lyman 6_spectrum image (fri)

PASI - Electron Microscopy - Chile

1Lyman - Spectrum Imaging

Spectrum Imaging Charles Lyman

Lehigh University, Bethlehem, PA

Based on presentations by John Hunt (Gatan, Inc.), John Titchmarsh (Oxford University), and Masashi Watanabe (Lehigh University)



Spectrum Imaging (SI)

Collect entire spectrum at each pixel» No a priori of specimen knowledge required» Can detect small amounts of elements in local

regions of x-y images» Away from microscope:

– Repeatedly apply sophisticated spectrum processing

– “Mine the data cube” for features

Concept» Jeanguillaume & Colliex, Ultramicroscopy 28 (1989), 252

Demonstration» Hunt & Williams, Ultramicroscopy 38 (1991), 47

x

E

Scan

y

Incidentelectron

probe

“x-y-energy” data cube



Elemental Maps from Data Cube

Data courtesy of David Rohde Specimen: polished granite

0200

400600

8001000

12001400

16001800

2000Energy

x

y

x

yEnergy

ElementalX-ray map

X-ray Spectrum



Quantitative Phase Analysis

Sum spectra for pixels within box » Enough counts for quatitative analysis

Data courtesy of David Rohde Specimen: polished granite



Compositional Maps in TEM/STEM

Collection by:» STEM X-ray

– Sequentially acquire EDS x-ray spectrum at each pixel (original concept)– Each x-ray entering detector assigned “x-y-energy” tag (Mott & Friel, 1999)

» STEM EELS– Sequentially acquire EELS spectrum at each pixel

» EFTEM (Energy-filtered imaging)– Sequentially acquire images at specific energies– One energy window for each energy channel in spectrum (E)



A few Words about EFTEM

Elemental Maps without

Employing Spectrum Imaging



EFTEM: In-Column and Post-Column Energy Filters

Omega Filter Gatan Imaging Filter (GIF)

From Williams and Carter, Transmission Electron Microscopy, Springer, 1996



RGB compositeOxygenNitrogenCarbon

Elemental Maps of a SiC/Si3N4 ceramic

Short Acquisition Time (3 maps, 250K pixels) = 50s

Energy-Filtered TEM (EFTEM) Element Maps - Not Spectrum Images

Courtesy John Hunt, Gatan



Energy-Filtering TEM

Images of only a small range of energies» Energy window of 1-100eV » Just above or just below energy-loss edge

EFTEM compositional mapping» Elemental maps using multiple energy-filtered images

– 2 images to determine background before edge – Scale background and subtract to obtain elemental signal– 1 image to collect elemental signal (edge above background)

Only one electron energy can be precisely in focus» All other energies will be suffer resolution loss (blurring)

The blurr is given by: » d = Cc * *E/E

– Cc = chromatic aberration constant– = the acceptance angle of the objective aperture– E = range of energies contributing to the image

» Blurr will be especially large for thick, high-Z specimens.» Reduce blurr by:

– Using a small energy window (E) – Select energy loss E by changing the gun voltage (vary kV)



EFTEM Elemental Mapping


Three-Window Method» Subtract edge background

using two pre-edge images (dotted line)

» Element concentration proportional to area of edge above background (outlined in red)

» Absolute concentration can be determined if thickness and elemental cross-sections are known



O AlTi

Aluminum

Superimpose three color layers to form RGB composite

Titanium

Oxygen

6 layer metallization test structure

3 images each around:

O K edge:@ 532 eVTi L23 edge: @ 455 eVAl K edge: @ 1560 eV

EFTEM Elemental Mapping: Example 1

1 µm




EFTEM Elemental Mapping: Example 2

O

N

Si

Ti

Al

BF image

Unfiltered bright-field TEM image of semiconductor device structure and elemental maps from ionization-edge signals of N-K, Ti-L, O-K, Al-K, and Si-K.

Color composite of all 5 elemental maps displayed on the left,showing the device construction.




