Neural network estimations of annealed and non-annealed Schottky diode characteristics at wide temperatures range

DOĞAN H., Duman S., TORUN Y., AKKOYUN S., Doğan S., ATİCİ U.

Materials Science in Semiconductor Processing, cilt.149, 2022 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 149
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1016/j.mssp.2022.106854
  • Dergi Adı: Materials Science in Semiconductor Processing
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Applied Science & Technology Source, Chemical Abstracts Core, Chimica, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex
  • Anahtar Kelimeler: Schottky diode, Artificial neural network, Modelling, SILICON-CARBIDE, CURRENT-VOLTAGE, CONTACTS, SEMICONDUCTOR
  • Sivas Cumhuriyet Üniversitesi Adresli: Evet

Özet

© 2022 Elsevier LtdIn 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.