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

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.