Automated HEMT Model Construction from Datasheets via Multi-Modal Intelligence and Prior-Knowledge-Free Optimization
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866908470732652544 |
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| author | Peng, Yuang Zhong, Jiarui Zhang, Yang Chen, Hong Cai |
| author_facet | Peng, Yuang Zhong, Jiarui Zhang, Yang Chen, Hong Cai |
| contents | Parameter extraction for industry-standard device models like ASM-HEMT is crucial in circuit design workflows. However, many manufacturers do not provide such models, leaving users to build them using only datasheets. Unfortunately, datasheets lack sufficient information for standard step-by-step extraction. Moreover, manual data extraction from datasheets is highly time-consuming, and the absence of a fully automated method forces engineers to perform tedious manual work. To address this challenge, this paper introduces a novel, end-to-end framework that fully automates the generation of simulation-ready ASM-HEMT SPICE models directly from PDF datasheets. Our framework is founded on two core innovations: 1) a multi-modal AI pipeline that integrates computer vision with a large language model (LLM) to robustly parse heterogeneous datasheet layouts and digitize characteristic curves, and 2) a novel Iterative-Focusing Tree-structured Parzen Estimator (IF-TPE) optimization algorithm is specifically designed for device parameter extraction under the high-dimensional, sparse-data condition by adaptively refining the parameter search space. Experimental validation on a diverse set of 17 commercial HEMT devices from 10 manufacturers confirms the framework's accuracy and robustness. The generated models demonstrate excellent agreement with published DC and RF characteristics. As the first fully automated workflow of its kind, our proposed solution offers a transformative approach to device modeling, poised to significantly accelerate the circuit design cycle by eliminating the need for manual parameter extraction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_21430 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Automated HEMT Model Construction from Datasheets via Multi-Modal Intelligence and Prior-Knowledge-Free Optimization Peng, Yuang Zhong, Jiarui Zhang, Yang Chen, Hong Cai Hardware Architecture Parameter extraction for industry-standard device models like ASM-HEMT is crucial in circuit design workflows. However, many manufacturers do not provide such models, leaving users to build them using only datasheets. Unfortunately, datasheets lack sufficient information for standard step-by-step extraction. Moreover, manual data extraction from datasheets is highly time-consuming, and the absence of a fully automated method forces engineers to perform tedious manual work. To address this challenge, this paper introduces a novel, end-to-end framework that fully automates the generation of simulation-ready ASM-HEMT SPICE models directly from PDF datasheets. Our framework is founded on two core innovations: 1) a multi-modal AI pipeline that integrates computer vision with a large language model (LLM) to robustly parse heterogeneous datasheet layouts and digitize characteristic curves, and 2) a novel Iterative-Focusing Tree-structured Parzen Estimator (IF-TPE) optimization algorithm is specifically designed for device parameter extraction under the high-dimensional, sparse-data condition by adaptively refining the parameter search space. Experimental validation on a diverse set of 17 commercial HEMT devices from 10 manufacturers confirms the framework's accuracy and robustness. The generated models demonstrate excellent agreement with published DC and RF characteristics. As the first fully automated workflow of its kind, our proposed solution offers a transformative approach to device modeling, poised to significantly accelerate the circuit design cycle by eliminating the need for manual parameter extraction. |
| title | Automated HEMT Model Construction from Datasheets via Multi-Modal Intelligence and Prior-Knowledge-Free Optimization |
| topic | Hardware Architecture |
| url | https://arxiv.org/abs/2507.21430 |