
Are detected by the “thick”
Si(Li) sensor.•
Penetrate through the “thin”
SDD sensor
•
Si(Li) sensors are more sensitive for high energy x-rays•
(S)TEM applications
14
Spectral Resolution Prediction
•
FWHM = (5.5 F ε
E + N2
)½
•
Where:•
F = Fano Factor, ~0.1 for Si•
ε
= 3.8 eV for Si•
E = X-ray Energy of interest•
N is FWHM of electronic Noise
•
For a given sensor material and x-ray energy, the “F ε
E”
term is a constant
•
If N = 0 eV, then limiting resolution for a Silicon-based detector is ~100eV at Mn-Kα
•
Current Mn-Kα
resolutions are ~124 eV, placing N ~ 55 eV
•
Reducing the electronic noise is the primary method to improving
spectral resolution!
15
Electronic Noise (ENC) Analysis
•
Total capacitance Ctot
•
1/f noise coeff. af
•
Leakage current IL•
Transconductance gm
•
Filter constants Ai
•
Shaping time constant T•
Electron charge q•
α
= 2/3 for FET
•
Ctot
: Si(Li) >> SDD (intrinsic design)•
IL
: Si(Li) << SDD (but dropping)
•
For Si(Li):•
Leakage current is not a factor•
1/f noise dominates at high shaping times
•
For SDD:•
Small capacitance “eliminates”
1/f contribution
•
Small capacitance reduces thermal noise to very small shaping times
thermal noise 1/f noise leakage current
τAIqACaπ2τ1AC
gkT2αENC 3L2
2totf1
2tot
m
2 ++=
16
Effect of Temperature on SDD Spectral Resolution
Decreasing the device temperature makes the resolution better
17
Effect of Temperature on SDD Spectral Resolution -
2
•
Decreased temperature improves resolution•
Sensor can be run at Room Temperature, but does not meet specifications.•
Effect is less dramatic with more cooling
18
Mn Resolution Comparison
•
Both Si(Li) and SDD start at low values•
Specifications appear similar•
Si(Li) degrades faster with increasing input count rate•
Slow response of diode geometry•
SDD degrades slowly with increasing count rate•
Fast response of radial field geometry•
Better resolution at 7x input count rate
•
SDD maintain their good resolution
throughout the input range
•
Specification resolution is not the primary advantage of SDD
Resolution Comparison
0
20
40
60
80
100
120
140
160
180
200
0 100 200 300 400 500 600 700 800 900
Input Count Rate (k cps)
Mn-
Ka
Res
olut
ion
(eV)
UD - 10+ NT - 10
120
130
140
150
0 10 20 30 40 50 60 70
19
New Generation Electronics Offer Spectral Stability
•
As input count rate increases using a constant shaping time:•
Old self-reset mode produced varying resolution and peak locations •
New pulse-reset mode produces stable resolution and peak locations•
FWHM of Mn-Kα
increases less than 4 eV•
Peak shift reduced to less than 8 eV
20
SDD Peak and Resolution Stability for Mn-K
Manganese Test - 10mm2 Droplet
5.8
5.85
5.9
5.95
6
0 20 40 60 80Deadtime
eV
1uSec2uSec4uSec8uSec
•
Resolution•
Does not vary with dead time %•
Degrades as shaping time decreases
Manganese Test - 10mm2 Droplet
100
105
110
115
120
125
130
135
140
145
150
0 20 40 60 80Deadtime
FWH
M
1uSec2uSec4uSec8uSec
•
Peak location •
Does not vary with dead time %•
Does not vary with shaping time
21
SDD Spectral Resolution Stability
•
Superior electronics permit stable resolution values across the spectrum as the count rate increases.
Resolution Changes
0
20
40
60
80
100
120
140
0 20 40 60 80 100 120
Input Count Rate (k cps)
Peak
Res
olut
ion
(eV)
C-KaSi-KaMn-KaAl-Ka
22
SDD Spectral Resolution Display
•
All peak shapes are indistinguishable from 5k to 100k cps input
23
SDD Mn Throughput
•
A variety of electronic shaping time settings permits the best possible spectral resolution at each input count rate.
•
Slower shaping times provide better resolution but lower output rates•
Faster shaping times provide higher output rates but at lower resolution
UD 10+ Throughput
50% dead time
0
50
100
150
200
250
300
350
0 100 200 300 400 500 600 700 800 900
Input Rate (k cps)
Out
put R
ate
(k c
ps)
200 ns400 ns600 ns800 ns1000 ns1600 ns2000 ns3200 ns4000 ns6400 ns
Resolution Changes - UD
120
130
140
150
160
170
180
0 100 200 300 400 500 600 700 800 900
Input Count Rate (k cps)
Mn-
Ka
FWH
M (e
V)
200 ns400 ns600 ns800 ns1000 ns1600 ns2000 ns3200 ns4000 ns6400 ns
24
SDD Mn Throughput Display
•
The resolution display does not change significantly until the final shaping time of 200 ns (800k cps) is used.
