Comparative analysis of parameter extraction techniques for AlGaN/GaN HEMT on silicon/sapphire substrate

Majumdar, Shubhankar; Bag, Ankush; Biswas, Dhrubes

doi:10.1016/j.microrel.2017.08.016

Cited by 6 publications

(4 citation statements)

References 27 publications

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“…The forward‐bias method is usually used to extract parasitic resistances. Unfortunately, excessive gate current flowing through the Schottky junction will cause gate degradation 34 . Therefore, in order to avoid this phenomenon, the parasitic resistances are extracted by the reverse‐cutoff method ( V GS = −2.5 V, V DS = 0 V).…”

Section: Parasitic Parameter Extraction Processmentioning

confidence: 99%

“…Unfortunately, excessive gate current flowing through the Schottky junction will cause gate degradation. 34 Therefore, in order to avoid this phenomenon, the parasitic resistances are extracted by the reverse-cutoff method (V GS = À2.5 V, V DS = 0 V). In formulas ( 11)-( 13), the formulas for extracting the parasitic resistances are shown.…”

Section: Parasitic Parameter Extraction Processmentioning

confidence: 99%

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A reliable parameter extraction method for the augmented GaN high electron mobility transistor small‐signal model

Zhai

Cai

Zhang

et al. 2022

Int J RF Mic Comp-Aid Eng

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An improved GaN high electron mobility transistor (HEMT) small-signal equivalent model is proposed. To model the effects of extrinsic parameters that vary with the bias voltage, R L and L ds are added to the model. Moreover, substrate-related parameters (R sub and C sub ) are used to model the substrate effect. In order to avoid damaging the gate under high forward gate pressure conditions, an accurate parasitic parameter extraction method is proposed, which improves the reliability and validity of the parameter extraction. The S-parameters of the proposed model are in good agreement with the measured S-parameters in the range of 0.5-20.5 GHz.

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Section: Parasitic Parameter Extraction Processmentioning

confidence: 99%

Section: Parasitic Parameter Extraction Processmentioning

confidence: 99%

A reliable parameter extraction method for the augmented GaN high electron mobility transistor small‐signal model

Zhai

Cai

Zhang

et al. 2022

Int J RF Mic Comp-Aid Eng

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“…They utilize Gaussian distribution kernel functions and Bayesian‐based modeling techniques. Majumdar et al 21 presented the utilization of an ANN model for extracting the parameters of GaN HEMTs on silicon (Si) and sapphire (Al 2 O 3 ). After briefly reviewing the work reported over the years in the literature, we can now differentiate our work from previous studies as it mainly focuses on the fitting of a model through measured S ‐parameter data in terms of training sets and predictions with the utilization of XGBoost regressor and auto‐encoder.…”

Section: Literature Overviewmentioning

confidence: 99%

Auto‐encoder based hybrid machine learning model for microwave scaled GaAs pHEMT devices

Bhargava

Vadalà

Majumdar

et al. 2022

Int J RF Mic Comp-Aid Eng

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In this article, a study of performing machine learning (ML) based modeling for semiconductor devices has been developed using experimental microwave data. Characterization of gallium arsenide (GaAs) pseudomorphic high electron mobility transistors (pHEMTs) with different gate widths is used as the illustrative example to demonstrate the accuracy and effectiveness of the presented modeling procedure. The tested devices are based on the multifinger layout, in which the total gate width (W) is obtained by multiplying the number of fingers (N f ) and their length (W 0 ). Machines are trained with scattering (S-)parameter measurements up to 65 GHz by using the extreme gradient boosting (XGBoost) algorithm with K-fold cross-validation. Then, the output of the trained machine is utilized by the parameters such as N f and W 0 inside the Auto-encoder (AE) model. In particular, the ML model with AE has a maximum of 99.88% prediction accuracy, despite the uncertainty inherent in the microwave measurements and the unavoidable deviations from the ideal behavior of the analyzed devices.

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“…Several studies have reported on pinch-off issues and early soft/hard breakdown [8]. This technology holds promise for high-power applications, including RF switches [10,[12][13][14][15][16] power amplifiers [11], wireless communication [16,17] and radar systems [12]. Scandiumalloyed Wurtzite III-nitrides (Sc-III-Ns) represent a novel class of ferroelectrics (FEs) characterized by a broad bandgap ranging from 4.9 to 5.6 eV.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive empirical modeling of ScAlN/AlGaN/GaN ferroelectric HEMT

Pyngrope,

Chaturvedi,

Dasgupta

et al. 2024

Semicond. Sci. Technol.

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This correspondence introduces a concise depiction of a High Electron Mobility transistor (HEMT) utilizing a model that incorporates theeffect of ferroelectric (FE) polarization on the empirical expression for drain current. This is accomplished by modifying the basic Shockley Equation (S.E) for MESFET. The model employed in this investigation is particularly geared towards investigating the impact of the counterclockwise hysteresis effect due to the use of dielectric ScAlN on the transfer characteristics (Id − Vgs), which is mainly attributed to trapping and de-trapping of charge carriers at the dielectric/semiconductor interface. Modifying the S.E. made it possible to replicate the behavior observed in experimental HEMT accurately. We reported the modified drain current empirical expressions for the ScAlN/AlGaN/GaN HEMT that considers the FE polarization. The results of this study showed good agreement between the Id − Vgs characteristics obtained from the analytical and experimental data, with a root mean square deviation (RMSD) error of 0.97% and a mean squared error (MSE) error of 0.94%. This analytical drain current model, presented herein for the first time, incorporates a limited number of fitting parameters. Therefore, enhancing the computational efficiency to assess the performance of ferroelectric GaN HEMTs for usage in wide-area electronics.

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Comparative analysis of parameter extraction techniques for AlGaN/GaN HEMT on silicon/sapphire substrate

Cited by 6 publications

References 27 publications

A reliable parameter extraction method for the augmented GaN high electron mobility transistor small‐signal model

A reliable parameter extraction method for the augmented GaN high electron mobility transistor small‐signal model

Auto‐encoder based hybrid machine learning model for microwave scaled GaAs pHEMT devices

Comprehensive empirical modeling of ScAlN/AlGaN/GaN ferroelectric HEMT

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