ANN-Based Large-Signal Model of AlGaN/GaN HEMTs With Accurate Buffer-Related Trapping Effects Characterization

Husain

Hashmi

et al. 2021

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This paper presents an extensive study and demonstration of efficient electrothermal largesignal GaN HEMT modeling approaches based on combined techniques of Genetic Algorithm (GA) with Artificial Neural Networks (ANN), and Particle Swarm optimization (PSO) with Support Vector Regression (SVR). Another promising Gaussian Process Regression (GPR) based large-signal modeling approach is also explored and presented. The GA-ANN addresses the typical problem of local minima associated with the backpropagation (BP) based ANN. The GA successfully aids in the determination of optimal initial values for BP-ANN and enables it to find a unique optimal solution after subsequent of iterations with higher rate of convergence. This is also achieved using PSO-SVR with lower optimization variables. The developed modeling techniques are demonstrated and used to simulate the gate and drain currents of a 2-mm GaN device. All the models are relatively simple, practical, and easy to implement. The gate and drain currents models are embedded in an equivalent large-signal circuit's model and built in Advanced Design System (ADS) software. The implemented model is validated by large-signal measurements and very good fitting results have been obtained. The model also showed an accurate simulation for a nonlinear power amplifier with very good computational speed and convergence.

Section: Appendix Bmentioning

confidence: 99%

Large-Signal Modeling of GaN HEMTs Using Hybrid GA-ANN, PSO-SVR, and GPR-Based Approaches

Husain

Hashmi

et al. 2021

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“…All model elements are then used to simulate the pinch-off Sparameters and find the error 𝜀 𝑟 according to (2). The same extrinsic elements are de-embedded from active S-parameters measurements at different bias conditions to find the stranded deviations 𝜎 𝐶𝑔𝑠 , 𝜎 𝐶𝑔𝑑 , 𝜎 𝐶𝑑𝑠 and 𝜎 𝐺𝑚 of the corresponding intrinsic elements.…”

Section: Gray Wolf Optimizationmentioning

confidence: 99%

Gray Wolf Optimization-Based Modeling Technique Applied to GaN High Mobility Electron Transistors

2021

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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.

“…The small-signal lumped-element equivalent-circuit modeling of microwave transistors has been much debated and is still being debated [1][2][3][4][5][6][7][8], since this area of research is of great interest but also very challenging. The large interest comes mostly from the fact that the small-signal lumpedelement equivalent-circuit models often act as foundation for large-signal and noise modeling [9][10][11][12][13][14][15]. However, smallsignal lumped-element equivalent-circuit modeling becomes more and more challenging due to the continuous increase in transistor operating frequencies and gate width enlargement, in order to enable higher-frequency and higher-power applications.…”

Section: Introductionmentioning

confidence: 99%

An Improved Transistor Modeling Methodology Exploiting the Quasi-Static Approximation

Crupi

Raffo

et al. 2021

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In this paper, a new modeling technique is proposed for extracting small-signal lumped-element equivalent-circuit models for microwave transistors. The proposed procedure is based on using an optimization approach that is improved by targeting a quasi-static behavior as additional objective function rather than only minimizing the error between the simulated and measured scattering parameters. The validity of the developed modeling methodology is successfully demonstrated by considering a 0.25x1000 m 2 gallium nitride (GaN) highelectron-mobility transistor (HEMT) as a case study.