Adaptive process control for crystal growth using machine learning for high-speed prediction: application to SiC solution growth-Reference-Cited by-同舟云学术

Adaptive process control for crystal growth using machine learning for high-speed prediction: application to SiC solution growth

Published:2021 Issue:9 Volume:23 Page:1982-1990
ISSN:1466-8033
Container-title:CrystEngComm
language:en
Short-container-title:CrystEngComm

Author:

Dang Yifan¹²³⁴^ORCID,Zhu Can⁵²³⁴,Ikumi Motoki¹²³⁴,Takaishi Masaki¹²³⁴,Yu Wancheng⁵²³⁴,Huang Wei¹²³⁴,Liu Xinbo¹²³⁴^ORCID,Kutsukake Kentaro⁵²³⁴⁶,Harada Shunta¹²³⁴⁵,Tagawa Miho¹²³⁴⁵,Ujihara Toru¹²³⁴⁵

Affiliation:

1. Graduate School of Engineering

2. Nagoya University

3. Nagoya 464-8603

4. Japan

5. Institute of Materials and Systems for Sustainability (IMaSS)

6. Center for Advanced Intelligence Project

Abstract

A time-dependent recipe designed by an adaptive control method can consistently maintain the optimal growth conditions despite the unsteady growth environment.

Funder

Advanced Science Institute

New Energy and Industrial Technology Development Organization

Japan Society for the Promotion of Science

Publisher

Royal Society of Chemistry (RSC)

Subject

Condensed Matter Physics,General Materials Science,General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2021/CE/D0CE01824D

Reference32 articles.

1. Application of Artificial Neural Networks in Crystal Growth of Electronic and Opto-Electronic Materials