Artificial intelligence approach on predicting current values of polymer interface Schottky diode based on temperature and voltage: An experimental study

• Published in: Superlattices and microstructures Vol. 153; p. 106864
• Main Authors: Güzel, Tamer, Çolak, Andaç Batur
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.05.2021
• Subjects: Artificial neural network; Barrier diode; Current-voltage characteristics; MEH-PPV; Schottky diode; MEH-PPV; Artificial neural network; Current-voltage characteristics; Barrier diode; Schottky diode
• ISSN: 0749-6036; 1096-3677
• DOI: 10.1016/j.spmi.2021.106864

In this study, an artificial neural network model has been developed to predict the current values of a 6H–SiC/MEH-PPV Schottky diode with polymer-interface, depending on temperature and voltage. In the training of the multi-layer perceptron network model with 13 neurons in its hidden layer, the experimentally measured current values between 100 and 250 K temperature and -3V to + 3V voltage range have been used. In the input layer of the model developed with a total of 244 experimental data, temperature, and voltage values have been defined and current values were obtained in the output layer. The mean square error value of the artificial neural network is 1.63E-08 and the R-value is 0.99999. The developed model has been able to predict the current values of the polymer-interfaced 6H–SiC/MEH-PPV Schottky diode with an average error rate of −0.15% depending on temperature and voltage, with high accuracy. •Experimentally measured current values of 6H–SiC/MEH-PPV Schottky diode with polymer-interface.•Designing the optimal ANN model using experimental data of 6H–SiC/MEH-PPV Schottky diode with polymer-interface.•Analyzing the prediction performance of the developed ANN model.•Comparison of experimental current values and ANN outputs.