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Optimization of laser annealing parameters based on bayesian reinforcement learning

Author

Listed:
  • Chung-Yuan Chang

    (National Yang-Ming Chiao-Tung University)

  • Yen-Wei Feng

    (National Yang-Ming Chiao-Tung University)

  • Tejender Singh Rawat

    (National Yang-Ming Chiao-Tung University)

  • Shih-Wei Chen

    (Taiwan Semiconductor Research Institute)

  • Albert Shihchun Lin

    (National Yang-Ming Chiao-Tung University)

Abstract

Developing new semiconductor processes consumes tremendous time and cost. Therefore, we applied Bayesian reinforcement learning (BRL) with the assistance of technology computer-aided design (TCAD). The fixed or variable prior BRL is tested where the TCAD prior is fixed or is changed by the experimental sampling and decays during the entire RL procedure. The sheet resistance (Rs) of the samples treated by laser annealing is the optimization target. In both cases, the experimentally sampled data points are added to the training dataset to enhance the RL agent. The model-based experimental agent and a model-free TCAD Q-Table are used in this study. The results of BRL proved that it can achieve lower Rs minimum values and variances at different hyperparameter settings. Besides, two action types, i.e., point to state and increment of levels, are proven to have similar results, which implies the method used in this study is insensitive to the different action types.

Suggested Citation

  • Chung-Yuan Chang & Yen-Wei Feng & Tejender Singh Rawat & Shih-Wei Chen & Albert Shihchun Lin, 2025. "Optimization of laser annealing parameters based on bayesian reinforcement learning," Journal of Intelligent Manufacturing, Springer, vol. 36(4), pages 2479-2492, April.
    • Handle: RePEc:spr:joinma:v:36:y:2025:i:4:d:10.1007_s10845-024-02363-w
    DOI: 10.1007/s10845-024-02363-w
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