This Is AuburnElectronic Theses and Dissertations

Principal Component Regression Models for Thermo-Mechanical Reliability of Plastic Ball Grid Arrays on Cu-Core and No Cu-Core PCB Assemblies in Harsh Environments




Shirgaokar, Aniket

Type of Degree



Mechanical Engineering


In the current work, Goldmann constants and Norris-Landzberg acceleration factors have been developed for eutectic Tin Lead and Lead free solders (SAC 305) with the help of statistical tools including Principal Component Regression for reliability prediction and part selection of Plastic Ball grid array packages. Two types of PCB assemblies including PCBs with integral copper core and PCBs with no integral copper core have been tested. The models have been developed based on thermo-mechanical reliability data acquired on packages subjected to several different thermal cycling conditions. The thermal cycling conditions differ in temperature range, dwell times, maximum temperature, minimum temperature to enable development of constants needed for life prediction and assessment of acceleration factors. Goldmann constants and the Norris-Landzberg acceleration factors have been benchmarked against previously published values. In addition, model predictors have been validated against validation datasets which have not been used for model development. Convergence of statistical models with experimental data has been demonstrated using a single factor design of experiment study for individual factors including temperature cycle magnitude, relative coefficient of thermal expansion, solder volume, diagonal length of chip, etc. The predicted and measured acceleration factors have also been computed and correlated. The correlations achieved are of a good accuracy for different parameters examined. Statistics based log transformed models have been presented to show their power dependencies. Box – Tidwell power law modeling has been demonstrated. The presented methodology is valuable in development of fatigue damage constants for the application specific accelerated test data-sets and provide a method to develop institutional learning based on prior accelerated test data.