The Study of the Polydispersivity Effect on the Thermal Conductivity of Particulate Thermal Interface Materials by Finite Element Method

Thermal interface materials (TIMs) are particulate composite materials widely used in the microelectronics industry to reduce the thermal resistance between the device and the heat sink. Predictive modeling using fundamental physical principles is critical to developing new TIMs, since it can be used to quantify the effect of polydispersivity, volume fraction and arrangements on the effective thermal conductivity. A random network model that can efficiently capture the near-percolation transport in these particle-filled systems was developed by the authors, which can take into account the interparticle interactions and random size distributions. In this paper, a Java-based code is used to generate the microstructures at different volume fraction and different particle-size distribution (PSD). COMSOL was used to investigate the impact of polydispersivity on the effective thermal conductivity of particulate TIMs. The log-normal distribution was used to capture the filler PSD. From the simulation results, there exists an optimum value of the polydispersivity which has the largest thermal conductivity for a given volume fraction.