Fuzzy and Kohonen SOM based classification of different 0D nanostructures

Abstract

In this paper, the clustering of the GaAs-based droplet epitaxially grown self-assembled nanostructures was investigated by soft-computing methods. The properties and the operation of these devices, depend on the type, the shape, the size, and their distribution of these 0 dimensional nanostructures. Because of this, it is very important to know, how and what kind of nanostructures can form, at the given technological parameters. Our goal is the classification of these nanostructures, in order to support the research and the production of these devices. Our solution is based on the shape factor calculation of the given nanostructure. In this work, two possible classification methods of nanostructures were introduced as well. First, the classification potential of the Kohonen Self-Organizing Mapping (SOM) was investigated. Second, the fuzzy inference system based classification was studied. In this case, the shape factor was determined by geometrical sizes of the nanostructures. In this paper the clustering was introduced, which supports many kinds of technology as well.