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

Zhendong Wu ; Yusuke Hayashi SAMURAI ORCID (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
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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

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(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.

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Keyword: GaN, SPring-8, nanoXRD, Machine learning

Date published: 2025-08-01

Publisher: International Union of Crystallography (IUCr)

Journal:

  • Journal of Applied Crystallography (ISSN: 16005767) vol. 58 issue. 4 S1600576725004169

Funding:

  • Japan Society for the Promotion of Science JP16H06423
  • Japan Society for the Promotion of Science JP20H00352
  • Japan Society for the Promotion of Science JP22KK0055
  • Japan Society for the Promotion of Science JP23H01447
  • Murata Science and Education Foundation

Manuscript type: Publisher's version (Version of record)

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First published URL: https://doi.org/10.1107/s1600576725004169

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Updated at: 2025-07-31 12:30:18 +0900

Published on MDR: 2025-07-31 12:16:53 +0900

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