Physical and Fuzzy Logic Modeling of a Flip-Chip Thermocompression Bonding Process

Abstract

Flip-Chip connections using gold-to-gold, gold-to-aluminum, or gold-to-solder bondings or contacts enhanced by epoxy are low-cost alternatives to soldering. To assist their technology advancements, we have developed yield models for a representative assembly process with flip-chip, thermocompression bondings. Based on bonding mechanics, a physical yield model has been developed to characterize the process. Then, a fuzzy logic model has been established to improve the modeling’s accuracy by including experimental data. The physical yield model can predict the assembly yield as a function of forces and planarities of the end effector, bump height variations, bump geometries, mechanical properties corresponding to different materials and temperatures, and distribution patterns of bumps. Consistent with our experimental experience, the calculated force level for a high-yield process was around 3000 gmf for a 30-gold-bump chip with a bump diameter of 60 μm and a height of 50 μm. The fuzzy logic model can be trained and adjusted by the results of physical models and experiments. It correlates very well to the nonlinear relationships between the yield and the assembly parameters, and has a self-learning capability to update itself with new data. Such capabilities have been demonstrated by studying the bonding on a substrate with or without a compliant layer.