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

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.