Application of machine learning in rapid analysis of solder joint geometry and type on thermomechanical useful lifetime of electronic components

Chen, Tzu-Chia; Alazzawi, Fouad Jameel Ibrahim; Salameh, Anas A.; Ahmed, Alim Al Ayub; Pustokhina, Inna; Aravindhan, Surendar; Oudah, Atheer Y.

doi:10.1080/15376494.2021.2014002

Cited by 12 publications

(4 citation statements)

References 48 publications

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“…24,25 However, the proposed ML models have mainly focused on the physical parameters of solder joints for estimating the fatigue lifetime under different states. [26][27][28][29] To be specific, the models collected the input parameters, such as geometry features, thermal load specifications, and physical properties of solder interconnections, and established a ML-based algorithm to predict the fatigue lifetime as the target. Hence, until now there has been no published work characterizing the fatigue microstructure of solder joints through ML-based approaches.…”

Section: Introductionmentioning

confidence: 99%

A Micromechanical Data-Driven Machine-Learning Approach for Microstructural Characterization of Solder Balls in Electronic Packages Subjected to Thermomechanical Fatigue

Kurniawan

Sayed

Luna

et al. 2023

J. Electron. Mater.

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A combination of nanoindentation mapping and machine-earning (ML) modeling has been used to characterize the micro-structural changes in SnPb solder balls exposed to thermal cycling. The model facilitated the microstructural evaluation of solder bumps through the prediction of microscale variations of Young's modulus in the joint zone. The outcomes revealed that the micromechanical data-driven ML model precisely classified the microstructural constituents, i.e., β-Sn and α-Pb, along with the grain boundary (GB) regions. However, some deviations were observed in GB recognition, when the elastic modulus gradient was not sharp enough. The predictive results also revealed that the increase in number of thermal cycles led to stiffening and grain coarsening of α-Pb, while the β-Sn matrix mainly remained stable. Moreover, it was found that the thermal cycling intensified structural heterogeneity in the solder and sharpened the elastic modulus variations at the GB regions. In summary, the outcomes of this study demonstrate the prediction possibility of microstructural features in SnPb solder balls with a predefined thermal cycle numbers, and unfolded the relationship between morphological characteristics and microscale mechanical properties.

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Section: Introductionmentioning

confidence: 99%

A Micromechanical Data-Driven Machine-Learning Approach for Microstructural Characterization of Solder Balls in Electronic Packages Subjected to Thermomechanical Fatigue

Kurniawan

Sayed

Luna

et al. 2023

J. Electron. Mater.

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“…Increasing solder joint length in double cantilever beam samples under bending revealed that increasing solder joint length up to about 30 mm, increases the breaking force noticeably [19]. Due to the worldwide use of artificial intelligence in recent years in various areas of science, researchers in the field of solder joints have also benefited from this issue, and a large number of research has been done regarding the application of machine learning in the field of solder joints [20][21][22][23][24]. Samavatian [25], identified important factors in printed circuit board failure caused by thermal cycling.…”

Section: Introductionmentioning

confidence: 99%

“…They trained an artificial neural network which can predict the lifetime for desired solder joints in electronic components with 82% accuracy. Chen et al [20] tried to estimate solder joint lifetime in a PCB with ball gay array configuration by means of neural networks. They considered 14 parameters influencing the lifetime of the solder joints as the network input vector and could predict the lifetime with 89% accuracy by using 360 data points.…”

Section: Introductionmentioning

confidence: 99%

Application of data mining techniques for assessment of fracture load and energy in double cantilever beam solder joints

Soroush,

Nourani

2023

JDAF

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Predicting the exact values of fracture load and energy has a significant effect in preventing solder joint failure. In this research, various double cantilever beam (DCB) solder joints with different geometric constraints, including adherend thickness, adherend width, loading arm length, and solder thickness were tested under mode I crack propagation and at a strain rate of 0.03 . According to ANOVA test results, all the mentioned geometric factors influence the fracture load, but adherend width and solder thickness don't change the failure energy remarkably, considering the type I error is equal to 0.05. The failure load and energy forecasting using the random forest algorithm showed that the prediction accuracy is 92% and 81% respectively. Linear regression and lasso regression were also utilized to identify the solder joint's fracture behavior. The coefficient of determination in both methods is acceptable for solder joint fracture force prediction, but for fracture energy, it has decreased to about 70%. Finally, multi-objective optimization was done with the help of the NSGA-II method, and the Pareto front diagram drawn according to the problem's constraints to find the optimal values for fracture force and energy.

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“…These defects are also correlated with lifetime prediction for the 3D IC. Chen et al 8 considered certain types of solder joint geometry and generated their dataset by using finite element models. The dataset was used to train their neural networks to estimate the lifetime of the simulated solder joints and provided some design and manufacture guidelines.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence deep learning for 3D IC reliability prediction

Hsu

Shie

Chen

et al. 2022

Sci Rep

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Three-dimensional integrated circuit (3D IC) technologies have been receiving much attention recently due to the near-ending of Moore’s law of minimization in 2D IC. However, the reliability of 3D IC, which is greatly influenced by voids and failure in interconnects during the fabrication processes, typically requires slow testing and relies on human’s judgement. Thus, the growing demand for 3D IC has generated considerable attention on the importance of reliability analysis and failure prediction. This research conducts 3D X-ray tomographic images combining with AI deep learning based on a convolutional neural network (CNN) for non-destructive analysis of solder interconnects. By training the AI machine using a reliable database of collected images, the AI can quickly detect and predict the interconnect operational faults of solder joints with an accuracy of up to 89.9% based on non-destructive 3D X-ray tomographic images. The important features which determine the “Good” or “Failure” condition for a reflowed microbump, such as area loss percentage at the middle cross-section, are also revealed.

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Application of machine learning in rapid analysis of solder joint geometry and type on thermomechanical useful lifetime of electronic components

Cited by 12 publications

References 48 publications

A Micromechanical Data-Driven Machine-Learning Approach for Microstructural Characterization of Solder Balls in Electronic Packages Subjected to Thermomechanical Fatigue

A Micromechanical Data-Driven Machine-Learning Approach for Microstructural Characterization of Solder Balls in Electronic Packages Subjected to Thermomechanical Fatigue

Application of data mining techniques for assessment of fracture load and energy in double cantilever beam solder joints

Artificial intelligence deep learning for 3D IC reliability prediction

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