Abstract
This paper presents an efficient artificial neural network (ANN) electrothermal modeling approach applied to GaN devices. The proposed method is based on decomposing the device nonlinearity into intrinsic trapping-induced and thermal-induced nonlinearities that can be simulated by low-order ANN models. The ANN models are then interconnected in the physics-relevant equivalent circuit to accurately simulate the transistor. Genetic algorithm (GA)-based training procedure has been implemented to find optimal values for the weights of the ANN models. The modeling approach is used to develop a large-signal model for a 1-mm gate-width GaN high-electron mobility transistor (HMET). The model has been implemented in the advanced design system (ADS) and it has been validated by pulsed and continues small- and large-signal measurements. The model simulations showed a very good agreement with the measurements and verify the validity of the developed technique for dynamic electrothermal modeling of active devices.
Highlights
GAN high electron mobility transistor (HEMT) is currently an outstanding device for designing RF and microwave circuits
Even though the thermal performance of GaN HEMTs is better than other technologies such as Si, this effect must be considered in the modeling phase of the device for accurate and reliable circuit design, especially for larger devices
Jarndal: On Neural Network-Based Electrothermal Modeling of GaN Devices limitations have been overcome by using analytical modeling based on closed formulas [6], [11]
Summary
GAN high electron mobility transistor (HEMT) is currently an outstanding device for designing RF and microwave circuits. The higher electron saturation velocity, electron mobility, breakdown voltage, and operating temperature qualify it to be an outstanding device for designing advanced communication-electronic circuits such as power amplifiers and low noise amplifiers [1]. This makes the GaN HEMT an optimal choice for designing transceivers for advanced wireless communication systems such as 5G, WiMAX, ultrawideband radar systems, and Ku-band space communication systems [2]. Under linear- or quasi-linear-mode of operations [5] Another important effect is the typical inherent surface and buffer trapping of the GaN HEMT, which results in current collapse under RF (> 10 MHz) and kink effects in the dc and pulsed IV measurements [6].
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