EvoDRC: A Self-Evolving Agentic Framework for Automated DRC Violation Repair· EvoDRC：自动化DRC违规修复的自我进化框架
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 initializes layer-specific repair skills using knowledge distilled from an unrelated reference design and continuously evolves these skills using traceable repair experience collected from the target design. EvoDRC decomposes the layout into bounded repair regions and assigns an LLM repair agent to each
提出EvoDRC框架自动化修复DRC违规，减少人工干预。
- 核心方法
- EvoDRC通过从无关参考设计中提取知识初始化层特定修复技能，并通过目标设计中的可追踪修复经验不断进化这些技能。框架将版图分解为有界修复区域，为每个区域分配一个基于LLM的修复代理。
- 适合谁读
- 研究者与工程师
- 要解决的问题
- 先进工艺节点物理设计中DRC违规闭合仍需大量手动迭代修复。
- 关键实验
- 论文未提供具体实验数据，但描述了EvoDRC在目标设计中的应用流程。
- 主要贡献
- 实现自动化DRC违规修复，减少手动工程变更指令迭代，提高设计效率。
- 意义与局限
- 该框架有望显著提升先进工艺节点物理设计的自动化水平，但其实际效果和广泛适用性仍需进一步验证。