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.
In this paper an improved Gray-Wolf-Optimization (GWO) based small-signal modeling is developed. The proposed method is demonstrated by modeling GaN High Electron Mobility Transistors (HEMTs) on SiC ...and Diamond substrates. The technique bases on engineering the optimization objective function to provide reliable values for the model parameters; while keeping a better fitting for the targeted measurements. The reliability of extraction has been further improved by using physical relevant condition to remove any unrealistic values during the optimization process. The modeling procedure was applied on 2x50-μm, 8x150-μm, 8x250-μm and 16x250-μm GaN HEMTs on SiC substrate in addition to 2x125-μm and 4x125-μm GaN HEMTs on Diamond substrate. Very good results were obtained for both technologies with an excellent fitting for the related measurements. The results also show the reliability of the developed technique and validate its applicability for small-and large-signal modeling applications.
This paper presents an efficient parameter extraction method applied to GaN high electron mobility transistors. The procedure only relies on <inline-formula> <tex-math notation="LaTeX">{S} ...</tex-math></inline-formula>-parameter measurements at cold bias conditions to extract the extrinsic parameters of a 19-element small-signal model. Hybrid technique of particle-swarm-optimization and direct fitting has been developed and implemented. The extraction procedure has been optimized to consider measurements uncertainty and improve the reliability of the extraction. The procedure has been validated by multibias extraction for different device sizes. A very good agreement between simulations and measurements has been obtained.
Machine learning‐based efficient temperature‐dependent small‐signal modelling approaches for GaN high electron mobility transistors (HEMTs) are presented by the authors here. The first method is an ...artificial neural network (ANN)‐based and makes use of the well‐known multilayer perceptron (MLP) architecture whereas the second technique is developed using support vector regression (SVR). The models are trained on a large set of measurement data obtained from a 2‐mm GaN‐on‐silicon device operating under varying operating conditions (bias voltages and ambient temperatures) over a wide frequency range of 0.1 to 20 GHz. An excellent agreement is found between the measured and the simulated S‐parameters for both models over the entire frequency range. It is identified that the training process and prediction capability of ANN is superior to SVR. However, the SVR is more robust when compared to the artificial neural network (ANN) in term of its sensitivity to local minima and uniqueness of the final solution. Subsequently, the performances of the proposed ANN‐ and SVR‐based models are improved by incorporating particle swarm optimization (PSO) in the model development process. The PSO improves the uniqueness of the ANN model whereas it enhances the performance of the SVR by optimising its control parameters. The proposed models exhibit very good accuracy and scalability.
A reliable small‐signal modelling approach has been developed and applied on GaN‐on‐diamond high electron mobility transistor. The extrinsic elements' extraction procedure was improved to provide an ...accurate characterization for the quasistatic behaviour of the intrinsic transistor. The frequency independence of the intrinsic elements at active multibias condition has been considered as another objective in addition to measurements' fitting. Physical relevant values for the model elements have been obtained. The model accuracy was also validated by means of S‐parameters simulation, which showed a very good fitting of the measured data.
This paper presents a simple approach to model the self-heating effect in GaN high electron mobility transistors (HEMTs) using a particle swarm neural network and also reports the extraction ...procedure of the model parameters. The main advantage of the developed method is its simplicity of construction and implementation in computer-aided-design tools. The developed modeling procedure is applied to a packaged GaN HEMT and validated by DC and AC small/large-signal simulations, which showed a very good agreement with the measurements.
This article presents efficient parameters extraction procedure applied to GaN High electron mobility transistor (HEMT) on Si and SiC substrates. The method depends on combined technique of direct ...and optimization‐based to extract the elements of small‐signal equivalent circuit model (SSECM) for GaN‐on‐Si HEMT. The same model has been also applied to GaN‐on‐SiC substrate to evaluate the effect of the substrates on the model parameters. The quality of extraction was evaluated by means of S‐parameter fitting at pinch‐off and active bias conditions.
Artificial Neural Network (ANN) is frequently utilized for the development of behavioral models of Gallium Nitride (GaN) High Electron Mobility Transistors (HEMTs). However, exhaustive investigation ...concerning the ANN algorithms implemented in major programming platforms for small-signal behavioral models of GaN HEMTs is generally not available. To fill this void, this paper carefully examines and evaluates ANN algorithms implemented in MATLAB, Python and R software environments for the development of accurate and efficient GaN HEMTs modelling. At first, the ANN based models are developed using MATLAB, Python's major frameworks namely Keras, PyTorch and Scikit-learn, and R's ANN framework namely H2O to model the GaN devices. Thereafter, an in-depth analysis is carried out to comprehend the usefulness of each framework in different application scenarios. At last, a detailed evaluation of the developed models in terms of generalization capability, training and prediction speed, seamless integration with the standard circuit design tool advanced design system, and of the development environments in respect of support and documentation, user-friendly interface, ease of model development, open-access and cost is carried out.
This article reports a Microstrip design for low noise amplifier (LNA) using a packaged commercial GaN‐on‐SiC high electron mobility transistor (HEMT). A cascode configuration with an inter‐stage ...matching and an independent biasing technique was used. A lumped elements design was first developed, analyzed, and simulated in ADS. Then the design was implemented using microstrip technology and simulated using the momentum EM simulation in ADS. The LNA is easy to fabricate, has a low cost, and can be easily modified for other applications. The proposed GaN LNA showed a gain of 13.5 dB with a noise figure (NF) of 3 dB from 2.8 to 3.8 GHz.
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