Deep Learning Method for Breakdown Voltage and Forward I-V 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 (<i>V</i>_br) and forward <i>I-V</i> 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 <i>V</i>_br 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 <i>V</i>_br but also curves, such as forward <i>I-V</i> characteristics, with a mean squared error (MSE) of less than 10^-3. Our research shows the applicability and high efficiency of introducing deep learning into device characteristic prediction.