Printed Circuit Board (PCB) schematic design plays an essential role in all areas of electronic industries. Unlike prior works that focus on digital or analog circuits alone, PCB design must handle heterogeneous digital, analog, and power signals while adhering to real-world IC packages and pin constraints. Automated PCB schematic design remains unexplored due to the scarcity of open-source data and the absence of simulation-based verification. We introduce PCBSchemaGen, the first training-free framework for PCB schematic design that comprises LLM agent and Constraint-guided synthesis. Our approach makes three contributions: 1. an LLM-based code generation paradigm with iterative feedback with domain-specific prompts. 2. a verification framework leveraging a real-world IC datasheet derived Knowledge Graph (KG) and Subgraph Isomorphism encoding pin-role semantics and topological constraints. 3. an extensive experiment on 23 PCB schematic tasks spanning digital, analog, and power domains. Results demonstrate that PCBSchemaGen significantly improves design accuracy and computational efficiency.

翻译：印刷电路板（PCB）原理图设计在电子工业的各个领域均发挥着至关重要的作用。与以往仅关注数字或模拟电路的研究不同，PCB设计必须处理异构的数字、模拟及电源信号，同时遵循现实世界中的集成电路封装与引脚约束。由于开源数据的匮乏以及缺乏基于仿真的验证方法，自动化PCB原理图设计领域仍未被充分探索。我们提出了PCBSchemaGen，这是首个无需训练的PCB原理图设计框架，它包含大语言模型（LLM）智能体与约束引导的综合生成。我们的方法做出了三项贡献：1. 一种基于LLM的代码生成范式，通过领域特定的提示进行迭代反馈。2. 一个验证框架，利用从真实集成电路数据手册中构建的知识图谱（KG）以及子图同构算法，对引脚角色语义与拓扑约束进行编码。3. 在涵盖数字、模拟及电源领域的23项PCB原理图设计任务上进行了广泛的实验。结果表明，PCBSchemaGen显著提升了设计准确性与计算效率。