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Masashi Watanabe Dept. of Mater. Sci. & Eng. Lehigh University masashi.watanabe@lehigh

The Imaging in Electron Microscopy II Workshop on Feb./18/’10. Efficient Processing of Large Scale Spectrum-imaging Datasets by Multivariate Statistical Analysis: Toward Atomic-resolution Analysis. Masashi Watanabe Dept. of Mater. Sci. & Eng. Lehigh University masashi.watanabe@lehigh.edu

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Masashi Watanabe Dept. of Mater. Sci. & Eng. Lehigh University masashi.watanabe@lehigh

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  1. The Imaging in Electron Microscopy II Workshopon Feb./18/’10 Efficient Processing of Large Scale Spectrum-imaging Datasets by Multivariate Statistical Analysis: Toward Atomic-resolution Analysis Masashi Watanabe Dept. of Mater. Sci. & Eng. Lehigh University masashi.watanabe@lehigh.edu http://www.lehigh.edu/~maw3/index.html

  2. Signals for Analysis in TEM (AEM) Beam-specimen interaction in TEM • These signals can be • used for conventional TEM • Imaging • Electron diffraction • for analysis • Spectrometry • Characteristic X-rays • Energy-loss electron With these signals, the thin specimen can be analyzed in TEM Analytical Electron Microscope

  3. Signal Generation for Microanalysis in AEM Ionization process due to inelastic scattering • Two kinds of signals • generated from a thin • specimen are used for microanalysis in AEM: • Characteristic X-rays: • X-ray energy dispersive • spectrometry (XEDS) • Energy-loss electrons: • electron energy-loss • spectrometry (EELS)

  4. Analysis Modes for Small Features II. Line I. Point III. Area Map / Image Line Profile Differences across/ along the object Differences across the object Comparison of on/off the object Typical approaches in AEM/STEM If elemental distribution may fluctuate locally, individual point analyses couldmiss such variations.

  5. Principle of Energy-Filtering TEM Courtesy of Gatan

  6. Aberration-Corrected STEM/TEM Schottky- FEG CEOS corrector Clam shell HA-ADF detector Energy slit W filter NCEM VG HB 603 with a Nion corrector TEAM 0.5 with CEOS correctors JEOL JEM-2200FS with a CEOS corrector Watanabe et al., Microsc. Microana., 12 (2006), 515. Kisilowski et al. Microsc. Microana. 14 (2008), 469. Watanabe et al., JEOL News, 41 (2006) 1. NCEM/LBNL Lehigh Lehigh

  7. Aberration Correction in JEM-2200FS HAADF-STEM image Si(110) Simulated probe Ronchigram Hexapoleoff Conventional Ip = 10 pA 0.16 nm(HM) 0.76 nm(TM) 11 mrad 1 nm Hexapole on Cs-corrected Ip = 50 pA 0.12 nm(HM) 0.35 nm(TM) 25 mrad 1 nm 34 mrad

  8. Atomic-Column EELS Analysis/Imaging La0.7Sr0.3MnO3/SrTiO3 La1.2Sr1.8Mn2O7 Bi0.5Sr0.5MnO3 Muller et al. Science 319 (2008), 1073. CaMnO3 Bosman et al. Phys. Rev. Lett.99 (2007), 086102. Kimoto et al. Nature 450 (2007), 702. Varela et al. Phys. Rev. B79 (2009), 085117 .

  9. Elemental Mapping Approaches • Elemental mapping is the best approach to analyze nano-scale features such as: • small precipitates • interfaces/boundaries With aberration-correction Spatial resolution ~few Ångstroms ~few Ångstroms ~few Ångstroms Spatial resolution below 1 nm ~few Ångstroms 1-2 nanometers • Technique • EFTEM • STEM-PEELS • STEM-XEDS Ni-base superalloy STEM-XEDS maps EFTEM maps CrL (3window) TiL (3window) CrK TiK 0 0 0 0 800 600 20 150 200nm (counts) 100nm (counts)

  10. Signal Detection for Analysis in AEM Signal collection XEDS spectrum EELS spectrum W&C Fig. 38.7 • XEDS in AEM • poorgenerationefficiency • poorcollectionefficiency • EELS in AEM • poorS/B

  11. Incident probe Scan x y E Spectrum Imaging (SI) • Concept: Jeanguillaume & Colliex, • Ultramicroscopy, 28 (1989), 252 • Demonstration: Hunt & Williams, • Ultramicroscopy, 38 (1991), 47 Spectrum imaging (SI): a mapping technique to collect whole spectrum at individual pixels • STEM-XEDS SI • STEM-EELS SI • EFTEM SI • Advantages • less chance to miss minor peaks • Post-acquisition processing • potentialcapability for regular spectral processing Additional dimension always provides more (useful) information! Requires efficient ways to handle large data

  12. Atomic-Column EEL SI of SrTiO3 3.9 Å EEL spectrum image: 15 x 15 pixels, 256 channels dwell time: 0.1 s current: 250 pA (~1.3 Å probe)

  13. Principal Component Analysis (PCA) PCA is one of the basic MSA approaches and can extractthe 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)

