Combining Intelligence With Rules for Device Modeling: Approximating the Behavior of AlGaN/GaN HEMTs Using a Hybrid Neural Network and Fuzzy Logic Inference System

This paper uses the Adaptive Neuro-Fuzzy Inference System (ANFIS) to investigate and propose a new alternative behavioral modeling technique for microwave power transistors. Utilizing measured I-V characteristics, associated parameters like transconductance (gm) and output conductance (gds), etc., S-parameters characteristics, and RF performance parameters such as unity current gain frequency (fT), maximum unilateral gain frequency (fmax), ANFIS-based behavioral models are developed for Gallium Nitride (GaN) High Electron Mobility Transistors (HEMTs) and validated. The models have been developed using two distinct devices with dimensions of 10×200μm and 10×250μm for multi-bias conditions and over a broad frequency range (0.5 to 43.5 GHz). Subsequently, the proposed model performance is validated on devices with geometries of 10×220μm, 4×100μm, and 2×200μm to examine the interpolation accuracy, extrapolation potential, and scalability. Here, ANFIS utilizes the subtractive clustering method to process the measurement characteristics by computing the clusters and opts for the best-performing model using error and number of fuzzy rules as criteria. The parameters involved in the fuzzy representation are trained using neural network algorithms, namely gradient-descent and least squares estimate. The proposed models are subsequently incorporated in a commercial circuit simulator (Keysight's ADS) and the class-F power amplifier's gain and stability characteristics are computed and studied.