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基于强化学习的准分子激光器能量控制算法研究

Energy Control of Excimer Laser Based on Reinforcement Learning

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摘要

光刻用准分子激光器的能量特性在集成电路的光刻过程中至关重要,直接影响光刻机曝光线条的精度。为了实现对于衡量能量特性的能量稳定性和剂量精度的精确控制,从放电电压调节的角度对激光脉冲的能量特性控制进行了研究。为了设计能量特性控制算法,首先对准分子激光器的放电特性建立了仿真模型,并验证了模型的有效性。然后,设计了基于强化学习的准分子激光器能量特性控制算法。最后在仿真模型上,分别采用Z-N(Ziegler-Nichol)参数整定的比例积分(PI)算法、粒子群优化(PSO)整定的PI算法和基于强化学习的算法对出光脉冲进行了控制,将最终的结果进行对比。实验结果证明,在基于强化学习的能量控制算法的控制下,激光器的能量稳定性小于4%,剂量精度小于0.3%,并且动态性能要优于Z-N参数整定的PI算法、PSO整定的PI算法。证明了算法的优越性,提高了光刻用准分子激光器的鲁棒性和实用性,满足了半导体光刻需求。

Abstract

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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中图分类号:TN248.2

DOI:10.3788/CJL202047.0901002

所属栏目:激光器件与激光物理

基金项目:国家科技重大专项;

收稿日期:2020-02-21

修改稿日期:2020-04-20

网络出版日期:2020-09-01

作者单位    点击查看

孙泽旭:中国科学院微电子研究所光电研发中心, 北京 100029中国科学院大学, 北京 100049
冯泽斌:中国科学院微电子研究所光电研发中心, 北京 100029中国科学院大学, 北京 100049
周翊:中国科学院微电子研究所光电研发中心, 北京 100029中国科学院大学, 北京 100049
刘广义:中国科学院微电子研究所光电研发中心, 北京 100029中国科学院大学, 北京 100049
韩晓泉:中国科学院微电子研究所光电研发中心, 北京 100029中国科学院大学, 北京 100049

联系人作者:韩晓泉(hanxiaoquan@ime.ac.cn)

备注:国家科技重大专项;

【1】Yu Y S, You L B, Liang X, et al. Progress of excimer lasers technology [J]. Chinese Journal of Lasers. 2010, 37(9): 2253-2270.
余吟山, 游利兵, 梁勖, 等. 准分子激光技术发展 [J]. 中国激光. 2010, 37(9): 2253-2270.

【2】Watanabe H, Komae S, Tanaka S, et al. Reliable high-power injection locked 6 kHz 60 W laser for ArF immersion lithography [J]. Proceedings of SPIE. 2007, 6520: 652031.

【3】Basting D. Excimer laser technology [M]. New York: Springer. 2001.

【4】Shi H Y, Zhao J S, Song X L, et al. Analysis on factors affecting energy stability of excimer laser for lithography [J]. Infrared and Laser Engineering. 2014, 43(11): 3540-3546.
石海燕, 赵江山, 宋兴亮, 等. 光刻用准分子激光器能量稳定性影响因素分析 [J]. 红外与激光工程. 2014, 43(11): 3540-3546.

【5】Cacouris T, Conley W, Thornes J, et al. New ArF immersion light source introduces technologies for high-volume 14 nm manufacturing and beyond [J]. Proceedings of SPIE. 2015, 9426: 942618.

【6】Cacouris T, Rechtsteiner G, Conley W. Next-generation DUV light source technologies for 10 nm and below [J]. Proceedings of SPIE. 2017, 10147: 1014718.

【7】Cacouris T, Thornes J, Sells M, et al. Advanced light source technologies for memory and logic processes [J]. Proceedings of SPIE. 2018, 10587: 105870Y.

【8】Tanaka S, Tsushima H, Nakaike T, et al. GT40A: durable 45 W ArF injection-lock laser light source for dry/immersion lithography [J]. Proceedings of SPIE. 2006, 6154: 61542O.

【9】Miyamoto H, Kumazaki T, Tsushima H, et al. The ArF laser for the next generation multiple-patterning immersion lithography supporting green operations and leading edge processes [J]. Proceedings of SPIE. 2017, 10147: 1014719.

