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 $I_{\mathrm{ ds}}$ , 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 $K_{T}$ and an improved Shockley diode equation are given in the proposed model as well. The developed LSM has been fully verified by a $2\times 100\,\,\mu \text{m}$ AlGaN/GaN HEMT with the pulsed I–V, small-signal S-parameters, power sweep, and load—pull measurements.
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