Large Language Models (LLMs) have shown great potential in automating code generation; however, their ability to generate accurate circuit-level SPICE code remains limited due to a lack of hardware-specific knowledge. In this paper, we analyze and identify the typical limitations of existing LLMs in SPICE code generation. To address these limitations, we present SPICEPilot a novel Python-based dataset generated using PySpice, along with its accompanying framework. This marks a significant step forward in automating SPICE code generation across various circuit configurations. Our framework automates the creation of SPICE simulation scripts, introduces standardized benchmarking metrics to evaluate LLM's ability for circuit generation, and outlines a roadmap for integrating LLMs into the hardware design process. SPICEPilot is open-sourced under the permissive MIT license at https://github.com/ACADLab/SPICEPilot.git.

翻译：大型语言模型（LLMs）在自动化代码生成方面展现出巨大潜力；然而，由于缺乏硬件特定知识，其生成精确电路级SPICE代码的能力仍然有限。本文分析并识别了现有LLMs在SPICE代码生成中的典型局限性。为应对这些局限，我们提出了SPICEPilot——一个基于Python、利用PySpice生成的新型数据集及其配套框架。这标志着在跨多种电路配置的SPICE代码生成自动化方面迈出了重要一步。我们的框架实现了SPICE仿真脚本的自动创建，引入了标准化基准指标以评估LLMs的电路生成能力，并规划了将LLMs集成到硬件设计流程的路线图。SPICEPilot已在MIT许可下开源，项目地址为https://github.com/ACADLab/SPICEPilot.git。