Exploring effective charge in electromigration using machine learning

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.

9 pages, 5 figures

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• A General-Purpose Machine Learning Framework for Predicting Properties of Inorganic Materials
• Predicting the thermodynamic stability of perovskite oxides using machine learning models
• Robust FCC solute diffusion predictions from ab-initio machine learning methods
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Cited by in corpus (3)

• Opportunities and Challenges for Machine Learning in Materials Science
• The Materials Simulation Toolkit for Machine Learning (MAST-ML): an automated open source toolkit to accelerate data-driven materials research
• Machine learning for impurity charge-state transition levels in semiconductors from elemental properties using multi-fidelity datasets