An Experimental Study on Artificial Intelligence-Based Prediction of Capacitance-Voltage Parameters of Polymer-Interface 6H-SiC/MEH-PPV/Al Schottky Diodes
Küçük Resim Yok
Tarih
2022
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Wiley-V C H Verlag Gmbh
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Herein, an artificial neural network (ANN) model has been developed to predict the capacitance values of the polymer-interface 6H-SiC/MEH-PPV/Al Schottky diode depending on the frequency. In the training of the feed-forward back-propagation network model with five neurons in its hidden layer, 480 experimental data have been used. Of these, 70% of the data used in the development of the multilayer perceptron network has been used for network training, 15% for validation, and 15% for the test phase. The predictive performance of the network model has been analyzed by comparing the predicted values obtained from the ANN with the experimental data. For the developed ANN, the mean square error value is 4.34E-06, the R-value is 0.99728, and the average margin of deviation value is 0.03%.
Açıklama
Anahtar Kelimeler
artificial neural network, barrier height, capacitance-voltage, MEH-PPV, Schottky barrier
Kaynak
Physica Status Solidi A-Applications and Materials Science
WoS Q Değeri
Q3
Scopus Q Değeri
Q2
Cilt
219
Sayı
5
