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

Abstract

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.

Keywords

Artificial neural networks / Sintered nano-Ag / Effective thermal conductivity / Finite element modeling

Acknowledgments

The authors would like to express their gratitude for the support from the National Natural Science Foundation of China (No. 12272012) and acknowledge the AI tool SparkDesk (iFLYTEK) for enhancing the language quality and readability of the manuscript.

Funding

This research was financially supported by the National Natural Science Foundation of China (No. 12272012).

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Author contributions

Conceptualization: Jiahui Wei, Yanwei Dai

Formal analysis: Libo Zhao, Jiahui Wei

Investigation: Jiahui Wei, Daowei Wu, Yuting Zhang, Kui Li, Fei Qin

Methodology: Yanwei Dai, Libo Zhao, Jiahui Wei

Writing - original draft: Libo Zhao, Jiahui Wei, Yanwei Dai

Writing - review & editing: Libo Zhao, Yanwei Dai, Jiahui Wei

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Availability of data

The data presented in this study are available upon request from the corresponding author due to data protection.

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