Research on quantum dot STM morphological image recognition based on machine vision

In order to alleviate artificial work in the process of surface topography analysis of quantum dots and to make the STM image analysis process more automation, substrate miscut angle and morphological characteristics of quantum dots are investigated based on machine vision. Firstly, the step shape is extracted by erosion and edge detection, thus the miscut angle is calculated by inverse triangulation. And then, quantum dot number and corresponding spatial coordinates are extracted by binarization and threshold descent, based on which its uniformity is calculated by the neighborhood density, and size is found out after solving adhesive problem in image. Experimental results show that the average errors for miscut angle calculation, quantum dot number and size statistics are 5.02%, 0.7788% and 1.12%, respectively compared with the manual method;and further provided the automotive capability for quantum dot uniformity statistics. The automatic recognition process based on machine vision algorithm has practical value for assisting researchers to analyze surface morphology of quantum dots. (authors)