Modeling temperature dependent chemical reaction of intermetallic compound growth

Morozov, Aleksandr; Freidin, A. B.; Müller, Wolfgang; Семенча, А. В.; Tribunskiy, M.

doi:10.1109/eurosime.2019.8724531

Cited by 8 publications

(3 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Arafat et al [49] also presented a multicomponent diffusion-based model and confirmed that the intermetallic-growth rate becomes slow at the end of an aging process in the solder joints. By modeling temperature dependency and chemical reaction of intermetallics under thermal cycling, Morozov et al [50] suggested that the growth mechanism is originated from the multi-component diffusion in solids and affected by the residual stresses and strains in the joint zone.…”

Section: Detailed Microstructural/mechanical Characterizationmentioning

confidence: 99%

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

Chen

2023

J. Phys.: Condens. Matter

View full text Add to dashboard Cite

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.

show abstract

Section: Detailed Microstructural/mechanical Characterizationmentioning

confidence: 99%

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

Chen

2023

J. Phys.: Condens. Matter

View full text Add to dashboard Cite

show abstract

“…Finite element modelling of the reaction front propagation in a 2D plate with a groove was presented in Freidin et al (2016b). Applications for intermetallics formations were considered in Morozov et al (2018aMorozov et al ( , 2019 with numerical simulation based on isogeometric analysis (Morozov et al 2018b). Furthermore, the theory has been used to simulate two-phase lithiation of Si particles utilized in Li-ion batteries (Poluektov et al 2018), where the constituent materials undergoing finite elastoviscoplastic deformations were considered.…”

Section: Boundary Value Problems Of Mechanochemistry: Forbidden Zonesmentioning

confidence: 99%

Chemical Affinity Tensor in Coupled Problems of Mechanochemistry

Freidin

Vilchevskaya

2019

Encyclopedia of Continuum Mechanics

View full text Add to dashboard Cite

show abstract

“…As the soldering process is depending on diffusion between solder and substrate, the concentration changes in the Cu-Sn system also depends on the diffusion coefficient. As the diffusion coefficient is temperature dependent [14], it changes with temperature in every time steps. So, both Sn and Cu were taken as a global variable which changes in every time step during the simulation.…”

Section: Setting the Diffusion Coefficient As Variablementioning

confidence: 99%

Development of a Simplistic Method to Simulate the Formation of Intermetallic Compounds in Diffusion Soldering Process

Chaki¹

2019

AJMSP

View full text Add to dashboard Cite

A simplistic simulation technique has been developed for computing the individual intermetallic compound (IMC) thickness which is formed in substrate-solder (Cu-Sn) systems during the diffusion soldering process in high-temperature power electronic applications. The method requires the time-dependent temperature profile for the soldering process and the growth rate parameters (e.g. concentration gradient, diffusion coefficient, activation energy, etc.) for the development of IMC layers as input. The method is suitable for predicting the thickness of an intermetallic phase layer during the diffusion soldering process. As such, it can be used in high-temperature power electronic application's solder processing to enhance the reliability and lifetime of solder interconnections by allowing the control of the thickness of IMC layers. The method is demonstrated for IMC growth between pure copper as substrate and pure Sn as solder material. The growth behavior of the IMC layer is increased with increasing temperature over time according to the Arrhenius theory in the temperature range between 24°C to 260°C. To simulate the formation of IMC thickness in diffusion soldering interconnections, a simplistic way has been attempted using the popular commercial finite element simulation tool Comsol Multiphysics and scientific computing application 'Matlab'. By means of transient thermal input, the diffusion-controlled intermetallic phase formation is simulated here. Few assumptions are taken care of this simulation process, for example, no convection, no reaction, solid-solid diffusion, no the pressure effect on the computational domain.

show abstract

Modeling temperature dependent chemical reaction of intermetallic compound growth

Cited by 8 publications

References 25 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

Chemical Affinity Tensor in Coupled Problems of Mechanochemistry

Development of a Simplistic Method to Simulate the Formation of Intermetallic Compounds in Diffusion Soldering Process

Contact Info

Product

Resources

About