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Comprehensive Characterization of Extended Defects in Semiconductor Materials by a Scanning Electron Microscope

Comprehensive Characterization of Extended Defects in Semiconductor Materials by a Scanning Electron Microscope

Published on: May 28, 2016

Defect identification and statistics toolbox: automated defect analysis for scanning probe microscopy images.

Alana Gudinas¹, Jason Moscatello¹, Shawna M Hollen¹

¹University of New Hampshire, Department of Physics and Astronomy, 9 Library Way, Durham, NH 03824, United States of America.

Journal of Physics. Condensed Matter : an Institute of Physics Journal

|October 15, 2020

Summary

This study introduces the Defect Identification and Statistics Toolbox (DIST), a new software for analyzing atomic defects in scanning probe microscopy (SPM) images. DIST automates defect analysis, improving accuracy and efficiency.

Keywords:

SPM STM automated defect identification data analysis defect defect statistics

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Area of Science:

Materials Science
Surface Science
Nanotechnology

Background:

Manual identification of atomic defects in Scanning Probe Microscopy (SPM) images is time-consuming and prone to error.
Accurate defect analysis is crucial for understanding material properties and performance.

Purpose of the Study:

To present the Defect Identification and Statistics Toolbox (DIST), a novel image processing solution for SPM data.
To enable accurate and efficient identification and analysis of atomic defects in SPM images.

Main Methods:

Development of an image processing toolbox (DIST) integrating automated algorithms with user interaction.
Implementation of contour plotting for isolating image features from background noise.
Utilizing DIST for interactive SPM image processing and defect identification.

Main Results:

DIST successfully identifies and classifies atomic defects in SPM images with high accuracy.
Automated computation of critical defect statistics is achieved.
Interactive processing capabilities enhance user control and analysis efficiency.

Conclusions:

The Defect Identification and Statistics Toolbox (DIST) offers a significant advancement for SPM image analysis.
DIST streamlines the process of defect identification, leading to more efficient research and development.
This toolbox facilitates a deeper understanding of material properties through precise defect characterization.