Reliability analysis and condition monitoring of SAC+ solder joints under high thermomechanical stress conditions using neuronal networks

Zippelius, Andreas; Hanß, Alexander; Schmid, Maximilian; Pérez-Velázquez, Judith; Elger, Gordon

doi:10.1016/j.microrel.2021.114461

Cited by 9 publications

(3 citation statements)

References 10 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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“…Hou et al (2022) proposed a novel meshless model for computing stress evolution induced by electromigration in ultra-large-scale integrated circuits, which eliminates the need for time discretization and grid generation in traditional numerical stress evolution analysis, thereby saving computation time while ensuring satisfactory accuracy. Zippelius et al (2022) investigated the thermal-mechanical fatigue of different Sn-Ag-Cu (SAC) solders by transient thermal analysis (TTA), and predicted their thermal-mechanical fatigue behavior using ANNs. Zeng and Yan (2023) used the backpropagation (BP) neural network algorithm to construct a network model for predicting the fatigue life of micro-scale single crystal copper.…”

Section: Introductionmentioning

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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“…Most electronic failures that occur in devices are because of thermomechanical fatigue, a form of low-cycle fatigue (Vianco, 2017). Thermomechanical failures happen when the solder joints are subjected to thermal changes, because the different materials present have different coefficient of thermal expansions (CTEs) (Zippelius, 2022; Libot, 2018; Yan, 2022). Usually, in a PCBA, a solder joint and its surroundings involve materials such as the PCB, mainly constituted by an FR4 substrate (anisotropic CTE values dependent on PCB type: CTE XY = approximately 13–15 and CTE ZZ = approximately 45–350 ppm/°C), and the pad made of copper (CTE of 17–19 ppm/°C), on which the electronic components (CTE between 1.4–10.9 ppm/°C) are connected by solder (CTE of 17–22 ppm/°C) (Hinojosa, 2019; George, 2018; Multi-cb, 2022; Akbari, 2019).…”

Section: Introductionmentioning

confidence: 99%

Effect of the IMC layer geometry on a solder joint thermomechanical behavior

Capela

Souza

Costa

et al. 2022

SSMT

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Purpose In a printed circuit board assembly (PCBA), the coefficient of thermal expansion (CTE) mismatch between the solder joint materials has a detrimental impact on reliability. The mechanical stresses caused by the thermal changes of the assembly lead to fatigue and sometimes the failure of the solder joints. The purpose of this study is to propose a novel pad design to obtain an interrupted solder/substrate interface, to improve the PCBA reliability. Design/methodology/approach An interruption in the continuous intermetallic compound (IMC) layer of a solder joint was implemented, by the deposition of a silicone film in the pad, changing its geometry. That change allows a redistribution of stresses in the most ductile zone of the solder joint, the solder. The stress concentration at the solder/substrate interface is reduced, as well as the general state of stress at the solder joint. Findings A new way was developed to reduce the stress on the solder joints, caused by thermal variations, because of the different components CTEs mismatch. This new method consists of interrupting the IMC layers of the solder joint, strategically, redirecting the usual stresses to a more ductile area of the joint, the solder. This is an innovative method that allows increase the lifetime of PCBAs and the equipments. Originality/value In this study, a new pad design concept for higher solder joint reliability was developed to reduce the shear stress in the solder joints because of the CTE mismatch between all the solder joint components.

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Reliability analysis and condition monitoring of SAC+ solder joints under high thermomechanical stress conditions using neuronal networks

Cited by 9 publications

References 10 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

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

Effect of the IMC layer geometry on a solder joint thermomechanical behavior

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