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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.

Currently, the GaN-on-silicon high electron mobility transistor (HEMT) is a promising candidate to replace the Si Metal Oxide Semiconductor Field Effect Transistor (MOSFET) for high power electronics circuits. However, self-heating is still a challenging issue to be addressed, especially for high-current applications. In this paper, a GaN-on-Si HEMT with a diamond (Dia) heat spreader is proposed to suppress the self-heating effect. The performance of the proposed device is analyzed and compared with conventional GaN-on-Si and also GaN-on-SiC devices. The analysis was carried-out using technology computer aided design. The GaN-on-Si with diamond heat spreader suppresses the self-heating in the device and achieves higher saturation drain current than conventional GaN-on-Si. In addition, GaN-on-Si with Diamond heat spreader yields a higher transconductance and cut-off frequency than GaN-on-Si. This improved structure will provide a low cost device with enhanced thermal characteristics for higher power applications.

Currently, gallium nitride (GaN) on silicon (Si) high-electron-mobility transistors (HEMT) are a promising candidate for designing improved power electronic circuits. The lattice mismatch between GaN and Si induces buffer/substrate leakage currents that impact the performance of the device. In this paper, four different model topologies have been demonstrated to simulate the buffer/substrate loading effect in GaN on a Si HEMT. A particle swarm optimization-based procedure has been developed to extract reliable values for the model elements. The models are evaluated in term of reliability of extraction and accuracy of S-parameter fitting. The results showed that the RC-based model topologies have more accurate simulation for buffer/substrate leakage than the RLC-based topology. This will contribute to reliable and accurate small-/large-signal modeling of the device for designing efficient power circuits.

In this article, an artificial neural network (ANN)-based large-signal model (LSM) of AlGaN/GaN high electron mobility transistors (HEMTs) with accurate buffer-related trapping effects characterization and modeling is proposed. A hybrid small-signal parameter-extraction method for AlGaN/GaN HEMTs is used to acquire the parasitic parameters. To simplify the modeling procedure of the drain–source current $I_{\mathrm{ ds}}$ , an ANN-based model associated with the empirical equations taking into account the trapping effects, self-heating effects, and breakdown issue is developed. The low-frequency dispersions related to the buffer-related trapping effects have been well modeled by using a new empirical equation, which has been verified by small-signal S-parameters. Also, a new thermal factor $K_{T}$ and an improved Shockley diode equation are given in the proposed model as well. The developed LSM has been fully verified by a $2\times 100\,\,\mu \text{m}$ AlGaN/GaN HEMT with the pulsed I–V, small-signal S-parameters, power sweep, and load—pull measurements.

The purpose of this experimental study is to evaluate quantitatively the impact of the temperature on the behavior of various high electron-mobility transistor (HEMT) technologies through the analysis of the DC and RF transconductance. The experimental data are reported for six different HEMT devices, in order to develop a comparative analysis based on various technologies, including gallium arsenide (GaAs) and gallium nitride (GaN) materials, matched and pseudomorphic HEMTs, single- (S-H) and double-heterojunction (D-H) HEMTs, and both virgin and multi-layer devices. The reported findings show that the impact of the ambient temperature on the HEMT behavior strongly depend on the tested technology and operating conditions. As a matter of fact, a higher temperature can lead to increased or degraded transconductance, depending on the device technologies and bias point. In the GaAs-based devices, an operating bias condition at which the DC and RF transconductance are temperature insensitive can be defined, owing to two-opposite temperature-dependent effects counteracting with each other.

This work investigates the applicability of a two-stage Colpitts oscillator based on GaN high electron mobility transistors (HEMT) for wireless power transfer applications. An inductive load is used both as part of the resonance network in the oscillator and as a power transmitting coil. Simulations and experimental results using commercial GaN transistors are provided.

This paper presents three different approaches for designing dc-ac converter based on GaN transistor. The proposed circuits could be used to design the transmitter side of Wireless Power Transfer (WPT) systems. The first two approaches are based on single-stage and double-stage Colpitts Oscillators, and the third one is based on an H-bridge converter. The three circuits have been simulated using LTSpice software, then implemented on PCB boards. The whole WPT system has been implemented, cased, and tested.

