Large‐signal modeling methodology for GaN HEMTs for RF switching‐mode power amplifiers design

A large‐signal model for GaN HEMT transistor suitable for designing radio frequency power amplifiers (PAs) is presented along with its parameters extraction procedure. This model is relatively easy to construct and implement in CAD software since it requires only DC and S‐parameter measurements. The modeling procedure was applied to a 4‐W packaged GaN‐on‐Si HEMT, and the developed model is validated by comparing its small‐ and large‐signal simulation to measured data. The model has been employed for designing a switching‐mode inverse class‐F PA. Very good agreement between the amplifier simulation and measurement shows the validity of the model. © 2010 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2011.     

Bayesian inference‐based small‐signal modeling technique for GaN HEMTs

A new modeling methodology for gallium nitride (GaN) high‐electron‐mobility transistors (HEMTs) based on Bayesian inference theory, a core method of machine learning, is presented in this article. Gaussian distribution kernel functions are utilized for the Bayesian‐based modeling technique. A new small‐signal model of a GaN HEMT device is proposed based on combining a machine learning technique with a conventional equivalent circuit model topology. This new modeling approach takes advantage of machine learning methods while retaining the physical interpretation inherent in the equivalent circuit topology. The new small‐signal model is tested and validated in this article, and excellent agreement is obtained between the extracted model and the experimental data in the form of dc I–V curves and S‐parameters. This verification is carried out on an 8 × 125 μm GaN HEMT with a 0.25 μm gate feature size, over a wide range of operating conditions. The dc I–V curves from an artificial neural network (ANN) model are also provided and compared with the proposed new model, with the latter displaying a more accurate prediction benefiting, in particular, from the absence of overfitting that may be observed in the ANN‐derived I–V curves.     

Neural network electrothermal modeling approach for microwave active devices

This article presents an artificial neural network (ANN) approaches for small‐ and large‐signal modeling of active devices. The small‐signal characteristics were modeled by S‐parameters based feedforward NN models. The models have been implemented to simulate the bias, frequency and temperature dependence of measured S‐parameters. Feedback NN based large‐signal model was developed and implemented to simulate the drain current and its inherent thermal effect due to self‐heating and ambient temperature. Both small‐ and large‐signal models have been validated by measurements for 100‐μm and 1‐mm GaN high electron mobility transistors and very good agreement was obtained.     

Fuzzy neural‐based approaches for efficient RF/microwave transistor modeling

In today's RF and microwave circuits, there is an ever‐increasing demand for higher level of system integration that leads to massive computational tasks during simulation, optimization, and statistical analyses, requiring efficient modeling methods so that the whole process can be achieved reliably. Since active devices such as transistors are the core of modern RF/microwave systems, the way they are modeled in terms of accuracy and flexibility will critically influence the system design, and thus, the overall system performance. In this article, the authors present neural‐ and fuzzy neural‐based computer‐aided design techniques that can efficiently characterize and model RF/microwave transistors such as field‐effect transistors and heterojunction bipolar transistors. The proposed techniques based on multilayer perceptrons neural networks and c‐means clustering algorithms are demonstrated through examples. © 2008 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2009.     

AlGaN/GaN HEMTs on SiC and Si substrates: A review from the small‐signal‐modeling's perspective

In this article, small‐signal modeling approaches for GaN HEMTs on SiC and Si substrates have been developed. The main advantage of these approaches is their accuracy, reliability, and dependency on only cold S‐parameter measurements to extract the parasitic elements of the device. The proposed equivalent circuit model for GaN on Si HEMT considers extra effects due to parasitic conduction through substrate or buffer layers. S‐parameter measurements at different bias conditions in addition to physical based analysis have been used to validate the accuracy and reliability of the developed modeling methods. © 2013 Wiley Periodicals, Inc. Int J RF and Microwave CAE 24:389–400, 2014.     

Growth and characterization of 0.8-μm gate length AlGaN/GaN HEMTs on sapphire substrates

AlGaN/GaN high electron mobility transistor (HEMT) structures were grown on 2 inch sapphire substrates by MOCVD, and 0.8-μm gate length devices were fabricated and measured. It is shown by resistance mapping that the HEMT structures have an average sheet resistance of approximately 380 Θ/sq with a uniformity of more than 96%. The 1-mm gate width devices using the materials yielded a pulsed drain current of 784 mA/mm atV _gs=0.5 V andV _ds=7 V with an extrinsic transconductance of 200 mS/mm. A 20-GHz unity current gain cutoff frequency (f _T) and a 28-GHz maximum oscillation frequency (f _max) were obtained. The device with a 0.6-mm gate width yielded a total output power of 2.0 W/mm (power density of 3.33 W/mm) with 41% power added efficiency (PAE) at 4 GHz.     

