Neural Network-Based Approach for Failure and Life Prediction of Electronic Components under Accelerated Life Stress

Author:

Qiu Yunfeng12,Li Zehong1345

Affiliation:

1. School of Integrated Circuit Science and Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China

2. Institute of Guizhou Aerospace Measuring and Testing Technology, Guiyang 550009, China

3. State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China

4. Chongqing Institute of Microelectronics Industry Technology, University of Electronic Science and Technology of China (UESTC), Chongqing 401331, China

5. Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China (UESTC), Shenzhen 518110, China

Abstract

Researchers worldwide have been focusing on accurately predicting the remaining useful life of electronic devices to ensure reliability in various industries. This has been made possible by advancements in artificial intelligence (AI), machine learning, and Internet of Things (IoT) technologies. However, accurately forecasting device life with minimal data sets, especially in industrial applications, remains a challenge. This paper aims to address this challenge by utilizing machine learning algorithms, specifically BP, XGBOOST, and KNN, to predict device reliability with limited data. The remaining life dataset of electronic components is obtained through simulation for training and testing the algorithms, and the experimental results show that the algorithms achieve a certain level of accuracy, with the error rates being as follows: BP algorithm, 0.01–0.02%; XGBOOST algorithm, 0.01–0.02%; and KNN algorithm, 0–0.07%. By benchmarking these algorithms, the study demonstrates the feasibility of deploying machine learning models for device life prediction with acceptable accuracy loss, and highlights the potential of AI algorithms in predicting the reliability of electronic devices.

Publisher

MDPI AG

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