In this paper a Genetic Algorithm (GA) augmented Convolutional Neural Network (CNN) procedure has been developed. The proposed approach was implemented in Python and applied on a simple three layer convolutional neural network to explore its effects on training the process. The global searching capability of the GA is exploited to initiate the training process of the conventional Back Propagation (BP) based CNN. The weights of the network are optimally initiated using the GA genetic algorithm rather than using random initializers before training. The proposed method of GA-BP based CNN shows better performance in terms of the training time and accuracy with respect to the conventional BP based CNN.

COVID-19 pandemic now affects the entire world and has a major effect on the global economy. A number of medical researchers are currently working in various fields to tackle this pandemic and its circumstances. This paper aims of developing a model that can estimate the number of deaths in the affected cases based on the documented number of older (above 65 years of age), diabetic and smoking cases. The Gaussian Process Regression (GPR) approach has been used to build the model and its performance was compared with a corresponding Artificial Neural Network (ANN) model. The model was applied to reliable data published by the World Health Organization (WHO) for different countries in North America, Europe and the Gulf region. The model provided impressive results with an excellent prediction of data from all the countries under investigation. The model may be useful in estimating the number of deaths due to any arbitrary number of inputs. It would also help to prepare effective measures to minimize the number of deaths.

This work addresses the increasing number of diabetic patients around the world. Neural network models have been developed and used to predict and classify the likelihood of a person to become diabetic. It presents three models based on neural network for the classification and prediction of diabetes. These models include a feedforward network, a pattern network, and a cascade forward architecture. The performance of the three models are compared in terms of accuracy, sensitivity, and specificity. All models are implemented and tested in MATLAB. The highest accuracy reported among the models is 91.1 %.

In this study, a 4.4 MHz wireless power transfer (WPT) system was designed and implemented using a class-AB Armstrong oscillator that uses a packaged GaN on Si high electron mobility transistor. The oscillator is designed and simulated in advanced design system and then integrated with a magnetically coupled resonant WPT system. The transmitting planar coil of the WPT contains both the main and the feedback coils of the Armstrong oscillator. The whole system was implemented in a printed circuit board and tested. The DC-to-AC conversion efficiency of the simulated Armstrong oscillator is 69%. The maximum measured power transfer efficiency is 40.3% at a 2 cm distance between the transmitting and receiving coils. The input DC power of 37.5 mW provides 15.1 mW of AC power to the load with low distortion, making it suitable for low electromagnetic interference, size, power and cost applications such as biomedical implants.

Impact of substrate thickness on self-heating effect for AlGaN/GaN HEMT is analyzed. Self-heating effect is analyzed using Technology Computer Aided Design (TCAD) simulation. GaN-HEMT with gate width of 1mm and gate length of 0.7 µm is considered for the analysis. In the simulation, substrate thickness is varied from 100 µm to 200 µm to study its impact on self-heating effect and drain current. In addition, the ambient temperature is varied from 300K to 700K and its impact is analyzed. Impact of gate-gate spacing on thermal performance is also analyzed. Furthermore, Kink effect on drain current at V GS =-2V is observed.

Millimeter-wave technology is promising for the next generation of wireless communication. An appropriate channel model for typical scenarios in a built-up environment is crucially needed for development of future technologies such as 5G. This paper presents an efficient millimeter wave wideband propagation model that can be used for analysis and design purposes. Unlike the commonly used two-ray and statistical models, the proposed model has higher reliability and accuracy to simulate the considered environment. The uniform theory of diffraction has been used to calculate the total received signal due to multi-reflection–diffraction for both linear and circular polarization. Mathematical formulas have been derived for each considered ray contribution in terms of the building dimensions and the distances between the building, mobile and transmitter. The model has been used to evaluate the performance of the communication system at 28 and 73 GHz based on signal fading characteristics and power delay profile, and the simulation results have been compared with other published works

