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arXiv · 1907.01480

Exploring effective charge in electromigration using machine learning

Yu-chen Liu·Benjamin Afflerbach·Ryan Jacobs·Shih-kang Lin·Dane Morgan
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Abstract

The effective charge of an element is a parameter characterizing the electromgration 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 5-fold (leave-out-alloy-group) cross-validation yielded root-mean-square-error divided by whole data set standard deviation (RMSE/$\sigma$) values of 0.37 $\pm$ 0.01 (0.22 $\pm$ 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.

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Yu-chen Liu, Benjamin Afflerbach, Ryan Jacobs, Shih-kang Lin, Dane Morgan. 2019-07-02. Exploring effective charge in electromigration using machine learning. https://doi.org/10.1557/mrc.2019.63

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