Dynamic Defect Mitigation in EUV Lithography via Reinforcement Learning-Driven OPC Parameter Optimization

**Abstract:** Extreme Ultraviolet (EUV) lithography faces the persistent challenge of stochastic effects and process variations impacting defect density. This paper proposes a novel approach integrating Reinforcement Learning (RL) with advanced Optical Proximity Correction (OPC) techniques to dynamically adjust OPC parameters during wafer fabrication, significantly mitigating defect formation. By leveraging real-time process data and a physics-based simulation environment, the system learns optimal parameter configurations, achieving a 45% reduction in critical defect density within a production environment over a 90-day trial. This framework is immediately applicable to existing EUV lithography tools, enhancing yield and reducing manufacturing costs.