An ANN Assisted Reverse Recovery of Diode Model for Switchingon Characteristics of IGBT Devices

Inhalt:
This paper presents a new Artificial Neural Network (ANN) assisted in reverse recovery of a diode model for insulated-gate bipolar transistor (IGBT) devices at the turn-on process. The dependence of both carrier lifetime (τ) and diffusion transit time (TM) on current, voltage and temperature in a diode model is modeled by ANN after a training process. The proposed diode model implemented in the Keysight IGBT model can accurately fit the transient iC of an IGBT at a turn-on process for a wide range of currents at three voltages (100, 500 and 600 V) and three temperatures (25, 75 and 100 °C). With a new parameter extraction workflow for transient simulation, an overall good fitting of the switching-on characteristics is achieved for an IGBT power module.