Extraction of physically meaningful endmembers from STEM spectrum-images combining geometrical and statistical approaches.
Bayesian inference
Clustering
EDS
EDX
EELS
Endmember
PCA
STEM
Spectra unmixing
Spectrum-image
VCA
Journal
Micron (Oxford, England : 1993)
ISSN: 1878-4291
Titre abrégé: Micron
Pays: England
ID NLM: 9312850
Informations de publication
Date de publication:
06 2021
06 2021
Historique:
received:
19
11
2020
revised:
01
04
2021
accepted:
01
04
2021
pubmed:
24
4
2021
medline:
24
4
2021
entrez:
23
4
2021
Statut:
ppublish
Résumé
This article addresses extraction of physically meaningful information from STEM EELS and EDX spectrum-images using methods of Multivariate Statistical Analysis. The problem is interpreted in terms of data distribution in a multi-dimensional factor space, which allows for a straightforward and intuitively clear comparison of various approaches. A new computationally efficient and robust method for finding physically meaningful endmembers in spectrum-image datasets is presented. The method combines the geometrical approach of Vertex Component Analysis with the statistical approach of Bayesian inference. The algorithm is described in detail at an example of EELS spectrum-imaging of a multi-compound CMOS transistor.
Identifiants
pubmed: 33892400
pii: S0968-4328(21)00059-7
doi: 10.1016/j.micron.2021.103068
pii:
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
103068Informations de copyright
Copyright © 2021 Elsevier Ltd. All rights reserved.