LLM Agents Tackle Analog Circuit Design with ATLAS Framework

Researchers have developed a new framework called ATLAS that leverages Large Language Models (LLMs) for analog Electronic Design Automation (EDA). This multi-step agentic framework is designed to generate functional Successive Approximation Register (SAR) Analog-to-Digital Converter (ADC) circuits that can pass rigorous SPICE simulations. By grounding LLMs with expert knowledge for planning, selection, parameterization, and iterative modification, ATLAS aims to overcome the limitations of direct LLM prompting in analog design, which often results in hallucinations and failed simulations. AI

IMPACT This research demonstrates a novel application of LLM agents for complex engineering tasks, potentially accelerating the design process for specialized electronic components.

RANK_REASON The cluster contains an academic paper detailing a new methodology for AI-assisted analog circuit design. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• Dimple Vijay Kochar
• large-language models
• SAR ADC
• Spice
• Successive Approximation Register Analog-to-Digital Converter circuit
• Template-Constrained Generation

• paper
• product

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