Sensing COVID-19, GOx (glucose oxidase enzyme) in exhaled breath condensate/saliva, bio-molecules like KIM (Kidney Injury Molecule) in human body and pH value in human body fluids have gained huge attention in the present scenario as well as in the past decade. Hence, for the first time, double channel technique in AlGaN/GaN High Electron Mobility Transistor (HEMT) is proposed and its applicability is demonstrated by biosensing application. Simulation using SILVACO Technology Computer Aided Design (TCAD) based on numerical solid state models has been extensively used for investigation and analysis. The sensitivity of double channel device is compared with single channel device and its performance is evaluated in terms of the transconductance. Unlike the single channel device, double channel device exhibited wide range of transconductance with respect to gate bias. The device recorded a sensitivity of 136%, which is 74% higher than the sensitivity of single channel device. Hence, it is inferred that the sensitivity enhances with the use of multiple channels and could be increased by increasing the number of channels. The results of this research show that the proposed sensor stands a promising candidate for future biosensing applications that demand high detection limits.

Diabetes affect many people around the world. While current treatments such as insulin injections and insulin pump devices are effective at managing diabetes, it still requires a manual effort that if not acted upon fast enough will lead to fatal results. This work designs and implements an artificial pancreas to improve the quality of life for diabetics by regulating the blood glucose levels and ease the burden of therapy for the insulin dependent. It presents a portable artificial pancreas system based on continuous glucose monitor (CGM), an insulin pump, and a microprocessor for implementing a control algorithm for automatic injection of the proper insulin dosage. The system also provides an efficient tool for monitoring the patient situation by submitting a regular report to the health care center and sending an alarm in case of any critical issue. The presented system is cost-effective compared to commercially available systems.

This paper reports the design and implementation of an Armstrong power oscillator for wireless power transfer (WPT) using GaN HEMT (high electron mobility transistor) at 4.6 GHz and GaN FET at 40 kHz. In the first design, a low power depletion-type device is used, simulated and integrated with a magnetically coupled resonant (MRC) WPT system using PCBs. The second design is based on a higher power enhancement-type transistor, where it is simulated and compared with the first design. High quality AC output waveforms with low distortion are obtained, which leads to low electromagnetic interference (EMI) levels that are required for sensitive applications such as biomedical technologies.

In this paper, four different equivalent circuit models to describe substrate/buffer loading effect in GaN HEMT on Si substrate have been investigated. The effect is characterized by Z-parameter measurements of open de-embedding structure for the considered device. A meta-heuristic optimization techniques of Grey-Wolf has been utilized to extract optimal values for the model elements. The models are validated by comparing their simulation results with the measured data. The extraction results are evaluated in terms of their accuracy and rate of convergence.

This work proposes a simpler automatic fault classifier that uses multi-layer perceptron (MLP) to identify faults in rotating machines. For classification, only statistical features and rotation frequency was taken in to consideration. Synthetic Minority Over-sampling Technique (SMOTE) was incorporated to handle imbalance classes in the machinery fault database. The proposed approach was evaluated on Machinery Fault Database (MAFAULDA) database. The proposed system has achieved an accuracy of 96.2%, which is comparable with the reported results in the literature. Results indicate that handling imbalance classes greatly increases the generalizability power of the MLP classifier on the machinery fault database.

Training artificial neural networks (ANNs) using global optimization techniques is becoming an attractive area of research. In this paper, combined modeling techniques of ANNs with genetic algorithm (ANN-GA), particles warm optimization (ANN-PSO) and grey wolf optimization (ANN-GWO) will be presented. The performance of these three techniques will be investigated in terms of efficiency and effectiveness for solving practical modeling problem. It has been found the recently developed ANN-GWO has a comparable performance with respect to the other techniques. ANN-PSO showed higher rate of convergence, which makes it more practical for real-time applications

In this paper, an improved modeling approach has been developed and validated for GaN high electron mobility transistors (HEMTs). The proposed analytical model accurately simulates the drain current and its inherent trapping and thermal effects. Genetic-algorithm-based procedure is developed to automatically find the fitting parameters of the model. The developed modeling technique is implemented on a packaged GaN-on-Si HEMT and validated by DC and small-/large-signal RF measurements. The model is also employed for designing and realizing a switch-mode inverse class-F power amplifier. The amplifier simulations showed a very good agreement with RF large-signal measurements.

