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.

翻译：芯片设计与制造的进步使得处理深度学习与自然语言处理等复杂任务成为可能，为通用人工智能（AGI）的发展铺平了道路。另一方面，人工智能可被用于从规划、实施到制造的半导体技术创新与流程优化。本文提出\textit{智能OPC工程师助手}，这是一种基于人工智能/大语言模型的方法论，旨在解决被称为光学邻近效应修正（OPC）的核心制造感知优化问题。该方法论包含基于强化学习的OPC配方搜索，以及用于配方总结的定制化多模态智能体系统。实验表明，我们的方法论能够针对具有特殊处理设计拓扑的各种芯片设计高效构建OPC配方，而这项任务通常需要具备多年经验的OPC工程师投入全职工作量才能完成。