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Quantitative optical mapping of two-dimensional materials.

Bjarke S Jessen1,2, Patrick R Whelan3, David M A Mackenzie4,3

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Summary
This summary is machine-generated.

Automated optical contrast analysis rapidly identifies two-dimensional materials (2DM). This machine vision strategy eliminates false positives, enabling accurate layer number and coverage determination for 2DM research.

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Area of Science:

  • Materials Science
  • Condensed Matter Physics
  • Nanotechnology

Background:

  • Optical contrast is crucial for identifying two-dimensional materials (2DM) down to monolayer thickness.
  • Manual analysis of optical contrast is time-consuming and prone to errors, hindering rapid 2DM research.
  • Automated schemes are essential for fast and reliable 2DM identification.

Purpose of the Study:

  • To develop a fast and reliable automated method for identifying two-dimensional materials (2DM) using optical contrast.
  • To eliminate false-positives in 2DM identification through a computationally light machine vision strategy.
  • To enable accurate determination of 2DM coverage, contamination, and layer number.

Main Methods:

  • Modeling expected 2DM contrast in digital images to identify candidate regions.
  • Employing a machine vision strategy with binary thresholding, opening/closing filters, and edge detection for shape analysis.
  • Utilizing data pyramids for high-resolution optical coverage maps and a zoomable interface for real-time processing.

Main Results:

  • Successfully modeled 2DM contrast to automatically identify candidate regions.
  • Developed a computationally light strategy to eliminate false-positives, improving accuracy.
  • Enabled real-time processing and analysis of large datasets for 2DM characterization.

Conclusions:

  • A standard optical microscope with a CCD camera can serve as an analysis tool for 2DM.
  • The automated method accurately determines 2DM coverage, contamination, and layer number.
  • This approach accelerates two-dimensional materials research by providing efficient and reliable analysis.