Prognostication of the effect of mean temperature of thermal cycle on SAC305 leadfree reliability using damage pre-cursors

Abstract

Electronics in automotive underhood applications may be subjected to temperatures in the neighborhood of 150°C to 175°C. Several electronic functions such as lane departure warning systems, collision avoidance systems are critical to vehicle operation. Prior studies have shown that low silver leadfree SnAgCu alloys exhibit pronounced deterioration in mechanical properties even after short exposure to high temperatures. Current life prediction models for second level interconnects do not provide a method for quick-turn assessment of the effect of mean temperature on cyclic life. In this paper, a method has been developed for assessment of the effect of mean cyclic temperature on the thermal fatigue reliability based on physics based leading damage indicators including phase-growth rate and the intermetallic thickness. Since the quantification of the thermal profile in the field applications may be often very difficult, the proposed method does not require the acquisition of the thermal profile history. Three environments of -50°C to +50°C, 0°C to 100°C, 50°C to 150°C with identical thermal excursion and different mean temperatures have been studied. Test assemblies with three different packages including CABGA 144, PBGA 324, and PBGA 676 have been used for the study. Damage-proxy based damage-equivalency relationships have been derived for the three thermal cycles. Weibull distributions have been developed for the three test assemblies to evaluate the effect of the mean cyclic temperature on the thermal fatigue life. Data indicates that the thermal fatigue lie drops with the increase in mean temperature of the thermal cycle even if the thermal excursion magnitude is kept constant. Damage equivalency model predictions of the effect of mean temperature of the thermal cycle have been validated versus weibull life distributions. The damage proxy based damage equivalency methodology shows good correlation with experimental data.