【10】Chen X L, Lou F G, He Y, et al. Home-made 10 kW fiber laser with high efficiency [J]. Acta Optica Sinica. 2019, 39(3): 0336001.
陈晓龙, 楼风光, 何宇, 等. 高效率全国产化10 kW光纤激光器 [J]. 光学学报. 2019, 39(3): 0336001.

【11】Liu X D, Qin Y X, Liu J, et al. Research on parabolic band integrating mirror for high-power large-width rectangular laser beams [J]. Laser & Optoelectronics Progress. 2019, 56(19): 191403.
刘晓东, 秦应雄, 柳洁, 等. 高功率激光大宽度矩形光束抛物带式积分镜研究 [J]. 激光与光电子学进展. 2019, 56(19): 191403.

【12】Zhu N W, Fang X D. FLUENT-based numerical simulation of gas flow field of excimer laser [J]. Chinese Journal of Lasers. 2016, 43(9): 0901007.
朱能伟, 方晓东. 基于FLUENT的准分子激光器气体流场数值仿真 [J]. 中国激光. 2016, 43(9): 0901007.

【13】Pan N, Liang X, Lin Y, et al. Transmission method of analog signal in excimer laser system [J]. Infrared and Laser Engineering. 2019, 48(9): 0905003.
潘宁, 梁勖, 林颖, 等. 准分子激光系统中模拟信号的传输方法 [J]. 红外与激光工程. 2019, 48(9): 0905003.

【14】Fan Y Y, Zhou Y, Liu G Y, et al. Compound cavity ArF excimer laser with high efficiency [J]. Chinese Journal of Lasers. 2016, 43(2): 0202001.
范元媛, 周翊, 刘广义, 等. 高效率ArF准分子激光复合腔技术研究 [J]. 中国激光. 2016, 43(2): 0202001.

【15】Liu B, Ding J B, Wang K B, et al. Experimental study of characteristics of discharge shock waves in high-repetition-rate excimer lasers [J]. Chinese Journal of Lasers. 2019, 46(12): 1201001.
刘斌, 丁金滨, 王魁波, 等. 高重复频率准分子激光器中放电冲击波特性的实验研究 [J]. 中国激光. 2019, 46(12): 1201001.

【16】Zhu F, Huang K, Tao M M, et al. Theoretical analysis of energy stability of repetitively pulsed HF laser [J]. Acta Optica Sinica. 2019, 39(4): 0414001.
朱峰, 黄珂, 陶蒙蒙, 等. 重复频率HF激光脉冲能量稳定性的理论分析 [J]. 光学学报. 2019, 39(4): 0414001.

【17】Wang X S, Liang X, You L B, et al. Study on energy control algorithm for high-repetition-rate ArF excimer lasers [J]. Laser Technology. 2012, 36(6): 763-766.
王效顺, 梁勖, 游利兵, 等. 高重复频率ArF准分子激光器能量控制算法研究 [J]. 激光技术. 2012, 36(6): 763-766.

【18】Zhao D L, Li W J, Liang X, et al. Study on energy stability for excimer laser skin therapeutic apparatus [J]. Infrared and Laser Engineering. 2017, 46(12): 1206001.
赵读亮, 李文洁, 梁勖, 等. 准分子激光皮肤治疗仪能量稳定性研究 [J]. 红外与激光工程. 2017, 46(12): 1206001.

【19】Sandstrom R L, Besaucele H A, Fomenkov I V, et al. -12-21 . 1999.

【20】Dayan P. Q-learning [J]. Machine Learning. 1992, 8(3/4): 279-292.

【21】Jacobs R A. Increased rates of convergence through learning rate adaptation [J]. Neural Networks. 1988, 1(4): 295-307.

【22】Huang Y R, Qu L G. PID controller parameter tuning and implementation[M]. Beijing: Science Press, 2010.
黄友锐, 曲立国. PID控制器参数整定与实现[M]. 北京: 科学出版社, 2010.

【23】Chiou J S, Tsai S H, Liu M T. A PSO-based adaptive fuzzy PID-controllers [J]. Simulation Modelling Practice and Theory. 2012, 26: 49-59.

引用该论文

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): 0901002

孙泽旭,冯泽斌,周翊,刘广义,韩晓泉. 基于强化学习的准分子激光器能量控制算法研究[J]. 中国激光, 2020, 47(9): 0901002

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