@article{0f92a52011ae4c139e3960d4c9f88234,
title = "Exploring effective charge in electromigration using machine learning",
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 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.",
author = "Liu, {Yu Chen} and Benjamin Afflerbach and Ryan Jacobs and Lin, {Shih Kang} and Dane Morgan",
note = "Funding Information: 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. Ministry of Science and Technology http://dx.doi.org/10.13039/100007225 106-2628-E-006-002-MY3 107-2917-I-006-008 National Science Foundation (NSF) Software Infrastructure for Sustained Innovation (SI2) No. 1148011 pdf S2159685919000636a.pdf Publisher Copyright: Copyright {\textcopyright} Materials Research Society 2019.",
year = "2019",
month = jun,
day = "1",
doi = "10.1557/mrc.2019.63",
language = "English",
volume = "9",
pages = "567--575",
journal = "MRS Communications",
issn = "2159-6859",
publisher = "Cambridge University Press",
number = "2",
}