Back Propagation Neural Network in Predicting the Thermal Fatigue Life of Microelectronic Chips

Abstract

The present study gives an efficient approach to predict the thermal fatigue lives of microelectronic chips under cyclic thermal load using back propagation (BP) artificial neural network method. Strain based and stress-strain based thermal fatigue life models are established, respectively, according to the experimental results of thermal fatigue tests and the singularity parameters at the failure interface calculated by finite element method (FEM). According to the existing FEM results, the BP approach is configured to predict the singularity parameters at the failure interface in the new chips once the dimensions and thermal-mechanical properties of solders are obtained. By comparison, the predicted thermal fatigue lives according to the established thermal fatigue life models are in good agreement with the experimental results. The thermal fatigue life prediction of microelectronic chips based on the BP network approach is feasible.