Phase-field data fusion model for intermetallic compound growth at the SnAgCu/Cu interface under gradient temperature fields

Abstract With the continuous advancement of electronic packaging technology, accurately studying the growth mechanisms of intermetallic compounds (IMCs) at the SnAgCu/Cu interface under gradient temperature fields has become a critical issue. This paper proposes a multi-scale model that integrates phase-field modeling (Phase-Field Modeling) with multi-source data fusion technology (Data Fusion) to address this challenge. The model describes the diffusion-reaction kinetics of IMCs using phase-field variables and introduces a data fusion algorithm to optimize the influence of temperature gradients on interfacial energy and diffusion coefficients. By comparing experimental data with simulation results, the model’s effectiveness in predicting IMC layer thickness, morphological evolution, and thermomechanical reliability is validated.

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Publication Details

Journal
Journal of Physics Conference Series
Published
2025-08-01
DOI
https://doi.org/10.1088/1742-6596/3068/1/012035
Primary Topic
Model Reduction and Neural Networks
Type
article
Field-Weighted Citation Impact
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article

Phase-field data fusion model for intermetallic compound growth at the SnAgCu/Cu interface under gradient temperature fields

Zhongcheng Wang
Journal of Physics Conference Series
Model Reduction and Neural Networks
article

Phase-field data fusion model for intermetallic compound growth at the SnAgCu/Cu interface under gradient temperature fields

Zhongcheng Wang
article en

Abstract

Abstract With the continuous advancement of electronic packaging technology, accurately studying the growth mechanisms of intermetallic compounds (IMCs) at the SnAgCu/Cu interface under gradient temperature fields has become a critical issue. This paper proposes a multi-scale model that integrates phase-field modeling (Phase-Field Modeling) with multi-source data fusion technology (Data Fusion) to address this challenge. The model describes the diffusion-reaction kinetics of IMCs using phase-field variables and introduces a data fusion algorithm to optimize the influence of temperature gradients on interfacial energy and diffusion coefficients. By comparing experimental data with simulation results, the model’s effectiveness in predicting IMC layer thickness, morphological evolution, and thermomechanical reliability is validated.

Journal of Physics Conference SeriesVol. 3068(1)
NARI Group (China) (CN)
Openalex Percentile: Top 84%
Model Reduction and Neural Networks
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