Reliability life evaluation of bipolar transistors based on deep learning

Liang Zhang; Xuesong Xie; Xiaoling Zhang; Minghao Wang

doi:10.1117/12.2638601

23 May 2022 Reliability life evaluation of bipolar transistors based on deep learning

Liang Zhang, Xuesong Xie, Xiaoling Zhang, Minghao Wang

Proceedings Volume 12254, International Conference on Electronic Information Technology (EIT 2022); 1225437 (2022) https://doi.org/10.1117/12.2638601
Event: International Conference on Electronic Information Technology (EIT 2022), 2022, Chengdu, China

Abstract

Quickly predicting the remaining useful life of bipolar transistors is an effective means of assessing their health and reliability. Aiming at the problems of high modeling difficulty and poor fitting accuracy in the traditional physical model method and data fitting method, this paper proposes a reliability life prediction method for bipolar transistors based on deep learning. Firstly, the experiment is carried out with the accelerated storage experimental data of transistors in the Microelectronics Reliability Laboratory of Beijing University of Technology, and the reverse leakage current I_CBO is selected as the failure sensitive parameter, and a reasonable failure judgment is set. The lifetime of the sample is calculated; Secondly, the average lifetime under three common distributions is given, and combined with the Peak temperature and humidity model, the storage lifetime of the transistor under natural storage conditions is extrapolated; finally, the mean absolute error (MAE) and mean square error (MSE) are selected as evaluation functions, and the life prediction results of the deep learning model and the data fitting method are compared. The results show that compared with the data fitting method, the MAE of the life prediction results of the deep learning model decreases by 82.00%, and the MSE decreases by 98.16%, which verifies the effectiveness of the method.

Citation Download Citation

Liang Zhang, Xuesong Xie, Xiaoling Zhang, and Minghao Wang "Reliability life evaluation of bipolar transistors based on deep learning", Proc. SPIE 12254, International Conference on Electronic Information Technology (EIT 2022), 1225437 (23 May 2022); https://doi.org/10.1117/12.2638601

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KEYWORDS

Data modeling

Transistors

Reliability

Data storage

Neural networks

Statistical modeling

Humidity

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