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

TL;DR

LayoutCopilot leverages LLMs to create a multi-agent framework that simplifies interactive analog layout design, making it more accessible, efficient, and user-friendly by translating natural language into actionable design commands.

Contribution

The paper introduces LayoutCopilot, a novel LLM-powered multi-agent framework that enhances usability and automation in analog layout design, addressing existing usability barriers.

Findings

Demonstrates improved efficiency in analog layout tasks.

Shows increased accessibility for designers with varying expertise.

Validates effectiveness on real-world analog designs.

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.