构建更完整电路图数据集，用AI分割提升网表识别准确率
AMSnet 2.0: A Large AMS Database with AI Segmentation for Net Detection
- 基于图像分割实现鲁棒的电路网络检测，无需依赖人工规则
- 新数据集含2686个电路，支持位置信息与数字重建
- 适合芯片设计、AI辅助电路分析的研究者使用
当前多模态大模型因识图能力不足，难以理解电路原理图。这主要源于高质量原理图-网表训练数据的缺乏。现有方法如AMSnet虽可解析原理图生成网表，但依赖硬编码规则，难处理复杂或含噪图。本文提出一种基于分割的新型网络检测机制，具备高鲁棒性，并能恢复元件与连线的位置信息，支持电路的数字化重建。在此基础上，我们整合多源图数据，构建了新版AMSnet 2.0数据集。该数据集包含2,686个电路，提供原理图图像、Spectre格式网表、OpenAccess数字版图及元件与网线的位置信息；相较原AMSnet仅有的792个电路与SPICE网表，显著提升规模与可用性。
原文摘要 · Abstract (English)
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.
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