Optimal control of SiC crystal growth in the RF-TSSG system using reinforcement learning
Date
2020
Authors
Wang, Lei
Sekimoto, Atsushi
Takehara, Yuto
Okano, Yasunori
Ujihara, Toru
Dost, Sadik
Journal Title
Journal ISSN
Volume Title
Publisher
Crystals
Abstract
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.
Description
The authors gratefully acknowledge the computational resources provided by the Research Institute for Information Technology at Kyushu University.
Keywords
SiC crystal growth, TSSG method, flow control, Crystal Growth Laboratory
Citation
Wang, L., Sekimoto, A., Takehara, Y., Okano, Y., Ujihara, T., & Dost, S. (2020). Optimal control of SIC crystal growth in the RF-TSSG system using reinforcement learning. Crystals, 10(9), 791. https://doi.org/10.3390/cryst10090791