25
Low Energy Performance•
Be spectrum
•
BN spectrum
•
Same sensitivity as Si(Li)
26
Sensor Size: Bigger is Better
•
A larger sensor size has the potential to collect more x-rays than a smaller sensor size.
•
However, the real metric of x-ray detection is the solid-angle subtended by the detector.
•
The solid-angle (SA) is defined as:•
SA = A / D2
•
Where:•
A = active area of the sensor in mm2
•
D = sample-to-sensor distance•
For the same D, a larger A detector is preferred.
•
HOWEVER …..
27
Sensor Size: When Bigger is Not Always Better
•
The physical geometry of the detector housing may restrict the location of the detector inside a chamber.
•
A larger area sensor may require a larger diameter housing.•
If that housing conflicts with other structure inside the chamber, then its location may need to be changed.
•
This change may require and increase in D to a clear location.•
This new D adversely affects the SA value.
•
It is entirely possible that a larger area detector in a large housing mounted at a larger distance may actually collect less x-rays than a smaller area detector in a smaller housing at a shorter distance!
28
Detector Solid-Angle Comparison
Solid Angle = Area / Distance2
30 mm2
sensor in 19 mm tube @ 43 mm: SA = 16.2 msr80 mm2
sensor in 35 mm tube @ 71 mm: SA = 15.9 msr
29
Spectral Resolution Requirements
•
Good spectral resolution permits easy isolation, identification, and measurement of peaks
•
If peak locations are greater than ~2x resolution:
•
Peaks are isolated•
Peak identification is trivial•
Net peak counts can be measured manually
•
Software is needed for quantification•
If peak locations are less than ~2x resolution:
•
Peaks overlap in display•
Peak identification becomes difficult•
Software is needed for proper background removal and net counts measurement
•
Software is needed for quantification
30
Spectral Resolution Limits
•
In practice, there are potentials for many peak overlaps
•
Most spectra require software to analyze peaks, even at highest resolution
•
Robust routines were developed in the 1970’s and 1980’s to deal with peak overlaps
•
Peak identification•
Peak deconvolution•
Net count measurement•
These routines were designed for detectors with a best resolution of ~145 eV @ Mn-K
31
Example –
Carbide Tool
•
Detector: Si(Li) 10mm2•
Resolution: 155eV
MnK
FWHM•
Accelerating Voltage: 20kV•
Magnification: 10,000x•
Map resolution: 256x192
32
Example –
Carbide Tool
Raw Counts
Net Counts
1500 1600 1700 1800 1900 2000
eV
155eV!WMTaM
33
Example –
Mo, S, Ba Multiphase Sample
•
Detector: UltraDry 10mm2 SDD•
Resolution: 129eV
MnK
FWHM•
Accelerating Voltage: 7kV•
Magnification: 500x•
Map resolution: 256x192•
Acquisition Time: 3 minutes
34
Example –
Mo, S, Ba –
Raw Count Element Maps
35
Example –
Mo, S, Ba –
Net Count Maps
36
Example –
Mo, S, Ba –
Phase Maps
Distinguishing the three main phases is not possible without robust peak deconvolution2100 2150 2200 2250 2300 2350 2400 2450 2500
eV
MoL SK
37
•
“Low Beam”
= 10k cps
“High BeamHigh Beam”
= 250k cps•
Longer shaping time produced more visible peaks•
Al-K resolution: 78 eV vs. 139 eV
•
Not enough resolution to separate all peaks, even at “Low Beam”
(best resolution)
•
Software is required for data analysis•
With equal data quality, what is more important: data display or
acquisition time?
Spectral Appearance
38
SDD Demonstration
•
Spectral Acquisitions•
Spectral Imaging (Mapping) Acquisitions
39
Summary
•
Silicon-drift technology is relatively young•
Still being enhanced•
Silicon-drift detectors have very few limitations for EDS analyses•
High-energy x-rays•
SDD have many advantages, especially for high throughput applications•
Cooling •
Resolution degradation•
Maximum storage rate•
Detector electronics are just as important as the sensor•
Solid angle is a more important metric than sensor size•
Spectral resolution is visually appealing, but software processing is still required for spectral (and mapping) analyses
40
Conclusions
•
Analytical results are obtained faster and at the same confidence with an SDD.
•
SDD performance can only get better.
