Exploring effective charge in electromigration using machine learning. (June 2019) - Liu, Yu-chen; Afflerbach, Benjamin; Jacobs, Ryan; Lin, Shih-kang; Morgan, Dane
Abstract: Abstract : 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 R 2 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.