Decision-Support Models for Thermo-Mechanical Reliability of Leadfree Flip-Chip Electronics in Extreme Environments

Abstract

Modeling tools and techniques for assessment of component reliability in extreme environments are scarce. Previous studies have focused on development of modeling tools at sub-scale or component level. The tools are often available only in an offline manner for decision support and risk assessment of advanced technology programs. There is need for a turn key approach, for making trade-offs between geometry and materials and quantitatively evaluating the impact on reliability. Application of flip-chip assemblies and underfills in benign office environments and wireless applications is not new, however their reliability in extreme environments is still not very well understood. In the current work, the decision-support models for deployment of flip-chip devices under various harsh thermal environments have been presented. The current work is targeted towards government contractors, OEMs, and 3rd party contract manufacturers who intend to select part architectures and board designs based on specified mission requirements. In addition, the mathematical models presented in this paper provide decision guidance for smart selection of component packaging technologies and perturbing presentlydeployed product designs for minimal risk insertion of new packaging technologies. The models serve as an aid for understanding the sensitivity of component reliability to geometry, package architecture, material properties and board attributes to enable educated selection of appropriate device formats. 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. Convergence between statistical model sensitivities and failure mechanics based model sensitivities has been demonstrated. Predictions of sensitivities have also been validated against the experimental test data.