Behavioural SiC IGBT Modelling Using Non-Linear Voltage and Current Dependent Capacitances

Almpanis, Ioannis; Evans, Paul; Li, Ke; Lophitis, Neophytos

Abstract

This paper presents a behavioural silicon carbide (SiC) IGBT model that utilizes voltage and current dependent capacitances to simulate its switching characteristics, and a voltage dependent current source to simulate the static characteristics. The non-linear capacitances are extracted from dynamic Current-Voltage (IV) measurements, eliminating the need for non-standard Capacitance-Voltage (C-V) characterization methods under high voltage and high current. The accuracy of the compact model is compared with previously validated numerical Technology Computer Aided Design (TCAD) simulation results across a wide range of operational conditions. The model performance is demonstrated by accurately predicting the unique characteristics of a 27kV SiC IGBT, including dV/dt, dI/dt and losses, while significantly reducing the simulation time by 4-5 orders of magnitude. Additionally, the model convergence is tested using a buck converter topology with non-ideal parasitic elements.

• Conference Name: 2023 IEEE Design Methodologies Conference (DMC)
• Conference Location: Miami, FL, USA
• Start Date: Sep 24, 2023
• End Date: Sep 26, 2023
• Acceptance Date: Sep 1, 2023
• Online Publication Date: Jan 29, 2024
• Publication Date: Sep 24, 2023
• Deposit Date: May 14, 2024
• Publicly Available Date: Jun 5, 2024
• Publisher: Institute of Electrical and Electronics Engineers
• Book Title: 2023 IEEE Design Methodologies Conference (DMC)
• ISBN: 979-8-3503-1555-4
• DOI: https://doi.org/10.1109/dmc58182.2023.10412584
• Public URL: https://nottingham-repository.worktribe.com/output/31435415
• Publisher URL: https://ieeexplore.ieee.org/document/10412584