Probabilistic Lifetime Prediction of Electronic Packages Using Advanced Uncertainty Propagation Analysis and Model Calibration

We propose a novel methodology for calibrating the physics-based lifetime models of the electronic packages using the eigenvector dimension-reduction (EDR) method and a censored data analysis. The methodology enables to overcome two challenges that are encountered in typical electronic packaging applications: 1) the minimum computational cost without sacrificing the prediction accuracy and 2) the proper handling of the censored data. The EDR method is first employed for uncertainty propagation for the computational efficiency when multiple unknown variables are to be used in nonlinear damage models. Next, the likelihood function is modified to handle the failure data as well as the censored data in the likelihood analysis, and thus establishes the correlation between the model response and the experimental result. Finally, through an unconstrained optimization process, a calibrated parameter set of statistical distributions for unknown input variables is obtained while maximizing the modified likelihood. The proposed statistical calibration approach is implemented for solder joint fatigue reliability. The results confirm the claimed computational effectiveness for an accurate physics-based lifetime model.