New small‐signal extraction method applied to GaN HEMTs on different substrates

In this article, a new extraction technique is proposed to extract the small‐signal parameters of gallium nitride (GaN) high electron mobility transistors (HEMTs) on three different substrates namely, Si, SiC, and Diamond. This extraction technique used a single small‐signal circuit model to efficiently describe the physical and electrical properties of GaN on different substrates. This technique takes into account any asymmetry between the gate‐source and gate‐drain capacitances on the asymmetrical GaN HEMT structure, charge‐trapping effects, passivation layer inclusion, as well as leakage currents associated with the nucleation layer between the GaN buffer layer and the different substrates. The extracted values were then optimized using the grey wolf optimizer. The proposed technique was demonstrated through a close agreement between simulated and measured S‐parameters.     

Hybrid small‐signal model parameter extraction of GaN HEMTs on Si and SiC substrates based on global optimization

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.     

Modeling power and intermodulation behavior of microwave transistors with unified small‐signal/large‐signal neural network models

This article presents a detailed procedure to learn a nonlinear model and its derivatives to as many orders as desired with multilayer perceptron (MLP) neural networks. A modular neural network modeling a nonlinear function and its derivatives is introduced. The method has been used for the extraction of the large‐signal model of a power MESFET device, modeling the nonlinear relationship of drain‐source current I_ds as well as gate and drain charge Q_g and Q_d with respect to intrinsic voltages V_gs and V_ds over the whole operational bias region. The neural models have been implemented into a user‐defined nonlinear model of a commercial microwave simulator to predict output power performance as well as intermodulation distortion. The accuracy of the device model is verified by harmonic load‐pull measurements. This neural network approach has demonstrated to predict nonlinear behavior with enough accuracy even if based only on first‐order derivative information. © 2003 Wiley Periodicals, Inc. Int J RF and Microwave CAE 13: 276–284, 2003.     

Neural‐space mapping‐based large‐signal modeling for MOSFET

In this article, a large‐signal modeling approach based on the combination of equivalent circuit and neuro‐space mapping modeling techniques is proposed for MOSFET. In order to account for the dispersion effects, two neuro‐space (S) mapping based models are used to model the drain current at DC and RF conditions, respectively. Corresponding training process in our approach is also presented. Good agreement is obtained between the model and data of the DC, S parameter, and harmonic performance for a 0.13 μm channel length, 5 μm channel width per finger and 20 fingers MOSFET over a wide range of bias points, demonstrating the proposed model is valid for DC, small‐signal and nonlinear operation. Comparison of DC, S‐parameter, and harmonic performance between proposed model and empirical model further reveals the better accuracy of the proposed model. © 2011 Wiley Periodicals, Inc. Int J RF and Microwave CAE , 2011.     

Trust region‐based optimization of PKI neural models for RF/microwave devices

This article proposes a new trust region‐based optimization technique for Radio Frequency (RF)/microwave devices. The proposed approach is apt for modeling scenarios, where standard ANN multilayer perceptron (MLP) and Prior Knowledge Input (PKI) models fail to deliver a satisfactory model. This approach feeds output of standard ANN model as knowledge input to PKI model. The ANN model and the PKI model form a symbiotic pair to yield accurate results. In this paper, the dogleg routine is exploited in the process of optimization to obtain valid trust region steps. The proposed method is compared with sensitivity technique via several RF/microwave components. © 2012 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2013.     

Small signal behavioral modeling technique of GaN high electron mobility transistor using artificial neural network: An accurate,fast, and reliable approach

This article reports a comparative study of two artificial neural network structures and associated variants used to describe and predict the behavior of 2 × 200 μm² GaN high electron mobility transistors (HEMTs), utilizing radiofrequency characterization. Two architectures namely multilayer perceptron and cascade feedforward, have been investigated in this work to develop the behavioral model. A study is conducted utilizing the two architectures, all trained using Levenberg‐Marquardt, in terms of accuracy, convergence rate, and generalization capability to develop the behavioral model of GaN HEMT. However, to ensure the robustness of the model, accuracy, convergence rate, time elapsed, and generalization capability of the proposed model is also tested under couple of training algorithms, activation functions, number of hidden layers and neuron embedded inside it, methods for initialization of weights and bias and certain other vital parameters playing vital role in influencing the model accuracy and effectiveness. An excellent agreement found between measured S‐parameters and the proposed model proves the effectiveness of the proposed approach and excellent prediction ability for a sweeping multibias set and broad frequency range of 1 to 18 GHz. Moreover, a very good generalization capability is also recorded under variation of crucial parameters of GaN HEMT‐based neural model.     

Construction of behavioral models for microwave devices from time domain large‐signal measurements to speed up high‐level design simulations

Behavioral models for microwave devices from time domain large‐signal measurements are developed. For the presented examples, the model is defined by representing the terminal currents as a function of the terminal voltages and their derivatives. When using these models as building blocks of higher level designs, the simulation speed is significantly improved. © 2003 Wiley Periodicals, Inc. Int J RF and Microwave CAE 13: 54–61, 2003.     

