Machine learning for board-level drop response of BGA packaging structure

Mao, Minghui; Wang, Wenwu; Lu, Changheng; Jia, Fuchen; Long, Xu

doi:10.1016/j.microrel.2022.114553

Cited by 13 publications

(3 citation statements)

References 27 publications

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“…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%

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: 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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“…A machine learning (ML) framework has recently been utilized to analyze various engineering problems, including cardiovascular organs [14][15][16] , cantilevered structures 17,18 , and composite materials 19,20 . In particular, the ML framework allows the prediction of critical reliability parameters in chip package structures, such as energy release rates 21 , warpage behaviors 22 , and drop responses 23 . Furthermore, several studies have presented analysis models for the accelerated reliability of solder joints under thermal cycling conditions using arti cial neural network (ANN) architectures [24][25][26] .…”

Section: Introductionmentioning

confidence: 99%

“…[19,20] In particular, the ML framework allows the prediction of critical reliability parameters in chip package structures, such as energy release rates, [21] warpage behaviors, [22] and drop responses. [23] Furthermore, several studies have presented analysis models for the accelerated reliability of solder joints under thermal cycling conditions using artificial neural network (ANN) architectures. [24][25][26] These ML approaches driven by FEM simulation data have been regarded as a fast and accurate surrogate of the simulation.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning‐Based Solution for Thermomechanical Analysis of MMIC Packaging

Kang

Lee

Kim

et al. 2022

Adv Materials Technologies

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Thermo-mechanical analysis of monolithic microwave integrated circuit (MMIC) packaging is essential to guarantee the reliability of radio frequency/microwave applications. However, a method for fast and accurate analysis of MMIC packaging structures has not been developed. Here, we demonstrate a machine learning (ML)-based solution for thermo-mechanical analysis of MMIC packaging. This MLbased solution analyzes temperature and thermal stresses considering 13 design parameters categorized into material properties, geometric characteristics, and thermal boundary conditions. Finite element simulation with the Monte Carlo method is utilized to prepare 40,000 data samples for supervised learning and validation of the ML solution, and a laser-assisted thermal experiment veri es the accuracy of the simulation. After data preparation, regression tree ensemble and arti cial neural network (ANN) learning models are investigated. The results indicate that the ANN models accurately predict the temperature and thermal stresses, showing a 1.69 % minimum error. Finally, the developed ML solution is deployed as a web application format for facile approaches. We believe that this study will provide a guideline for developing ML-based solutions in chip packaging design technology.

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Numerical Simulation of Impact Response of Board-Level Packaging Structure

Long,

Hu,

et al. 2023

Mechanisms and Machine Science

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Machine learning for board-level drop response of BGA packaging structure

Cited by 13 publications

References 27 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

Machine Learning‐Based Solution for Thermomechanical Analysis of MMIC Packaging

Numerical Simulation of Impact Response of Board-Level Packaging Structure

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