Real-Time Scheduling for Multi-Cluster Tools with Milti-Type Wafers by Deep Reinforcement Learning

Multi-cluster tools are extensively utilized in semiconductor manufacturing. As these tools integrate more functionalities and handle increasingly complex processing recipes under numerous constraints, cyclic scheduling strategies become impractical. This paper focuses on the non-cyclic scheduling problem of multi-cluster tools with multi-type wafers and residency time constraints. A generic model is developed to describe dual-armed multi-cluster tools and propose an optimization scheduling algorithm based on Proximal Policy Optimization (PPO). Numerical experiments demonstrate that the proposed algorithm enables rapid scheduling, achieving scheduling performance close to optimal and minimizing residency time.