AccScience Publishing / IJAMD / Online First / DOI: 10.36922/ijamd.5744
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ORIGINAL RESEARCH ARTICLE

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

Libo Zhao1† ,  Jiahui Wei1† ,  Yanwei Dai1* ,  Daowei Wu2 ,  Yuting Zhang2 ,  Kui Li3 ,  Fei Qin1
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1 Department of Mechanics, Institute of Electronics Packaging Technology and Reliability, Beijing University of Technology, Beijing, China
2 Advanced Packaging Division, Xi’an Institute of Microelectronics Technology, Xi’an, Shaanxi, China
3 R&d Innovation Center, Xi’an Institute of Microelectronics Technology, Xi’an, Shaanxi, China
IJAMD, 5744 https://doi.org/10.36922/ijamd.5744
Submitted: 1 November 2024 | Revised: 13 January 2025 | Accepted: 15 January 2025 | Published: 6 February 2025
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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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.

Graphical abstract
Keywords
Artificial neural networks
Sintered nano-Ag
Effective thermal conductivity
Finite element modeling
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.
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International Journal of AI for Materials and Design, Electronic ISSN: 3029-2573 Print ISSN: 3041-0746, Published by AccScience Publishing
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