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  • Received: Feb. 21, 2020

    Accepted: Apr. 20, 2020

    Posted: Sep. 1, 2020

    Published Online: Sep. 16, 2020

    The Author Email: Han Xiaoquan (

    DOI: 10.3788/CJL202047.0901002

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    Zexu Sun, Zebin Feng, Yi Zhou, Guangyi Liu, Xiaoquan Han. Energy Control of Excimer Laser Based on Reinforcement Learning[J]. Chinese Journal of Lasers, 2020, 47(9): 0901002

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Chinese Journal of Lasers, Vol. 47, Issue 9, 0901002 (2020)

Energy Control of Excimer Laser Based on Reinforcement Learning

Sun Zexu1,2, Feng Zebin1,2, Zhou Yi1,2, Liu Guangyi1,2, and Han Xiaoquan1,2,*

Author Affiliations

  • 1Optoelectronics Research and Development Center, Institute of Microelectronics of the Chinese Academy of Sciences,Beijing 100029, China
  • 2University of Chinese Academy of Sciences, Beijing 100049, China


The energy characteristics of lithography excimer lasers are critical in the lithography process of integrated circuits and directly affect the accuracy of the exposure lines of the lithography machine. In order to design a laser energy control algorithm, a simulation model is built for the discharge characteristics of the excimer laser, and the validity of the model is verified. Then, design an energy control algorithm for excimer laser based on reinforcement learning. Finally, on the simulation model, the Z-N (Ziegler-Nichol) parameter tuning proportion integral (PI) algorithm, particle swarm optimization (PSO) tuning PI algorithm and reinforcement learning-based algorithm are used to control the pulse of laser output, and compare the final results. The experimental results show that under the control of the energy control algorithm based on reinforcement learning, the laser energy stability is less than 4%, the dose accuracy of is less than 0.3%, and the dynamic performance is better than the Z-N parameter tuning PI algorithm and PSO tuning PI algorithm. Prove the superiority of the algorithm, improve the robustness and practicability of lithography excimer laser, and meet the needs of photolithography.


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