PCBnet: A Dataset and Automatic Construction of SPICE Netlists from Schematic Images
Computer Vision and Pattern RecognitionArtificial Intelligence
Summary
The authors created PCBnet, a big collection of over 300 real printed circuit board (PCB) designs with detailed labels for parts and connections. This dataset helps train AI to better understand complex PCB drawings, which are usually hard because of many parts, wires, and messy text. They also built a system that reads PCB images, figures out how parts connect, and fixes mistakes using expert knowledge. Their method accurately detects parts, reads text, and maps connections, making PCBnet a useful tool for improving AI-based PCB design.
Authors
Zhen Huang, Yuhao Gao, Yuzhi Liu, Daian Cheng, Chengyuan Shao, Yucheng Chen, Yongjian Jia, Futing Zhang, Yichen Shi, Wenhao Wang, Zuyan He, Yangbo Wei, Zhanfei Chen, Jinlong Yan, Yu Zhang, Haoying Wu, Ting-Jung Lin, Lei He
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.