Reinforcement Learning-Based Scheduling for Dual-Arm Cluster Tool with Multifunctional Process Modules

Abstract

Cluster tools are vital in semiconductor manufacturing, where multifunctional process modules (MPMs) enhance flexibility and efficiency. However, variable MPMs and processing time in dual-arm cluster tools (DACTs) complicate scheduling, as variable MPM allocation patterns yield distinct productivity. This paper proposes a reinforcement learning-based method for DACTs with MPMs. Firstly, an algorithm enumerates all valid MPM allocation patterns. Then, an adaptive deep Q-Network (DQN) with masking techniques efficiently selects the most efficient pattern and generates robot schedules, minimizing makespan and wafer post-processing residency time across diverse DACT configurations. Experiments validate the proposed approach that offers robust, flexible scheduling solutions to boost semiconductor manufacturing productivity.