AMSnet 2.0: A Large AMS Database with AI Segmentation for Net Detection
This addresses the lack of high-quality training data for AI in electronic design automation, though it is incremental as it builds on existing work.
The authors tackled the problem of multimodal large language models struggling with circuit schematics by creating AMSnet 2.0, a dataset with 2,686 circuits including schematic images, netlists, digital schematics, and positional information, compared to the previous AMSnet with only 792 circuits.
Current multimodal large language models (MLLMs) struggle to understand circuit schematics due to their limited recognition capabilities. This could be attributed to the lack of high-quality schematic-netlist training data. Existing work such as AMSnet applies schematic parsing to generate netlists. However, these methods rely on hard-coded heuristics and are difficult to apply to complex or noisy schematics in this paper. We therefore propose a novel net detection mechanism based on segmentation with high robustness. The proposed method also recovers positional information, allowing digital reconstruction of schematics. We then expand AMSnet dataset with schematic images from various sources and create AMSnet 2.0. AMSnet 2.0 contains 2,686 circuits with schematic images, Spectre-formatted netlists, OpenAccess digital schematics, and positional information for circuit components and nets, whereas AMSnet only includes 792 circuits with SPICE netlists but no digital schematics.