Hyperbolic Deep Learning System for Intelligent Gate-Driving and Health Monitoring of Silicon-Carbide Power MOSFETs

Accurate monitoring of the gate–oxide health of Silicon-Carbide (SiC) MOSFET devices is pivotal for condition-based maintenance of electrified vehicles, high-frequency converters and consumer electronic applications. A MOSFET parameter closely related to the device oxide interface is the threshold voltage. The SiC MOSFETs threshold voltage ( $V_{th}$ ) drifts with interface oxide–trap accumulation and it is therefore a direct early-marker of the device degradation. The $V_{th}$ drift monitoring is then significantly relevant in the mentioned application fields. This work proposes a non-intrusive, single-shot intelligent pipeline that infers $V_{th}$ solely from an input data-snapshot of the steady-state MOSFET static behavior based on current-to-voltage $I\!-\!V$ family curves sampled during routine gate driving. We introduce HyperDeep, a novel Hyperbolic Möbius Deep architecture that embeds each $I\!-\!V$ input snapshot in the hyperbolic Poincaré ball, processes it through ad-hoc designed residual Möbius layers followed by hyperbolic self-attention blocks. An adaptive exponential Lipschitz regularization block compensates process-spread-induced statistical drift in SiC devices, preserving mapping stability across SiC MOSFET's wide threshold-voltage window. The proposed HyperDeep system was trained on large dataset that includes both synthetic and real data obtained through Dynamic Gate Stress (DGS) methodology applied to SiC MOSFET GEN3 devices delivered by STMicroelectronics. The proposed deep pipeline attains in average of 0.015 MSE (Mean Squared Error) in retrieving actual $V_{th}$ , outperforming compared state-of-the-art architectures.