Performance prediction of current-voltage characteristics of Schottky diodes at low temperatures using artificial intelligence

Abstract

Schottky diodes are still one of the most important elements of electronics. Therefore, investigating the properties of diodes is very important in determining their usage areas. In this study, the performance of the artificial neural network model trained using high temperature data in predicting the current-voltage properties at low temperatures was investigated. An artificial neural network is modeled using the experimentally measured current and voltage values at the temperature range of 80 and 375 K. In the developed network model, temperature and voltage values are defined as input parameters and current values are estimated. Levenberg-Marquardt training algorithm was used as the training algorithm in the neural network, which was developed using a total of 1584 data. The current values obtained from the artificial neural network were compared with the experimental current values, and the prediction performance of the network model was extensively analyzed by using various performance parameters. The results showed that the developed artificial neural network can predict current values at low temperatures with high accuracy depending on voltage. In addition, it was found that the current-voltage characteristics of the Schottky diode at low temperatures could be predicted with an error rate of approximately ±7 %. On the other hand, the error rates in the prediction of diode characteristics by artificial intelligence were determined to be independent of temperature.