# Exploring effective charge in electromigration using machine learning

## TL;DR

This paper employs machine learning to model the effective charge in electromigration, achieving high accuracy and demonstrating potential for predicting alloy behavior and reliability in electronic interconnections.

## Contribution

The study introduces a machine learning model that predicts effective charge as a linear function of elemental properties, with validated accuracy and extrapolation capabilities.

## Key findings

- RMSE/$\sigma$ of 0.37 ± 0.01 indicating high model accuracy
- R^2 value of 0.86 demonstrating strong predictive power
- Limited but useful extrapolation to new alloys

## 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.

---
Source: https://tomesphere.com/paper/1907.01480