关键词: 封装结构, 力学可靠性, 有限元模拟, 机器学习, 动力响应

Abstract: In the electronic packaging industry, the evaluation of mechanical reliability of the packaging structures is usually studied by means of finite element simulations, which is however considerably time-consuming due to modeling creation and iterative analysis. To eliminate these technical limitations, the dynamic response of the packaging structure during the drop process was firstly simulated by ABAQUS in the present study, and the values of feature candidates were obtained to construct a 4×3 data set. In fact, the main feature variables of the drop resistance evaluation is taken as the input features, while the stress and equivalent plastic strain are taken as the output feature values. Consequently, the data set was trained by the correlation-driven neural network to obtain the corresponding machine-learning based prediction model. Finally, compared with the finite element simulation, the predicted results of machine-learning based neural network are similar to those of finite element simulation results. The results of this paper show that the machine learning method based on finite element simulations has great potential in predicting the reliability of the mechanical performance of the packaging structure under complicated working conditions.

Key words: packagingstructure, mechanicalreliability, finiteelementmethod, machinelearning, dynamicresponse