A Physical-Based Deep Learning Model for Thermal Process Simulation in FC-BGA Package

Heat transfer analysis is crucial in electronic packaging design due to the coefficient of thermal expansion mismatch, which can cause mechanical failure. However, the classical numerical computation method is expensive which makes it hard to satisfy the electronic packaging design. With the rapid development of deep learning, it has widely been used in plenty of areas. In this work, we design a deep-learning model to study the transient thermal simulation with complex structures and various materials. The Rothe method is used to train the model. Numerical simulations have verified the high efficiency of the DL model.