Antal Ürmös

Soft-computing based classification and design of quantum dot nanostructures on GaAs substrate

The parameters of the semiconductor devices can be improved by nanostructures significantly. For this reason, it is necessary to produce nanostructures with given parameters. The soft-computing design of the self-organized nanostructures and a new classification model will be discussed in this paper. These nanostructures are formed by droplet epitaxy on compound semiconductor substrate. The parameters of the nanostructures (type, size, distribution) depend on the applied technology. The key factors of the technology (substrate temperature, Ga flux, As pressure, annealing time and annealing temperature) will be determined as design parameters. These parameters are set in order to produce nanostructures with the desired property. The revised version of previously introduced nanostructure fuzzy-based classification model will be also discussed.

Fuzzy and Kohonen SOM based classification of different 0D nanostructures

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.

Nanostructure Growth Supported by In Situ RHEED Evaluation

The in-situ monitoring of the MBE grown nanostructures can be carried out using the RHEED method. During the droplet epitaxal growth, the observation of the nanostructure formation is very important to understand the growth kinetics. In the present work, a novel in-situ RHEED evaluation and further MBE related developments are introduced, with which the quality of the nanostructure preparation can be improved.

Study of urbanistical complexity on two Hungarian regions

In our present work, we study and compare two Hungarian regions, from the viewpoint of complexity and self-assembly. In the size-order log-log diagram, we can observe two linear sections. There are two types of linear sections, which are corresponds to the two types of driving forces. We study, how the talents of the two regions in the diagrams appear and what do they mean from the viewpoint of driving forces.

Quantum structure classification by Kohonen Self-Organizing Map and by Fuzzy C-Means algorithm

Nowadays the nanostructures, formed on the way of self assembly are intensively investigated both in the basic and the applied sciences. In our paper, we investigate the structures on III-V compund semiconductor based materials, which are grown by epitaxial process. This process is analized by the beta version of Quantum Structure Analyzer 1.0, which is developed in C# langague, in the Microsoft© Visual Studio 2008 development environment. This software operates with the help of the Kohonen Self-Organizing Maps (SOM) algorithm and with the help of the Fuzzy C-Means algorithm. In present work, in the preface we give a short introduction of Molecular Beam Epitaxy (MBE), after this we introduce the algorithms, applied in this software. Finally, we demonstrate the results of the program.

Correction of the concentration profile of the epitaxially grown GaAs thin film layers, measured by electrolytic capacitance-voltage method

In this work we are dealing with the solution of a concentration profile measurement problem in the epitaxially grown III/V type compound semiconductor layers. The III/V based materials have specific electronic and optical properties, so they have large significance in the semiconductor device production. At these materials, the epitaxial growth is the fundamental technological step. During this epitaxial process is possible to change the doping concentration in the grown layer. In most cases, the electrolytic capacitance-voltage (ECV) technique serves for the measurement of this concentration profile. This method combines the conventional capacitance-voltage (CV) measurement with the anodic oxidation, to avoid the electrical break-down at the junction. The disadvantage of this method is that the contact surface increases during the measurement, because of the dissolution of the material. This surface growth - in certain layer structure - causes error in the measurement. In present work, we give a computational solution for this problem by our software, which we have developed in Matlab™. This algorithm calculates and summarizes the wall capacitance and subtract from the measured data, in each step. We demonstrate the operation of the program on different sample data series.