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

arXiv: https://arxiv.org/abs/2508.13157 · HTML: https://mlsanity.com/paper/2508.13157/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 21 references that are in the map.
- First posted on arXiv 27 Jun 2025.

## Builds on (most cited first, 10 of 10)

- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [VerilogEval: Evaluating Large Language Models for Verilog Code Generation](https://mlsanity.com/paper/2309.07544/) (2023)
- [RTLLM: An Open-Source Benchmark for Design RTL Generation with Large Language Model](https://mlsanity.com/paper/2308.05345/) (2023)
- [Chip-Chat: Challenges and Opportunities in Conversational Hardware Design](https://mlsanity.com/paper/2305.13243/) (2023)
- [RTLFixer: Automatically Fixing RTL Syntax Errors with Large Language Models](https://mlsanity.com/paper/2311.16543/) (2023)
- [AutoChip: Automating HDL Generation Using LLM Feedback](https://mlsanity.com/paper/2311.04887/) (2023)
- [AmpAgent: An LLM-based Multi-Agent System for Multi-stage Amplifier Schematic Design from Literature for Process and Performance Porting](https://mlsanity.com/paper/2409.14739/) (2024)
- [MEIC: Re-thinking RTL Debug Automation using LLMs](https://mlsanity.com/paper/2405.06840/) (2024)
- [AMSNet: Netlist Dataset for AMS Circuits](https://mlsanity.com/paper/2405.09045/) (2024)
- [Masala-CHAI: A Large-Scale SPICE Netlist Dataset for Analog Circuits by Harnessing AI](https://mlsanity.com/paper/2411.14299/) (2024)

## Built on by (most cited first, 7 of 7)

- [TopoSizing: An LLM-aided Framework of Topology-based Understanding and Sizing for AMS Circuits](https://mlsanity.com/paper/2509.14169/) (2025)
- [HeaRT: A Hierarchical Circuit Reasoning Tree-Based Agentic Framework for AMS Design Optimization](https://mlsanity.com/paper/2511.19669/) (2025)
- [OmniSch: A Multimodal PCB Schematic Benchmark For Structured Diagram Visual Reasoning](https://mlsanity.com/paper/2604.00270/) (2026)
- [AnalogRetriever: Learning Cross-Modal Representations for Analog Circuit Retrieval](https://mlsanity.com/paper/2604.23195/) (2026)
- [NetlistBench: Evaluating LLM Reliability in SPICE Netlist Recognition and Manipulation](https://mlsanity.com/paper/2608.12197/) (2026)
- [DiagramNet: An End-to-End Recognition Framework and Dataset for Non-Standard System-Level Diagrams](https://mlsanity.com/paper/2605.01338/) (2026)
- [SchGen: PCB Schematic Generation with Semantic-Grounded Code Representations](https://mlsanity.com/paper/2605.30345/) (2026)

## Abstract (arXiv)

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.