用AI自动检查电路图连接是否符合元件规格，避免返工
DRCY: Agentic Hardware Design Reviews
- 多智能体系统自动获取元件数据手册并逐针比对
- 在真实设计平台部署，支持汽车到航天级项目
- 通过多轮共识提升关键设计分析的可靠性
硬件设计错误在制造后发现会导致成本高昂的物理返工，使产品推迟数月。现有电子设计自动化（EDA）工具仅能验证结构连接性，无法确保连接在语义上正确——例如引脚定义是否匹配制造商规格，或电压调节器反馈电阻是否产生预期输出。我们提出DRCY，首个可投入生产的多智能体大模型系统，能自主获取元件数据手册，执行逐针分析，并将发现以行内评论形式反馈至设计评审中。DRCY已部署于AllSpice Hub协作硬件设计平台，作为提交设计评审时触发的CI/CD流程运行。目前被多家大型硬件公司用于从多智能体车辆设计到太空探索等场景。本文详述DRCY五智能体流水线架构、具备自评估能力的智能数据手册检索系统，以及提升安全关键分析可靠性的多轮共识机制。
原文摘要 · Abstract (English)
Hardware design errors discovered after fabrication require costly physical respins that can delay products by months. Existing electronic design automation (EDA) tools enforce structural connectivity rules. However, they cannot verify that connections are \emph{semantically} correct with respect to component datasheets. For example, that a symbol's pinout matches the manufacturer's specification, or that a voltage regulator's feedback resistors produce the intended output. We present DRCY, the first production-ready multi-agent LLM system that automates first-pass schematic connection review by autonomously fetching component datasheets, performing pin-by-pin analysis against extracted specifications, and posting findings as inline comments on design reviews. DRCY is deployed in production on AllSpice Hub, a collaborative hardware design platform, where it runs as a CI/CD action triggered on design review submissions. DRCY is used regularly by major hardware companies for use-cases ranging from multi-agent vehicle design to space exploration. We describe DRCY's five-agent pipeline architecture, its agentic datasheet retrieval system with self-evaluation, and its multi-run consensus mechanism for improving reliability on safety-critical analyses
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