Optimal principal component analysis of STEM XEDS spectrum images

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 princip...

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Detalles Bibliográficos
Autores principales: Potapov, Pavel, Lubk, Axel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2019
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6456488/
https://www.ncbi.nlm.nih.gov/pubmed/31032174
http://dx.doi.org/10.1186/s40679-019-0066-0
Descripción
Sumario: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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s40679-019-0066-0) contains supplementary material, which is available to authorized users.

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