Evaluation criterion of the neural network model of heterostructural nanoelectronic devices for predicting their electrical parameters

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Abstract

The paper is devoted to the neural network approach, which is proposed to be used to predict the operational parameters of heterostructural nanoscale devices. The advantage of this approach is a clear methodology for evaluating the weighting coefficients as part of a trained artificial neural network, which makes it possible to solve the problem for devices with an arbitrary structure. Learning is a complex iterative process, at the end of which it is important to evaluate the functioning of the neural network model. Therefore, it is necessary to determine the achieved accuracy and to identify negative effects that may occur during the learning process, when such a model is being developed. The project presents a criterion for evaluation the training quality of the neural network model of heterostructural nanoelectronic devices for predicting their electrical parameters. The main advantage of this criterion is its sensitivity to negative effects arising in the learning process, which was demonstrated by an example with two input training parameters and confirmed by visual control of 3D surfaces. The applicability of the developed criterion in the selection of neural networks with arbitrary architecture for solving design problems in the development of semi-conductor devices has been proved.

About the authors

Natalia A. Vetrova

Bauman Moscow State Technical University (National Research University of Technology); Peoples’ Friendship University of Russia (RUDN University)

Email: vetrova@bmstu.ru
ORCID iD: 0000-0002-6218-4111

Candidate of Technical Sciences, Associate Professor of the Department of Instrument Engineering Technologies, Bauman Moscow State Technical University (National Research University of Technology), ; Associate Professor of the Department of Nanotechnology and Microsystems Engineering, Academy of Engineering, Peoples’ Friendship University of Russia (RUDN University)

5 2-ya Baumanskaya St, bldg 1, Moscow, 105005, Russian Federation; 6 Miklukho-Maklaya St, Moscow, 117198, Russian Federation

Alexandr A. Filyaev

Bauman Moscow State Technical University (National Research University of Technology); National University of Science and Technology “MISIS”

Author for correspondence.
Email: alex.filyaev.98@gmail.com
ORCID iD: 0000-0001-7319-8001

master student, Department of Instrument Engineering Technologies, Bauman Moscow State Technical University (National Research University of Technology), ; engineer of the scientific project, National Technology Initiative Center for Quantum Communications, National University of Science and Technology “MISIS,”

5 2-ya Baumanskaya St, bldg 1, Moscow, 105005, Russian Federation; 4 Leninskii Prospekt, Moscow, 119049, Russian Federation

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Copyright (c) 2022 Vetrova N.A., Filyaev A.A.

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