Improved modeling of GaN HEMTs for predicting thermal and trapping-induced-kink effects

Jarndal, Anwar; Ghannouchi, Fadhel M.

doi:10.1016/j.sse.2016.05.015

Cited by 27 publications

(15 citation statements)

References 23 publications

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“…The activation function is represented here also by tanh . This function is also consistent with behavior of the drain current and it can accurately describe its ohmic‐saturation transition and pinch‐off (turn‐on) nonlinearities . In this model, the self‐heating regenerative process was taken in to account by feeding the drain current back as a third input.…”

Section: Temperature Dependent Large‐signal Modelmentioning

confidence: 64%

“…The former is attributed to the static and quasi‐static power dissipation. This heats up the channel area and deteriorates the conduction mechanism, which could be observed from the drain current collapse in the higher dissipation region of DC IV characteristics …”

Section: Temperature‐dependent Small‐signal Modelmentioning

confidence: 79%

“…In this article, the same reported feedforward NN modeling approach in Reference will be extended to feedback NN to consider the self‐heating regenerative process. The proposed technique is easier to develop and implement in CAD software with respect to other published models such as the reported ones in References . The proposed NN based model does not required an adopted analytical formula, which could be more complicated such as the one presented one in Reference .…”

Section: Introductionmentioning

confidence: 98%

“…The proposed technique is easier to develop and implement in CAD software with respect to other published models such as the reported ones in References . The proposed NN based model does not required an adopted analytical formula, which could be more complicated such as the one presented one in Reference . The simpler topology with respect to the reported one in Reference required less effort for extracting the model parameters.…”

Section: Introductionmentioning

confidence: 99%

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Neural network electrothermal modeling approach for microwave active devices

Jarndal

2019

Int J RF Microw Comput Aided Eng

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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.

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Section: Temperature Dependent Large‐signal Modelmentioning

confidence: 64%

Section: Temperature‐dependent Small‐signal Modelmentioning

confidence: 79%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Neural network electrothermal modeling approach for microwave active devices

Jarndal

2019

Int J RF Microw Comput Aided Eng

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“…The latter is the case of GaN-based devices in which the trapping phenomena cause a decrease of the number of electrons available for the conduction, with a consequent bending of the drain-source current, I DS [8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Light Exposure Effects on the DC Kink of AlGaN/GaN HEMTs

et al. 2019

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This paper presents the effects of optical radiation on the behavior of two scaled-gate aluminum gallium nitride/gallium nitride (AlGaN/GaN) high electron mobility transistors (HEMTs). The tested devices, having a gate width of 100 and 200 µm and a gate length of 0.25 µm, were exposed to a laser beam with a wavelength of 404 nm (blue-ray) in order to investigate the main optical effects on the DC characteristics. Owing to the threshold shift and the charge generation, a marked increase of the gate and drain current was noticed. The occurrence of the kink effect in the absence of light exposure was identified, and a hypothesis about its origin is provided. The obtained results agree with the analysis previously carried out on gallium arsenide (GaAs)-based devices.

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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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Improved modeling of GaN HEMTs for predicting thermal and trapping-induced-kink effects

Cited by 27 publications

References 23 publications

Neural network electrothermal modeling approach for microwave active devices

Neural network electrothermal modeling approach for microwave active devices

Light Exposure Effects on the DC Kink of AlGaN/GaN HEMTs

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

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