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

2024-06-27

arXiv.org

AI Summary

To address the high learning barrier and non-intuitive interaction of EDA tools in analog circuit layout design, this paper proposes the first LLM-based multi-agent collaborative framework tailored for EDA analog design. The framework integrates natural language understanding with domain-specific EDA knowledge to enable end-to-end mapping from high-level design intent to executable scripts, while supporting context-aware interactive suggestions. It achieves tight integration with industrial EDA toolchains—particularly Cadence Virtuoso—to establish a closed-loop automated layout system. Experimental evaluation demonstrates over 70% reduction in user interaction steps and a 55% decrease in task completion time for novice designers, significantly improving both design efficiency and usability. The core contribution lies in pioneering an EDA-specialized LLM multi-agent collaboration paradigm, overcoming the limitations of conventional command-line and GUI-based interactions.

Technology Category

• Multiagent Systems: Adversarial Agents
• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

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.

Problem

• Electronic Design Automation
• Analog Layout Design
• User Experience

Innovation

• LayoutCopilot
• Natural Language Commands
• Analog Circuit Layout