Presentation
Drcagent: A Design Rule Aware LLM Agent for DRC Auto-Fix with Adversarial Reinforcement Learning
DescriptionAs semiconductor manufacturing advances toward leading-edge nodes, such as 7 nm and below, Design Rule Checking (DRC) and violation correction have emerged as critical bottlenecks in achieving design closure. This escalation arises from intricate geometric constraints and complex rule dependencies, which traditional template-based or heuristic approaches are inadequate to resolve efficiently. These methods offer limited adaptability when migrating to new process nodes. To overcome these challenges, we propose drcAgent, a novel framework for automated DRC violation correction. The framework integrates a multimodal Large Language Model (LLM) agent with a Retrieval-Augmented Generation (RAG) mechanism grounded in a Design Rule Knowledge Graph (DRKG). We develop a self-play adversarial multi-turn reinforcement learning framework where a Generator agent and a Fixer agent co-evolve, enabling the agent to iteratively improve its correction policy through real interactions with commercial EDA tools. Experimental results on real industrial-scale design cases demonstrate that the proposed framework can be effective at fixing violations, offering a promising new learning-based solution for chip design.
Event Type
Research Manuscript
TimeMonday, July 275:06pm - 5:18pm PDT
LocationMtg Room 101A