Applications of a Combined Approach of Kinetic Monte Carlo Simulations and Machine Learning to Model Atomic Layer Deposition (ALD) of Metal Oxides

Author

Emily Justus, Missouri State UniversityFollow

Date of Graduation

Spring 2022

Degree

Master of Science in Materials Science

Department

Physics, Astronomy, and Materials Science

Committee Chair

Ridwan Sakidja

Abstract

Metal-oxides such as ZnO or Al₂O₃ synthesized through Atomic Layer Deposition (ALD) have been of great research interest as the candidate materials for ultra-thin tunnel barriers. In this study, I have applied a 3D on-lattice Kinetic Monte Carlo (kMC) code developed by Timo Weckman’s group to simulate the growth mechanisms of the tunnel barrier layer and to evaluate the role of various experimentally relevant factors in the ALD processes. I have systematically studied the effect of parameters such as the chamber pressure temperature, pulse, and purge times. The database generated from the kMC simulations was subsequently used as descriptors in the subsequent analyses via Machine Learning algorithms. The simulated results of a combined approach of kMC and ML were then compared to the experimental results.

Keywords

kinetic Monte Carlo algorithm, simulation, zinc oxide, atomic layer deposition, machine learning, linear regression, multilayer perceptron, M5P

Subject Categories

Atomic, Molecular and Optical Physics

Copyright

© Emily Justus

Recommended Citation

Justus, Emily, "Applications of a Combined Approach of Kinetic Monte Carlo Simulations and Machine Learning to Model Atomic Layer Deposition (ALD) of Metal Oxides" (2022). MSU Graduate Theses. 3736.
https://bearworks.missouristate.edu/theses/3736