EFTEM detection limits

Typically 2-5% local atomic concentration of most elements» 1% is attainable for many elements in ideal samples » 10% for difficult specimens that are thick or of rapidly varying thickness

Sensitivity limited by:» Diffraction contrast» Small number of background windows» Signal-to-noise» Thickness» Artifacts

If you can see the edge in the spectrum, you can probably map it

EFTEM spectrum image can map lower concentrations than the 3-window method

» Better background fits because there are more fitting channels




STEM & EFTEM EELS Spectrum Imaging



STEM spectrum image acquisition

STEM spectrum image» acquired by stepping a focused

electron probe from one pixel to the next

EDXSTEM

EELSDF

Specimen

The spectrum image data cube is filled one spectrum column at a time

» In STEM it is possible to collect x-ray, EELS, BF, and ADF simultaneously

» Use of the ADF or SE signal during acquisition permits spatial drift correction

x

y

ECourtesy John Hunt, Gatan



yx

E

image at E1image at E2

image at Ei

.

.

.

.

.

.

.

.

.

EFTEM spectrum image acquisition

EFTEM spectrum image» Acquire an image containing

a narrow range of energies

» The spectrum image data cube is filled one energy plane at a time

» Image plane retains full spatial resolution of TEM image




STEM EELS spectrum imaging

EELS STEM SI acq. at 200keV (cold FEG)» xy: 50*29 pixels» E: 1024 channels (75eV, =0.5eV)» Acquisition time: ~ 5 minutes» Processing time: ~ 5 minutes




Quantitative EFTEM Spectrum Imaging

EFTEM Spectrum Image» 2.9 nm resolution» Si-L23 : 75-150eV

{3eV steps} (1.5 min)» N-K, Ti-L, O-K : 350-650eV

{5eV steps} (8 min) FEI CM120 + BioFilter

» 120keV» Corrections: x-rays, MTF, spatial drift» Scaled by hydrogenic x-sections




STEM vs. EFTEM Spectrum Imaging

Quantitative elemental mapping» Both STEM SI and EFTEM SI can do this

EELS STEM Spectrum Imaging» Good quality spectra» All artifacts / instabilities correctable» Usually safer w/unknowns

EFTEM Spectrum Imaging» Fast mapping» Uncorrected artifacts / instabilities are very dangerous» Very useful for well characterized systems» Excellent spatial resolution



X-ray Spectrum Imaging



Mining the SI Data Cube

Masashi Watanabe Lehigh University

Nb(wt%)1.5

0

Nb(wt%)1.5

0

Multivariate Statistical Analysis of X-ray Spectrum Images



X-ray Spectrum Imaging

Collection of SIHuge data set e.g. 256x256 = 65,536 spectra each spectrum 1024 channels cannot analyze manuallyNoisier spectrum for XEDS than EELSMany possible variables composition, thickness, multiple phases

100 nm

NiK

TiKAlK CrK

FeKWhat can we do?

Specimen: Ni-based superalloy

Courtesy M. Watanabe



Multivariate Statistical Analysis

Problems for which MSA may be useful 1. Investigation of data of great complexity2. Handling large quantities of data3. Simplifying data and reducing noise4. Identifying specific features (components) can be interpreted in useful ways E.R. Malinowski, Factor Analysis in Chemistry, 3rd ed. (2002)

Multivariate statistical analysis (MSA) is a group of processing techniques to:

(1) identify specific features from large data sets (such as a series of XEDS and EELS spectra, i.e. spectrum images) and

(2) reduce random noise components efficiently in a statistical manner.



Nb map in Ni-base superalloy

Nb(at%)1

0

Nb(at%)1

0 100 nm

original MSA-processed

Multivariate Statistical Analysis• identify specific features in the spectrum image• reduce random noise




The Data Cloud

Find greatest variancein data

x1, x2, x3 are first three channels of spectrum or image

Manipulate matrices Principal component

analysis finds new axes for data cloud that correspond to the largest changes in the data

These few components can represent data



Principal Component Analysis (PCA)

PCA is one of the basic MSA approaches and can extract the smallest number of specific features to describe the original data sets.