In this article, a simple, direct and reliable extraction method has been developed and applied to different sizes of Gallium Nitride (GaN) high electron mobility transistors (HEMTs). Instead of high-voltage gate-forward measurements with risk of device damage, the proposed approach uses only uncritical cold S-parameters at pinch-off (VGS

This paper presents a design of a 3.5 GHz GaN Low Noise Amplifier (LNA) using Microstrip interconnect technology on a gold plated PCB and using packaged commercial GaN-on-SiC HEMTs. Cascode configuration with inter-stage matching and independent biasing networks was used. The whole design was fully implemented using Microstrip except for the capacitors and was optimized to reduce the noise figure (NF) and improve the gain and stability. The LNA provided a gain of 13.5 dB with a noise figure (NF) less than 3dB from 2.9 to 3.7 GHz with power consumption 2.92W.

A novel reliable small signal model parameter extraction of asymmetric GaN-based Heterojunction Field Effect Transistors (HFETs) is proposed in this paper. An efficient systematic searching procedure based on pinch-off and cold weak-forward S-parameters has been developed to consider the asymmetric structure of GaN HFETs and find the optimal values of the gate and drain pad capacitances Cpg and Cpd. Considering that the depletion region extension is varied with the gate bias voltage V gs slightly even below pinch-off voltage, the obtained initial values of the extracted parameters are optimized by artificial bee colony (ABC) algorithm to improve the reliability of parameter extraction. The developed parameter extraction method shows excellent accuracy and reliability. The proposed procedure can be extended to asymmetric GaN devices with various process technologies. The developed approach has been validated by an asymmetric 0.15 μm GaN HFET over a wide range of bias conditions and frequencies., 2018 13th European Microwave Integrated Circuits Conference (EuMIC), September 23-25, 2018, Madrid, Spain

In this paper, a reliable small-signal model parameter extraction method for GaN high electron mobility transistor (HEMT) on Si substrate has been developed and validated with respect to different gate width devices. The main advantage of this approach is its accuracy and dependency on only pinched-off and unbiased ${S}$ -parameter measurements. The developed procedure shows reliable and physically relevant results for the investigated devices and scaled with the gate width. A very good agreement is obtained between small-and large-signal simulations and measurements of the considered GaN HEMTs.

To overcome the limitation of energy supply for artificial cardiac pacemaker and the need for continuous monitoring, this paper presents a design and implementation of a wireless-powering and heart-monitoring system for patients with an implanted artificial cardiac pacemaker. The monitoring part of the system uses an Electrocardiography (ECG) device in addition to a microprocessor to monitor the heart condition of the patient and gives a periodic report to the physician in charge. The wireless-powering system includes a transmitter outside the patient's body to transfer power to the pacemaker's battery to charge it.

Various local optimization techniques such as Interior Point Algorithm have been widely used to optimize the parameters of models representing biodynamic responses to vibration. The quality of the obtained solutions depends on the initial guesses. This paper presents a comparison between the performance of Particle Swarm Optimization and Genetic Algorithm in optimizing the parameters of a human body model, where these techniques do not require initial guesses. The model represents the vertical apparent mass and the fore-and-aft cross-axis apparent mass of the seated human body during vertical excitation. With both optimization methods, the model provided close fits to the moduli and phases for both median data and the responses of 12 individual subjects. However, it was noted that using PSO provided a better solution with less mean error than GA and a faster solution in most of the cases.

Electrical load forecasting is essential in the field of power systems to enhance the operation and economical utilization In this paper, a combined approaches of artificial neural networks (ANN) with particle-swarm-optimization (PSO) and genetic algorithm optimization (GA) for short and mid-term load forecasting is developed. The model identifies the relationship among load, temperature and humidity using a case study of Sharjah City in United Arab Emirates. The ANN model trains the hourly peak load data for a set of days and then forecasts the load for next day. Actual data obtained from Sharjah Electricity and Water Authority (SEWA) is used to validate the results.

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.

This paper presents an approach for designing and implementing a smart glove for deaf and dumb people. There have been several researches done in order to find an easier way for non-vocal people to communicate with vocal people and express themselves to the hearing world. Developments have been made in sign language but mainly in American Sign Language. This research aims to develop a sign to Arabic language translator based on smart glove interfaced wirelessly with microcontroller and text/voice presenting devices. An approach has been developed and programmed to display Arabic text. The whole system has been implemented, programmed, cased and tested with very good results.

In this paper, a multi-purpose wireless charger design and implementation procedure is presented. A simple design based on Colpitts oscillator has been used to implement the transmitter side. The structure of the receiver coil has been optimized to improve the coupling efficiency. The system has been fabricated, cased and tested with different mobile devices and tablets.

This paper presents a design for 1–6 GHz low noise amplifier (LNA) based on cascade GaN HEMTs with LC ladder input matching and stabilizing output impedance. The designed LNA is simulated using Advanced Design Software (ADS) based on a commercial packaged transistor from Cree Inc. The LNA shows a maximum noise figure of 2.95 dB in the 1–6 GHz range with a gain of 14 dB. It also has a maximum S 11 of −2.4 dB, an input IP3 of 29.4dBm and is unconditionally stable over the whole bandwidth while drawing 13 Watts of DC power. The designed LNA also shows high linearity with more than 60 dBc carrier-to-intermediation distortion.

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.

This paper presents a propagation model for land-mobile-satellite (LMS) wideband radio channel in built-up environment. The model characterizes the behavior of the radio channel, under shadowing and multipath effects due to buildings, with variation of the elevation angle of the satellite. The wideband parameters (coherent bandwidth and time delay spreading) for LMS channel, in residential and urban environments, are computed. These parameters can be considered as a measure of the amount of ISI (inter-symbol interference) of the radio channel, which distorts the received signal and accordingly increases the bit error rate. The calculated values for these parameters using our model, show very good agreement with the corresponding measured ones, which accordingly shows the validity of the developed model for radio channel design in satellite mobile communication systems.

An improved large-signal modeling approach of GaN on Si devices for RF high-power applications is presented. This approach accounts for the parasitic buffer loading effect under microwave RF operation in addition to the self-heating and trap ping effects associated with high-power operation conditions. A hybrid optimization (genetic and Simplex) technique based procedure is used to determine the optimal values of the model extrinsic elements. These elements are de-embedded from multibias S-parameter measurements to find the intrinsic part of the device, which is then used to construct a nonlinear current-charge (represented by nonlinear charge and current sources) based model for the gate current of the device. Pulsed and static dc IV characteristics are used for modeling of the drain current. The validity of the developed modeling approach is verified by comparing simulated large-signal single- and two-tone simulation with measured data of a 2-mm (10 × 200 μm) GaN HEMT on Si substrate. The model has been employed for designing a class-AB power amplifier. Very good agreement between the amplifier simulation and measurement shows the validity of the model.

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. © 2011 Wiley Periodicals, Inc.

A new parasitic elements extraction technique for GaN HEMT transistors on Si substrate is presented. This technique is based on the use of two de-embedding GaN structures: open and thru-short. The equivalent circuit models along with the extraction procedure are detailed. A very good agreement between measurements and simulations validate the developed extraction method.

In this paper a reliable and cost-effective modeling approach for GaN HEMT on both Si and SiC substrates is presented. The developed model is validated in terms of small-and large-signal simulations for 2-mm GaN-on-Si and 1-mm GaN-on-SiC HEMTs.

A new small-signal modeling approach applied to GaN-based devices is presented. In this approach, a new method for extracting the parasitic elements of the GaN device is developed. This method is based on two steps, which are: 1) using cold S-parameter measurements, high-quality starting values for the extrinsic parameters that would place the extraction close to the global minimum of the objective function for the distributed equivalent circuit model are generated and 2) the optimal model parameter values are searched through optimization using the starting values already obtained. The bias-dependent intrinsic parameter extraction procedure is improved for optimal extraction. The validity of the developed modeling approach and the proposed small-signal model is verified by comparing the simulated wide-band small-signal S-parameter, over a wide bias range, with measured data of a 0.5-/spl mu/m GaN high electron-mobility transistor with a 2/spl times/50 /spl mu/m gatewidth.

In this paper, a parameter extraction method for GaN HEMTs is developed. The main advantage of this approach is its accuracy, reliability and dependence on only pinch-off Sparameter measurements to extract the parasitic elements of the device. The extraction results are compared with other extraction results based on pinch-off and forward measurements. The comparison result demonstrates the validity of the proposed method for small- and large-signal modeling of GaN devices.

This letter mainly focuses on providing theoretical justification for possible gallium-nitride (GaN) device linearity improvement, interpreting the key physical origins of third-order distortion (IMD3). Based on the bias-dependent S-parameter measurement data of field-plate (FP)-free 8times125 mum GaN high-electron mobility transistor (HEMT), IMD3 is modeled using classical Volterra series theory. Through this technique, device diagnosis is carried out for efficiently localizing the distortion behavior. Further, device linearity is shown to improve by appropriately tuning the gate-drain feedback capacitance by taking advantage of FP technology proving the analysis to be a powerful tool for developing GaN HEMT technology

The validity of the proposed small-signal model (SSM) and the developed extraction method in for large GaN devices is investigated. Extraction of parasitic elements is performed for different size devices to show the scaling of these elements with the gate width. The model shows a very good result for describing the parasitic distributed effect, which is considerable for large devices.

Prediction of future load demand is important for secure operation of power systems and their economical utilization. A number of algorithms have been suggested for solving this problem. In this paper, a genetic-fuzzy-neural networks approach for mid-term load forecasting is proposed. In this paper the relationship between humidity, temperature and load is identified with a case study for a particular region in Oman. The output load obtained is corrected using a correction factor from neural networks model, which depends on previous set of loads. Data for monthly peak load of four years has been used for training the model, which then forecasts the load of the fifth year. The model has been validated using actual data from an electricity company.

A genetic neural network based large-signal model for GaN HEMT transistor suitable for designing power amplifiers (PAs) is presented along with its parameters extraction procedure. This model is relatively easy to construct and implement in CAD software and 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 large-signal simulation to measured data. The model has been employed for simulating a switching-mode inverse class-F power amplifier. Very good agreement between the amplifier simulation and measurement shows the validity of the model.

A table-based large-signal model for GaN HEMT transistor suitable for designing switching-mode power amplifiers (SMPAs) 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 intrinsic drain and gate currents are described as a table-based to provide an accurate prediction for the device in the switching states as well as the transition one. The modeling procedure was applied to a 4-W packaged GaN-on-Si HEMT. The 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 power amplifier. Very good agreement between the amplifier simulation and measurement shows the validity of the model.

In this paper, a large-signal model for GaN HEMT transistors for designing RF power amplifiers is presented. This model is relatively easy to construct and implement in CAD software since it requires only DC and S-parameter measurements. The modeling procedure is applied to a 4-W packaged GaN-on-Si HEMT and the developed model is validated by comparing its large-signal simulation to measured data under different classes of operation.

An improved parasitic elements extraction method applied to GaN on Si devices is presented. Genetic-algorithm based procedure is used to determine a high quality reliable starting values for the extrinsic parameters of proposed small-signal model. Local optimization technique is then used to refine the initial value and find the optimal value for each model element. The validity of the developed method and the proposed small-signal model is verified by comparing simulated small-signal S-parameters with measured ones of a 2-mm (10×200 µm) GaN HEMT-on-Si.