Investigation of features for prediction modeling of nano-scale conduction with time-dependent calculation of electron wave packet

The model to predict the electron transmission probability from the random impurity distribution in a two-dimensional nanowire system by combining the time evolution of the electron wave function and machine learning is proposed. We have shown that the intermediate state of the time evolution calculation is a great advantage for efficient modeling by machine learning. The features for machine learning are extracted by analyzing the time variation of the electron density distribution using time evolution calculations. Consequently, the prediction error of the model is improved by performing machine learning based on the features. The proposed method provides a useful perspective for analyzing the motion of electrons in nanoscale semiconductors.

The electron transmission properties in a non-planar system of two chained rings

The electron transmission properties in a model of two chained orthogonal quantum rings with input and output wires were investigated by using the quantum graph theory and quantum waveguide theory. The model was obtained for three-dimensional space. It also was shown that changing of the orientation of second ring in the respect to the field is a way to control the electron transmission and reflection.

A 64 bit quantum dragon data-set for machine learning

Design and examples of a sixty-four bit quantum dragon data-set are presented. A quantum dragon is a tight-binding model for a strongly disordered nanodevice, but when connected to appropriate semi-infinite leads has complete electron transmission for a finite interval of energies. The labeled data-set contains records which are quantum dragons, which are not quantum dragons, and which are indeterminate. The quantum dragon data-set is designed to be difficult for trained humans and machines to label a nanodevice with regard to its quantum dragon property. The 64 bit record length allows the data-set to be utilized in restricted Boltzmann machines which fit well onto the D-Wave 2000Q quantum annealer architecture.

Fast Response GaAs Photodetector Based on Constructing Electron Transmission Channel

Low-dimensional GaAs photodetectors have drawn a great deal of attention because of their unique absorption properties and superior responsivity. However, their slow response speed caused by surface states presents challenges. In this paper, a mixed-dimensional GaAs photodetector is fabricated utilizing a single GaAs nanowire (NW) and a GaAs 2D non-layer sheet (2DNLS). The photodetector exhibits a fast response with a rise time of ~4.7 ms and decay time of ~6.1 ms. The high-speed performance is attributed to an electron transmission channel at the interface between the GaAs NW and GaAs 2DNLS. Furthermore, the fast electron channel is confirmed by eliminating interface states via wet passivation. This work puts forward an effective way to realize a high-speed photodetector by utilizing the surface states of low-dimensional materials.

In Silico Study of the Influence of Various Substrates on the Electronic Properties and Electrical Conductivity of Mono- and Bilayer Films of Armchair Single-Walled Carbon Nanotubes

We investigate electronic and electro-physical properties of mono- and bilayer armchair single-walled carbon nanotube (SWCNT) films located on substrates of different types, including substrates in the form of crystalline silicon dioxide (SiO2) films with P42/mnm and P3121 space symmetry groups. The SWCNT films interact with substrate only by van der Waals forces. The densities of electronic states (DOS) and the electron transmission functions are calculated for SWCNT films with various substrates. The electrical conductivity of SWCNT films is calculated based on the electron transmission function. It is found that the substrate plays an important role in the formation of DOS of the SWCNT films, and the surface topology determines the degree and nature of the mutual influence of the nanotube and the substrate. It is shown that the substrate affects the electronic properties of monolayer films, changing the electrical resistance value from 2% to 17%. However, the substrate has practically no effect on the electrical conductivity and resistance of the bilayer film in both directions of current transfer. In this case, the values of the resistances of the bilayer film in both directions of current transfer approach the value of ~6.4 kΩ, which is the lowest for individual SWCNT.