Principal component analysis based development of Norris-Landzberg acceleration factors and Goldmann Constants for leadfree electronics

Goldmann Constants and Norris-Landzberg acceleration factors for lead-free solders have been developed based on principal component regression models (PCR) for reliability prediction and part selection of area-array packaging architectures under thermo-mechanical loads. Models have been developed in conjunction with Stepwise Regression Methods for identification of the main effects. Package architectures studied include, BGA packages mounted on copper-core and no-core printed circuit assemblies in harsh environments. The models have been developed based on thermo-mechanical reliability data acquired on copper-core and no-core assemblies in four different thermal cycling conditions. Packages with Sn3Ag0.5Cu solder alloy interconnects have been examined. The models have been developed based on perturbation of accelerated test thermo-mechanical failure data. Data has been gathered on nine different thermal cycle conditions with SAC305 alloys. The thermal cycle conditions differ in temperature range, dwell times, maximum temperature and minimum temperature to enable development of constants needed for the 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 predictions have been validated against validation data-sets 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, and diagonal length of the chip. The predicted and measured acceleration factors have also been computed and correlated. Good correlations have been achieved for parameters examined. Previously, the feasibility of using multiple linear regression models for reliability prediction has been demonstrated for flex-substrate BGA packages [Lall 2004, 2005], flip-chip packages [Lall 2005] and ceramic BGA packages [Lall 2007]. The presented methodology is valuable in the development of fatigue damage constants for the application specific accelerated test data-sets and provides a method to develop institutional learning based on prior accelerated test data.