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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%.

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The effect of the TiO2 interfacial layer on rectifying junction parameters of Ag/TiO2/n-InP/Au Schottky diodes has been investigated using current-voltage (I-V) measurements in the temperature range of 120-420 K with steps of 20 K. The barrier height is found to be 0.19 eV and 0.68 eV from current-voltage characteristics at 120 K and 420 K, respectively. At 120 K and 420 K, the ideality factor is found to be 3.52 and 1.01 for the Ag/TiO2/n-InP/Au Schottky barrier diode, respectively. These results are gained by the thermionic emission theory at room temperature. Values of series resistances gained from the Cheung-Cheung method are compared with results gained from a modified Norde method. These experimental results indicate that series resistance decreases with an increase in temperature. The current-voltage (I-V) measurements showed that the diode with the TiO2 interfacial layer gave a double Gaussian property in the examined temperature range. The Richardson constant is also calculated from a modified Richardson plot and is found to be very compatible with the theoretical value. Interface state density is also examined by using I-V characteristics.

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