Reliability Characterization of Solder Joints in Electronic Systems Through a Neural Network Aided Approach

Reihanisaransari, Reza; Samadifam, Farshad; Salameh, Anas A.; Mohammadiazar, Farzam; Amiri, Nafiseh; Channumsin, Sittiporn

doi:10.1109/access.2022.3224008

Cited by 7 publications

(1 citation statement)

References 45 publications

(55 reference statements)

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“…The training process of a neural network primarily entails the use of the BP algorithm. During training, the neural network adjusts the weights and biases of its nodes iteratively in response to the discrepancy between its predicted outputs and the desired target values, thereby enhancing its accuracy and generalization capabilities (Reihanisaransari et al, 2022). The flowchart in Figure 4 illustrates the methodology used for neural network prediction in this work.…”

Section: Neural Networkmentioning

confidence: 99%

Temperature and current density prediction in solder joints using artificial neural network method

Liu,

Xu,

et al. 2023

SSMT

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Purpose Due to the miniaturization of electronic devices, the increased current density through solder joints leads to the occurrence of electromigration failure, thereby reducing the reliability of electronic devices. The purpose of this study is to propose a finite element-artificial neural network method for the prediction of temperature and current density of solder joints, and thus provide reference information for the reliability evaluation of solder joints. Design/methodology/approach The temperature distribution and current density distribution of the interconnect structure of electronic devices were investigated through finite element simulations. During the experimental process, the actual temperature of the solder joints was measured and was used to optimize the finite element model. A large amount of simulation data was obtained to analyze the neural network by varying the height of solder joints, the diameter of solder pads and the magnitude of current loads. The constructed neural network was trained, tested and optimized using this data. Findings Based on the finite element simulation results, the current is more concentrated in the corners of the solder joints, generating a significant amount of Joule heating, which leads to localized temperature rise. The constructed neural network is trained, tested and optimized using the simulation results. The ANN 1, used for predicting solder joint temperature, achieves a prediction accuracy of 96.9%, while the ANN 2, used for predicting solder joint current density, achieves a prediction accuracy of 93.4%. Originality/value The proposed method can effectively improve the estimation efficiency of temperature and current density in the packaging structure. This method prevails in the field of packaging, and other factors that affect the thermal, mechanical and electrical properties of the packaging structure can be introduced into the model.

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Section: Neural Networkmentioning

confidence: 99%