Paper Info
Title
Do Artificial Neural Networks Always Provide High Prediction Performance? An Experimental Study on the Insufficiency of Artificial Neural Networks in Capacitance Prediction of the 6H-SiC/MEH-PPV/Al Diode
Abstract
In this paper, we study a new model that represents the symmetric connection between capacitance-voltage and Schottky diode. This model has a symmetrical shape towards the horizontal direction. In recent times, works conducted on artificial neural network structure, which is one of the greatest actual artificial intelligence apparatuses used in various fields, stated that artificial neural networks are apparatuses that proposal very high forecast performance by the side of conventional structures. In the current investigation, an artificial neural network structure has been generated to guess the capacitance voltage productions of the Schottky diode with organic polymer edge, contingent on the frequency with a symmetrical shape. Of the dataset, 130 were grouped for training, 28 for validation, and 28 for testing. In order to evaluate the effect of the number of neurons on the prediction accuracy, three different models with different neuron numbers have been developed. This study, in which an artificial neural network model, although well-trained, could not predict the output values correctly, is a first in the literature. With this aspect, the study can be considered as a pioneering study that brings a novelty to the literature.
Year
DOI
Venue
2022
10.3390/sym14081511
SYMMETRY-BASEL
Keywords
DocType
Volume
artificial neural network, MEH-PPV, capacitance-voltage, Schottky diode, barrier height
Journal
14
Issue
ISSN
Citations 
8
2073-8994
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Andac Batur Colak100.34
Tamer Guzel200.34
Anum Shafiq303.38
Kamsing Nonlaopon401.69