Rapid Flow Behavior Modeling of Thermal Interface Materials Using Deep Neural Networks

TL;DR

This paper introduces a deep neural network-based method to rapidly model and optimize the spreading behavior of thermal interface materials on complex surfaces, improving efficiency over traditional CFD simulations.

Contribution

It presents a lightweight heuristic and neural network approach for fast TIM spreading modeling and automated pattern optimization on complex geometries.

Findings

Neural network achieves rapid predictions of TIM spreading.

Method outperforms traditional CFD in speed and usability.

Validated with real product samples.

Abstract

Thermal Interface Materials (TIMs) are widely used in electronic packaging. Increasing power density and limited assembly space pose high demands on thermal management. Large cooling surfaces need to be covered efficiently. When joining the heatsink, previously dispensed TIM spreads over the cooling surface. Recommendations on the dispensing pattern exist only for simple surface geometries such as rectangles. For more complex geometries, Computational Fluid Dynamics (CFD) simulations are used in combination with manual experiments. While CFD simulations offer a high accuracy, they involve simulation experts and are rather expensive to set up. We propose a lightweight heuristic to model the spreading behavior of TIM. We further speed up the calculation by training an Artificial Neural Network (ANN) on data from this model. This offers rapid computation times and further supplies gradient information. This ANN can not only be used to aid manual pattern design of TIM, but also enables an automated pattern optimization. We compare this approach against the state-of-the-art and use real product samples for validation.