发表机构
Eastern Institute of Technology; Ningbo Institute of Digital Twin; University of Science and Technology of China; Wuhan University of Technology（东方理工大学; 宁波数字孪生研究院; 中国科学技术大学; 武汉理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究构建了含300余个真实PCB设计的PCBnet数据集，开发了结合视觉识别等的自动化原理图转网表流程，实现了高检测与连接准确率，为PCB设计自动化提供了基准。
AI 中文摘要
印刷电路板（PCBs）是现代电子系统的基础，但AI驱动的PCB设计自动化仍受限于缺乏大规模配对的原理图-网表数据集。PCB原理图因元件类型多样、布线拓扑复杂以及文本标注存在噪声而极具挑战性。为解决这一缺口，我们提出PCBnet，这是一个包含300余个真实设计的大规模PCB原理图数据集，带有引脚标注及配对的SPICE网表。该数据集包含超过50000个元件实例、150000条导线、100000个文本区域和400000个字符。我们还开发了一套自动化的原理图转网表流程，结合了视觉识别、拓扑构建以及领域知识引导的多智能体校正。所提方法实现了94.54%的元件检测mAP、98.57%的文本识别准确率和84.47%的端到端连接准确率。PCBnet为未来AI驱动的PCB设计自动化提供了基准和数据基础。
英文摘要
Printed circuit boards (PCBs) are fundamental to modern electronic systems, yet AI-driven PCB design automation remains constrained by the lack of large-scale paired schematic-netlist datasets. PCB schematics are particularly challenging due to diverse component types, complex wiring topologies, and noisy textual annotations. To address this gap, we present PCBnet, a large-scale PCB schematic dataset comprising over 300 real-world designs with annotated pins and paired SPICE netlists. It contains more than 50,000 component instances, 150,000 wires, 100,000 text regions, and 400,000 characters. We further develop an automated schematic-to-netlist pipeline that combines visual recognition, topology construction, and domain-knowledge-guided multi-agent correction. The proposed method achieves 94.54% component detection mAP, 98.57% text recognition accuracy, and 84.47% end-to-end connectivity accuracy. PCBnet provides a benchmark and data foundation for future AI-driven PCB design automation.
CommentsAccepted at the 2026 IEEE International Conference on LLM-Aided Design (ICLAD 2026)
