Chinese Journal of Lasers, Volume. 47, Issue 9, 901002(2020)
Energy Control of Excimer Laser Based on Reinforcement Learning
Fig. 1. Single pulse energy change under constant high voltage working mode of excimer laser
Fig. 3. Comparison of Burst's pulse energy change measured and model under the same working mode. (a) Measured Burst pulse energy; (b) model Burst pulse energy
Fig. 5. Comparison of light energy values. (a) Z-N parameter tuning PI algorithm; (b) PSO tuning PI algorithm; (c) algorithm based on reinforcement learning
Fig. 6. Comparison of algorithm single pulse energy histograms. (a) Z-N parameter tuning PI algorithm; (b) PSO tuning PI algorithm; (c) algorithm based on reinforcement learning
Fig. 7. Comparison of Burst energy stability. (a) Z-N parameter tuning PI algorithm; (b) PSO tuning PI algorithm; (c) algorithm based on reinforcement learning
Fig. 8. Comparison of Burst dose stability. (a) Z-N parameter tuning PI algorithm; (b) PSO tuning PI algorithm; (c) algorithm based on reinforcement learning
Fig. 9. Comparison of dynamic stability. (a) Z-N parameter tuning PI algorithm; (b) PSO tuning PI algorithm; (c) algorithm based on reinforcement learning
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Sun Zexu, Feng Zebin, Zhou Yi, Liu Guangyi, Han Xiaoquan. Energy Control of Excimer Laser Based on Reinforcement Learning[J]. Chinese Journal of Lasers, 2020, 47(9): 901002
Category: laser devices and laser physics
Received: Feb. 21, 2020
Accepted: --
Published Online: Sep. 16, 2020
The Author Email: Xiaoquan Han (hanxiaoquan@ime.ac.cn)