# LLM-based AI Agent for Sizing of Analog and Mixed Signal Circuit

arXiv: https://arxiv.org/abs/2504.11497 · HTML: https://mlsanity.com/paper/2504.11497/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 7 of its 18 references that are in the map.
- First posted on arXiv 14 Apr 2025.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 7 of 7)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [AutoCkt: Deep Reinforcement Learning of Analog Circuit Designs](https://mlsanity.com/paper/2001.01808/) (2020)
- [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)
- [LEDRO: LLM-Enhanced Design Space Reduction and Optimization for Analog Circuits](https://mlsanity.com/paper/2411.12930/) (2024)

## Built on by (most cited first, 6 of 6)

- [EEsizer: LLM-Based AI Agent for Sizing of Analog and Mixed Signal Circuit](https://mlsanity.com/paper/2509.25510/) (2025)
- [Dataset Construction for Training LLM to Learn Analog Circuit Knowledge](https://mlsanity.com/paper/2508.10409/) (2025)
- [LLM-Assisted Op-Amp Behavioral-Level Design via Agentic Human-Mimicking Reasoning](https://mlsanity.com/paper/2601.21321/) (2026)
- [EM-Aware Physical Synthesis: Neural Inductor Modeling and Intelligent Placement & Routing for RF Circuits](https://mlsanity.com/paper/2602.11461/) (2026)
- [Surveying GenAI-based Automation in Printed Circuit Board Design and Test](https://mlsanity.com/paper/2606.17074/) (2026)
- [Simulation-Aware In-Context Policy Improvement for LLM-Aided Analog Layout Refinement](https://mlsanity.com/paper/2608.13767/) (2026)

## Abstract (arXiv)

The design of Analog and Mixed-Signal (AMS) integrated circuits (ICs) often involves significant manual effort, especially during the transistor sizing process. While Machine Learning techniques in Electronic Design Automation (EDA) have shown promise in reducing complexity and minimizing human intervention, they still face challenges such as numerous iterations and a lack of knowledge about AMS circuit design. Recently, Large Language Models (LLMs) have demonstrated significant potential across various fields, showing a certain level of knowledge in circuit design and indicating their potential to automate the transistor sizing process. In this work, we propose an LLM-based AI agent for AMS circuit design to assist in the sizing process. By integrating LLMs with external circuit simulation tools and data analysis functions and employing prompt engineering strategies, the agent successfully optimized multiple circuits to achieve target performance metrics. We evaluated the performance of different LLMs to assess their applicability and optimization effectiveness across seven basic circuits, and selected the best-performing model Claude 3.5 Sonnet for further exploration on an operational amplifier, with complementary input stage and class AB output stage. This circuit was evaluated against nine performance metrics, and we conducted experiments under three distinct performance requirement groups. A success rate of up to 60% was achieved for reaching the target requirements. Overall, this work demonstrates the potential of LLMs to improve AMS circuit design.