Prediction of JTE breakdown performance in SiC PiN diode radiation detectors using TCAD augmented machine learning

Abstract

The design of Junction Termination Extension (JTE) is an important step for meeting the reliability requirement of SiC PiN diode radiation detectors. For early evaluation, Technology Computer Aided Design (TCAD) software is often used to simulate the electronic property of detectors with different JTE parameters. But it is time consuming, which need 1 h or even longer for one case. Here, a TCAD augmented Machine Learning (ML) method based on the fully connected Neural Network (NN) algorithm is proposed to predict the breakdown performance quickly with different parameters of Spatial Modulation (SM) JTE. Utilized ∼5000 datum generated by TCAD simulation, the ML model could be established and achieve good prediction of breakdown voltage and location within a few seconds. As a semi-supervised learning model, its prediction accuracy of breakdown location is higher than 89.4 % and the determination coefficient R2 of breakdown voltage could be up to 0.97 compared with TCAD simulations. Moreover, this model could give the relationship curve of breakdown voltages and doping concentration, which is useful to choose an ideal structure with a wide implantation dose window. Based on this ML prediction model, the design cycle of SiC PiN diode radiation detectors could be reduced significantly.