An Experimental Study on Artificial Intelligence‐Based Prediction of Capacitance–Voltage Parameters of Polymer‐Interface 6H‐SiC/MEH‐PPV/Al Schottky Diodes

Tamer Güzel, Andaç Batur Çolak

2022physica status solidi (a)17 citationsDOI

Abstract

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%.

Topics & Concepts

Artificial neural networkCapacitanceMaterials scienceSchottky diodeDiodePerceptronStandard deviationVoltageMultilayer perceptronTest dataMean squared errorInterface (matter)Computer scienceOptoelectronicsElectronic engineeringArtificial intelligenceElectrical engineeringComposite materialChemistryEngineeringMathematicsStatisticsCapillary actionProgramming languageElectrodeCapillary numberPhysical chemistrySemiconductor materials and interfacesIntegrated Circuits and Semiconductor Failure AnalysisSilicon Carbide Semiconductor Technologies