Temperature and Time-dependent Property Prediction and Validation for Nano-Underfills using RSA based RVE Algorithms

Presently, no-flow underfills have very low or no filler content because micron-size filler particles hinder solder joint formation. Nano-silica underfills have the potential of attaining higher filler loading in no-flow underfills without hindering solder interconnect formation (Kraszewshi et al., 2001 and Shi and Wong, 1999). The silica particles reduce coefficient of thermal expansion of the underfill-epoxy matrix. In traditional underfills, the size of silica particles is in the micrometer range. In this paper, property prediction models based on representative volume element (RVE) and modified random spatial adsorption (RSA) have been developed. The models utilize statistically isotropic random-placement of nano-particles, in addition to random size-distribution of particles for analysis of material. Volume fractions up to 40 percent of nano-silica filler have been studied. Properties predicted and correlated with experimental data include, coefficient of thermal expansion, elastic modulus, poisson´s ratio, and viscoelastic properties including stress relaxation under applied strain. All properties have been measured in the temperature range of -175 degC to +150degC. Nano-underfills with 10 percent and 22 percent volume fraction of filler assembled with 63Sn37Pb eutectic and 95.5Sn3.5Ag1.0Cu leadfree flip-chip devices have been subjected to thermal shock tests in the range of -55 to 125degC and -55 to 150degC respectively. The trade-offs between using nano-fillers instead of micron-fillers on thermo-mechanical properties and reliability has been benchmarked