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.

热量过多会缩短使用寿命，并威胁电子设备的可靠性。为了优化扇出（FO）封装中的热传导，我们提出了一种使用Taguchi实验设计，径向基神经网络（RBNN）和遗传算法（GA）的混合方法。构造了FO封装的传热模型。混合方法用于检查八个几何参数对散热的影响。通过Taguchi设计方法发现，芯片尺寸（A），封装边长与芯片边长之比（D）以及RDL的临界尺寸（G）等参数对散热的影响最大。 FO封装的抗性，而其他方面则不那么重要。建立了RBNN模型来预测热阻，并通过GA对其进行了优化。我们使用结构参数向量[100，250，50，2，2，2，10，100]获得了FO封装的最佳设计。RBNN-GA优化模型的热阻为280.58 K / W。最大结温与环境温度之间的差异减少了约33.22％。证明了混合方法对于优化FO封装的散热是有效的，可用于电子设备的结构设计和热管理。