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Exploring effective charge in electromigration using machine learning

  • Yu-chen Liu (a1) (a2), Benjamin Afflerbach (a2), Ryan Jacobs (a2), Shih-kang Lin (a1) (a3) (a4) and Dane Morgan (a2)...


The effective charge of an element is a parameter characterizing the electromigration effect, which can determine the reliability of interconnection in electronic technologies. In this work, machine learning approaches were employed to model the effective charge (z*) as a linear function of physically meaningful elemental properties. Average fivefold (leave-out-alloy-group) cross-validation yielded root-mean-square-error divided by whole data set standard deviation (RMSE/σ) values of 0.37 ± 0.01 (0.22 ± 0.18), respectively, and R2 values of 0.86. Extrapolation to z* of totally new alloys showed limited but potentially useful predictive ability. The model was used in predicting z* for technologically relevant host–impurity pairs.


Corresponding author

Address all correspondence to Dane Morgan at


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