Image2Net: Datasets, Benchmark and Hybrid Framework to Convert Analog Circuit Diagrams into Netlists

TL;DR

This paper introduces Image2Net, a hybrid framework and dataset for converting complex analog circuit diagrams into netlists, significantly improving accuracy and supporting diverse styles to aid LLM-based analog IC design.

Contribution

It presents a new dataset with diverse circuit diagram styles and a hybrid conversion framework that outperforms previous methods in accuracy and robustness.

Findings

Achieves 80.77% successful conversion rate, 34.62%-45.19% higher than prior methods.

Introduces netlist edit distance (NED) for precise assessment of conversion quality.

Provides open-source datasets and benchmarks for future research.

Abstract

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.