将电路图转为网表，让大模型能更好设计模拟集成电路。
Image2Net: Datasets, Benchmark and Hybrid Framework to Convert Analog Circuit Diagrams into Netlists
- 构建混合框架Image2Net，结合图像识别与规则推理转换电路图。
- 在新数据集上成功率达80.77%，优于之前方法34.62%-45.19%。
- 首次引入网表编辑距离评估，适合芯片设计与AI辅助工程研究者。
大型语言模型（LLM）在模拟集成电路（IC）设计中展现出巨大潜力，因其具备出色的抽象与知识泛化能力。然而，基于LLM的模拟IC发展仍受限于文本形式的描述，而现有模拟IC多以图像形式的电路图呈现，而非文本网表。将电路图转化为网表可丰富LLM对模拟IC的认知。但以往转换框架因图像风格和电路元件支持有限，难以进一步应用。为此，本文构建并开源了一个包含多种电路图风格、简单与复杂模拟IC均衡分布的新数据集，并提出名为Image2Net的混合转换框架，实现从电路图到网表的实用化转换。同时引入网表编辑距离（NED）精准评估转换结果与真实网表间的差异。在本基准测试中，Image2Net取得80.77%的成功率，比先前工作高出34.62%-45.19%；平均NED为0.116，较现有最优方法降低62.1%-69.6%。相关数据集与基准测试代码已公开于https://github.com/LAD021/ci2n_datasets。
原文摘要 · Abstract (English)
Large Language Model (LLM) exhibits great potential in designing of analog integrated circuits (IC) because of its excellence in abstraction and generalization for knowledge. However, further development of LLM-based analog ICs heavily relies on textual description of analog ICs, while existing analog ICs are mostly illustrated in image-based circuit diagrams rather than text-based netlists. Converting circuit diagrams to netlists help LLMs to enrich the knowledge of analog IC. Nevertheless, previously proposed conversion frameworks face challenges in further application because of limited support of image styles and circuit elements. Up to now, it still remains a challenging task to effectively convert complex circuit diagrams into netlists. To this end, this paper constructs and opensources a new dataset with rich styles of circuit diagrams as well as balanced distribution of simple and complex analog ICs. And a hybrid framework, named Image2Net, is proposed for practical conversion from circuit diagrams to netlists. The netlist edit distance (NED) is also introduced to precisely assess the difference between the converted netlists and ground truth. Based on our benchmark, Image2Net achieves 80.77% successful rate, which is 34.62%-45.19% higher than previous works. Specifically, the proposed work shows 0.116 averaged NED, which is 62.1%-69.6% lower than state-of-the-arts. Our datasets and benchmark are available at https://github.com/LAD021/ci2n_datasets.
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