发表机构
Purdue University（普渡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对布局中设计规则违规问题，提出DRC-Aid闭环智能体框架，利用确定性规则引擎、大语言模型等，通过预算深度优先搜索等方法进行局部DRC修复，在相关布局评估中效果良好，基于LLM的选择优于其他策略。
AI 中文摘要
解决布局中的设计规则违规（DRV）需要几何编辑和验证的迭代循环。我们提出了DRC-Aid，一个闭环智能体框架，通过将局部DRC修复制定为循环验证搜索来实现自动化。确定性规则引擎将物理验证工具报告的违规转换为有限的几何编辑菜单，以限制组合几何修复空间。现成的大语言模型（LLM）评估局部几何上下文，通过预算深度优先搜索和回溯从该菜单中选择编辑。验证工具（如Calibre nmDRC/nmLVS）的即时反馈确保几何合规并防止电气拓扑退化，而全局内存库防止循环重新探索。在包含DRV的FreePDK45布局上进行评估，DRC-Aid在约92.5%的情况下实现了DRC清洁、LVS等效修复，总违规减少约98%，而剩余情况产生部分修复的LVS等效候选方案。在相同的搜索和验证基础设施下，基于LLM的选择优于随机（54.4%）和确定性启发式（83.3%）策略，在有六个或更多违规的情况下差距扩大。
英文摘要
Resolving Design Rule Violations (DRVs) in layouts entails an iterative loop of geometric edits and verification. We present DRC-Aid, a closed-loop agentic framework that automates local DRC repair by formulating it as verification-in-the-loop search. To constrain the combinatorial geometric repair space, a deterministic Rule Engine converts physical verification tool-reported violations into a bounded menu of geometric edits. An off-the-shelf Large Language Model (LLM) evaluates local geometric context to select edits from this menu, with budgeted depth-first search and backtracking. Immediate feedback from verification tools such as Calibre nmDRC/nmLVS enforces geometric compliance and guards against electrical-topology degradation, while a global Memory Bank prevents cyclic re-exploration. Evaluated on FreePDK45 layouts containing DRVs, DRC-Aid achieves DRC-clean, LVS-equivalent repairs in ~92.5% of cases with a ~98% total violation reduction, while residual cases yield partially repaired LVS-equivalent candidates. Under an identical search and verification infrastructure, LLM-based selection outperforms random (54.4%) and deterministic-heuristic (83.3%) policies, with the gap widening on cases with six or more violations.
Comments7 pages
