Predicting effective thermal conductivity of sintered nano-Ag with artificial neural networks

Due to the demand for high reliability and thermal conductivity of high-power modules operating at high temperatures, sintered nano-silver (Ag) has garnered significant attention as an excellent interconnect and heat transfer layer, particularly for its thermal conductivity and other reliability research. Since the mechanical behavior and heat conduction capacity of sintered Ag is generally regulated by changes in temperature, its microstructure will change accordingly, affecting its performance. In this study, a machine learning model was used to evaluate and predict the thermal conductivity of sintered Ag, providing an effective method to analyze the influence of microstructural characteristics on its heat transfer properties. Image processing and model simulation of scanning electron microscopy images of sintered nano-Ag nanostructures were performed using MATLAB and Ansys software. A batch calculation of the thermal conductivity of 2D images of sintered nano-Ag nanostructures was performed to obtain sufficient data sets. Based on the artificial neural network model of Bayesian optimization, the equivalent thermal conductivity of different sintered nano-Ag microstructures was predicted with high accuracy using the microstructure image and characteristic parameters of sintered nano-Ag. The proposed method enables rapid, effective, and accurate evaluation and prediction of the thermal conductivity of sintered nano-Ag, contributing significantly to the reliability of power modules.