dc.contributor.author | Doğan, Hülya | |
dc.contributor.author | Duman, Songül | |
dc.contributor.author | Torun, Yunis | |
dc.contributor.author | Akkoyun, Serkan | |
dc.contributor.author | Doğan, Seydi | |
dc.contributor.author | Atici, Uğur | |
dc.date.accessioned | 2023-06-20T08:06:01Z | |
dc.date.available | 2023-06-20T08:06:01Z | |
dc.date.issued | 15/06/2022 | tr |
dc.identifier.uri | https://hdl.handle.net/20.500.12418/13767 | |
dc.description.abstract | n this study, Artificial Neural Network (ANN) model has been proposed to characterize the annealed and the
non-annealed Schottky diode from experimental data. The experimental current values of Ni/n-type 6H–SiC
Schottky diode for the voltages applied to the diode terminal starting from 80 K with 20 K steps up to 500 K
temperature were measured for both non-annealed and annealed Schottky diodes. The applied voltage has been
varied starting from -2 V with 10 mV steps up to +2 V for each temperature value. The modeling performance
has been assessed according to the varying number of neurons in the hidden layer, starting from 5 to 50 neurons,
thereafter the optimum number of neurons has been obtained for both annealed and non-annealed ANN models.
The minimum Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) indices values for both annealed
and non-annealed diodes have been obtained with 40 neurons for both the training and test phase. | tr |
dc.language.iso | eng | tr |
dc.rights | info:eu-repo/semantics/closedAccess | tr |
dc.subject | Schottky diode Artificial neural network Modelling | tr |
dc.title | Neural network estimations of annealed and non-annealed Schottky diode characteristics at wide temperatures range | tr |
dc.type | article | tr |
dc.contributor.department | Mühendislik Fakültesi | tr |
dc.relation.publicationcategory | Uluslararası Hakemli Dergide Makale - Kurum Öğretim Elemanı | tr |