发表机构
New Jersey Institute of Technology; University of Michigan; Colorado State University; AMD; Case Western Reserve; University of Illinois, Chicago（新泽西理工学院; 密歇根大学; 科罗拉多州立大学; 超威半导体公司; 凯斯西储大学; 伊利诺伊大学芝加哥分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
PICasso是一种AI辅助的光子集成线路设计框架，结合结构化生成流程与多环节验证优化，可提升LLM生成的线路规范满意度，降低插入损耗，生成可制造的版图。
AI 中文摘要
我们提出了PICasso，这是一种基于AI的框架，可根据自然语言规范自动合成、验证和优化光子集成线路（PICs）。PICasso将结构化的自然语言（NL）→YAML→GDS生成流程与工艺设计套件（PDK）感知知识注入、自动布局布线、设计规则检查（DRC）/线路与版图对比（LVS）验证以及基于SAX的光子模拟相结合。为了系统评估AI驱动的光子设计，我们引入了PIC-Set，这是一个包含36个参数化PIC设计任务的基准，涵盖核心光子基本单元和多组件线路。我们使用PIC-Set在统一评估协议下对多种最先进的大型语言模型（LLMs）进行基准测试，包括结构和功能Spec@k、优化效率以及扰动下的鲁棒性等新指标。在基准测试中，与普通LLM生成相比，PICasso显著提高了端到端规范满意度，高复杂度线路的结构Spec@3最高达92.7%，功能Spec@3最高达52%。此外，PICasso通过模拟引导优化持续降低线路插入损耗，将平均损耗从4.98 dB降至3.25 dB，降低了1.74 dB。这些结果表明，结构化领域约束、物理验证和模拟反馈使LLMs从脆弱的网表生成器转变为实用的PIC设计智能体，能够生成可制造的版图，且运行时间与手动基于图形用户界面（GUI）的工作流程相比具有竞争力。
英文摘要
We present PICasso, an AI-assisted framework for automated synthesis, verification, and optimization of photonic integrated circuits (PICs) from natural-language specifications. PICasso couples a structured NL -> YAML -> GDS generation pipeline with PDK aware knowledge injection, automated placement and routing, DRC/LVS validation, and SAX-based photonic simulation. To systematically evaluate AI-driven photonic design, we introduce PIC-Set, a benchmark of 36 parameterized PIC design tasks spanning core photonic primitives and multi-component circuits. Using PIC-Set, we benchmark several state-of-the-art Large Language Models (LLMs) under a unified evaluation protocol, including new metrics such as structural and functional $Spec@k$, optimization efficiency, and robustness under perturbations. Across the benchmark, PICasso significantly improves end-to-end specification satisfaction compared to vanilla LLM generation. Structural $Spec@3$ reaches up to 92.7% and functional $Spec@3$ up to 52% on high-complexity circuits. In addition, PICasso consistently reduces circuit insertion loss, lowering the mean loss from 4.98 dB to 3.25 dB (1.74 dB improvement) through simulation-guided optimization. These results demonstrate that structured domain constraints, physical verification, and simulation feedback transform LLMs from brittle netlist generators into practical PIC design agents capable of producing manufacturable layouts with competitive runtimes relative to manual GUI-based workflows.
Comments5 Tables, 4 Figures
