BP neural network in predicting thermal fatigue life of microelectronic chip

xiaoguang huang, Dianhao Zhang, Zhiqiang Wang


The present study gives an efficient approach for the prediction of thermal fatigue 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 respectively established according to the experimental results of thermal fatigue and singularity parameters at the failure interface by finite element method (FEM). The BP approach is configured to predict the singularity parameters at failure interface based on the dimensions, thermal-mechanical properties of solders in the new chips. Therefore, their thermal fatigue lives can be calculated. From the results of the present investigation, it is observed that the prediction of thermal fatigue lives by BP model are in good agreement with the experimental results.


thermal fatigue, microelectronic chips, BP, singularity parameters, life prediction

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DOI: https://doi.org/10.33180/InfMIDEM2020.102


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