ANN-Based Large-Signal Model of AlGaN/GaN HEMTs With Accurate Buffer-Related Trapping Effects Characterization

Abstract

In this article, an artificial neural network (ANN)-based large-signal model (LSM) of AlGaN/GaN high electron mobility transistors (HEMTs) with accurate buffer-related trapping effects characterization and modeling is proposed. A hybrid small-signal parameter-extraction method for AlGaN/GaN HEMTs is used to acquire the parasitic parameters. To simplify the modeling procedure of the drain-source current Ids, an ANN-based model associated with the empirical equations taking into account the trapping effects, self-heating effects, and breakdown issue is developed. The low-frequency dispersions related to the buffer-related trapping effects have been well modeled by using a new empirical equation, which has been verified by small-signal S-parameters. Also, a new thermal factor KT and an improved Shockley diode equation are given in the proposed model as well. The developed LSM has been fully verified by a 2 × 100 μm AlGaN/GaN HEMT with the pulsed I-V, small-signal S-parameters, power sweep, and load-pull measurements.