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@ARTICLE{Kunwar:454551,
      author       = {Kunwar, Anil and Coutinho, Yuri Amorim and Hektor, Johan
                      and Ma, Haitao and Moelans, Nele},
      title        = {{I}ntegration of machine learning with phase field method
                      to model the electromigration induced {C}u$_{6}${S}n$_{5}$
                      {IMC} growth at anode side {C}u/{S}n interface},
      journal      = {Journal of materials science $\&$ technology},
      volume       = {59},
      issn         = {1005-0302},
      address      = {Shenyang},
      publisher    = {Ed. Board, Journal of Materials Science $\&$ Technology},
      reportid     = {PUBDB-2021-00602},
      pages        = {203 - 219},
      year         = {2020},
      abstract     = {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 (k$_{em}$) of the anode IMC at several
                      magnitudes of applied low current density (j = 1 × 10$^6$
                      to 10 × 10$^6$ A/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 k$_{em}$ data 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.},
      cin          = {FS-PET-D},
      ddc          = {670},
      cid          = {I:(DE-H253)FS-PET-D-20190712},
      pnm          = {6213 - Materials and Processes for Energy and Transport
                      Technologies (POF3-621) / SWEDEN-DESY - SWEDEN-DESY
                      Collaboration $(2020_Join2-SWEDEN-DESY)$},
      pid          = {G:(DE-HGF)POF3-6213 / $G:(DE-HGF)2020_Join2-SWEDEN-DESY$},
      experiment   = {EXP:(DE-MLZ)NOSPEC-20140101},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000588638600021},
      doi          = {10.1016/j.jmst.2020.04.046},
      url          = {https://bib-pubdb1.desy.de/record/454551},
}