Nanoscale light element identification using machine learning aided STEM-EDS
- Nanoscale light element identification using machine learning aided STEM-EDS
- 서진유; 천동원; 조민경; 김홍규; 배지환; 김긍호; 김주영; 한정우; Heon-Young Ha; Tae-Ho Lee; Jae Hoon Jang
- Issue Date
- Scientific Reports
- VOL 10, 13699
- 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.
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