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A Multiresolution Analysis-Assisted Reinforcement Learning Approach to Run-by-Run Control

机译：分步控制的多分辨率分析辅助强化学习方法

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摘要

In recent years, the run-by run (RbR) control mechanism has emerged as a useful tool for keeping complex semiconductor manufacturing processes on target during repeated short production runs. Many types of RbR controllers exist in the literature of which the exponentially weighted moving average (EWMA) controller is widely used in the industry. However, EWMA controllers are known to have several limitations. For example, in the presence of multiscale disturbances and lack of accurate process models, the performance of EWMA controller deteriorates and often fails to control the process. Also, the control of complex manufacturing processes requires sensing of multiple parameters that may be spatially distributed. New control strategies that can successfully use spatially distributed sensor data are required. This paper presents a new multiresolution analysis (wavelet) assisted reinforcement learning (RL)-based control strategy that can effectively deal with both multiscale disturbances in processes and the lack of process models. The novel idea of a wavelet-aided RL-based controller represents a paradigm shift in the control of large-scale stochastic dynamic systems of which the control problem is a subset. Henceforth, we refer our new control strategy as a WRL-RbR controller. The WRL-RbR controller is tested on a multiple-input-multiple-output chemical mechanical planarization process of wafer fabrication for which the process model is available. Results show that the RL controller outperforms EWMA-based controllers for low autocorrelation. The new controller also performs quite well for strongly autocorrelated processes for which the EWMA controllers are known to fail. Convergence analysis of the new breed of the WRL-RbR controller is presented. Further enhancement of the controller to deal with model-free processes and for inputs coming from spatially distributed environments are also discussed. Note to Practitioners-This work was motivated by the need to develop a- - n intelligent and efficient RbR process controller, especially for the control of processes with short production runs as in the case of the semiconductor manufacturing industry. A novel controller that is presented here is capable of generating optimal control actions in the presence of multiple time-frequency disturbances, and allows the use of realistic (often complex) process models without sacrificing robustness and speed of execution. Performance measures, such as reduction of variability in process output and control recipe, minimization of initial bias, and ability to control processes with high autocorrelations are shown to be superior in comparison to the commercially available exponentially weighted moving average controllers. The WRL-RbR controller is very generic, and can be easily extended to processes with drifts and sudden shifts in the mean and variance. The viability of extending the controller to distributed input parameter sensing environments, including those for which process models are not available, is also discussed
机译：近年来，逐行运行（RbR）控制机制已成为一种有用的工具，可用于在反复的短暂生产运行中将复杂的半导体制造工艺保持在目标位置。文献中存在许多类型的RbR控制器，其中指数加权移动平均（EWMA）控制器已在行业中广泛使用。但是，已知EWMA控制器具有多个限制。例如，在存在多尺度干扰且缺乏准确的过程模型的情况下，EWMA控制器的性能会下降，并且通常无法控制过程。同样，复杂制造过程的控制要求感测可能在空间上分布的多个参数。需要可以成功使用空间分布的传感器数据的新控制策略。本文提出了一种新的基于多分辨率分析（小波）辅助强化学习（RL）的控制策略，该策略可有效处理过程中的多尺度干扰和过程模型的缺乏。基于小波的基于RL的控制器的新颖思想代表了控制问题是其子集的大规模随机动态系统的控制范式转变。从今以后，我们将新的控制策略称为WRL-RbR控制器。 WRL-RbR控制器在晶圆制造的多输入多输出化学机械平面化工艺上进行了测试，该工艺模型可用。结果表明，对于低自相关，RL控制器优于基于EWMA的控制器。对于已知的EWMA控制器发生故障的高度自相关的过程，新的控制器也表现出色。介绍了新型WRL-RbR控制器的收敛性分析。还讨论了控制器的进一步增强，以处理无模型的过程以及来自空间分布环境的输入。给从业者的注意-这项工作的动机是需要开发a--n智能高效的RbR过程控制器，尤其是对于半导体生产行业中生产周期短的过程的控制。此处介绍的新型控制器能够在存在多个时频干扰的情况下生成最佳控制动作，并允许在不牺牲鲁棒性和执行速度的情况下使用实际的（通常是复杂的）过程模型。与市场上可买到的指数加权移动平均值控制器相比，性能度量（例如降低过程输出和控制配方中的可变性，最小化初始偏差以及具有高自相关性的过程控制能力）表现出优异的性能。 WRL-RbR控制器非常通用，可以轻松扩展到均值和方差有漂移和突然变化的过程。还讨论了将控制器扩展到分布式输入参数感测环境（包括那些过程模型不可用的环境）的可行性。

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