The Prediction Model of Crystal Growth Simulation Built by Machine Learning and Its Applications

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  • UJIHARA Toru
    Institute of Materials and Systems for Sustainability (IMaSS), Nagoya University GaN-OIL, National Institute of Advanced Industrial Science and Technology (AIST) Department of Materials Process Engineering, Nagoya University Center for dvanced Intelligence Project (AIP), RIKEN
  • TSUNOOKA Yosuke
    GaN-OIL, National Institute of Advanced Industrial Science and Technology (AIST) Department of Materials Process Engineering, Nagoya University
  • HATASA Goki
    Department of Materials Process Engineering, Nagoya University
  • KUTSUKAKE Kentaro
    Institute of Materials and Systems for Sustainability (IMaSS), Nagoya University Center for dvanced Intelligence Project (AIP), RIKEN
  • ISHIGURO Akio
    Institute of Innovation for Future Society, Nagoya University
  • MURAYAMA Kenta
    Institute of Materials and Systems for Sustainability (IMaSS), Nagoya University
  • NARUMI Taka
    Institute of Materials and Systems for Sustainability (IMaSS), Nagoya University
  • HARADA Shunta
    Institute of Materials and Systems for Sustainability (IMaSS), Nagoya University Department of Materials Process Engineering, Nagoya University
  • TAGAWA Miho
    Institute of Materials and Systems for Sustainability (IMaSS), Nagoya University Department of Materials Process Engineering, Nagoya University

Bibliographic Information

Other Title
  • 機械学習を用いた結晶成長予測モデルの構築とその応用
  • キカイ ガクシュウ オ モチイタ ケッショウ セイチョウ ヨソク モデル ノ コウチク ト ソノ オウヨウ

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Abstract

<p>The prediction model of the result of computed fluid dynamics simulation in SiC solution growth was constructed on neural network using machine learning. Utilizing the prediction model, we can optimize quickly crystal growth conditions. In addition, the real-time visualization system was also made using the prediction model.</p>

Journal

  • Vacuum and Surface Science

    Vacuum and Surface Science 62 (3), 136-140, 2019-03-10

    The Japan Society of Vacuum and Surface Science

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