PICopilot：一种基于大语言模型的智能体框架，通过脚本生成辅助光子集成芯片设计
PICopilot: An LLM-based Agentic Framework for Assisting Photonic Integrated Circuit Design via Script Generation
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中文总结 AI 辅助
PICopilot是首个基于LLM的智能体框架，通过带反馈机制的多智能体架构与专用RAG流程，成功完成全部48项PIC脚本任务，性能优于其他LLM方法及通用RAG的GPT-5。
中文摘要 AI 辅助
光子集成芯片（PIC）的快速发展正推动设计流程从传统的基于图形用户界面（GUI）的方法转向基于脚本的方法，以获得更高的灵活性、可移植性和可维护性。然而，基于脚本的设计带来了新的挑战，要求设计人员具备工具应用程序编程接口（API）和编程方面的额外熟练程度；同时，由于其本质上不如基于GUI的方法直观且更复杂，因此需要更多的精力和时间。随着PIC规模和复杂性的增长，设计需求与手动脚本编写能力之间的生产力差距持续扩大。为解决这一差距，我们推出了PICopilot，这是首个基于大语言模型（LLM）的智能体框架，可通过从自然语言指令自动生成设计脚本辅助PIC设计。PICopilot采用带有反馈机制的多智能体架构，以及专门设计的检索增强生成（RAG）流程，实现了高成功率和可靠性。在包含多种PIC脚本任务的基准测试中，实验结果表明，PICopilot成功完成了全部48项任务，且优于其他基于LLM的方法，不会产生显著的额外延迟或成本，甚至比配备通用RAG流程的先进GPT-5模型多解决21项任务。
英文摘要
The rapid development of photonic integrated circuits (PICs) is shifting the design flow from traditional graphical user interface (GUI)-based methods to script-based methods for higher flexibility, portability, and maintainability. However, script-based design introduces new challenges, requiring designers to possess additional proficiency in tool application programming interfaces (APIs) and programming. It also demands greater effort and time because it is inherently less intuitive and more complex than GUI-based methods. As PICs grow in scale and complexity, the productivity gap between design needs and manual scripting capabilities continues to widen. To address this gap, we introduce PICopilot, the first large language model (LLM)-based agentic framework that assists in PIC design via automated design script generation from natural language instructions. PICopilot leverages a multi-agent architecture with a feedback mechanism and a specifically designed retrieval-augmented generation (RAG) pipeline, achieving a high success rate and reliability. Experimental results on a benchmark of diverse PIC scripting tasks demonstrate that PICopilot successfully completes all 48 tasks and outperforms other LLM-based approaches without incurring substantial extra latency or cost, even solving 21 more tasks than the advanced GPT-5 model with a general RAG pipeline.
发表机构
- The Hong Kong University of Science and Technology（香港科技大学）
- The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
机构由 AI 辅助整理，请以论文原文为准。
