Investigation of failure mechanism of aluminum-scandium wire bond contact under active power cycle test

Yamaguchi, Tadashi; Suto, Yuya; Araki, Noritoshi; Eto, Miki; Fischer, Felix R.; Groth, Anne

doi:10.1016/j.microrel.2023.114956

Cited by 4 publications

(1 citation statement)

References 7 publications

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“…In practical applications, the bonded interface is subjected to large thermal stresses due to the difference between the coefficients of thermal expansion (CTEs) of the Al bonding wires and the Si chip. As the service time increases, cracks develop at the bonded interface and gradually expand, degrading the mechanical properties of the bonded interface [ 3 ]. The degradation of the mechanical properties of the bonded interface seriously affects the conductive and heat transfer characteristics of the IGBT module and ultimately leads to the IGBT module failing.…”

Section: Introductionmentioning

confidence: 99%

Using Machine Learning and Finite Element Analysis to Extract Traction-Separation Relations at Bonding Wire Interfaces of Insulated Gate Bipolar Transistor Modules

Zhao,

An,

Wang

et al. 2024

Materials

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For insulated gate bipolar transistor (IGBT) modules using wire bonding as the interconnection method, the main failure mechanism is cracking of the bonded interface. Studying the mechanical properties of the bonded interface is crucial for assessing the reliability of IGBT modules. In this paper, first, shear tests are conducted on the bonded interface to test the bonded interface’s strength. Then, finite element–cohesive zone modeling (FE-CZM) is established to describe the mechanical behavior of the bonded interface. A novel machine learning (ML) architecture integrating a convolutional neural network (CNN) and a long short-term memory (LSTM) network is used to identify the shape and parameters of the traction separation law (TSL) of the FE-CZM model accurately and efficiently. The CNN-LSTM architecture not only has excellent feature extraction and sequence-data-processing abilities but can also effectively address the long-term dependency problem. A total of 1800 sets of datasets are obtained based on numerical computations, and the CNN-LSTM architecture is trained with load–displacement (F–δ) curves as input parameters and TSL shapes and parameters as output parameters. The results show that the error rate of the model for TSL shape prediction is only 0.186%. The performance metric’s mean absolute percentage error (MAPE) is less than 3.5044% for all the predictions of the TSL parameters. Compared with separate CNN and LSTM architectures, the proposed CNN-LSTM-architecture approach exhibits obvious advantages in recognizing TSL shapes and parameters. A combination of the FE-CZM and ML methods in this paper provides a promising and effective solution for identifying the mechanical parameters of the bonded interfaces of IGBT modules.

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Section: Introductionmentioning

confidence: 99%