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

Bingyang Liu, Haoyi Zhang, Xiaohan Gao, Zichen Kong, Xiyuan Tang, Yibo Lin, Runsheng Wang, Ru Huang

TL;DR

The paper tackles usability barriers in analog layout design by bridging natural language with executable layout-tool commands using a multi-agent LLM framework. LayoutCopilot employs a two-stage Abstract/Concrete Request Processor along with four specialized agents, guided by prompt engineering and a shared knowledge base, to translate designer intents into precise commands while offering actionable design suggestions. Experimental results across GPT-3.5, GPT-4, and Claude-3 on OTA and COMP scenarios demonstrate high classification accuracy, strong sanity and functionality checks, and meaningful post-layout performance improvements, validating the approach’s practicality. The work suggests LayoutCopilot can reduce the learning curve for interactive EDA tools, enhance design efficiency, and broaden adoption of automated supports for analog circuit layout.

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.

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

TL;DR

The paper tackles usability barriers in analog layout design by bridging natural language with executable layout-tool commands using a multi-agent LLM framework. LayoutCopilot employs a two-stage Abstract/Concrete Request Processor along with four specialized agents, guided by prompt engineering and a shared knowledge base, to translate designer intents into precise commands while offering actionable design suggestions. Experimental results across GPT-3.5, GPT-4, and Claude-3 on OTA and COMP scenarios demonstrate high classification accuracy, strong sanity and functionality checks, and meaningful post-layout performance improvements, validating the approach’s practicality. The work suggests LayoutCopilot can reduce the learning curve for interactive EDA tools, enhance design efficiency, and broaden adoption of automated supports for analog circuit layout.

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.
Paper Structure (28 sections, 10 figures, 5 tables)

This paper contains 28 sections, 10 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Preliminaries
  3. Integrating LLMs into EDA Applications
  4. Prompt Engineering
  5. Introduction to Prompt Engineering
  6. Application and Advanced Techniques
  7. Automatic Prompt Generation and Retrieval-Augmented Generation (RAG)
  8. Multi-Agent Collaboration with LLMs
  9. Interactive Analog Layout Design
  10. The Scope of LayoutCopilot
  11. LayoutCopilot Framework
  12. Abstract Request Processor
  13. Classifier Agent
  14. Analyzer Agent
  15. Solution Refiner Agent
  16. ...and 13 more sections

Figures (10)

  • Figure 1: Comparison of workflows in analog layout automation, highlighting key advantages and disadvantages of manual, fully automated, interactive, and LLM-powered interactive tools (LayoutCopilot).
  • Figure 2: System comparison: overcoming single agent limitations through multi-agent collaboration.
  • Figure 3: Illustration of LayoutCopilot's functionalities.
  • Figure 4: Overview of LayoutCopilot: a multi-agent framework that interprets natural language design intents through abstract and concrete request processors, coordinating agents to execute precise layout adjustments for interactive placement and routing.
  • Figure 5: Illustration of the configuration for a single agent in LayoutCopilot.
  • ...and 5 more figures