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Exploring effective charge in electromigration using machine learning

Published online by Cambridge University Press:  27 May 2019

Yu-chen Liu ,
Benjamin Afflerbach ,
Ryan Jacobs ,
Shih-kang Lin  and
Dane Morgan
Yu-chen Liu
Affiliation:
Department of Materials Science and Engineering, National Cheng Kung University, Tainan city 70101, Taiwan Department of Materials Science and Engineering, University of Wisconsin-Madison, Madison, USA
Benjamin Afflerbach
Affiliation:
Department of Materials Science and Engineering, University of Wisconsin-Madison, Madison, USA
Ryan Jacobs
Affiliation:
Department of Materials Science and Engineering, University of Wisconsin-Madison, Madison, USA
Shih-kang Lin
Affiliation:
Department of Materials Science and Engineering, National Cheng Kung University, Tainan city 70101, Taiwan Center for Micro/Nano Science and Technology, National Cheng Kung University, Tainan city 70101, Taiwan Hierarchical Green-Energy Materials (Hi-GEM) Research Center, National Cheng Kung University, Tainan 70101, Taiwan
Dane Morgan*
Affiliation:
Department of Materials Science and Engineering, University of Wisconsin-Madison, Madison, USA
*
Address all correspondence to Dane Morgan at [email protected]
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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.

Type
Artificial Intelligence Research Letters
Information
MRS Communications , Volume 9 , Issue 2 , June 2019 , pp. 567 - 575
Copyright
Copyright © Materials Research Society 2019 

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