用自进化智能体自动修复芯片设计中的规则错误，减少人工干预。
EvoDRC: A Self-Evolving Agentic Framework for Automated DRC Violation Repair
- 通过分解版图区域并分配大模型智能体进行局部修复
- 在7个模块上实现73.5%的违规项减少，优于基线
- 能持续学习修复经验，适合先进制程芯片设计
先进节点物理设计中，设计规则检查（DRC）闭合仍是主要瓶颈。尽管详细布线器具备规则感知能力，但残留的设计规则违规（DRVs）常需人工工程变更迭代。自动化修复面临挑战：须考虑复杂几何交互、保持电路连通性，并避免引入新违规。我们提出EvoDRC，一种面向模块级DRC修复的自进化智能体框架。EvoDRC利用无关参考设计提取的知识初始化分层修复技能，并基于目标设计收集的可追溯修复经验持续演化这些技能。该框架将版图分解为有界修复区域，为每个区域分配一个LLM修复智能体。局部DRC分析、连通性检查与影响预览工具提供修改反馈。修复操作及其引发的违规变化被存入知识库，用于技能演化。在DAC26 DRC基准的7个模块级设计上，EvoDRC相较报告基线实现73.5%的整体违规减少。
原文摘要 · Abstract (English)
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.
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