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.