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Universal image segmentation for optical identification of 2D materials.

Randy M Sterbentz1, Kristine L Haley1, Joshua O Island2

  • 1Department of Physics and Astronomy, University of Nevada Las Vegas, Las Vegas, NV, 89154, USA.

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|March 12, 2021
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Summary

This study introduces an automated image segmentation program using unsupervised clustering to identify the thickness of 2D materials from optical microscopy images. The program achieves high accuracy for various materials and substrates, offering a universal solution.

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Area of Science:

  • Materials Science
  • Computer Science
  • Image Analysis

Background:

  • Machine learning enhances data analysis, particularly in image segmentation.
  • Accurate identification of 2D material thickness is crucial for their application.

Purpose of the Study:

  • To develop an automated program for identifying the thickness of 2D materials using image segmentation.
  • To achieve universal applicability across different materials and substrates.

Main Methods:

  • Utilized unsupervised clustering algorithms for image segmentation.
  • Incorporated analysis of all three digital color channels.
  • Applied Gaussian mixture models for cluster fitting.

Main Results:

  • Achieved automatic thickness identification of 2D materials from optical microscopy images.
  • Reached approximately 95% pixel accuracy for mono- and few-layer flakes.
  • Demonstrated generality across opaque and transparent substrates.

Conclusions:

  • The developed program offers a facile and universal method for automatic 2D material identification.
  • This approach overcomes limitations of previous methods by analyzing all color channels and using flexible cluster fitting.