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Author:

Yu, P. (Yu, P..) | Dai, Y. (Dai, Y..) | Qin, F. (Qin, F..)

Indexed by:

CPCI-S EI Scopus

Abstract:

Sintered nano-silver is a widely used interconnect material in the third generation of power semiconductors, and is often used for service in extremely high-temperature environments due to its advantages of high melting temperature, making the reliability of interconnection more and more important for the overall reliability of the third-generation power semiconductor device. Different from traditional fatigue life prediction methods, a thermal fatigue lifetime dataset is collected based on Coffin-Manson thermal fatigue model and finite element simulations in this paper. A data-driven method based on the Support Vector Regression (SVR) model for predicting fatigue lifetime of sintered nano-silver layer is developed. The analysis includes identifying the key factors influencing interconnect reliability, and determining the main factors affecting the thermal fatigue lifetime of SiC modules. The results show that increasing the thickness of the sintered nano-silver interconnecting layer can enhance the lifetime of the interconnect structure, while elastic modulus and chip thickness negatively impact the lifetime of the sintered silver layer. The prediction accuracy and stability of the proposed data-driven SVR model are discussed and studied. © 2024 IEEE.

Keyword:

Reliability Fatigue life Sintered silver Machine learning Power module

Author Community:

  • [ 1 ] [Yu P.]Institute of Electronics Packaging Technology and Reliability, Beijing University of Technology, Department of Mechanics, Beijing, 100124, China
  • [ 2 ] [Dai Y.]Institute of Electronics Packaging Technology and Reliability, Beijing University of Technology, Department of Mechanics, Beijing, 100124, China
  • [ 3 ] [Qin F.]Institute of Electronics Packaging Technology and Reliability, Beijing University of Technology, Department of Mechanics, Beijing, 100124, China

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Year: 2024

Language: English

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ESI Highly Cited Papers on the List: 0 Unfold All

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Chinese Cited Count:

30 Days PV: 12

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