Integration of machine learning with phase field method to model the electromigration induced Cu6Sn5 IMC growth at anode side Cu/Sn interface
Anil Kunwara,*(
), Yuri Amorim Coutinhoa, Johan Hektorb,c, Haitao Mad, Nele Moelansa
), Yuri Amorim Coutinhoa, Johan Hektorb,c, Haitao Mad, Nele Moelansa
.
Fig. 1.. Experimental tools (paradigm I) are utilized to ascertain the phases at the material interface, and also to perform the validation work during inverse design of electromigration parameters. The theoretical equations (paradigm II) are solved computationally in the finite element method based computational model (paradigm III). The artificial neural network (ANN, paradigm IV) is used to predict the effective charge number. And finally, with all the quantities determined, the FEM is used to compute the electrical resistance in the multiphase system.
