@inproceedings{47eba092ba654264b03daeafc726d46a,
title = "APEX: Automating Parameter Extraction of Compact Models with Differential Neural Network Approximation",
abstract = "The traditional compact model parameter extraction highly depends on engineers' expertise, leading to a time-consuming and iterative process. To address the above issue, this paper proposes an automatic parameter extraction method for compact models, APEX. The proposed APEX framework adopts an artificial neural network (ANN) method as an approximation of compact models using model parameters as inputs and IV/CV data as outputs. The model parameters are efficiently extracted using an automatic differential mechanism based on the ANN-approximated compact model. Experimental results demonstrate that our proposed framework achieves good fitting accuracy and scalability across device structures when evaluating GAA and FinFET devices. A fitting error of less than 4\% is achieved on the open-source benchmark.",
keywords = "Compact model, automatic differentiation, neural network, parameter extraction",
author = "Jianing Zhang and Jiaqi Dou and Yang Shen and Bingyi Ye and Xiaojin Li and Yanling Shi and Yuhang Zhang and Yabin Sun",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 2025 International Symposium of Electronics Design Automation, ISEDA 2025 ; Conference date: 09-05-2025 Through 12-05-2025",
year = "2025",
doi = "10.1109/ISEDA65950.2025.11100460",
language = "英语",
series = "2025 International Symposium of Electronics Design Automation, ISEDA 2025",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "622--627",
booktitle = "2025 International Symposium of Electronics Design Automation, ISEDA 2025",
address = "美国",
}