Principal Components Regression Model for Prediction of Acceleration Factors for Copper-Aluminum Wirebonds Subjected to Harsh Environments

Migration to Cu wirebond from traditional gold wirebond is recent trend in packaging industry. Selection of different materials, such as EMC used in the molding process plays key role in defining lifetime for wirebond system. Effects of the individual variables such as pH value, ionic contamination, filler content on reliability of wirebond is available in literature. However, since all these parameters have combined effect on reliability, joint effect of all parameters on acceleration factor and their quantification is necessary for smooth transition to Cu wirebond system, keeping transition and testing time minimum. Effect of pH value and ionic contamination of EMC on life distribution was individually studied in the past. In this paper, predictive model for acceleration factor of copper based on PCR approach is presented. A set of parts, molded with different EMC's were subjected to high temperature environment (150°C-225°C). Resistance, IMC change and shear strength change were monitored during this study. Resistance spectroscopy was used for accurate resistance measurement. Dage 2400PC was used to calculate change in shear strength. Parts were cross-sectioned and polished along Cu-Al interface using SEM and EDX system after failure. Relation between resistance changes with change in shear strength was established. 20% change in resistance was considered as failure threshold. All parts were tested till failure. Time-to-failure data was used to calculate acceleration factor, with 150°C as a base temperature. Different AF's obtained for different EMC's were then regressed against environmental conditions, mechanical and chemical properties of molding compound. In this study, all packages had same architecture, wire diameter and pad thickness. Principal component analysis was used to identify influential variables and to remove multicollinearity in the data set. PCA technique allows dataset to be projected into lower dimension vector space, and remove variables which do not contribute towards variance of predictive variable. Prediction model will be able to predict AF when environmental conditions, properties of EMC are known. Model was validated by performing self-validation test, and by predicting AF for the dataset which was not part of study. This model will provide, educated estimation of time to failure for new set of EMC's, for desired operating condition.