Evaluation of 4H‐SiC MOSFET transfer characteristics using machine‐learning techniques

Abstract The performance of 4H silicon carbide (SiC) MOSFETs critically depends on the quality of the SiC/silicon oxide interface, which typically contains a high density of interface traps. To solve this problem, fast and reliable characterization methods are required. The commonly used evaluation schemes for 3‐terminal transfer characteristics, however, neglect the presence of interface traps. Here, a method based on machine‐learning techniques is presented which extracts reliable performance parameters from transfer characteristics of 4H‐SiC MOSFETs including a quantitative estimate of the density of interface traps. This method is successfully validated by comparison with Hall‐effect measurements and applied to various MOSFET types.