Heat dissipation optimization and prediction for three-dimensional fan-out package

Abstract

Excessive heat would reduce the service life and threaten the reliability of electronic devices. To optimize the heat conduction in Fan-out (FO) package, we proposed a hybrid method using Taguchi design of experiments, radial basis neural network (RBNN) and genetic algorithm (GA). The heat transfer models of the FO package were constructed. The hybrid method was used to examine the influence of eight geometric parameters on heat dissipation. It was revealed by using the Taguchi design method that the parameters the chip size (A), the ratio of package side length to chip side length (D), and the critical dimension of RDL (G) have the most significant impact on the thermal resistance of FO package, and others are less important. The RBNN model was established to predict the thermal resistance, which was optimized by using the GA. We obtained the optimal design of the FO package with the structure parameter vector [100, 250, 50, 2, 2, 2, 10, 100]. The thermal resistance of the RBNN-GA optimized model is 280.58 K/W. The difference between the maximum junction temperature and the ambient temperature was reduced by around 33.22%. It proved that the hybrid method is effective for optimizing the heat dissipation of FO package, which can be used for structure design and thermal management of the electronic devices.