arXiv subjects

Arkadiusz Janas

Publications and source records attributed to Arkadiusz Janas.

5 recordsLinked to original sources

arXiv2021

Into the Origin of Electrical Conductivity for the Metal-Semiconductor Junction at the Atomic Level

The metal-semiconductor (M-S) junction based devices are commonly used in all sorts of electronic devices. Their electrical properties are defined by the metallic phase properties with a respect to the semiconductor used. Here we make an in-depth survey on the origin of the M-S junction at the atomic scale by studying the properties of the AuIn2 nanoelectrodes formed on the InP(001) surface by the in situ electrical measurements in combination with a detailed investigation of atomically resolved structure supported by the first-principle calculations of its local electrical properties. We have found that a different crystallographic orientation of the same metallic phase with a respect to the semiconductor structure influences strongly the M-S junction rectifying properties by subtle change of the metal Fermi level and influencing the band edge moving at the interface. This ultimately changes conductivity regime between Ohmic and Schottky type. The effect of crystallographic orientation has to be taken into account in the engineering of the M-S junction-based electronic devices.

cond-mat.mtrl-sci↗

arXiv2021

Towards Understanding of Gold Interaction with AIII-BV Semiconductors at Atomic Level

AIII-BV semiconductors have been considered for decades to be a promising material in overcoming the limitations of silicon semiconductor devices. One of the important aspects within AIII-BV semiconductor technology are gold-semiconductor interactions on the nanoscale. We report on investigations into the basic chemical interactions of Au atoms with AIII-BV semiconductor crystals by an investigation of nanostructures formation in the process of thermally-induced Au self-assembly on various AIII-BV surfaces, and this by means of atomically resolved High Angle Annular Dark Field (HAADF) Scanning Transmission Electron Microscopy (STEM) measurements. We have found that the formation of nanostructures is a consequence of the surface diffusion and nucleation of adatoms produced by Au induced chemical reactions on AIII-BV semiconductor surfaces. Only for InSb crystal we have found that there is efficient diffusion of Au atoms into the bulk, which we experimentally studied by Machine Learning HAADF STEM image quantification and theoretically by Density Functional Theory (DFT) calculations with the inclusion of finite temperature effects. Furthermore, the effective number of Au atoms needed to release one AIII metal atom has been estimated. The experimental finding reveals a difference in the Au interactions with In- and Ga-based groups of AIII-BV semiconductors. Our comprehensive and systematic studies uncover details of the Au interactions with the AIII-BV surface at the atomic level with chemical sensitivity and shed new light on the fundamental Au/AIII-BV interactions at the atomic scale.

cond-mat.mes-hall↗

arXiv2019

Automatic microscopic image analysis by moving window local Fourier Transform and Machine Learning

Analysis of microscope images is a tedious work which requires patience and time, usually done manually by the microscopist after data collection. Here we introduce an approach of automatic image analysis, which is based on locally applied Fourier Transform and Machine Learning methods. In this approach, a whole image is scanned by a local moving window with defined size and the 2D Fourier Transform is calculated for each window. Then, all the Local Fourier Transforms are fed into Machine Learning processing. Firstly, a number of components in the data is estimated from Principal Component Analysis (PCA) Scree Plot performed on the data. Secondly, the data are decomposed blindly by Non-Negative Matrix Factorization (NMF) into interpretable spatial maps (loadings) and corresponding Fourier Transforms (factors). The microscopic image is analyzed and the features on the image are automatically discovered, based on the local changes in Fourier Transform. The user selects only a size and movement of the scanning local window which defines the final analysis resolution. This automatic approach was successfully applied to analysis of various microscopic images with and without local periodicity i.e. atomically resolved High Angle Annular Dark Field (HAADF) Scanning Transmission Electron Microscopy (STEM) image of Au nanoisland of fcc and Au hcp phases, Scanning Tunneling Microscopy (STM) image of Au-induced reconstruction on Ge(001) surface, Scanning Electron Microscopy (SEM) image of metallic nanoclusters grown on GaSb surface, and Fluorescence microscopy image of HeLa cell line of cervical cancer. The proposed approach could be used to automatically analyze the local structure of microscopic images within a time of about a minute for a single image on a modern desktop/notebook computer and it is freely available as a Python analysis notebook and Python program for batch processing.

eess.IV↗

arXiv2019

Nanostructure phase and interface engineering via controlled Au self-assembly on GaAs(001) surface

We have investigated the temperature-dependent morphology and composition changes occurring during a controlled self-assembling of thin Au film on the Gallium arsenide (001) surface utilizing electron microscopy at nano and atomic levels. It has been found that the deposition of 2 ML of Au at a substrate temperature lower than 798 K leads to the formation of pure Au nanoislands. For the deposition at a substrate temperature of about 798 K the nanostructures of the stoichiometric AuGa phase were/had been grown. Gold deposition at higher substrate temperatures results in the formation of octagonal nanostructures composed of an AuGa2 alloy. We have proved that the temperature-controlled efficiency of Au-induced etching-like of the GaAs substrate follows in a layer-by-layer manner leading to the enrichment of the substrate surface in gallium. The excess Ga together with Au forms liquid droplets which, while cooling the sample to room temperature, crystallize therein developing crystalline nanostructures of atomically-sharp interfaces with the substrate. The minimal stable cluster of 3 atoms and the activation energy for the surface diffusion Ed=0.816+-0.038eV was determined. We show that by changing the temperature of the self-assembling process one can control the phase, interface and the size of the nanostructures formed.

cond-mat.mes-hall↗

arXiv2017

Retrieving the quantitative chemical information at nanoscale from SEM EDX measurements by Machine Learning

The quantitative composition of metal alloy nanowires on InSb(001) semiconductor surface and gold nanostructures on germanium surface is determined by blind source separation (BSS) machine learning (ML) method using non negative matrix factorization (NMF) from energy dispersive X-ray spectroscopy (EDX) spectrum image maps measured in a scanning electron microscope (SEM). The BSS method blindly decomposes the collected EDX spectrum image into three source components, which correspond directly to the X-ray signals coming from the supported metal nanostructures, bulk semiconductor signal and carbon background. The recovered quantitative composition is validated by detailed Monte Carlo simulations and is confirmed by separate cross-sectional TEM EDX measurements of the nanostructures. This shows that SEM EDX measurements together with machine learning blind source separation processing could be successfully used for the nanostructures quantitative chemical composition determination.

cond-mat.mes-hall↗