PCBnet: A Dataset and Automatic Construction of SPICE Netlists from Schematic Images

2026-08-28

AI Summary

为解决AI驱动的PCB设计自动化中缺乏大规模配对数据集的问题，通过构建包含300多个实际设计的PCBnet数据集，并开发自动化的原理图到网表转换流程。

Abstract

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.

Problem

• AI-driven PCB design automation
• large-scale paired schematic-netlist datasets
• diverse component types
• complex wiring topologies
• noisy textual annotations

Innovation

• PCBnet
• schematic-to-netlist
• visual recognition
• topology construction
• multi-agent correction