A Hybrid Finite Element Modeling: Artificial Neural Network Approach for Predicting Solder Joint Fatigue Life in Wafer-Level Chip Scale Packages
A Hybrid Finite Element Modeling: Artificial Neural Network Approach for Predicting Solder Joint Fatigue Life in Wafer-Level Chip Scale Packages
复制标题
混合有限元建模：用于预测晶圆级芯片尺寸封装中焊点疲劳寿命的人工神经网络方法
DOI：
10.1115/1.4047227
复制
发表时间：
2021-03-01
影响因子：
1.6
通讯作者：
Liu, Li
中科院分区：
文献类型：
--
作者：
Chen, Zhiwen;Zhang, Zhao;Liu, Li
电子器件的疲劳寿命预测在科研和工业上都具有重要意义。传统上，疲劳试验和有限元建模是两种主要的方法。提出了一种新的疲劳寿命预测方法（有限元法与人工神经网络相结合）。建立了不同芯片厚度、PCB厚度和焊点间距的晶圆级芯片规模封装（WLCSP）的有限元模型，评估了结构参数对热疲劳载荷下最大蠕变应变增加的影响。然后采用修正的Coffin-Manson方程估算相应的疲劳寿命。构建人工神经网络，然后使用建模数据集进行训练、测试和优化，以预测最大蠕变应变和疲劳寿命的增加。对于用于应变预测的人工神经网络，最优网络在精度测试中的预测准确率为97%，在泛化测试中的预测准确率为93%。另一种人工神经网络预测疲劳寿命的精度试验为94.2%，泛化试验为88%。这种混合方法在电子器件疲劳寿命估算中具有较好的应用前景，可以节省时间和成本。
Fatigue life prediction of electronic devices is of great importance in both research and industry. Traditionally, fatigue tests and finite element modeling (FEM) are the two main methods. This paper presents a new hybrid approach (FEM combined with artificial neural network, (ANN)) for fatigue life prediction. Finite element models on wafer-level chip scale packages (WLCSP) with different chip thickness, PCB thickness, and solder joint pitches were created to evaluate the effect of structure parameters on the increase in maximum creep strain under thermal fatigue load. Modified Coffin–Manson equation was then employed to estimate the corresponding fatigue life. ANNs were built, and then trained, tested, and optimized with the datasets from modeling to predict increase in maximum creep strain and fatigue life. For the ANN built for strain prediction, prediction accuracy of the optimal network was 97% in accuracy tests and 93% in generalization tests. Accuracy of the other ANN for predicting fatigue life was 94.2% in accuracy tests and 88% in generalization tests. This hybrid method shows very promising application in fatigue life estimation of electronic devices which requires much less time and lower cost.