Models for Thermo-Mechanical Reliability Trade-offs for Ball Grid Array and Flip Chip Packages in Extreme Environments

Abstract

In the current work, decision-support models for deployment of various ball grid array devices and flip chip electronics 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 BGA and Flip Chip packaging technologies and for perturbing presently-deployed product designs for minimal risk insertion of new materials and architectures. 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. 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 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 BGA and Flip Chip assemblies in benign office environments and wireless applications is not new, however their reliability in extreme environments is still not very well understood. Multiple linear regression, principal components regression and power law based modeling methodologies have been used for developing prediction models that enables higher-accuracy prediction of characteristic life 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 multiple linear regression approach uses the potentially important variables from stepwise regression methods, and the principal components regression uses the principal components obtained from the eigen values and eigen vectors of correlation matrix for model building. The power law modeling is a non regression based approach that uses the method of maximum likelihood for developing power law relationship between characteristic life and the package parameters. 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.