Prediction of Deep Neural Network for TCAD Analysis of Degradation Induced by Near-Interface Displacement Defects in 4H-SiC MOSFETs

Silicon carbide (SiC) devices used in space and nuclear environments are exposed to radiation that creates displacement defects (DDs), but the effect of DDs near the gate oxide/SiC interface has not been analyzed systematically. Using technology computer-aided design (TCAD) simulation, this study analyzes how near-interface DDs in a 4H-SiC metal-oxide-semiconductor field-effect transistor (MOSFET) affect the threshold voltage (Vth), transconductance (gm), and on-current (Ion), and builds deep neural network (DNN) models that predict these effects. The DDs were modeled as acceptor-like traps with varied energy levels, concentration, depth, lateral position, and distribution width. The degradation was largest at the channel entrance and the edge of the junction field-effect transistor (JFET) region. The center depth mattered little while the trap region reached the interface, whereas a small gap between the trap region and the interface strongly reduced the degradation. Shallow-level traps reduced Ion without shifting Vth, whereas only deep-level traps caused an actual Vth shift. The DNNs accurately predicted the degradation for new TCAD conditions in which the trap parameters simultaneously took values not used in training and outperformed grid interpolation and other regression models. The trained DNNs thus rapidly predict the degradation at the simulated lateral positions for radiation reliability assessment.