Physical Fractal Phenomena in Nanostructured Semiconductors

Zhanabaev, Zeinulla Zh.; Grevtseva, T.Yu.

doi:10.1166/rits.2014.1023

Cited by 7 publications

(6 citation statements)

References 0 publications

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“…Therefore, the value of α provides us with information on the degree of anisotropy that the rate of variation of the concentration x(z) and the cross-sectional area involved in the direction in which the concentration, with anisotropy being null when α = 1.000 and anisotropy not null when 0.1000 ≤ α < 1.000. This same interpretative analysis of (20) can be applied to (21) and (22) for the effective mass of the electron and the confining potential, respectively.…”

Section: A Direct Application Of the Formalismmentioning

confidence: 92%

“…Also, in [18], it is found that an approximation can be established between the concepts of relativistic kinetic energy and fractional kinetic energy. The heterogeneous semiconductor structures do not escape this multitude of applications of the fractional calculation; for example, in [19][20][21][22][23][24][25] just some of them can be found. Thus, taking into account the favorable effect that the fractional derivative can have on the different natural systems, our present contribution consists in the direct application of the framework developed in [14] to find, for the first time, the approximate physical and geometric effect that produces the fractional derivative of the variable the concentration of a dopant, in this case, Aluminum deposited on a substrate, the position-dependent effective mass adopted by the confined electron, and the potential energy that the electron acquires due to the semiconducting medium.…”

Section: Introductionmentioning

confidence: 99%

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Approximate construction of new conservative physical magnitudes through the fractional derivative of polynomialtype functions: a particular case in semiconductors of type AlxGa1-xAs

Campos-García¹,

Molinar-Tabares

Figueroa-Navarro

et al. 2020

Rev. Mex. Fís.

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The fractional calculus has a very large diversification as it relates to applications from physical interpretations to experimental facts to modeling of new problems in the natural sciences. Within the framework of a recently published article, we obtained the fractional derivative of the variable concentration x (z), the effective mass of the electron dependent on the position m (z) and the potential energy V (z), produced by the confinement of the electron in a semiconductor of type AlxGa1-xAs, with which we can intuit a possible geometric and physical interpretation. As a consequence, it is proposed the existence of three physical and geometric conservative quantities approximate character, associated with each of these parameters of the semiconductor, which add to the many physical magnitudes that already exist in the literature within the context of fractional variation rates. Likewise, we find that the fractional derivatives of these magnitudes, apart from having a common critical point, manifest self-similar behavior, which could characterize them as a type of fractal associated with the type of semiconductor structures under study.

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Section: A Direct Application Of the Formalismmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Approximate construction of new conservative physical magnitudes through the fractional derivative of polynomialtype functions: a particular case in semiconductors of type AlxGa1-xAs

Campos-García¹,

Molinar-Tabares

Figueroa-Navarro

et al. 2020

Rev. Mex. Fís.

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show abstract

“…For a given reference speed * , the reference torque increment should be selected with a proper value which drives the motor speed to be equivalent to the reference speed at the next sample time, giving out n + 1 = n + 1 − n = * n − n = e n (8) Combining (7) and (8),…”

Section: Model Of the Speed Control Loopmentioning

confidence: 99%

Design and Analysis of a Self-Tuning Speed Controller for Permanent Magnet Synchronous Motors Based on the Neural Network

Tian¹,

Liu²,

Zhao³

2015

Jnl of Comp & Theo Nano

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A novel artificial neural network (ANN) based speed controller of permanent magnet synchronous motors (PMSM) is proposed in this paper. Small signal model of the speed loop of PMSM which is the foundation of the framework of the controller is analyzed. The gradient rule is utilized to update the weights of the neuron inputs, and auxiliary retrain process is used to improve the dynamic performance of the controller. Compared with traditional intelligent control algorithms, the cumbersome offline training is bypassed. Therefore, no extensive knowledge of the motor is required, and computation cost is reduced significantly. A comprehensive simulation was done to verify the validation of the ANN speed controller. Comparison with the conventional PID controller demonstrates the outstanding performance and robustness of the proposed controller. The feasibility of implementation of the ANN speed controller on physical platforms is also covered in this paper.

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“…Image Processing and Earth Remote Sensing Z Zh Zhanabaev, T Yu Grevtseva, K A Gonchar, G K Mussabek, D Yermukhamed, A A Serikbayev, R B Assilbayeva, A Zh Turmukhambetov and V Yu Timoshenko Nanocluster semiconductor films, including porous layers of silicon nanowire (SiNWs) grown in non-equilibrium conditions have scale-invariant, hierarchically self-similar, i.e. fractal and multifractal structure [10][11][12][13]. The scale invariance of a SiNW film is in their self-similarity (similarity coefficients on different variables are equal to each other) and self-affinity (similarity coefficients are different on different variables, that corresponds to anisotropic structure of the film).…”

Section: Introductionmentioning

confidence: 99%

Nonlinear analysis of the degree of order and chaos of morphology of porous silicon nanostructures

Zhanabaev

Grevtseva

Gonchar

et al. 2019

Proceedings of the v International Conference Information Technology and Nanotechnology 2019

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This work has been done to identify quantitative criteria the degree of order and chaos morphology of porous layers consisting of silicon nanowire arrays. In order to fulfill the work, a method of using metal-assisted chemical etching has been utilized to produce nanowires. There has been done a work of digital processing of porous film images which were extracted by scanning electron microscope. Informational-entropic and Fourier analysis have been applied to quantitatively describe the degree of order and chaos in nanostructure distribution in the layers. Self-similarity of the layer morphology has been quantitatively described via its fractal dimensions by correlation method. The applied approach for image processing allows us to distinguish the morphological features of as-called "black" (more ordered) and "white" (less ordered) silicon layers, which are characterized by minimal and maximal optical reflection, respectively. From all of the methods of digital techniques that we have used the method for determining the conditional information of a chaotic set was proved to be the most informative.

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Physical Fractal Phenomena in Nanostructured Semiconductors

Cited by 7 publications

References 0 publications

Approximate construction of new conservative physical magnitudes through the fractional derivative of polynomialtype functions: a particular case in semiconductors of type AlxGa1-xAs

Approximate construction of new conservative physical magnitudes through the fractional derivative of polynomialtype functions: a particular case in semiconductors of type AlxGa1-xAs

Design and Analysis of a Self-Tuning Speed Controller for Permanent Magnet Synchronous Motors Based on the Neural Network

Nonlinear analysis of the degree of order and chaos of morphology of porous silicon nanostructures

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