Optimal principal component analysis of STEM XEDS spectrum images.
Denoising
EDS
EDX
PCA
Reconstruction
STEM
Spectrum image
XEDS
Journal
Advanced structural and chemical imaging
ISSN: 2198-0926
Titre abrégé: Adv Struct Chem Imaging
Pays: Germany
ID NLM: 101687126
Informations de publication
Date de publication:
2019
2019
Historique:
received:
13
12
2018
accepted:
29
03
2019
entrez:
30
4
2019
pubmed:
30
4
2019
medline:
30
4
2019
Statut:
ppublish
Résumé
STEM XEDS spectrum images can be drastically denoised by application of the principal component analysis (PCA). This paper looks inside the PCA workflow step by step on an example of a complex semiconductor structure consisting of a number of different phases. Typical problems distorting the principal components decomposition are highlighted and solutions for the successful PCA are described. Particular attention is paid to the optimal truncation of principal components in the course of reconstructing denoised data. A novel accurate and robust method, which overperforms the existing truncation methods is suggested for the first time and described in details.
Identifiants
pubmed: 31032174
doi: 10.1186/s40679-019-0066-0
pii: 66
pmc: PMC6456488
doi:
Types de publication
Journal Article
Langues
eng
Pagination
4Déclaration de conflit d'intérêts
The authors declare that they have no competing interests.
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