Machine learning analysis for RHEED images using EM algorithm

DOI
  • Yoshinari Asako
    Graduate School of Advanced Engineering, Tokyo University of Science National Institute for Materials Science
  • Ando Yasunobu
    National Institute of Advanced Industrial Science and Technology
  • Matsumura Tarojiro
    National Institute of Advanced Industrial Science and Technology
  • Kotsugi Masato
    Graduate School of Advanced Engineering, Tokyo University of Science
  • Nagamura Naoka
    Graduate School of Advanced Engineering, Tokyo University of Science National Institute for Materials Science Japan Science and Technology Agency PRESTO

Bibliographic Information

Other Title
  • EMアルゴリズムを用いたRHEED画像の機械学習自動解析

Abstract

<p>RHEED (reflection high-energy electron diffraction) is a widely used method for in-situ surface structural analysis of thin films. Since it is difficult to interpret the entire patterns quantitatively, researchers often use limited information such as the intensity oscillation at a given diffraction spot in film thickness estimation. Here, we adopted machine learning techniques for feature extraction from the entire RHEED patterns. We performed peak fitting analysis of the luminance histogram obtained from the time-series image datasets of RHEED patterns of Si surface superstructures during Indium deposition using EM algorithm. One peak component corresponds to the background, and the other corresponds to the diffraction spots. By tracking the change in the dispersion value of the peak, the optimal time for preparing each surface superstructure could be estimated automatically. Our method is expected for the application in data-driven material synthesis.</p>

Journal

Details 詳細情報について

  • CRID
    1390571968054430080
  • NII Article ID
    130008134164
  • DOI
    10.14886/jvss.2021.0_2dp03s
  • ISSN
    24348589
  • Text Lang
    ja
  • Data Source
    • JaLC
    • CiNii Articles
  • Abstract License Flag
    Disallowed

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