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

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.

Automated summary

A neural network model was developed to predict the current values of a 6H?SiC/MEH-PPV Schottky diode with a polymer interface based on temperature and voltage. The model showed high accuracy with an average error rate of -0.15% in predicting the current values.