Estimation of Effective Thermal and Mechanical Properties of Particulate Thermal Interface Materials by a Random Network Model

Particulate thermal interface materials (TIMs) are commonly used to transport heat from chip to heat sink. While high thermal conductance is achieved by large volume loadings of highly conducting particles in a compliant matrix, small volume loadings of stiff particles will ensure reduced thermal stresses in the brittle silicon device. Developing numerical models to estimate effective thermal and mechanical properties of TIM systems would help optimize TIM performance with respect to these conflicting requirements. Classical models, often based on single particle solutions or regular arrangement of particles, are insufficient as real-life TIM systems contain a distribution of particles at high volume fractions, where classical models are invalid. In our earlier work, a computationally efficient random network model (RNM) was developed to estimate the effective thermal conductivity of TIM systems (Kanuparthi et al., 2008, “An Efficient Network Model for Determining the Effective Thermal Conductivity of Particulate Thermal Interface Materials,” IEEE Trans. Compon. Packag. Technol., 31(3), pp. 611–621; Dan et al., 2009, “An Improved Network Model for Determining the Effective Thermal Conductivity of Particulate Thermal Interface Materials,” ASME Paper No. InterPACK2009-89116.) . This model is extended in this paper to estimate the effective elastic modulus of TIMs. Realistic microstructures are simulated and analyzed using the proposed method. Factors affecting the modulus (volume fraction and particle size distribution (PSD)) are also studied.