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

Alana Gudinas; Jason Moscatello; Shawna M Hollen

doi:10.1088/1361-648X/abc1b2

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

J Phys Condens Matter. 2020 Oct 29;33(4). doi: 10.1088/1361-648X/abc1b2.

Authors

Alana Gudinas¹, Jason Moscatello¹, Shawna M Hollen¹

Affiliation

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

PMID: 33059332
DOI: 10.1088/1361-648X/abc1b2

Abstract

Identifying and classifying defects in scanning probe microscopy (SPM) images is an important task that is tedious to perform by hand. In this paper we present the defect identification and statistics toolbox (DIST), an image processing toolbox for identifying and analyzing atomic defects in SPM images. DIST combines automation with user input to accurately and efficiently identify defects and automatically compute critical statistics. We describe using DIST for interactive image processing, generating contour plots for isolating extrema from an image background, and processes for identifying defects.

Keywords: SPM; STM; automated defect identification; data analysis; defect; defect statistics.