Automatic HEMT Device Model Extraction from Document Using AI Agents

This study proposes an automated extraction method for HEMT device modeling from technical documents using AI - agent - based models. A data stream - based automation method is proposed, enabling direct HEMT model generation from datasheets. The system includes document parsing, graphical data extraction, parameter extraction using a Chain - of - Thought (CoT) prompting strategy, and SPICE modeling. The ASM - HEMT model, a physics - based compact model, accurately describes GaN HEMTs' properties. The proposed data stream employs EDocDLA for document parsing, EDocCurve for curve extraction, and integrates LLMs and optimization techniques for parameter extraction and model optimization. Experiments on the FHX76LP datasheet demonstrate the data stream's effectiveness. The generated SPICE model has a fitting error below 3% compared to the original data. This automated method significantly enhances modeling efficiency and accuracy, providing an efficient pathway for transforming technical documents into precise device models.