The key idea of PCA is to approximate the original huge data matrix D by a product of two small matrices T and PT by eigenanalysis or singular value decomposition (SVD)

D = T * PT

D: original data matrix (nX x nY x nE)T: score matrix (related to magnitude)PT: loading matrix (related to spectra)




Practical Operation of PCA

nXnY

nE

spectrum image

PCAD

nE

nX x nY

= T

PT

*nX x nY

nE

D = T * PT D: original data matrix (nX x nY x nE)T: score matrix (related to magnitude)PT: loading matrix (related to spectra)

line profile

nEnX

original data score loading

eigenanalysisor SVD

eigenvalues




Spectrum Image of Ni-Base Superalloy

100 nm• spectrum image: 256x256x1024• dwell time: 50 ms• 20 eV/channel

matrix

’

M23C6

NiK

CrK FeK

NiK

CrK

TiKAlK

NbL

Reconstructed spectraCourtesy M. Watanabe



Results of PCA 1

0 10 20

10–2

10–1

100

Component

Eige

nval

ue

STEM-ADF

200 nm

LoadingScore

scree plot

#1: average

#2: M23C6

#3: ’

Ti K

Al K

Ni KFe K

Cr K

Cr K

Ni K

Cr K Ni K


Noise



Results of PCA 2

0 10 20

10–2

10–1

100

Component

Eige

nval

ue

STEM-ADF

200 nm

LoadingScore

scree plot

#4: absorption

#5: noise

#6: noise

Ni K

Ni L

Cr K

Noise




Comparison of Maps

Alwt%

2

0wt%

2

0

wt%1.5

0wt%

1.5

0

Nb

100 nm Compositional fluctuations below 2 wt% can be revealed

Reconstructed

Original




Application to Fine Precipitates

Irradiation-induced hardening in low-alloy steelis caused by fine-scale precipitationAverage precipitate size: 2-5 nmX-ray mapping in VG HB 603 300 keV STEM

100 nm

BF-STEM image ADF-STEM image

Burke et al. J. Mater. Sci. (in press)



Application to Fine Precipitates in Steel

(wt%)

Thickness

10 20(nm)

STEM ADF Fe Cr

Ni Mn Cu50nm 85 95 5

0 8 2 3 0 0.5 1(wt%)(wt%)(wt%)

(wt%) (wt%)Mo

0

1

Too noisy




Application of MSA to Fine Precipitates

(wt%)

Thickness

10 20(nm)

STEM ADF Fe Cr

Ni Mn Cu50nm 85 95 5

0 8 1(wt%)

(wt%) (wt%)Mo

0

1

1.5 3(wt%)

0 0.8(wt%)




Some References to MSA Procedures

Multivariate statistical analysis – in general• S.J. Gould: “The Mismeasure of Man”, Norton, New York, NY, (1996).• E.R. Malinowski: “Factor Analysis in Chemistry, 3ed ed.”, Wiley, New York, NY, (2002).• P. Geladi & H. Grahn: “Multivariate Image Analysis”, Wiley, West Sussex, UK, (1996).

For microscopy applications• P. Trebbia & N. Bonnet: Ultramicroscopy 34 (1990) 165.• J.M. Titchmarsh & S. Dumbill: J. Microscopy 184 (1996) 195.• J.M. Titchmarsh: Ultramicroscopy 78 (1999) 241.• N. Bonnet, N. Brun & C. Colliex: Ultramicroscopy 77 (1999) 97.• P.G. Kotula, M.R. Keenan & J.R. Michael: M&M 9 (2003) 1.

• M.G. Burke, M. Watanabe, D.B. Williams & J.M. Hyde: J. Mater. Sci. (in press).• M. Bosman, M. Watanabe, D.T.L. Alexander, and V.J. Keast: Ultramicroscopy (in press)



Summary

• Spectrum Imaging• the way serious microanalysis should be done

• Mining the data cube• MSA is applicable for large data sets such as line

profiles and spectrum images • The large data sets can be described with a few

features by applying MSA• PCA is useful for noise reduction of data sets.• Be aware -- MSA can provide only hints of significant

features in the data sets (abstract components)

lyman 6_spectrum image (fri)

Documents