To efficiently select qualification and reliability monitoring programs, structural similarity rules for Integrated Circuit designs, wafer fabrication processes and/or package designs are currently used by the industry. By following the package structural similarity rules, the nu
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To efficiently select qualification and reliability monitoring programs, structural similarity rules for Integrated Circuit designs, wafer fabrication processes and/or package designs are currently used by the industry. By following the package structural similarity rules, the numbers of reliability qualification tests may be greatly reduced. However, when looking at the present rules it is clear that they are not reliably defined. For instance, geometrical parameters such as die-to-pad ratio are not quantitatively included and it seems that linear relationships are assumed. Besides that, these rules are mainly deducted from experience and industrial trial and error results, not from reliability physics. Driven by the present development trends of microelectronics (miniaturization, integration, cost reduction, etc) it is urgently needed to develop `advanced based structural similarity rules¿ based on reliability physics (physics of failures), to meet the industrial development trends. In this study, we have used DOE/RMS techniques to deduct advanced structural similarity rules through simulation-based optimisation techniques. Parametric 3D non-linear FE models are used to explore the responses of the complete ball grid array (BGA) package family for both the thermo-mechanical and moisture-diffusion responses as function of six parameters among which the die-to-pad ratio and the body size. In this way, advanced structural similarity rules are deduced which can be used to shorten design cycles. Even more, by using the accurate 3D non-linear reliability prediction models easy tools can be created for package designers. By using such a tool, the number of reliability qualification tests can be reduced. More importantly, possible failure mechanisms can be (better) understood and predicted.@en