VPEvolve：用于计算光刻的自进化虚拟工艺工程师
VPEvolve: A Self-Evolving Virtual Process Engineer for Computational Lithography
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中文总结 AI 辅助
VPEvolve提出自进化虚拟工艺工程师框架，结合技能库与LLM反射机制，在冻结模型下迭代优化OPC配方，显著降低光刻边缘放置误差。
中文摘要 AI 辅助
光学邻近校正（OPC）配方会随着工程师添加局部规则以修复新发现的光刻热点而不断增长。每次校正都可能与现有规则相互作用，而商业工具试验的经验教训仍分散在代码和日志中。\system将虚拟工艺工程师（VPE）框架与测量工程经验的技能库相结合。该框架为冻结的语言模型配备工艺手册、布局分析、配方编辑和商业工具评估功能。执行者提出对全局参数、局部目标规则或诊断试验的更改。每次评估后，LLM反射器和策展器将测量响应转化为基于证据的判断。执行者在下次试验前检索这些判断。可行的改进会更新保留的配方；每次测量的试验都会为指导下一次编辑的技能库提供信息。模型权重保持不变。在基于FreePDK45的基准测试中，每个案例进行十次商业工具评估，\system将Poly层上每个案例的最大边缘放置误差平均值从18.294纳米降至5.361纳米，Metal1层上从22.052纳米降至15.692纳米。每个最终配方都满足预定义的质量约束，并将最大误差至少改善0.1纳米。
英文摘要
Optical proximity correction (OPC) recipes grow as engineers add local rules to repair newly discovered lithography hotspots. Each correction can interact with existing rules, while lessons from commercial-tool trials remain scattered across code and logs. \system combines a Virtual Process Engineer (VPE) harness with a Skill Bank of measured engineering experience. The harness equips a frozen language model with process manuals, layout analysis, recipe editing, and commercial-tool evaluation. The actor proposes changes to the global parameters, local targeted rules, or diagnostic trials. After each evaluation, an LLM reflector and curator turn the measured response into evidence-linked judgments. The actor retrieves them before its next trial. Feasible improvements update the retained recipe; every measured trial informs the Skill Bank that guides the next edit. The model weights remain fixed. On a FreePDK45-derived benchmark with ten commercial-tool evaluations per case, \system reduces the mean per-case maximum edge placement error from 18.294 to 5.361 nm on Poly and from 22.052 to 15.692 nm on Metal1. Every final recipe satisfies the predefined quality constraints and improves the maximum error by at least 0.1 nm.
