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

Abstract

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.

A procedure for efficient generation and behavioral evaluation of ultra‐packed ellipsoidal particle systems

“…Recently, the above idea was analogously extended to estimate the inter-particle stiffness to estimate the effective elastic modulus. 45 Similarly, other properties of high contrast composites with ellipsoidal fillers such as permeability or electrical conductivity may also be estimated using the RNM.…”

“…More recently, the RNM was extended to evaluate the effective elastic modulus of particulate systems. 45 While interfacial resistance can have a significant influence on the effective conductance in particle systems, [46][47][48][49][50] experimentally validated RNM simulations by Kanuparthi et al 1 demonstrated that spatial arrangement of particles play a very significant role in transport of heat in high contrast composites. The overall effective thermal conductivity can be accurately described if an appropriate statistical distribution of filler-matrix gap is captured.…”

Effects of Filler Configuration and Moisture on Dissipation Factor and Critical Electric Field of Epoxy Composites for HV-ICs Encapsulation

“…6a. This simplified geometry can be also used for the actual case studies based on the fact that nearest neighbor function between an actual and a simplified structure exhibits similarly [34].…”

Thermal Interface Materials with High Thermal Conductivity and Low Young’s Modulus Using a Solid–Liquid Metal Codoping Strategy

“…Thermal interface materials are typically made up of polymers and highly thermally conductive fillers, such as metal powders, , metallic oxides, BN, AlN, and carbon materials. , According to the classical M–G model, the thermal conductivity of a TIM exponentially increases with an increase of doping volume. In addition, according to Eshelby’s theory, Young’s modulus will also sharply increase with an increase of solid filler content. The synchronized increase of the two physical properties will result in the dilemma of low thermal conductivity at a small doping volume and high hardness at a large doping volume.…”