Optimal control of sic crystal growth in the rf-tssg system using reinforcement learning

Lei Wang, Atsushi Sekimoto, Yuto Takehara, Yasunori Okano, Toru Ujihara, Sadik Dost

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


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.

Original languageEnglish
Article number791
Pages (from-to)1-13
Number of pages13
Issue number9
Publication statusPublished - Sept 2020
Externally publishedYes


  • Flow control
  • SiC crystal growth
  • TSSG method

ASJC Scopus subject areas

  • Chemical Engineering(all)
  • Materials Science(all)
  • Condensed Matter Physics
  • Inorganic Chemistry


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