2020年9月7日
Optimal Control of SiC Crystal Growth in the RF-TSSG System Using Reinforcement Learning
Crystals
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- 巻
- 10
- 号
- 9
- 開始ページ
- 791
- 終了ページ
- 791
- 記述言語
- 掲載種別
- 研究論文(学術雑誌)
- DOI
- 10.3390/cryst10090791
- 出版者・発行元
- MDPI AG
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.
- ID情報
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- DOI : 10.3390/cryst10090791
- eISSN : 2073-4352