发表机构
Massachusetts Institute of Technology（麻省理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对大语言模型在模拟电子设计自动化应用的瓶颈，提出端到端多步骤的ATLAS框架，利用专家知识结合模板约束生成，能生成成功通过仿真验证的SAR ADC，为集成LLMs到可靠模拟设计方法奠定基础。
AI 中文摘要
虽然大语言模型（LLMs）在软件代码生成方面展现出显著能力，但其在模拟电子设计自动化（EDA）中的应用存在瓶颈。由于对电路拓扑的理解和数据有限，直接提示LLMs和多模态模型会导致幻觉，无法生成能通过严格SPICE仿真的原理图。为此，我们提出了一个端到端、多步骤的LLM智能体框架ATLAS，它能生成功能正常且成功通过仿真验证的逐次逼近寄存器型（SAR）模数转换器（ADC）。为遵循模拟设计的严格约束，我们利用专家知识在规划、选择、参数化和迭代修改等方面对LLM进行指导。作为ATLAS的一部分，我们引入了模板约束生成，它不同于其他基于模板的工作，致力于构建更通用的SAR ADC生成流程。我们通过在不同技术节点和输入规格下开发SAR ADC，展示了该框架强大的概念验证。总体而言，我们基于专家知识的多步骤智能体ATLAS为将LLMs集成到可靠的模拟设计方法中奠定了实用基础。
英文摘要
While Large Language Models (LLMs) have demonstrated significant capability in software code generation, their application to analog Electronic Design Automation (EDA) is bottlenecked. Owing to limited circuit topology understanding and data, directly prompting LLMs and multimodal models leads to hallucinations and failure to produce schematics capable of passing rigorous SPICE simulations, as we show in our work. Instead, we propose an end-to-end, multi-step LLM agentic framework ATLAS, capable of generating a functional Successive Approximation Register (SAR) Analog-to-Digital Converter (ADC) that successfully passes simulation validation. To adhere to the rigid constraints of analog design, we utilize expert knowledge to ground the LLM in its planning, selection, parameterization, and iterative modification. As part of ATLAS, we introduce Template-Constrained Generation - which unlike other template-based works - builds towards a more generalized SAR ADC generation flow. We demonstrate a strong proof-of-concept of our framework by developing SAR ADCs across technology nodes and input specs. Overall, our expert-knowledge grounded multi-step agentic ATLAS establishes a pragmatic foundation for integrating LLMs into reliable analog design methodologies.