  14. Practical Operation of PCA eigenanalysis or SVD original data loading score nE nE nE nX D T PT line profile PCA = nX * nY nX x nY nX x nY eigenvalues nE D = T * PT spectrum image D: original data matrix (nX x nY x nE) T: score matrix (related to magnitude) PT: loading matrix (related to spectra)

  15. Application of PCA: EELS-SI of SrTiO3 loading spectrum score image HAADF-STEM image component #1 Ti L2,3 component #2 Ti L2,3 Sr M2,3 3.9 Å 200 300 400 500 Energy loss (eV)

  16. MSA on EEL SI By applying multivariate statistical analysis to spectrum images, statistically significant featurescan be extracted. loading spectrum HAADF score component #2 Ti L2,3 Sr M2,3 RGB overlay Sr M2,3 Ti L2,3 O K 3.9Å

  17. Application of PCA: EELS-SI of Si3N4 Si L2,3 Si L1 C K N K Component#1 Component#2 0.5 nm 0.5 nm 0.5 nm 200 300 400 500 600 700 100 Energy-loss (eV) Watanabe et al. Microscopy&Analysis (2009)

  18. Evaluation of Components by Scree Plot 15 10 Principal components Log of eigenvalue Noise components 5 15 25 5 10 20 0 Index of component

  19. MSA Plugin for Gatan DigitalMicrograph MSA plug-in for Gatan DigitalMicrograph is available through HREM Research Inc. http://www.hremresearch.com/Eng/plugin/MSAEng.html

  20. ~ ~ ~ D = T * PT Data Reduction by PCA Typically, the number of features < the number of data points a nE nE ~ a E D S E LT D ~ ~ = = + + * nX x nY nX x nY ~ S: truncated score matrix (related to spatial amplitude) LT: truncated loading matrix (related to spectra) D: reconstructed data matrix E: residual matrix ~ ~ Large original dataset can be reduced to much smaller dataset (reconstruction)

  21. Random Noise Removal via PCA Method Si L2,3 Original 1200 Reconstructed Si L1 800 Intensity (count) 400 C K N K 200 300 400 500 600 700 100 Energy-loss (eV)

  22. Random Noise Removal via PCA Method (a) Si L2,3 (106-136 eV) (b) Si L1 (154-194 eV) (c) N K (400-440 eV) original 0.5 nm PCA-reconstructed

  23. Application of Wiener Filter (a) Si L2,3 (106-136 eV) (b) Si L1 (154-194 eV) (c) N K (400-440 eV) 0.5 nm Si N1 N2 Wiener filter in the HREM-Filter Pro plug-ins Watanabe et al. Microscopy&Analysis (2009)

  24. Desirable Probe Currents for Analysis Required beam current in current corrected instruments XEDS Analysis EELS Analysis HAADF-STEM Imaging e.g. 30 pA or less the best you can 100 pA or more

  25. Current Dependence of Image Resolution Si 110 500 pA 120 pA 30 pA 1.92Å 1.36Å 224 224 115 004 004 004

  26. Combination of Advanced Techniques Ti Cr Ni-base superalloy g’ 15 0 25 0 (wt%) (wt%) 10 nm g g • Cs-corrected VG HB 603 • X-ray spectrum-imaging • Multivariate statistical analysis • z-factor quantification GB BF-STEM image by Lehigh Cs-corrected HB603 Watanabe et al., Microsc. Microana., 12 (2006), 515.

  27. Results of Advanced Analyses One of MSA components Extracted spectral feature Spatial amplitude Zr L Nb L Nb K Zr K Ti K Ni K Cr K GB Zr Nb Nominal composition Nb: 0.88 wt% Zr: 0.04 wt% 10 nm (wt%) (wt%) 0.5 3 0 0

  28. Zr-Segregation in Ni-Base Alloy Probe size: ~0.4 nm Probe current: 0.5 nA Zr spatial resolution 0.4-0.6 nm (wt%) 0.5 0 GB enrichment of Zr: 2-3 atoms/nm2 (0.12-0.17 monolayer) Minimum detectable mass (MDM) is also improved. (atoms/nm2) 0.8 0 Watanabe et al., Microsc. Microana., 12 (2006), 515.

  29. Quantitative Maps from Cu-0.5wt%Mn Mn org Mn MSA 1 1 0 0 (wt%) (wt%) # of Mn atoms Thickness 35 10 25 nm 0 0 (nm) (atoms)

  30. MSA-Reconstruction of High-Res. Images HAADF-STEM image from Fe2O3/Al2O3 interface MSA-reconstructed Original

  31. Summary • It is now possible to perform atomic-resolution elemental imaging by EELS, EFTEM and XEDS in aberration-corrected electron microscopes in combination with spectrum-imaging and multivariate statistical analysis. • By applying MSA, hidden information can be extracted without prior knowledge of datasets

  32. Acknowledgements David Williams (U. Alabama Huntsville) Chris Kiely (Lehigh) Dave Ackland (Lehigh) Masahiko Kanno (JEOL) Eiji Okunishi (JEOL) Uli Dahmen (NCEM, LBNL) Velimir Radmilovic (NCEM, LBNL) Marta Rossell (Currently at ETH , Switzerland) Rolf Erni (Currently at EMPA, Switzerland) Supported by NSF DMR-0804528 DOE DE-AC02-05CH11231 (NCEM) DOE DE-AC03-76SF00098 (TEAM)

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