Intelligent OPC Engineer Assistant for Semiconductor Manufacturing
This addresses the bottleneck of manual OPC recipe development for semiconductor manufacturers, though it appears incremental as it builds on existing AI and reinforcement learning techniques.
The paper tackles the problem of optical proximity correction (OPC) in semiconductor manufacturing by introducing an AI/LLM-powered methodology that efficiently builds OPC recipes on various chip designs, reducing the need for extensive human expertise.
Advancements in chip design and manufacturing have enabled the processing of complex tasks such as deep learning and natural language processing, paving the way for the development of artificial general intelligence (AGI). AI, on the other hand, can be leveraged to innovate and streamline semiconductor technology from planning and implementation to manufacturing. In this paper, we present \textit{Intelligent OPC Engineer Assistant}, an AI/LLM-powered methodology designed to solve the core manufacturing-aware optimization problem known as optical proximity correction (OPC). The methodology involves a reinforcement learning-based OPC recipe search and a customized multi-modal agent system for recipe summarization. Experiments demonstrate that our methodology can efficiently build OPC recipes on various chip designs with specially handled design topologies, a task that typically requires the full-time effort of OPC engineers with years of experience.