Systematic Quantitative Characterization of Surface Nanostructures
A. Al-Mousa, D.L. Niemann, N.G. Gunther, M. Rahman
Santa Clara University / PDF Solutions, US
Keywords: AFM, Characterization, Fuzzy Logic, Thin Film, Recognition
Abstract:In this paper, a systematic analysis technique is presented and applied to Atomic Force Microscopy (AFM) data. Our technique isolates surface nanostructures and extracts quantitative information such as their shape and size. Examples of features as recognized using our algorithm are presented. The technique presented is independent of feature size, and does not require any data-dependent threshold. Furthermore, it can be extended to process other types of Scanning Probe Microscopy (SPM) measurements. This technique is based on processing height and slope data along the scan lines and in the direction perpendicular to them separately. This enables the identification of features such as hills, valleys, and flat areas in each direction. Our approach utilizes a surface gradient calculation along with the height and the AFM phase information as inputs to a fuzzy logic recognition system. Based on the fuzzy logic rules sets developed, the system classifies surface areas as either a hill, flat, or pore. The advantage of using this system is that it is inherently tolerant of anomalous data and has high immunity to noise. We apply our technique to analyze AFM data for nanostructured thin-films and demonstrate its success in the recognition of different types of structures.