Predicting Lifetime of Semiconductor Power Devices Under Power Cycling Stress Using Artificial Neural Network

被引:9
|
作者
Vaccaro, Alessandro [1 ]
Magnone, Paolo [1 ]
Zilio, Andrea [1 ]
Mattavelli, Paolo [1 ]
机构
[1] Univ Padua, Dept Management & Engn, I-36100 Vicenza, Italy
关键词
Artificial neural network (ANN); insulated gate bipolar transistor (IGBT); power cycling; reliability; semiconductor power devices; thermal cycling; MODULES; RELIABILITY; FATIGUE; MODEL;
D O I
10.1109/JESTPE.2022.3194189
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
This article analyzes the problem of modeling the lifetime in semiconductor power devices subjected to power cycling stress using artificial neural networks (ANNs). This article discusses the optimal configuration of ANNs for the considered problem, aiming at minimizing the error in the predicted lifetime and at reducing the required number of training data. Moreover, being the device lifetime a stochastic parameter, the suitability of ANNs is verified in the case of variability in the input training data. Power cycling tests are conducted on insulated gate bipolar transistor (IGBT) devices and the experimental number of cycles to failure are adopted for the training process of the ANN.
引用
收藏
页码:5626 / 5635
页数:10
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