AI 中文总结
AutoDRI作为多智能体框架，结合多种技术弥合自然语言设计规则与CP-SAT约束的语义鸿沟，在41个单元基准及10余项复杂规则下实现高正确率，运行时间接近人工硬编码且通过相关验证。
AI 中文摘要
设计规则集成（DRI）仍是基于约束编程与SAT（CP-SAT）的标准单元综合及先进节点快速技术启用可扩展性的主要瓶颈，目前仍严重依赖人工投入与领域专业知识。此外，现有低级规则编码对多图案化技术下的切割规则等新兴约束表达能力不足。本文提出AutoDRI，这是一种用于标准单元综合中自动设计规则集成的多智能体框架，它结合几何语义库、标准化冲突集编码、构造性多色切割建模方法及反馈驱动的多智能体流程，以弥合自然语言设计规则与可执行CP-SAT约束间的语义鸿沟。在已报道的实验中，AutoDRI在41个单元基准及10余项复杂规则（包括彩色切割掩模间距规则）下实现近乎完美的规则集成正确性，与Gemini-3-pro的正确集成数达33/33，与GPT-5.4达32/33，同时保持与人工硬编码相当的运行时间，并通过KLayout DRC和Cadence LVS验证。
英文摘要
Design-rule integration (DRI) remains a major bottleneck for scalable (Constraint Programming with SAT) CP-SAT-based standard cell synthesis and rapid technology enablement at advanced nodes. It still depends heavily on manual effort and domain expertise. Moreover, existing low-level rule encodings are not expressive enough for emerging constraints such as cut-based rules under multi-patterning technology. This paper presents \textbf{AutoDRI}, a multi-agent framework for automated design-rule integration in standard cell synthesis. AutoDRI combines a geometric semantic library, a standardized conflict-set encoding, a constructive multicolor-cut modeling method, and a feedback-driven multi-agent flow to bridge the semantic gap between natural-language design rules and executable CP-SAT constraints. In the reported experiments, AutoDRI achieves near-perfect rule-integration correctness across 41 cell benchmarks under 10+ complex rules, including colored cut-mask spacing rules, reaching 33/33 correct integrations with Gemini-3-pro and 32/33 with GPT-5.4, while maintaining runtime comparable to manual hard-coding and passing KLayout DRC and Cadence LVS.
