Intelligent OPC Engineer Assistant for Semiconductor Manufacturing

TL;DR

This paper introduces an AI-powered assistant that uses reinforcement learning and multi-modal agents to automate and optimize optical proximity correction (OPC) in semiconductor manufacturing, reducing reliance on expert engineers.

Contribution

The paper presents a novel AI/LLM-based methodology for OPC recipe search and summarization, significantly improving efficiency and automation in semiconductor manufacturing processes.

Findings

Efficiently builds OPC recipes for various chip designs.

Reduces the need for extensive expert human effort.

Demonstrates effectiveness across complex design topologies.

Abstract

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.