# LayoutCopilot: An LLM-powered Multi-agent Collaborative Framework for Interactive Analog Layout Design

arXiv: https://arxiv.org/abs/2406.18873 · HTML: https://mlsanity.com/paper/2406.18873/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jun 2024.

## Built on by (most cited first, 25 of 25)

- [A Survey of Research in Large Language Models for Electronic Design Automation](https://mlsanity.com/paper/2501.09655/) (2025)
- [LEDRO: LLM-Enhanced Design Space Reduction and Optimization for Analog Circuits](https://mlsanity.com/paper/2411.12930/) (2024)
- [SPICEPilot: Navigating SPICE Code Generation and Simulation with AI Guidance](https://mlsanity.com/paper/2410.20553/) (2024)
- [AMSnet 2.0: A Large AMS Database with AI Segmentation for Net Detection](https://mlsanity.com/paper/2505.09155/) (2025)
- [MenTeR: A fully-automated Multi-agenT workflow for end-to-end RF/Analog Circuits Netlist Design](https://mlsanity.com/paper/2505.22990/) (2025)
- [FALCON: An ML Framework for Fully Automated Layout-Constrained Analog Circuit Design](https://mlsanity.com/paper/2505.21923/) (2025)
- [Bias in the Loop: How Humans Evaluate AI-Generated Suggestions](https://mlsanity.com/paper/2509.08514/) (2025)
- [Timing Analysis Agent: Autonomous Multi-Corner Multi-Mode (MCMM) Timing Debugging with Timing Debug Relation Graph](https://mlsanity.com/paper/2504.11502/) (2025)
- [Large Language Models (LLMs) for Electronic Design Automation (EDA)](https://mlsanity.com/paper/2508.20030/) (2025)
- [CktGen: Automated Analog Circuit Design with Generative Artificial Intelligence](https://mlsanity.com/paper/2410.00995/) (2024)
- [SANGAM: SystemVerilog Assertion Generation via Monte Carlo Tree Self-Refine](https://mlsanity.com/paper/2506.13983/) (2025)
- [AgenticTCAD: A LLM-based Multi-Agent Framework for Automated TCAD Code Generation and Device Optimization](https://mlsanity.com/paper/2512.23742/) (2025)
- [PANDA: An LLM-Enhanced Performance-Driven Analog Design Framework Bridging Design Intent and Layout Generation](https://mlsanity.com/paper/2606.15052/) (2026)
- [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)
- [Foundation-Model-Based Agents in Industrial Automation: Purposes, Capabilities, and Open Challenges](https://mlsanity.com/paper/2605.02592/) (2026)
- [AgenticECO: An Agentic Framework for ECO on 3D Integrated Circuits](https://mlsanity.com/paper/2608.03738/) (2026)
- [ZhuLong: Execution-Grounded LLM Agent for EDA Scripting with Offline API Self-Exploration](https://mlsanity.com/paper/2608.07925/) (2026)
- [PCEval: A Benchmark for Evaluating Physical Computing Capabilities of Large Language Models](https://mlsanity.com/paper/2601.02404/) (2025)
- [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)
- [Agentic Electronic Design Automation: A Handoff Perspective](https://mlsanity.com/paper/2606.19795/) (2026)
- [Simulation-Aware In-Context Policy Improvement for LLM-Aided Analog Layout Refinement](https://mlsanity.com/paper/2608.13767/) (2026)
- [PICasso: An AI-Enabled Design Framework for Autonomous Optimization of Silicon Photonic Devices](https://mlsanity.com/paper/2608.26113/) (2026)
- [2d-fet-bench: from spatial reasoning to fet design on flakes](https://mlsanity.com/paper/2610.07423/) (2026)

## Abstract (arXiv)

Analog layout design heavily involves interactive processes between humans and design tools. Electronic Design Automation (EDA) tools for this task are usually designed to use scripting commands or visualized buttons for manipulation, especially for interactive automation functionalities, which have a steep learning curve and cumbersome user experience, making a notable barrier to designers' adoption. Aiming to address such a usability issue, this paper introduces LayoutCopilot, a pioneering multi-agent collaborative framework powered by Large Language Models (LLMs) for interactive analog layout design. LayoutCopilot simplifies human-tool interaction by converting natural language instructions into executable script commands, and it interprets high-level design intents into actionable suggestions, significantly streamlining the design process. Experimental results demonstrate the flexibility, efficiency, and accessibility of LayoutCopilot in handling real-world analog designs.