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SPICEPilot: Navigating SPICE Code Generation and Simulation with AI Guidance

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arxiv 2410.20553 v1 pith:TYKW7E5H submitted 2024-10-27 cs.AR cs.AI

classification cs.ARcs.AI
keywords codegenerationspicespicepilotllmsabilityautomatingcircuit
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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.

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Forward citations

Cited by 3 Pith papers

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

  1. NetlistBench: Evaluating LLM Reliability in SPICE Netlist Recognition and Manipulation

    eess.SY 2026-08 accept novelty 7.0 of 10

    Using a deterministic canonical-circuit oracle, NetlistBench finds that LLM accuracy on SPICE netlist tasks drops sharply as structural complexity and edit horizon increase.

  2. DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining

    cs.LG 2025-02 conditional novelty 6.0 of 10

    A self-supervised graph encoder for device-level circuits improves prediction of circuit similarity, delays, and op-amp performance across analog and digital designs.

  3. PICBench: Benchmarking LLMs for Photonic Integrated Circuits Design

    cs.LG 2025-02 conditional novelty 6.0 of 10

    PICBench evaluates how well LLMs can generate photonic integrated circuit netlists and shows that simulator-driven error feedback greatly improves their syntax and functional correctness.

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