Machine learning assisted nanobeam X-ray diffraction based analysis on hydride vapor-phase epitaxy GaN

Zhendong Wu; Yusuke Hayashi; Tetsuya Tohei; Kazushi Sumitani; Yasuhiko Imai; Shigeru Kimura; Akira Sakai

doi:10.1107/s1600576725004169

論文 Machine learning assisted nanobeam X-ray diffraction based analysis on hydride vapor-phase epitaxy GaN

Zhendong Wu ; Yusuke Hayashi (National Institute for Materials Science) ; Tetsuya Tohei ; Kazushi Sumitani ; Yasuhiko Imai ; Shigeru Kimura ; Akira Sakai

Wu 2025 J Appl Crystallogr nanoXRD UMAP HVPE GaN.pdf

Download file (11.6MB)
Download as zip (11.3MB)

コレクション

引用

Zhendong Wu, Yusuke Hayashi, Tetsuya Tohei, Kazushi Sumitani, Yasuhiko Imai, Shigeru Kimura, Akira Sakai. Machine learning assisted nanobeam X-ray diffraction based analysis on hydride vapor-phase epitaxy GaN. Journal of Applied Crystallography. 2025, 58 (4), S1600576725004169. https://doi.org/10.1107/s1600576725004169

(BibTeX)

説明:

(abstract)

Nanobeam X-ray diffraction (nanoXRD) is a powerful tool for collecting in situ crystal structure information with high spatial resolution and data acquisition rate. However, analyzing the enormous amount of data produced by these high-throughput experiments for defect recognition or discovering hidden structural features becomes challenging. Machine learning (ML) methods have become attractive recently due to their outstanding performance in analyzing large data
sets. This research utilizes an ML algorithm, uniform manifold approximation and projection (UMAP), to enhance the nanoXRD-based crystal structure analysis of a cross-sectional hydride vapor-phase epitaxy GaN wafer.

権利情報: