Large‐signal distributed millimeter‐wave multifinger pHEMT modeling using time‐domain technique

The finite‐difference time‐domain (FDTD) method is used for the large‐signal modeling of a multifinger pHEMT, which is considered as five nonlinear coupled distributed transmission lines. The developed model, which is based on the exact physical layout of multifinger pHEMT, not only accurately describes the propagation effects along the electrodes at higher frequencies but it also includes major nonlinearities of the I–V and Q–V characteristics. Using the transmission line theory, a proper nonlinear equivalent lumped circuit model is allocated for the differential length of the quintuple‐line transistor and the nonlinear active multiconductor transmission line (NAMCTL) equations are derived. These nonlinear, coupled differential equations are numerically solved using the FDTD method. The proposed model is applied to a 100 nm GaAs pHEMT and the simulation results are compared with the results of conventional sliced model in Keysight ADS simulator. The developed transient nonlinear model accurately predicts both the S‐parameters (1–150 GHz) and large‐signal power performances especially at millimeter wave frequency range. The proposed model can be useful in design and analysis of various types of high‐frequency nonlinear integrated circuits.     

Experimental insight into the temperature effects on DC and microwave characteristics for a GaAs pHEMT in multilayer 3‐D MMIC technology

This paper is focused on studying the behavior of a GaAs pseudomorphic high electron mobility transistors (pHEMT) with respect to the temperature. The tested pHEMT is realized using the multilayer three‐dimensional (3‐D) monolithic microwave integrated circuit (MMIC) technology. The analysis is based on temperature‐dependent on‐wafer measurements carried out from 298 K to 373 K. The experiments consist of DC characteristics and scattering parameters in the broad frequency range from 45 MHz to 40 GHz. The effect of the temperature on the measured transistor performance is analyzed in detail and then, to gain a better insight and understanding of the device behavior, the achieved measurements are used for extraction and validation of a small‐signal equivalent‐circuit model for different temperature conditions. This study shows that, by heating the studied device, the observed performance variations depend remarkably on the selected bias condition. In particular, the output current and transconductance are degraded at higher gate‐source voltage and improved as the transistor is driven towards the pinch‐off. This is due to the counterbalancing of temperature‐dependent effects contributing in opposite ways to the resultant behavior of the transistor. Therefore, depending on the given application, an appropriate selection of the bias and temperature conditions is essential to guarantee adequate transistor performance.     

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.     

Microwave frequency small‐signal equivalent circuit parameter extraction for AlInN/GaN MOSHEMT

This article presents an accurate and efficient extraction procedure for microwave frequency small‐signal equivalent circuit parameters of AlInN/GaN metal‐oxide‐semiconductor high electron mobility transistor (MOSHEMT). The parameter extraction technique is based on the combination of conventional and optimization methods using the computer‐aided modeling approach. The S‐, Y‐, and Z‐ parameters of the model are extracted from extensive dynamic AC simulation of the proposed device. From the extracted Y‐ and Z‐ parameters the pad capacitances, parasitic inductances and resistances are extracted by operating the device at low and high frequency pinch‐off condition depending upon requirement. Then, the intrinsic elements are extracted quasi analytically by de‐embedding the extrinsic parameters. S‐parameter simulation of the developed small‐signal equivalent circuit model is carried out and is compared with TCAD device simulation results to validate the model. The gradient based optimization approach is used to optimize the small‐signal parameters to minimize the error between developed SSEC model and device simulation based s‐parameters. The microwave characteristics of optimized SSEC model is carried out (f_T = 169 GHz and f_max = 182 GHz) and compared with experimental data available from literature to validate the model.     

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.     

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.     

Accurate distributed and semidistributed models of field effect transistors for millimeter wave applications

An accurate distributed model of field effect transistors, including the parasitic impedances of the electrodes and the mutual coupling between them for analyzing the propagation effects along the electrodes working at millimeter wave frequencies, is presented. A numerical method is used to calculate the S‐parameters of the distributed model. Then, a corresponding simpler semidistributed model, which avoids solving coupled differential equations, is then presented. A GaAs pHEMT example is given to show the well agreement of the S‐parameters of the measurement and the distributed model ranging from 1 to 60 GHz. The S‐parameters of the semidistributed model agree well with that of the distributed model up to 100 GHz, and both of the models can be applied for S‐parameters prediction out of the measurement equipment range. © 2011 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2011.     

Artificial neural network approach for LNA design of GPS receiver

Paper presents an ANN modeling of microwave LNA for the global positioning front end receiver, operating at 1.57542 GHz. To design LNA, multilayer perceptron architecture is used. The scattering parameters of LNA are calculated using Levenberg Marquardt Backpropagation Algorithm for the frequency range 100 MHz to 8 GHz. The inputs given to this architecture are drain to source current, drain to source voltage, temperature and frequency and the outputs are maximum available gain, noise figure and scattering parameters (magnitude as well as angle). ANN model is trained using Agilent MGA 72543 GaAs pHEMT Low Noise Amplifier datasheet and this model shows high regression. The smith and polar charts are plotted for frequency range 100 MHz to 8 GHz.     

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.     

Small‐signal and noise modeling of class of HEMTs using knowledge‐based artificial neural networks

In this article, the neural network approach is exploited for development of bias‐dependent small‐signal and noise models of a class of microwave field effect transistor (FETs) made in the same technology but differing in the gate width. The prior knowledge neural approach is applied. Introducing gate width at the input of proposed neural networks, as well as the S/noise parameters of a device that belongs to the same class as the modeled device representing the prior knowledge, leads to very accurate scattering and noise parameters' modeling, as exemplified by modeling of class of pseudomorphic high electron mobility transistor (pHEMT) devices. © 2012 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2013.     

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.     

18‐31 GHz GaN wideband low noise amplifier (LNA) using a 0.1 μm T‐gate high electron mobility transistor (HEMT) process

GaN technology has attracted main attention towards its application to high‐power amplifier. Most recently, noise performance of GaN device has also won acceptance. Compared with GaAs low noise amplifier (LNA), GaN LNA has a unique superiority on power handling. In this work, we report a wideband Silicon‐substrate GaN MMIC LNA operating in 18‐31 GHz frequency range using a commercial 0.1 μm T‐Gate high electron mobility transistor process (OMMIC D01GH). The GaN MMIC LNA has an average noise figure of 1.43 dB over the band and a minimum value of 1.27 dB at 23.2 GHz, which can compete with GaAs and InP MMIC LNA. The small‐signal gain is between 22 and 25 dB across the band, the input and output return losses of the MMIC are less than ?10 dB. The P_1dB and OIP3 are at 17 dBm and 28 dBm level. The four‐stage MMIC is 2.3 × 1.0 mm² in area and consumes 280 mW DC power. Compared with GaAs and InP LNA, the GaN MMIC LNA in this work exhibits a comparative noise figure with higher linearity and power handling ability.     

Direct extraction method for stripline packaged field effect transistor small signal equivalent circuit model

A new method for the extraction of the small signal model parameters for packaged field effect transistors (FETs) is proposed in this article. This method differs from previous ones by extracting the packaging model parameters under active bias conditions without global numerical optimization techniques. The main advantage of this method is that a unique and physically meaningful set of extrinsic packaging model parameters are extracted by using a set of closed form expressions based on the coaxial measurement for packaged device and on‐wafer measurement for chip. Good agreement is obtained between simulated and measured results for a gallium arsenide MESFET with 0.5 μm gatelength and 400 μm gatewidth over a wide range of bias points up to 8 GHz. © 2013 Wiley Periodicals, Inc. Int J RF and Microwave CAE 24:306–313, 2014.     

Hybrid Ku‐band low‐noise amplifier for mobile DBS active phased‐array antennas

In this article we present a two‐stage Ku‐band low‐noise amplifier (LNA) using discrete pHEMT transistors on non‐PTFE substrates for low‐cost direct broadcast satellite (DBS) phased‐array systems (patent pending). The vertical input configuration of the LNA lends itself to direct integration with input port of antenna modules of the phased array, which minimizes preamplification losses. DC decoupling between LNA stages is realized using interdigital microstrip capacitors such that the implementation reduces the number of discrete microwave components and thereby not only reduces the component and assembly costs but also decreases the standard deviation of such crucial parameters of phased‐array systems as the end‐to‐end phase shift of the amplifier and the amplifier gain. Using the proposed printed decoupling capacitors, a cost reduction better than 30% of the original costs has been achieved. Additionally, we present a hybrid design procedure for the complete LNA, including its input and output connectors as well as packaging effects. This method is not based on parameter extraction, but encompasses electromagnetic (EM) field simulator results which are further combined using a high‐level circuit simulator. According to the presented measurement results, the implemented Ku‐band LNA has a noise figure better than 0.9 dB and a gain higher than 20 dB with a gain flatness of 0.3 dB over a 5% bandwidth. © 2006 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2006.     

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.     

Genetic algorithm‐based neural‐network modeling approach applied to AlGaN/GaN devices

An accurate equivalent circuit large‐signal model (ECLSM) for AlGaN‐GaN high electron mobility transistor (HEMT) is presented. The model is derived from a distributed small‐signal model that efficiently describes the physics of the device. A genetic neural‐network‐based model for the gate and drain currents and charges is presented along with its parameters extraction procedure. This model is embedded in the ECLSM, which is then implemented in CAD software and validated by pulsed and continuous large‐signal measurements of on‐wafer 8 × 125‐μm GaN on SiC substrate HEMT. Pulsed IV simulations show that the model can efficiently describe the bias dependency of trapping and self‐heating effects. Single‐ and two‐tone simulation results show that the model can accurately predict the output power and its harmonics and the associated intermodulation distortion (IMD) under different input‐power and bias conditions. © 2012 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2013.     

Artificial bee colony algorithm for small signal model parameter extraction of MESFET

This paper presents an application of swarm intelligence technique namely artificial bee colony (ABC) to extract the small signal equivalent circuit model parameters of GaAs metal extended semiconductor field effect transistor (MESFET) device and compares its performance with particle swarm optimization (PSO) algorithm. Parameter extraction in MESFET process involves minimizing the error, which is measured as the difference between modeled and measured S parameter over a broad frequency range. This error surface is viewed as a multi-modal error surface and robust optimization algorithms are required to solve this kind of problem. This paper proposes an ABC algorithm that simulates the foraging behavior of honey bee swarm for model parameter extraction. The performance comparison of both the algorithms (ABC and PSO) are compared with respect to computational time and the quality of solutions (QoS). The simulation results illustrate that these techniques extract accurately the 16—element small signal model parameters of MESFET. The efficiency of this approach is demonstrated by a good fit between the measured and modeled S-parameter data over a frequency range of 0.5–25 GHz.     

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 parameter extraction method of the PIN diode for physics‐based circuit simulation over a wide frequency range

The accurate physical parameters of the semiconductor devices are critical to the physics‐based circuit simulation, which solves the carrier transport equations to model the semiconductor devices. However, the conventional method extracts physical parameters from low‐frequency measurements such as the DC I‐V curve, which cannot work at high frequencies. To overcome this problem, we propose a physical parameter extraction method of the PIN diode working well from DC to microwave frequencies. Specifically, because the transit‐time effects are dependent on the working frequencies and input power levels, the operation modes of the PIN diode can be divided into three cases from DC to microwave frequencies; therefore, the proposed method extracts the parameters from three measured curves, including the DC I‐V curve, a small‐signal, and a large‐signal voltage waveform both at a microwave frequency. Experiments of a PIN diode SMP1330 circuit show that the error of the conventional method is about 45% at frequencies above 300 MHz, but the maximum error of the proposed method is only 9.5% from DC to 2 GHz. Moreover, the conventional method is unable to characterize the conductance modulation phenomenon, which leads to unexpected signal reflections in PIN limiter circuits and the missing of information in radio transceivers.     

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.