Deep Learning Method for Breakdown Voltage and Forward Characteristic Prediction of Silicon Carbide Schottky Barrier Diodes.

This work employs a deep learning method to develop a high-precision model for predicting the breakdown voltage () and forward characteristics of silicon carbide Schottky barrier diodes (SiC SBDs). The model significantly reduces the testing costs associated with destructive experiments, such as breakdown voltage testing. Although the model requires a certain amount of time to establish itself, it supports linear variations in related variables once developed. A predicted model for with an accuracy of up to 99% was successfully developed using 600 sets of input data after 200 epochs of training. After training for 1000 epochs, the deep learning-based model could predict not only point values like but also curves, such as forward characteristics, with a mean squared error (MSE) of less than 10. Our research shows the applicability and high efficiency of introducing deep learning into device characteristic prediction.