Journal
ULTRAMICROSCOPY
Volume 111, Issue 3, Pages 251-257Publisher
ELSEVIER
DOI: 10.1016/j.ultramic.2010.11.033
Keywords
STEM; Multivariate statistical analysis; Point defect; Image processing
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Funding
- United States Department of Energy [DE-FG02-03ER46057]
- Materials Design Institute, Los Alamos National Laboratory, LANS [25110-002-06]
- University of California
- EPSRC
- LLNL
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Quantitative analysis of point defects is demonstrated through the use of multivariate statistical analysis. This analysis consists of principal component analysis for dimensional estimation and reduction, followed by independent component analysis to obtain physically meaningful, statistically independent factor images. Results from these analyses are presented in the form of factor images and scores. Factor images show characteristic intensity variations corresponding to physical structure changes, while scores relate how much those variations are present in the original data. The application of this technique is demonstrated on a set of experimental images of dislocation cores along a low-angle tilt grain boundary in strontium titanate. A relationship between chemical composition and lattice strain is highlighted in the analysis results, with picometer-scale shifts in several columns measurable from compositional changes in a separate column. (C) 2010 Elsevier B.V. All rights reserved.
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