Skip to Main Content
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 polydisersivity, volume fraction and arrangements on the effective thermal conductivity. A Random Network Model (RNM) that can efficiently capture the near-percolation transport in these particle-filled systems was developed by the authors, which can take into account the inter-particle interactions and random size distributions. The accuracy of the RNM is dependent on the parameters inherent in analytical description of thermal transport between two spherical particles, and their numerical approximation into a network model. In the present study, COMSOL™ was used to conduct polydispersivity studies that enabled the refinement of the analytical model. Comparing RNM results with FE results, the relation of a critical parameter with the polydispersivity and the volume fraction of the fillers in TIMs was found that provides a more accurate prediction of the effective thermal conductivity of the particulate TIMs using RNM.