Quantified Differentiation of Surface Topography for Nano-materials As-Obtained from Atomic Force Microscopy Images

Surface texture is an important issue to realize the nature (crest and trough) of surfaces. Atomic force microscopy (AFM) image is a key analysis for surface topography. However, in nano-scale, the nature (i.e., deflection or crack) as well as quantification (i.e., height or depth) of deposited layers is essential information for material scientist. In this paper, a gradient-based K-means algorithm is used to differentiate the layered surfaces depending on their color contrast of as-obtained from AFM images. A transformation using wavelet decomposition is initiated to extract the information about deflection or crack on the material surfaces from the same images. Z-axis depth analysis from wavelet coefficients provides information about the crack present in the material. Using the above method corresponding surface information for the material is obtained. In addition, the Gaussian filter is applied to remove the unwanted lines, which occurred during AFM scanning. Few known samples are taken as input, and validity of the above approaches is shown.