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

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arxiv 2406.18873 v3 pith:WBIYE7GI submitted 2024-06-27 cs.AR

classification cs.AR
keywords designanaloginteractivelayoutcopilotlayoutautomationcollaborativecommands
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. LEDRO: LLM-Enhanced Design Space Reduction and Optimization for Analog Circuits

    cs.LG 2024-11 conditional novelty 7.0 of 10

    LEDRO uses an LLM to propose refined parameter ranges, then runs TuRBO inside those ranges, beating full-space Bayesian optimization on a 22-topology, 4-node op-amp benchmark.

  2. A Survey of Research in Large Language Models for Electronic Design Automation

    cs.LG 2025-01 conditional novelty 2.0 of 10

    A survey of LLM applications in electronic design automation, organized by design stage and adaptation technique.

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