Nanoscale light element identification using machine learning aided STEM-EDS

Title
Nanoscale light element identification using machine learning aided STEM-EDS
Authors
서진유천동원조민경김홍규배지환김긍호김주영한정우Heon-Young HaTae-Ho LeeJae Hoon Jang
Issue Date
2020-08
Publisher
Scientific Reports
Citation
VOL 10, 13699
Abstract
Light element identifcation is necessary in materials research to obtain detailed insight into various material properties. However, reported techniques, such as scanning transmission electron microscopy (STEM)-energy dispersive X-ray spectroscopy (EDS) have inadequate detection limits, which impairs identifcation. In this study, we achieved light element identifcation with nanoscale spatial resolution in a multi-component metal alloy through unsupervised machine learning algorithms of singular value decomposition (SVD) and independent component analysis (ICA). Improvement of the signal-to-noise ratio (SNR) in the STEM-EDS spectrum images was achieved by combining SVD and ICA, leading to the identifcation of a nanoscale N-depleted region that was not observed in as-measured STEM-EDS. Additionally, the formation of the nanoscale N-depleted region was validated using STEM– electron energy loss spectroscopy and multicomponent difusional transformation simulation. The enhancement of SNR in STEM-EDS spectrum images by machine learning algorithms can provide an efcient, economical chemical analysis method to identify light elements at the nanoscale.
URI
http://pubs.kist.re.kr/handle/201004/71834
ISSN
2045-2322
Appears in Collections:
KIST Publication > Article
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