Exploring effective charge in electromigration using machine learning. (June 2019)

• Record Type: Journal Article
• Authors: Liu, Yu-chen; Afflerbach, Benjamin; Jacobs, Ryan; Lin, Shih-kang; Morgan, Dane
• Is Part Of: MRS communications. Volume 9:Number 2(2019)
• Page Start: 567
• Page End: 575
• Publication Date: 2019-06
• Subjects: Materials -- Periodicals; 620.11
• DOI: 10.1557/mrc.2019.63
• Languages: English
• ISSNs: 2159-6859

Abstract

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.