强化学习
EWMA图表
控制理论(社会学)
马尔可夫决策过程
控制器(灌溉)
计算机科学
半导体器件制造
马尔可夫过程
过程(计算)
数学优化
人工智能
工程类
数学
控制(管理)
统计
电气工程
操作系统
生物
薄脆饼
控制图
农学
出处
期刊:IEEE Transactions on Semiconductor Manufacturing
[Institute of Electrical and Electronics Engineers]
日期:2023-02-01
卷期号:36 (1): 91-99
标识
DOI:10.1109/tsm.2022.3225480
摘要
Exponentially weighted moving average (EWMA) controllers have been extensively studied for run-to-run (RtR) control in semiconductor manufacturing processes. However, the EWMA controller with a fixed weight struggles to achieve excellent performance under unknown stochastic disturbances. To improve the performance of EMWA via online parameter tuning, an intelligent strategy using deep reinforcement learning (DRL) technique is developed in this work. To begin with, the weight adjusting problem is established as a Markov decision process. Meanwhile, simple state space, action space and reward function are designed. Then, the classical deep deterministic policy gradient (DDPG) algorithm is utilized to adjust the weight online. Moreover, a quantile regression-based DDPG (QR-DDPG) algorithm is further verified the effectiveness of the proposed method. Finally, the developed scheme is implemented on a deep reactive ion etching process. Comparisons are conducted to show the superiority of the presented approach in terms of disturbance rejection and target tracking.
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