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

• URL: http://arxiv.org/abs/2608.27923v1
• Date: Fri, 28 Aug 2026 04:58:03 GMT
• Title: PCBnet: A Dataset and Automatic Construction of SPICE Netlists from Schematic Images
• Abstract summary: PCBnet is a large-scale schematic dataset comprising over 300 real-world designs with annotated pins and paired SPICE netlists.<n>It contains more than 50,000 component instances, 150,000 wires, 100,000 text regions, and 400,000 characters.<n>We develop an automated schematic-to-netlist pipeline that combines visual recognition, topology construction, and domain-knowledge-guided multi-agent correction.
• Score: 12.081285864366889
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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