Iterative Machine Learning-Aided Framework Bridges Between Fatigue and Creep Damages in Solder Interconnections

Samavatian, Vahid; Fotuhi‐Firuzabad, Mahmud; Samavatian, Majid; Dehghanian, Payman; Blaabjerg, Frede

doi:10.1109/tcpmt.2021.3136751

Cited by 21 publications

(10 citation statements)

References 54 publications

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“…Secondly, the data normalization has to be applied into the input and target datasets. Previous literatures [23,24] have shown the importance of the data normalization on the AI training process. The results show that a good normalization range such as [0.2 0.8] can improve the AI performance during the training process.…”

Section: Frameworkmentioning

confidence: 99%

“…In this work, we aim to propose a machine learning (ML) model, which enables to predict the microstructural items, which are the indicators of IMC-growth mechanism under thermal cycling. In the microelectronics industry, the ML-based model has been extensively used to predict the reliability of components on the basis of physical and mechanical parameters without considering the microstructural characteristics [22][23][24][25][26]. Hence, our ML model can open a new solution for evaluating the solder microstructure in a wide range of thermal cycles by just examining few samples.…”

Section: Introductionmentioning

confidence: 99%

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Expectation–maximization machine learning model for micromechanical evaluation of thermally-cycled solder joints in a semiconductor

Chen

2023

J. Phys.: Condens. Matter

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This paper aims to study the microstructural and micromechanical variations of solder joints in a semiconductor under the evolution of thermal-cycling loading. For this purpose, a model was developed on the basis of expectation-maximization machine learning (ML) and nanoindentation mapping. Using this model, it is possible to predict and interpret the microstructural features of solder joints through the micromechanical variations (i.e. elastic modulus) of interconnection. According to the results, the classification of Sn-based matrix, intermetallic compounds and the grain boundaries with specified elastic-modulus ranges was successfully performed through the ML model. However, it was detected some overestimations in regression process when the interfacial regions got thickened in the microstructure. The ML outcomes also revealed that the thermal-cycling evolution was accompanied with stiffening and growth of intermetallic compounds; while the spatial portion of Sn-based matrix decreased in the microstructure. It was also figured out that the stiffness gradient becomes intensified in the treated samples, which is consistent with this fact that the thermal cycling increases the mechanical mismatch between the matrix and the intermetallic compounds.

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Section: Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Expectation–maximization machine learning model for micromechanical evaluation of thermally-cycled solder joints in a semiconductor

Chen

2023

J. Phys.: Condens. Matter

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“…Artificial intelligence has been widely used as a prediction model in many applications [38]- [40]. However, it has been recently become popular in the reliability assessment of power electronic components [41], [42]. In this study, we established our proposed method based on the fully connected deep neural network.…”

Section: Proposed Neural Network For Low-cycle Fatigue Solder Joint L...mentioning

confidence: 99%

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

et al. 2022

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Rapid and precise reliability evaluation of electronic circuits plays a key role in the design stage of the electronic systems. The task becomes even more difficult when several major parameters contribute into the reliability evaluation. This paper proposes a neural network aided approach as a prediction tool for estimating the useful lifetime of the ball shaped solder joint as the most resistless part under accidental drops in the electronic devices. Several contributory factors including ball grid array (BGA) chip location, printed circuit board (PCB) thickness, solder alloy composition and solder ball volume are considered in our proposed rapid prediction model and their effects are investigated. 480 finite element simulations as well as 20 experimental tests are performed to obtain an enriched database for our neural network based prediction model. The accuracy of the proposed model is calculated as 97.55% in comparison with the finite element and the experimental results. Ability of considering multi contributory factors in the reliability evaluation of the BGA chip makes our proposed approach be a suitable candidate in design for reliability of the electronic systems.INDEX TERMS Solder joint, drop test, lifetime estimation, machine learning.

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“…Machine learning algorithm has been widely used in power electronics [49]- [51] along with several other domains [9], [52]. In the different applications, it works as the universal mapping function for capturing the desired target through the best structure of the input data.…”

Section: Iiimachine Learning For Controlling Dc-dc Power Electronics ...mentioning

confidence: 99%

Application of Mixture of Experts in Machine Learning-Based Controlling of DC-DC Power Electronics Converter

et al. 2022

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Regardless of the application in which power electronic converters are deployed, their desired performances crucially depend on the controlling strategy while different impressive parameters are varied. This paper offers a novel controlling strategy originated from the mixture of two well-known controlling techniques, namely feedback (FBC) and model predictive (MPC) controllers. It uses the advantages of the above-mentioned controllers while their drawbacks or limitations are covered by each other using the mixture of experts (MoE) technique. Two neural networks for capturing the features of MPC and FBC along with a gating network as the main tool of MoE are employed in order to optimize the controlling of the DC-DC power electronic converters. These networks are trained through a set of pair data as the input vector and the target data. The results reveal that better performance can be obtained via benefit exploitation of both controlling techniques using a comprehensive MoE. The dynamic and steady state errors are decreased by 5% and 8%, respectively which demonstrate a global enhancement in the controlling of the DC-DC power electronic converters.

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Iterative Machine Learning-Aided Framework Bridges Between Fatigue and Creep Damages in Solder Interconnections

Cited by 21 publications

References 54 publications

Expectation–maximization machine learning model for micromechanical evaluation of thermally-cycled solder joints in a semiconductor

Expectation–maximization machine learning model for micromechanical evaluation of thermally-cycled solder joints in a semiconductor

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

Application of Mixture of Experts in Machine Learning-Based Controlling of DC-DC Power Electronics Converter

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