Quality enhancement of AZO thin films at various thicknesses by introducing ITO buffer layer

Due to the simultaneously superior optical transmittance and low electrical resistivity, transparent conductive electrodes play a significant role in semiconductor electronics. To enhance the electrical properties of these films, one approach is thickness increment which degrades the optical properties. However, a preferred way to optimize both electrical and optical properties of these layers is to introduce a buffer layer. In this work, the effects of buffer layer and film thickness on the structural, electrical, optical and morphological properties of AZO thin films are investigated. Al-doped zinc oxide (AZO) is prepared at various thicknesses of 100 to 300 nm on the bare and 100 nm-thick indium tin oxide (ITO) coated glass substrates by radio frequency sputtering. Results demonstrate that by introducing ITO as a buffer layer, the average values of sheet resistance and strain within the film are decreased (about 76 and 3.3 times lower than films deposited on bare glasses), respectively. Furthermore, the average transmittance of ITO/AZO bilayer is improved nearly 10% regarding single AZO thin film. This indicates that bilayer thin films show better physical properties rather than conventional monolayer thin films. As the AZO film thickness increases, the interplanar spacing, d₍₀₀₂₎, strain within the film and compressive stress of the film in the hexagonal lattice, decreases indicating the higher yield of AZO crystal. Moreover, with the growth in film thickness, carrier concentration and optical band gap (E_g) of AZO film are increased from 4.62?×?10¹⁹ to 8.21?×?10¹⁹ cm^?3 and from 3.55 to 3.62 eV, respectively due to the Burstein-Moss (BM) effect. The refractive index of AZO thin film is obtained in the range of 2.24–2.26. With the presence of ITO buffer layer, the AZO thin film exhibits a resistivity as low as 6?×?10^?4 Ω cm, a sheet resistance of 15 Ω/sq and a high figure of merit (FOM) of 1.19?×?10⁴ (Ω cm)^?1 at a film thickness of 300 nm. As a result, the quality of AZO thin films deposited on ITO buffer layer is found to be superior regarding those grown on a bare glass substrate. This study has been performed over these two substrates because of their significant usage in the organic light emitting diodes and photovoltaic applications as an enhanced carrier injecting electrodes.     

Numerical modelling of rough particle contacts subject to normal and tangential loading

Granular Matter - While there are plenty of experimental studies pertaining to the dust generation from and dustiness of powders, few of them aim at reaching a theoretical understanding of the...     

Experimental investigation and 3D finite element prediction of the white layer thickness, heat affected zone, and surface roughness in EDM process

An axisymmetric three-dimensional model for temperature distribution in the electrical discharge machining process has been developed using the finite element method to estimate the surface integrity characteristics of AISI H13 tool steel as workpiece. White layer thickness, depth of heat affected zone, and arithmetical mean roughness consisting of the studied surface integrity features on which the effect of process parameters, including pulse on-time and pulse current were investigated. Additionally, the experiments were carried out under the designed full factorial procedure to validate the numerical results. Both numerical and experimental results show that increasing the pulse on-time leads to a higher white layer thickness, depth of heat affected zone, and the surface roughness. On the other hand, an increase in the pulse current results in a slight decrease of the white layer thickness and depth of heat affected zone, but a coarser surface roughness. Generally, there is a good agreement between the experimental and the numerical results.     

On the ratio of independent complex Gaussian random variables

In this paper, we derive a closed form equation for the joint probability distribution \({{f_{{R}_{z}}},{\varTheta _{z}}}({r_{z}},{\theta _{z}})\) of the amplitude \({R_{z}}\) and phase \({\varTheta _{z}}\) of the ratio \({Z=\frac{X}{Y}}\) of two independent non-zero mean Complex Gaussian random variables \(X\sim CN(\nu _{x} \mathrm {e}^{j\phi _{x}},{\sigma ^{2}_{x}})\) and \(Y\sim CN(\nu _{y} \mathrm {e}^{j\phi _{y}},{\sigma ^{2}_{y}})\). The derived joint probability distribution only contains a confluent hypergeometric function of the first kind \({_1F_{1}}\) without infinite summations resulting in computational efficiency. We further derive the probability distribution for the ratio of two non-zero mean independent real Rician random variables containing an infinite summation generated by the estimation of the Cauchy product of equivalent series of two modified Bessel functions.     

A novel fuzzy linear regression model based on a non-equality possibility index and optimum uncertainty

Various kinds of fuzzy regression models are introduced in the literature and many different methods are proposed to estimate fuzzy parameters of the models. In this study, a new approach is introduced to find the parameters of a linear fuzzy regression, with fuzzy outputs, the input data of which is measured by crisp numbers. Based on a non-equality possibility index, a new objective function is designed and solved, by which a minimum degree of acceptable uncertainty (the h-level or h-cut) is found. Four numerical examples are presented to compare the proposed approach with some other methods. Results show superiority of the new approach based on the criterion used by Kim and Bishu in the cases studied here. A realistic application of the proposed method is also presented, by which the total energy consumption of the Residential-Commercial sector in Iran is modeled using three variables of the GDP, number of the Households and an Energy Price index as inputs (exogenous variables) to the model.     

A Neuro-fuzzy-stochastic frontier analysis approach for long-term natural gas consumption forecasting and behavior analysis: The cases of Bahrain,Saudi Arabia,Syria, and UAE

This paper presents an adaptive network-based fuzzy inference system-stochastic frontier analysis (ANFIS-SFA) approach for long-term natural gas (NG) consumption prediction and analysis of the behavior of NG consumption. The proposed models consist of input variables of Gross Domestic Product (GDP) and population (POP). Six distinct models based on different inputs are defined. All of trained ANFIS are then compared with respect to mean absolute percentage error (MAPE). To meet the best performance of the intelligent based approaches, data are pre-processed (scaled) and finally the outputs are post-processed (returned to its original scale). To show the applicability and superiority of the integrated ANFIS-SFA approach, gas consumption in four Middle Eastern countries i.e. Bahrain, Saudi Arabia, Syria, and United Arab Emirates is forecasted and analyzed based on the data of the time period 1980–2007. With the aid of autoregressive model, GDP and population are projected for the period 2008–2015. These projected data are used as the input of ANFIS model to predict the gas consumption in the selected countries for 2008–2015. SFA is then used to examine the behavior of gas consumption in the past and also to make insights for the forthcoming years. The ANFIS-SFA approach is capable of dealing with complexity, uncertainty, and randomness as well as several other unique features discussed in this paper.