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Open AccessEditor’s ChoiceArticle

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

1
Department of Materials Engineering Science, Osaka University, Toyonaka 560-8531, Osaka, Japan
2
Department of Materials Science, Nagoya University, Chikusa-ku 464-8603, Nagoya, Japan
3
Crystal Growth Laboratory, University of Victoria, Victoria, BC V8W 3P6, Canada
*
Author to whom correspondence should be addressed.
Crystals 2020, 10(9), 791; https://doi.org/10.3390/cryst10090791
Received: 7 August 2020 / Revised: 29 August 2020 / Accepted: 3 September 2020 / Published: 7 September 2020
(This article belongs to the Special Issue Crystal Growth from Liquid Phase)
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. View Full-Text
Keywords: SiC crystal growth; TSSG method; flow control SiC crystal growth; TSSG method; flow control
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MDPI and ACS Style

Wang, L.; Sekimoto, A.; Takehara, Y.; Okano, Y.; Ujihara, T.; Dost, S. Optimal Control of SiC Crystal Growth in the RF-TSSG System Using Reinforcement Learning. Crystals 2020, 10, 791. https://doi.org/10.3390/cryst10090791

AMA Style

Wang L, Sekimoto A, Takehara Y, Okano Y, Ujihara T, Dost S. Optimal Control of SiC Crystal Growth in the RF-TSSG System Using Reinforcement Learning. Crystals. 2020; 10(9):791. https://doi.org/10.3390/cryst10090791

Chicago/Turabian Style

Wang, Lei; Sekimoto, Atsushi; Takehara, Yuto; Okano, Yasunori; Ujihara, Toru; Dost, Sadik. 2020. "Optimal Control of SiC Crystal Growth in the RF-TSSG System Using Reinforcement Learning" Crystals 10, no. 9: 791. https://doi.org/10.3390/cryst10090791

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