evodrc-a-self-evolving-agentic-framework-for-autom

name	evodrc-a-self-evolving-agentic-framework-for-autom
description	Skill generated from arXiv paper 2607.20019: EvoDRC: A Self-Evolving Agentic Framework for Automated DRC Violation Repair
metadata	{"arxiv":{"id":"2607.20019","title":"EvoDRC: A Self-Evolving Agentic Framework for Automated DRC Violation Repair","authors":["Bing-Yue Wu","Chia-Tung Ho","Haoyu Yang","Brucek Khailany","Vidya A. Chhabria"],"published":"2026-07-22","categories":["cs.AI"],"url":"https://arxiv.org/abs/2607.20019","utility":1}}

EvoDRC: A Self-Evolving Agentic Framework for Automated DRC Violation Repair

arXiv: 2607.20019
Published: 2026-07-22
Authors: Bing-Yue Wu, Chia-Tung Ho, Haoyu Yang, Brucek Khailany, Vidya A. Chhabria
Categories: cs.AI
Utility: 1.00

Key Innovation

Design rule check (DRC) closure remains a major bottleneck in advanced-node physical design. Although detailed routers are rule-aware, residual design rule violations (DRVs) often require manual engineering change order iterations. Automating this process is challenging because repairs must account for complex geometric interactions, preserve circuit connectivity, and avoid introducing new violations. We present EvoDRC, a skill-evolution framework for agentic block-level DRC repair. EvoDRC initi...

Potential Application

This paper presents advancements that could be applied to enhance agent capabilities in the areas of cs.AI.

References

• arXiv: https://arxiv.org/abs/2607.20019