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您的检索式：作者名="Nele Moelans"
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| 1 | Integration of machine learning with phase field method to model the electromigration induced Cu_(6)Sn_(5) IMC growth at anode side Cu/Sn interface显示文摘Currently,in the era of big data and 5G communication technology,electromigration has become a serious reliability issue for the miniaturized solder joints used in microelectronic devices.Since the effective charge number(Z*)is considered as the driving force for electromigration,the lack of accurate experimental values for Z* poses severe challenges for the simulation-aided design of electronic materials.In this work,a data-driven framework is developed to predict the Z* values of Cu and Sn species at the anode based LIQUID,Cu_(6)Sn_(5) intermetallic compound(IMC)and FCC phases for the binary Cu-Sn system undergoing electromigration at 523.15 K.The growth rate constants(kem)of the anode IMC at several magnitudes of applied low current density(j=1×10^6 to 10×10^6A/m^2)are extracted from simulations based on a 1D multi-phase field model.A neural network employing Z* and j as input features,whereas utilizing these computed kemdata as the expected output is trained.The results of the neural network analysis are optimized with experimental growth rate constants to estimate the effective charge numbers.For a negligible increase in temperature at low j values,effective charge numbers of all phases are found to increase with current density and the increase is much more pronounced for the IMC phase.The predicted values of effective charge numbers Z* are then utilized in a 2D simulation to observe the anode IMC grain growth and electrical resistance changes in the multi-phase system.As the work consists of the aspects of experiments,theory,computation,and machine learning,it can be called the four paradigms approach for the study of electromigration in Pb-free solder.Such a combination of multiple paradigms of materials design can be problem-solving for any future research scenario that is marked by uncertainties regarding the determination of material properties. | Anil Kunwar Yuri Amorim Coutinho Johan Hektor Haitao Ma Nele Moelans | 2020 | Journal of Materials Science & Technology2020，59，24： | 3 |
| 2 | Combining thermodynamics with tensor completion techniques to enable multicomponent microstructure prediction显示文摘Multicomponent alloys show intricate microstructure evolution,providing materials engineers with a nearly inexhaustible variety of solutions to enhance material properties.Multicomponent microstructure evolution simulations are indispensable to exploit these opportunities.These simulations,however,require the handling of high-dimensional and prohibitively large data sets of thermodynamic quantities,of which the size grows exponentially with the number of elements in the alloy,making it virtually impossible to handle the effects of four or more elements.In this paper,we introduce the use of tensor completion for highdimensional data sets in materials science as a general and elegant solution to this problem.We show that we can obtain an accurate representation of the composition dependence of high-dimensional thermodynamic quantities,and that the decomposed tensor representation can be evaluated very efficiently in microstructure simulations.This realization enables true multicomponent thermodynamic and microstructure modeling for alloy design. | Yuri Amorim Coutinho Nico Vervliet Lieven De Lathauwer Nele Moelans | 2020 | npj Computational Materials2020，，1： | 0 |
| 3 | 硅灰石晶体在三元氧化物熔体中的生长行为研究(英文)显示文摘利用耦合FACTSage Toxide热力学数据库的定量相场模型,本研究模拟了硅灰石(CaSiO_3)在CaO-Al_2O_3-SiO_2体系中的恒温晶体生长过程,并研究了熔体组分和温度对CaSiO_3结晶过程的影响。结果表明,硅灰石的形貌主要由其表面能的各向异性所决定,而几乎不受界面动力学的各向异性所影响。此外,随着温度的降低,析出的硅灰石的生长方式由平面生长向枝晶生长方式转变,于此同时,更加精细的枝晶结构也逐渐呈现出来。模拟所得的枝晶生长速度和尖端半径与Ivanstov理论所得结果一致,和实验测得的数据也处于同一数量级。 | 刘晶晶 HEULENS Jeroen GUO Mu-Xing MOELANS Nele | 2016 | 无机材料学报2016，31，5： | 0 |
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