# AMSnet 2.0: A Large AMS Database with AI Segmentation for Net Detection

arXiv: https://arxiv.org/abs/2505.09155 · HTML: https://mlsanity.com/paper/2505.09155/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Placed in Chart understanding and anomaly detection, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 11 of its 17 references that are in the map.
- First posted on arXiv 14 May 2025.

## Placed in

- Multimodal LLMs / vision-language models / [Chart understanding and anomaly detection](https://mlsanity.com/mllm/chart-understanding-and-anomaly-detection/)

## Builds on (most cited first, 11 of 11)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [YOLACT: Real-time Instance Segmentation](https://mlsanity.com/paper/1904.02689/) (2019)
- [A Survey on Instance Segmentation: State of the art](https://mlsanity.com/paper/2007.00047/) (2020)
- [AnalogCoder: Analog Circuit Design via Training-Free Code Generation](https://mlsanity.com/paper/2405.14918/) (2024)
- [ADO-LLM: Analog Design Bayesian Optimization with In-Context Learning of Large Language Models](https://mlsanity.com/paper/2406.18770/) (2024)
- [AnalogGenie: A Generative Engine for Automatic Discovery of Analog Circuit Topologies](https://mlsanity.com/paper/2503.00205/) (2025)
- [LaMAGIC: Language-Model-based Topology Generation for Analog Integrated Circuits](https://mlsanity.com/paper/2407.18269/) (2024)
- [AMSNet: Netlist Dataset for AMS Circuits](https://mlsanity.com/paper/2405.09045/) (2024)
- [LayoutCopilot: An LLM-powered Multi-agent Collaborative Framework for Interactive Analog Layout Design](https://mlsanity.com/paper/2406.18873/) (2024)
- [AMSnet-KG: A Netlist Dataset for LLM-based AMS Circuit Auto-Design Using Knowledge Graph RAG](https://mlsanity.com/paper/2411.13560/) (2024)
- [DRC-Coder: Automated DRC Checker Code Generation Using LLM Autonomous Agent](https://mlsanity.com/paper/2412.05311/) (2024)

## Built on by (most cited first, 9 of 9)

- [OmniSch: A Multimodal PCB Schematic Benchmark For Structured Diagram Visual Reasoning](https://mlsanity.com/paper/2604.00270/) (2026)
- [Dataset Construction for Training LLM to Learn Analog Circuit Knowledge](https://mlsanity.com/paper/2508.10409/) (2025)
- [DiagramNet: An End-to-End Recognition Framework and Dataset for Non-Standard System-Level Diagrams](https://mlsanity.com/paper/2605.01338/) (2026)
- [AMSnet-q: Unsupervised Circuit Identification and Performance Labeling for AMS Circuits](https://mlsanity.com/paper/2605.01404/) (2026)
- [SPARC: A Multi-Agent System for Electrical Circuit Question Answering](https://mlsanity.com/paper/2606.20643/) (2026)
- [PCBnet: A Dataset and Automatic Construction of SPICE Netlists from Schematic Images](https://mlsanity.com/paper/2608.27923/) (2026)
- [Analog-DB: An Agent-First Analog Integrated Circuit Database, From Blocks to Systems](https://mlsanity.com/paper/2609.01286/) (2026)
- [Circuit-MLLM: Topological Logic-Guided Latent-Space Visual Reasoning for Circuit Schematic Understanding](https://mlsanity.com/paper/2609.15668/) (2026)
- [Validation and Simulation Catch Different Errors: Four Levels of Evaluation for LLM-Generated Circuits](https://mlsanity.com/paper/2609.26830/) (2026)

## Abstract (arXiv)

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.