An Improved Efficient Network Model for Determining the Effective Thermal Conductivity of Particulate Thermal Interface Materials

Abstract

Thermal interface materials (TIMs) are particulate composite materials widely used in the microelectronics industry to reduce the thermal resistance between the device and heat sink. Predictive modeling using fundamental physical principles is critical to developing new TIMs since it can be used to quantify the effect of particle volume fraction and arrangements on the effective thermal conductivity. The existing analytical descriptions of thermal transport in particulate systems do not accurately account for the effect of interparticle interactions, especially in the intermediate volume fractions of 30–80%. An efficient random network model (RNM) that captures the near-percolation transport in these particle-filled systems, taking into account the interparticle interactions and random size distributions, was previously developed by Kanuparthi et al. The RNM approach uses a cylindrical region to approximate the thermal transport within the filler particles and to capture the interparticle interactions. However, this approximation is less accurate when the polydispersivity of the particulate system increases. In addition, the accuracy of the RNM is dependent on the parameters inherent in an analytical description of thermal transport between two spherical particles and their numerical approximation into the network model. In the current paper, a novel semispherical approximation to the conductance of the fillers is presented as an alternative to the cylindrical region approximation used earlier. Compared with the cylindrical model, the thermal conductivities of the semispherical model are more closely to the finite element (FE) results. Based on the FE analysis, the network model is improved by developing an approximation of the critical cylindrical region between two spherical particles over which energy is transported. Comparing the RNM results with FE results and experimental data, a linear relationship of the critical parameter with the thermal conductivity ratio and the volume fraction was found that provides a more accurate prediction of the effective thermal conductivity of the particulate TIMs.