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Parameter estimation for pattern formation induced by ion bombardment of solid surfaces using deep learning.

Kevin M Loew1, R Mark Bradley2

  • 1Department of Physics, Colorado State University, Fort Collins, CO 80523, United States of America.

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|September 17, 2020
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Summary
This summary is machine-generated.

A new deep learning model accurately estimates parameters for modeling nanostructure formation during ion bombardment. This tool aids experimentalists in analyzing surface sputtering experiments.

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Area of Science:

  • Materials Science
  • Surface Science
  • Computational Physics

Background:

  • Surface nanostructuring via ion bombardment is often described by the anisotropic Kuramoto-Sivashinsky equation.
  • This equation requires five parameters dependent on experimental conditions (target material, ion properties, incidence angle).
  • Accurate parameter determination is crucial for understanding and predicting surface evolution.

Purpose of the Study:

  • To develop a rapid and accurate method for estimating the five parameters of the anisotropic Kuramoto-Sivashinsky equation.
  • To leverage deep learning for analyzing surface morphology images.
  • To provide a practical tool for experimentalists in ion sputtering.

Main Methods:

  • A deep learning model was trained using surface images generated by ion bombardment.
  • The model was designed to predict all five parameters of the anisotropic Kuramoto-Sivashinsky equation from a single input image.
  • Performance was evaluated using root-mean-square errors against known parameter ranges.

Main Results:

  • The deep learning model achieved root-mean-square errors below 3% of the parameter ranges used in training.
  • The model demonstrated high accuracy in estimating all five critical parameters.
  • This indicates a significant advancement in the speed and precision of parameter extraction.

Conclusions:

  • Deep learning offers an efficient and accurate approach to determine parameters governing ion beam sputtering.
  • The developed model can significantly expedite the analysis of surface nanostructure formation.
  • This tool can serve as a valuable complement or alternative to traditional parameter estimation methods.