Neuro‐space mapping modeling approach for trapping and self‐heating effects on GaAs and GaN devices

In recent years, neural networks have been successfully applied for modeling the nonlinear microwave devices as GaAs and GaN MESFETs/HEMTs. Many modeling approaches have been developed for small and large signal applications. In this contribution, a neuro‐space mapping approach is proposed for modeling the trapping and the self‐heating effects on GaAs and GaN devices. The Angelov empirical model is used as the coarse model, which can be adjusted using DC and Pulsed I/V measurements at different static bias points. The proposed approach is tested for the MGF1923 GaAs MESFET and for an AlGaN/GaN HEMT. DC and transient simulation results are compared to DC and Pulsed I/V measurements. Good results are obtained for the DC and dynamics I/V characteristics at different static bias points.     

A consensual modeling of the expert systems applied to microwave devices

In this work, a consensual approach is developed for modeling RF/microwave devices. In the proposed method, multiple individual models generated by an expert system ensemble are combined by a consensus rule that results in a consistent and improved generalization outputting with the highest possible reliability and accuracy. Here, the expert system ensemble is basically constructed by the competitor and diverse regressors which in our case are back‐propagation artificial neural network (ANN), support vector (SV) regression machine, k‐nearest neighbor and least squares algorithms that perform generalization independently from each other. In the case of excessive data, to reduce the amount of the data, the expert system ensemble of regressors can be shown to be trained by a subset consisting of the SVs. Main feature of the consensual modeling can be put forward as due to diversity in generalization process of each member of the ensemble, the resulted consensus model will effectively identify and encode more aspects of the nonlinear relationship between the independent and the dependent variables than will a single model. Thus, in the consensual modeling, an enhanced single model is built by combining the most successful sides of the competitor and the diverse contributors. Finally, consensual modeling is demonstrated typically for the two devices: the first is a passive device modeling which is synthesis of the conductor‐backed coplanar waveguide with upper shielding and the second is an active device modeling which is the noise modeling of a microwave transistor. © 2010 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2010.     

An improved empirical large‐signal model for the GaAs‐ and GaN‐based HEMTs

A complete empirical large‐signal model for the GaAs‐ and GaN‐based HEMTs is presented. Three generalized drain current I–V models characterized by the multi‐bias Pulsed I–V measurements are presented along with their dependence on temperature and quiescent bias state. The new I–V equations dedicated for different modeling cases are kept accurate enough to the higher‐order derivatives of drain‐current. Besides, an improved charge‐conservative gate charge Q–V formulation is proposed to extract and model the nonlinear gate capacitances. The composite nonlinear model is shown to accurately predict the S‐parameters, large‐signal power performances as well as the two‐tone intermodulation distortion products for various types of GaAs and GaN HEMTs. © 2011 Wiley Periodicals, Inc. Int J RF and Microwave CAE , 2011.     

An improved millimeter‐wave small‐signal modeling approach for HEMTs

An improved method to determine the small‐signal equivalent circuit model for HEMTs is presented in this study, which is combination of the analytical approach and empirical optimization procedure. The parasitic inductances and resistances are extracted under pinch‐off condition. The initial intrinsic elements are determined by conventional analytical method. Advanced design system (agilent commercial circuit simulator) is used to optimize the whole model parameters with small deviation of initial values. An excellent agreement between measured and simulated S‐parameters is obtained for 2 × 20 μm² gate width HEMT up to 40 GHz. © 2013 Wiley Periodicals, Inc. Int J RF and Microwave CAE 24:464–469, 2014.     

A physics‐based model of DC and microwave characteristics of GaN/AlGaN HEMTs

A physics‐based model of AlGaN/GaN High Electron Mobility Transistor (HEMT) is developed for the analysis of DC and microwave characteristics. Large‐ and small‐signal parameters are calculated for a given device dimensions and operating conditions. Spontaneous and piezoelectric polarizations at the heterointerface and finite effective width of the 2DEG gas have been incorporated in the analysis. The model predicts a maximum drain current of 523 mA/mm and transconductance of 138 mS/mm for a 1 μm × 75 μm device, which are in agreement with the experimental data. © 2007 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2007.     

High‐efficiency broadband GaN HEMT power amplifier based on harmonic‐tuned matching approach

A new type of broadband class‐F power amplifier is proposed with GaN HEMT device CGH40010F. And a new harmonic control network is designed by improving the traditional harmonic control network, with the second harmonic and third harmonic broadband matched, which effectively solves the problem of class‐F power amplifier in the design of the bandwidth. To improve the efficiency of power amplifier, all high‐order harmonics are controlled in a certain bandwidth. CGH40010F power transistor is utilized to build the power amplifier working from 1.5 to 2.6 GHz, with the measured saturated output power >10 W, drain efficiency 60%‐80%, and gain >10 dB. The second and the third harmonic suppression levels are maintained from ?19.13 to ?47.44 dBc and from ?16.18 to ?47.9 dBc, respectively. The simulation and measurement results of the proposed power amplifier show good consistency.