平均故障间隔时间
陶瓷电容器
可靠性(半导体)
电容器
电压
计算机科学
Boosting(机器学习)
电容感应
可靠性工程
理论(学习稳定性)
人工智能
机器学习
材料科学
失效物理学
故障率
工程类
电气工程
物理
功率(物理)
操作系统
量子力学
作者
Pedram Yousefian,Alireza Sepehrinezhad,Adri C. T. van Duin,Clive A. Randall
摘要
Multilayer ceramic capacitors (MLCC) play a vital role in electronic systems, and their reliability is of critical importance. The ongoing advancement in MLCC manufacturing has improved capacitive volumetric density for both low and high voltage devices; however, concerns about long-term stability under higher fields and temperatures are always a concern, which impact their reliability and lifespan. Consequently, predicting the mean time to failure (MTTF) for MLCCs remains a challenge due to the limitations of existing models. In this study, we develop a physics-based machine learning approach using the eXtreme Gradient Boosting method to predict the MTTF of X7R MLCCs under various temperature and voltage conditions. We employ a transfer learning framework to improve prediction accuracy for test conditions with limited data and to provide predictions for test conditions where no experimental data exists. We compare our model with the conventional Eyring model (EM) and, more recently, the tipping point model (TPM) in terms of accuracy and performance. Our results show that the machine learning model consistently outperforms both the EM and TPM, demonstrating superior accuracy and stability across different conditions. Our model also exhibits a reliable performance for untested voltage and temperature conditions, making it a promising approach for predicting MTTF in MLCCs.
科研通智能强力驱动
Strongly Powered by AbleSci AI