Microstructural characteristics influencing the effective thermal conductivity of particulate thermal interface materials

Particle laden polymer composites are widely used as thermal interface materials (TIMs) in the electronics cooling industry. A critical need in developing TIMs is apriori modeling from first principles to predict the effect of particle volume fraction and arrangements. This in turn will help optimize the material. In general, TIM systems contain random distributions of particles of a polydisperse (usually bimodal) nature. In addition to particle/matrix conductivities and volume loading of the particles in the matrix, the size distribution and the random arrangement of the particles in the matrix play an important role in determining the effective thermal conductivity of TIMs. A detailed analysis of the microstructural characteristics that influence the effective thermal conductivity of TIMs is presented in this paper. Random microstructural arrangements consisting of lognormal size-distributions of alumina particles in silicone matrix were generated using an algorithm implemented using a Java language code. The generated microstructures were statistically characterized using matrix-exclusion probability function. The filler particle volume loading was varied over a range of 40-55 %. For a given filler volume loading, the effect of polydispersivity in the microstructures was captured by varying the standard deviation(s) parameter in the lognormal filler particle size distribution function. The effective thermal conductivity of the microstructures was evaluated through simulations using a network model (previously developed by the authors). The influence of polydispersivity on the effective thermal conductivity of the microstructures is discussed.