Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5456997 | Micron | 2017 | 9 Pages |
Abstract
STEM spectrum-imaging with collecting EDX signal is considered in view of the extraction of maximum information from very noisy data. It is emphasized that spectrum-images with weak EDX signal often suffer from information loss in the course of PCA treatment. The loss occurs when the level of random noise exceeds a certain threshold. Weighted PCA, though potentially helpful in isolation of meaningful variations from noise, might provoke the complete loss of information in the situation of weak EDX signal. Filtering datasets prior PCA can improve the situation and recover the lost information. In particular, Gaussian kernel filters are found to be efficient. A new filter useful in the case of sparse atomic-resolution EDX spectrum-images is suggested.
Keywords
Related Topics
Physical Sciences and Engineering
Materials Science
Materials Science (General)
Authors
Pavel Potapov, Paolo Longo, Eiji Okunishi,