# AmpAgent: An LLM-based Multi-Agent System for Multi-stage Amplifier Schematic Design from Literature for Process and Performance Porting

arXiv: https://arxiv.org/abs/2409.14739 · HTML: https://mlsanity.com/paper/2409.14739/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Sep 2024.

## Built on by (most cited first, 29 of 29)

- [LEDRO: LLM-Enhanced Design Space Reduction and Optimization for Analog Circuits](https://mlsanity.com/paper/2411.12930/) (2024)
- [ControlAgent: Automating Control System Design via Novel Integration of LLM Agents and Domain Expertise](https://mlsanity.com/paper/2410.19811/) (2024)
- [AMSbench: A Comprehensive Benchmark for Evaluating MLLM Capabilities in AMS Circuits](https://mlsanity.com/paper/2505.24138/) (2025)
- [AnalogCoder-Pro: Unifying Analog Circuit Generation and Optimization via Multi-modal LLMs](https://mlsanity.com/paper/2508.02518/) (2025)
- [EEsizer: LLM-Based AI Agent for Sizing of Analog and Mixed Signal Circuit](https://mlsanity.com/paper/2509.25510/) (2025)
- [SPICEPilot: Navigating SPICE Code Generation and Simulation with AI Guidance](https://mlsanity.com/paper/2410.20553/) (2024)
- [MenTeR: A fully-automated Multi-agenT workflow for end-to-end RF/Analog Circuits Netlist Design](https://mlsanity.com/paper/2505.22990/) (2025)
- [Image2Net: Datasets, Benchmark and Hybrid Framework to Convert Analog Circuit Diagrams into Netlists](https://mlsanity.com/paper/2508.13157/) (2025)
- [TopoSizing: An LLM-aided Framework of Topology-based Understanding and Sizing for AMS Circuits](https://mlsanity.com/paper/2509.14169/) (2025)
- [LLM-based AI Agent for Sizing of Analog and Mixed Signal Circuit](https://mlsanity.com/paper/2504.11497/) (2025)
- [MMCircuitEval: A Comprehensive Multimodal Circuit-Focused Benchmark for Evaluating LLMs](https://mlsanity.com/paper/2507.19525/) (2025)
- [AutoSizer: Automatic Sizing of Analog and Mixed-Signal Circuits via Large Language Model (LLM) Agents](https://mlsanity.com/paper/2602.02849/) (2026)
- [A Large Language Model-based Multi-Agent Framework for Analog Circuits' Sizing Relationships Extraction](https://mlsanity.com/paper/2506.18424/) (2025)
- [CktGen: Automated Analog Circuit Design with Generative Artificial Intelligence](https://mlsanity.com/paper/2410.00995/) (2024)
- [Evaluating LLM-based Workflows for Switched-Mode Power Supply Design](https://mlsanity.com/paper/2507.10639/) (2025)
- [EEschematic: Multimodal-LLM Based AI Agent for Schematic Generation of Analog Circuit](https://mlsanity.com/paper/2510.17002/) (2025)
- [CircuitFormer: A Circuit Language Model for Analog Topology Design from Natural Language Prompt](https://mlsanity.com/paper/2605.05773/) (2026)
- [Emerging ML-AI Techniques for Analog and RF EDA](https://mlsanity.com/paper/2506.00007/) (2025)
- [Dataset Construction for Training LLM to Learn Analog Circuit Knowledge](https://mlsanity.com/paper/2508.10409/) (2025)
- [Can an Actor-Critic Optimization Framework Improve Analog Design?](https://mlsanity.com/paper/2603.24714/) (2026)
- [RF-Agent: A Practical Framework for Building Language Agents for RFIC Design](https://mlsanity.com/paper/2607.18772/) (2026)
- [VLM-CAD: VLM-Optimized Collaborative Agent Design Workflow for Analog Circuit Sizing](https://mlsanity.com/paper/2601.07315/) (2026)
- [LLM-Assisted Op-Amp Behavioral-Level Design via Agentic Human-Mimicking Reasoning](https://mlsanity.com/paper/2601.21321/) (2026)
- [A Self-Calibrating Framework for Analog Circuit Sizing Using LLM-Derived Analytical Equations](https://mlsanity.com/paper/2604.07387/) (2026)
- [Surveying GenAI-based Automation in Printed Circuit Board Design and Test](https://mlsanity.com/paper/2606.17074/) (2026)
- [Towards Reliable AI-Assisted Analog Design: Template-Constrained LLM Agents for SAR ADC Generation](https://mlsanity.com/paper/2607.14165/) (2026)
- [AaLLM: An End-to-End Analog Circuit Design Framework from Topology Generation to Sizing Using Large Language Models](https://mlsanity.com/paper/2608.13472/) (2026)
- [AgenticSizing: A Large Language Model-based Multi-Agent Framework for Analog Circuit Sizing](https://mlsanity.com/paper/2609.25873/) (2026)
- [Long-Horizon Analog Design Bench: Benchmarking Agents on Hours-Long Analog and Mixed-Signal Circuit Design Tasks](https://mlsanity.com/paper/2609.33356/) (2026)

## Abstract (arXiv)

Multi-stage amplifiers are widely applied in analog circuits. However, their large number of components, complex transfer functions, and intricate pole-zero distributions necessitate extensive manpower for derivation and param sizing to ensure their stability. In order to achieve efficient derivation of the transfer function and simplify the difficulty of circuit design, we propose AmpAgent: a multi-agent system based on large language models (LLMs) for efficiently designing such complex amplifiers from literature with process and performance porting. AmpAgent is composed of three agents: Literature Analysis Agent, Mathematics Reasoning Agent and Device Sizing Agent. They are separately responsible for retrieving key information (e.g. formulas and transfer functions) from the literature, decompose the whole circuit's design problem by deriving the key formulas, and address the decomposed problem iteratively. AmpAgent was employed in the schematic design of seven types of multi-stage amplifiers with different compensation techniques. In terms of design efficiency, AmpAgent has reduced the number of iterations by 1.32 ~ 4× and execution time by 1.19 ~ 2.99× compared to conventional optimization algorithms, with a success rate increased by 1.03 ~ 6.79×. In terms of circuit performance, it has improved by 1.63 ~ 27.25× compared to the original literature. The findings suggest that LLMs could play a crucial role in the field of complex analog circuit schematic design, as well as process and performance porting.