Abstract: The migration of high-reliability applications requiring sustained operation in harsh environments needs a better understanding of the acceleration factors under the stresses of operation. Prolonged exposure of the copper wire to elevated temperatures results in growth of excessive intermetallics and degradation of the interface. Behavior of Copper wirebond under high current-temperature conditions is not yet fully understood. Exposure to high current may induce Joule heating and electromigration, and thus significantly increase the degradation rate in comparison with low current operating conditions. Further, the accelerated test results of unbiased conditions cannot be used for life prediction of such high powered parts. EMCs used for encapsulation of the chip and the interconnects may vary widely in their formulation including pH, porosity, diffusion rates, levels and composition of the contaminants. Selection of different materials, such as EMC used in the molding process plays key role in defining lifetime for wirebond system. There is need for predictive models which can account for the exposure to environmental conditions, operating conditions and the EMC formulation in order to be realistically representative of the expected reliability. In this paper, a set of parts, molded with different EMCs were subjected to high temperature-current environment (temperature range of 150°C-200°C, 0.2A-1A). An artificial neural network (ANN) driven predictive model for estimation of the beta-sensitivities of the input variables has been developed for computation of the acceleration factor for the Cu-Al WB under high voltage and high temperature.