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SPICEPilot: Navigating SPICE Code Generation and Simulation with AI Guidance
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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.
Forward citations
Cited by 3 Pith papers
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NetlistBench: Evaluating LLM Reliability in SPICE Netlist Recognition and Manipulation
Using a deterministic canonical-circuit oracle, NetlistBench finds that LLM accuracy on SPICE netlist tasks drops sharply as structural complexity and edit horizon increase.
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DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining
A self-supervised graph encoder for device-level circuits improves prediction of circuit similarity, delays, and op-amp performance across analog and digital designs.
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PICBench: Benchmarking LLMs for Photonic Integrated Circuits Design
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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