发表机构
Arizona State University; NVIDIA Corporation（亚利桑那州立大学; 英伟达公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对先进节点物理设计中DRC闭合瓶颈问题，EvoDRC提出自进化智能体框架，利用参考设计知识和目标设计经验初始化并进化修复技能，将布局分解后分配智能体，通过工具反馈和知识数据库提升修复效果，实验显示总体减少73.5%。
AI 中文摘要
设计规则检查（DRC）闭合仍是先进节点物理设计中的主要瓶颈。尽管详细路由器有规则意识，但残留的设计规则违规（DRV）常需人工工程变更单迭代。自动化此过程具有挑战性，因为修复要考虑复杂几何交互、保持电路连通性并避免引入新违规。我们提出EvoDRC，一种用于智能体块级DRC修复的技能进化框架。它利用从无关参考设计中提取的知识初始化特定层修复技能，并通过从目标设计收集的可追溯修复经验不断进化这些技能。EvoDRC将布局分解为有界修复区域，为每个区域分配一个大语言模型修复智能体。局部DRC分析工具、连通性检查工具和影响预览工具为提议修改提供反馈。修复操作及其产生的DRV变化存储在知识数据库中，用于进化修复技能。在DAC26 DRC基准测试的七个块级设计上的实验表明，与已报道的基线相比，EvoDRC总体减少了73.5%。
英文摘要
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 region. Local DRC analysis, connectivity-checking, and impact-preview tools provide feedback on proposed modifications. Repair operations and their resulting DRV changes are stored in a knowledge database and used to evolve the repair skills. Experiments on seven block-level designs from the DAC26 DRC Benchmark show that EvoDRC achieves a 73.5\% overall reduction compared to the reported baseline.
