A Neural Network Modeling Method With Low-Rate Sampling for Wide Temperature Range SiC MOSFETs Application

航程(航空) 人工神经网络 材料科学 大气温度范围 采样(信号处理) 电子工程 光电子学 工程物理 计算机科学 人工智能 工程类 物理 热力学 电信 复合材料 探测器
作者
Wenhao Yang,Mengnan Qi,Yuyin Sun,Shasha Mao,Lei Yuan,Yimeng Zhang,Yuming Zhang
出处
期刊:IEEE Transactions on Electron Devices [Institute of Electrical and Electronics Engineers]
卷期号:71 (6): 3510-3517
标识
DOI:10.1109/ted.2024.3389628
摘要

With the rapid development of semiconductor technology, conventional modeling based on physical equations encounters challenges related to accuracy and development time. The study proposes a behavioral-level modeling approach based on artificial neural networks (ANNs), aiming to swiftly and accurately model SiC MOSFETs when used in CMOS integrated circuits over a wide temperature range. Nevertheless, achieving precise ANN model training typically demands a substantial volume of data, incurring costs related to measurements and lengthy training periods. To address this issue, sampling-based methods for acquiring training data play a crucial role, but they come with a notable limitation. Lower sampling rates result in a considerable reduction in model accuracy, whereas higher sampling rates fail to effectively tackle the time-consuming issue and the associated costs of model training. To train the ANN model with less data without compromising accuracy, this study uses the uniform random sampling (URS) method and the Latin hypercube sampling (LHS) method based on stratified sampling during the training set acquisition process. The results demonstrate that LHS significantly outperforms URS in terms of accuracy at the same sampling rate of 2%. For further enhancement of fitting accuracy in the transition region, a segmented LHS (SLHS) method is proposed, showcasing superior modeling capability. The ANN model constructed using this sampling method enhances fitting accuracy in the transition region between linear and saturation regions by 38.6% and overall fitting accuracy by 17.3%, when compared with LHS method.
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