Event-Driven Reinforcement Learning Enables Long-Horizon Control in Semiconductor Fabrication

The authors introduce a deep reinforcement learning framework tailored for optimizing multi-objective control in semiconductor fabrication, addressing the challenges of high-dimensional decision-making and long-horizon requirements. The framework employs a centralized-agent model with an event-driven temporal-difference formulation, allowing integration with various policy optimization methods. Validation experiments demonstrate substantial improvements in throughput and utilization, highlighting the framework's scalability and applicability to complex adaptive systems, which is crucial for practitioners aiming to enhance production efficiency in stochastic environments.