Extraction of physically meaningful endmembers from STEM spectrum-images combining geometrical and statistical approaches.
Sign Up to like & getrecommendations! Published in 2021 at "Micron"
DOI: 10.1016/j.micron.2021.103068
Abstract: 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… read more here.
Keywords: physically meaningful; meaningful endmembers; spectrum images; extraction physically ... See more keywords
On the loss of information in PCA of spectrum-images.
Sign Up to like & getrecommendations! Published in 2017 at "Ultramicroscopy"
DOI: 10.1016/j.ultramic.2017.06.023
Abstract: Principal Component Analysis (PCA) can drastically denoise STEM spectrum-images but might distort or cut off the important variations in data. The present paper analyzes various approaches to estimate such deviations and compares them with the… read more here.
Keywords: information pca; pca; spectrum images; loss information ... See more keywords
Optimal principal component analysis of STEM XEDS spectrum images
Sign Up to like & getrecommendations! Published in 2019 at "Advanced Structural and Chemical Imaging"
DOI: 10.1186/s40679-019-0066-0
Abstract: 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… read more here.
Keywords: principal component; xeds spectrum; spectrum images; stem xeds ... See more keywords