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

Jarndal, Anwar; Hussein, Ahmed S.

doi:10.1002/mmce.21555

Cited by 14 publications

(15 citation statements)

References 25 publications

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“…The onset of the distributed effects [16], [17] and non-quasi-static (NQS) effects [18], [19] make the model extraction much more critical. Over the years, optimizationbased model identification has demonstrated to be a powerful modeling tool to accomplish this critical task [20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

An Improved Transistor Modeling Methodology Exploiting the Quasi-Static Approximation

Jarndal

Crupi

Raffo

et al. 2021

IEEE J. Electron Devices Soc.

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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.

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Section: Introductionmentioning

confidence: 99%

An Improved Transistor Modeling Methodology Exploiting the Quasi-Static Approximation

Jarndal

Crupi

Raffo

et al. 2021

IEEE J. Electron Devices Soc.

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“…However, at higher frequencies, especially for large device periphery, extra elements are required in the EC to account for the parasitic effects. In such a case, the direct extraction procedure are replaced by optimization or combination of direct and optimization techniques 23 …”

Section: Introductionmentioning

confidence: 99%

Genetic algorithm initialized artificial neural network based temperature dependent small‐signal modeling technique for GaN high electron mobility transistors

Jarndal

Husain

Hashmi

2021

Int J RF Microw Comput Aided Eng

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This paper explores and develops efficient temperature‐dependent small‐signal modeling approaches for GaN high electron mobility transistors (HEMTs). The multilayer perceptron (MLP) architecture and cascaded MLP architecture of artificial neural network are employed to model temperature dependence of 2‐mm GaN‐on‐silicon device. It is identified that both architectures face problem of dependence on initials values of weights and biases. To overcome this issue, the genetic algorithm (GA) is incorporated in both MLP and cascaded MLP architectures. The models are trained on a large set of operating conditions (bias voltages and ambient temperatures) over a frequency range of 0.1 to 20 GHz and then tested for both temperature interpolation and extrapolation cases to assess their accuracy and robustness. An excellent agreement between the measured and the modeled S‐parameters over the entire frequency range demonstrate the quality and robustness of the proposed technique. It is also shown that the cascaded MLP with GA exhibits better performance but with increased complexity.

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“…Meanwhile, the optimization method has the drawback that the extracted elements could be physically meaningless and rely heavily on the initial value, local minima, and also optimization technique itself. Hence, most researchers combine these two methods to develop hybrid parameter‐extraction method 9‐13 . Usually, some assumptions on the capacitance partitioning of intrinsic part for cold pinch‐off GaN device are used, such as C gs = C gd in References 10 and 11 and three identical capacitances C b in 12 .…”

Section: Introductionmentioning

confidence: 99%

Improved small‐signal hybrid parameter‐extraction technique for AlGaN / GaN high electron mobility transistors

Helaoui

Cai

et al. 2021

Int J RF Mic Comp-Aid Eng

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An improved hybrid parameter‐extraction technique is proposed for the small‐signal modeling application in AlGaN/GaN high electron mobility transistors (HEMTs). The capacitance partitioning characteristics of cold pinch‐off GaN device with non‐field‐plate and field‐plate structures are reinvestigated and the scanning range of parasitic capacitances Cpg and Cpd is determined. A systematic optimization approach is proposed to obtain the initial values of Cpg and Cpd. By using the global optimization algorithm, particle swarm optimization, the reliable parasitic parameters can be acquired based on the proposed error function. The validity of the developed hybrid parameter‐extraction method is verified by 2 × 100 μm AlGaN/GaN HEMT with source‐connected FP structure from 0.1 to 40 GHz among the 442 biasing points.

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

Cited by 14 publications

References 25 publications

An Improved Transistor Modeling Methodology Exploiting the Quasi-Static Approximation

An Improved Transistor Modeling Methodology Exploiting the Quasi-Static Approximation

Genetic algorithm initialized artificial neural network based temperature dependent small‐signal modeling technique for GaN high electron mobility transistors

Improved small‐signal hybrid parameter‐extraction technique for AlGaN / GaN high electron mobility transistors

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