Optimal Control of SiC Crystal Growth in the RF-TSSG System Using Reinforcement Learning

Optimal Control of SiC Crystal Growth in the RF-TSSG System Using Reinforcement Learning

We have developed a reinforcement learning (RL) model to control the melt flow in the radio frequency (RF) top-seeded solution growth (TSSG) process for growing more uniform SiC crystals with a higher growth rate. In the study, the electromagnetic field (EM) strength is controlled by the RL model to...

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Bibliographic Details
Main Authors: Lei Wang, Atsushi Sekimoto, Yuto Takehara, Yasunori Okano, Toru Ujihara, Sadik Dost
Format: Article
Language:English
Published: MDPI AG 2020-09-01
Series:Crystals
Subjects:
SiC crystal growth
TSSG method
flow control
Online Access:https://www.mdpi.com/2073-4352/10/9/791
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spelling doaj-a11a38f39c8541e4a787b97e6cdc66552020-11-25T03:25:28ZengMDPI AGCrystals2073-43522020-09-011079179110.3390/cryst10090791Optimal Control of SiC Crystal Growth in the RF-TSSG System Using Reinforcement LearningLei Wang0Atsushi Sekimoto1Yuto Takehara2Yasunori Okano3Toru Ujihara4Sadik Dost5Department of Materials Engineering Science, Osaka University, Toyonaka 560-8531, Osaka, JapanDepartment of Materials Engineering Science, Osaka University, Toyonaka 560-8531, Osaka, JapanDepartment of Materials Engineering Science, Osaka University, Toyonaka 560-8531, Osaka, JapanDepartment of Materials Engineering Science, Osaka University, Toyonaka 560-8531, Osaka, JapanDepartment of Materials Science, Nagoya University, Chikusa-ku 464-8603, Nagoya, JapanCrystal Growth Laboratory, University of Victoria, Victoria, BC V8W 3P6, CanadaWe have developed a reinforcement learning (RL) model to control the melt flow in the radio frequency (RF) top-seeded solution growth (TSSG) process for growing more uniform SiC crystals with a higher growth rate. In the study, the electromagnetic field (EM) strength is controlled by the RL model to weaken the influence of Marangoni convection. The RL model is trained through a two-dimensional (2D) numerical simulation of the TSSG process. As a result, the growth rate under the control of the RL model is improved significantly. The optimized RF-coil parameters based on the control strategy for the 2D melt flow are used in a three-dimensional (3D) numerical simulation for model validation, which predicts a higher and more uniform growth rate. It is shown that the present RL model can significantly reduce the development cost and offers a useful means of finding the optimal RF-coil parameters.https://www.mdpi.com/2073-4352/10/9/791SiC crystal growthTSSG methodflow control
collection DOAJ
language English
format Article
sources DOAJ
author Lei Wang
Atsushi Sekimoto
Yuto Takehara
Yasunori Okano
Toru Ujihara
Sadik Dost
spellingShingle Lei Wang
Atsushi Sekimoto
Yuto Takehara
Yasunori Okano
Toru Ujihara
Sadik Dost
Optimal Control of SiC Crystal Growth in the RF-TSSG System Using Reinforcement Learning
Crystals
SiC crystal growth
TSSG method
flow control
author_facet Lei Wang
Atsushi Sekimoto
Yuto Takehara
Yasunori Okano
Toru Ujihara
Sadik Dost
author_sort Lei Wang
title Optimal Control of SiC Crystal Growth in the RF-TSSG System Using Reinforcement Learning
title_short Optimal Control of SiC Crystal Growth in the RF-TSSG System Using Reinforcement Learning
title_full Optimal Control of SiC Crystal Growth in the RF-TSSG System Using Reinforcement Learning
title_fullStr Optimal Control of SiC Crystal Growth in the RF-TSSG System Using Reinforcement Learning
title_full_unstemmed Optimal Control of SiC Crystal Growth in the RF-TSSG System Using Reinforcement Learning
title_sort optimal control of sic crystal growth in the rf-tssg system using reinforcement learning
publisher MDPI AG
series Crystals
issn 2073-4352
publishDate 2020-09-01
description We have developed a reinforcement learning (RL) model to control the melt flow in the radio frequency (RF) top-seeded solution growth (TSSG) process for growing more uniform SiC crystals with a higher growth rate. In the study, the electromagnetic field (EM) strength is controlled by the RL model to weaken the influence of Marangoni convection. The RL model is trained through a two-dimensional (2D) numerical simulation of the TSSG process. As a result, the growth rate under the control of the RL model is improved significantly. The optimized RF-coil parameters based on the control strategy for the 2D melt flow are used in a three-dimensional (3D) numerical simulation for model validation, which predicts a higher and more uniform growth rate. It is shown that the present RL model can significantly reduce the development cost and offers a useful means of finding the optimal RF-coil parameters.
topic SiC crystal growth
TSSG method
flow control
url https://www.mdpi.com/2073-4352/10/9/791
work_keys_str_mv AT leiwang optimalcontrolofsiccrystalgrowthintherftssgsystemusingreinforcementlearning
AT atsushisekimoto optimalcontrolofsiccrystalgrowthintherftssgsystemusingreinforcementlearning
AT yutotakehara optimalcontrolofsiccrystalgrowthintherftssgsystemusingreinforcementlearning
AT yasunoriokano optimalcontrolofsiccrystalgrowthintherftssgsystemusingreinforcementlearning
AT toruujihara optimalcontrolofsiccrystalgrowthintherftssgsystemusingreinforcementlearning
AT sadikdost optimalcontrolofsiccrystalgrowthintherftssgsystemusingreinforcementlearning
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