AnalogMaster: Large Language Model-based Automated Analog IC Design Framework from Image to Layout

2026-04-21

AI Summary

This work proposes the first end-to-end analog integrated circuit (IC) design automation framework powered by large language models (LLMs), addressing the limitations of existing tools that are typically confined to isolated design stages and rely heavily on manual intervention, particularly when handling unstructured inputs such as circuit schematic images. The proposed framework spans the entire design flow—from schematic image understanding and netlist generation to parameter optimization and placement routing—by integrating in-context learning with intent reasoning to achieve high-fidelity image-to-netlist translation. It further introduces self-augmented prompting and context truncation strategies to construct an efficient parameter search agent. Evaluated on 15 circuits of varying complexity, the framework achieves Pass@1 and Pass@5 success rates of 92.9% and 99.9%, respectively, using GPT-5, substantially outperforming current state-of-the-art methods.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Planning, Routing, and Scheduling: Planning with Language Models
• Natural Language Processing: (Large) Language Models

Application Category

• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Design automation has the potential to substantially improve the efficiency of analog integrated circuit (IC) design. However, existing algorithms and tools typically focus on individual stages, such as device sizing, placement, or routing, and still require significant manual intervention to complete the full design flow. While large language models (LLMs) have recently demonstrated remarkable success in automating digital IC design workflows, these advances cannot be directly transferred to analog IC design. Key challenges include strongly coupled performance metrics, the predominance of unstructured circuit schematic images, and the fact that most prior approaches address only isolated stages of the analog design process, limiting their ability to capture end-to-end performance impact. To address these challenges, we propose AnalogMaster, an extensible, LLM-based framework that enables end-to-end automation of analog IC design through a unified pipeline spanning circuit image-to-netlist generation, parameter optimization, placement, and routing. AnalogMaster integrates a joint reasoning mechanism that leverages in-context learning and intent reasoning to achieve accurate and robust image-to-netlist conversion. A parameter search agent integrating self-enhanced prompt engineering and context truncation is developed for effective device sizing and downstream physical design. Experimental evaluations on 15 representative circuits with varying levels of complexity demonstrate strong and consistent performance across multiple models. In particular, GPT-5 achieves success rates of 92.9% and 99.9% on Pass@1 and Pass@5, respectively. These results validate the effectiveness and robustness of the proposed framework and establish a practical paradigm for applying LLMs to full-stack analog IC design automation.

Problem

• analog IC design
• design automation
• end-to-end automation
• circuit schematic image
• performance coupling

Innovation

• Large Language Models
• Analog IC Design Automation
• Image-to-Netlist Conversion
• End-to-End Design
• Parameter Optimization