Scheduling Dual-Arm Cluster Tools With Multifunctional Process Modules Using Deep Reinforcement Learning
针对半导体制造中带多功能工艺模块的双臂集群工具，提出一种基于深度强化学习的调度方法，通过自适应算法生成可行配置并改进D3QN网络，以最小化完工时间并减少晶圆驻留时间。
With the advancement of semiconductor manufacturing technology, multifunctional process modules (MPMs) have been widely adopted in cluster tools to enhance production flexibility and efficiency by handling multiple operations concurrently. However, the MPMs lead to challenges in scheduling the robot due to a variety of configurations, complex robot action sequences, and regulating wafer postprocessing residency time. For scheduling such a dual-arm cluster tool (DACT) with MPMs, this article proposes a specific reinforcement learning-based scheduling method. We first develop an adaptive algorithm to generate all feasible MPM configurations. We then employ the masking technique and prioritize a replay experience buffer to improve the dueling double deep <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$</tex-math> </inline-formula>-network (D3QN), enabling it to train and identify scheduling strategies that minimize makespan and reduce wafer residency time under each configuration. We conduct experiments to demonstrate that the proposed method ensures high productivity, providing a robust and flexible scheduling solution for cluster tools in semiconductor manufacturing, and significantly enhances overall production performance.