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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

引用

Journal of Materials Science & Technology 2020年第24期59卷 203-219页

作者： Anil Kunwar Yuri Amorim Coutinho johan hektor Haitao Ma Nele Moelans Department of Materials Engineering KU LeuvenKasteelpark Arenberg 44LeuvenB-3001Belgium LUNARC-Centre for Scientific and Technical Computing at Lund University Lund UniversityP.O.Box 118SE-22100LundSweden Deutsches Elektronen-Synchrotron(DESY) Notkestrasse 85Hamburg22607Germany School of Materials Science and Engineering Dalian University of TechnologyDalian116024China

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 *** 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 *** 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 *** 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 *** results of the neural network analysis are optimized with experimental growth rate constants to estimate the effective charge *** 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 *** 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 *** 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 *** 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.

关键词： Phase field method Artificial neural network Intemetallic compound Current density Synchrotron radiation

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