AMSnet 2.0: A Large AMS Database with AI Segmentation for Net Detection
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arXiv
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| Format: | Preprint |
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2025
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| author | Shi, Yichen Tao, Zhuofu Gao, Yuhao Huang, Li Wang, Hongyang Yu, Zhiping Lin, Ting-Jung He, Lei |
| author_facet | Shi, Yichen Tao, Zhuofu Gao, Yuhao Huang, Li Wang, Hongyang Yu, Zhiping Lin, Ting-Jung He, Lei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_09155 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AMSnet 2.0: A Large AMS Database with AI Segmentation for Net Detection Shi, Yichen Tao, Zhuofu Gao, Yuhao Huang, Li Wang, Hongyang Yu, Zhiping Lin, Ting-Jung He, Lei Computer Vision and Pattern Recognition 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. |
| title | AMSnet 2.0: A Large AMS Database with AI Segmentation for Net Detection |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.09155 |