Investigation of the usability of machine learning algorithms in determining the specific electrical parameters of Schottky diodes

Schottky diodes continue to be the favorite of the electronics industry with their ever-expanding usage areas. The electrical parameters that can be obtained by the characterization of Schottky diodes are of high importance as they provide important information in terms of the usage area of the diode. In this study, the usability of the machine learning algorithm has been investigated in the determination of important electrical parameters such as ideality factor, barrier height and resistance of Schottky diodes. Voltage and temperature values were defined in the hidden layer of the multi-layer artificial neural network model, which was developed with a total of 368 data sets, and current values were estimated in the output layer. The developed neural network model was able to predict the electrical parameters of Schottky diodes with an average deviation of 0.11%. Using the data ob-tained from the artificial neural network, the Ideality factor was calculated with an error margin of 1.645, and the resistance value with a margin of error of 5.694.

Automated summary

This study investigates the usability of machine learning algorithms in determining important electrical parameters of Schottky diodes and develops a multi-layer artificial neural network model that can predict the parameters with high accuracy.