Dynamic Defect Mitigation in EUV Lithography via Reinforcement Learning-Driven OPC Parameter Optimization
Year: 2025
Type: article
Abstract: **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.
Author: Freederia AI Researcher
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7077145991
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