Thermo-mechanical Reliability Models for Life Prediction of Area Array Electronics in Extreme Environments
Type of DegreeThesis
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The increasing functionality in modern microelectronics requires more complexity in less space and more reliability at lower cost. Demands on miniaturization have lead to the evolution of several types of area array packages like PBGA, Flex-BGA, Flip Chip CSPs etc. Area array packages have been increasingly targeted for use in harsh environments such as automotive, military and space application but the thermo-mechanical reliability of these packages in such environments is a concern for the electronic industry. Several approaches are available today for reliability prediction including non-linear finite element models and first-order closed form models. The first-order models as the name suggests offer limited accuracy. In this thesis, a unique hybrid approach to reliability prediction, in order to achieve accuracy beyond the closed-form first-order approximations, has been presented. The perturbation approach presented in this paper enables higher-accuracy model prediction by perturbing known accelerated-test data-sets using models, using factors which quantify the sensitivity of reliability to various design, material, architecture and environmental parameters. The models are based on a combination of statistics and failure mechanics. In addition, parameter interaction effects, which are often ignored in closed form modeling, have been incorporated in the proposed hybrid approach. The statistics models are based on accelerated test data in harsh environments, while failure mechanics models are based on damage mechanics and material constitutive behavior. The framework formulated from the models is intended as an aid for understanding the sensitivity of the component reliability to geometry, package architecture, material properties and board attributes in different thermal environments. The intent is to develop a decision-support system for doing trade-offs between geometry, materials and quantitatively evaluating the impact on the reliability. Convergence between the statistical model sensitivities and failure mechanics based sensitivities has been demonstrated. Predictions of the sensitivities have been validated against the experimental test data.