Piecewise Small Signal Behavioral Model for GaN HEMTs based on Support Vector Regression

Geng, Mingqiang; Cai, Jialin; Yu, Chao; Su, Jiangtao; Li, Jun

doi:10.1109/nemo49486.2020.9343555

Cited by 11 publications

(16 citation statements)

References 7 publications

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“…After removing the effect of parasitic parameters by deembedding, the intrinsic Y ‐parameters can be extracted 35–37 . The intrinsic parameters can be obtained by the relevant expressions of the Y ‐parameters 17 .…”

Section: Extraction Of Intrinsic Parametersmentioning

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: Extraction Of Intrinsic Parametersmentioning

confidence: 99%

“…After removing the effect of parasitic parameters by deembedding, the intrinsic Y-parameters can be extracted. [35][36][37] The intrinsic parameters can be obtained by the relevant expressions of the Y-parameters. 17 The gate-source branch Y-parameters Y gs of the intrinsic model is shown as formula (14).…”

Section: Extraction Of Intrinsic Parametersmentioning

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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“…Often, the direct techniques are coupled with optimization algorithms, leading to the proliferation of the so called hybrid procedures. 6,15,16,18,21 The empowerment of the direct techniques with optimization algorithms allows increasing the complexity of the circuit topology, improving the extraction of the circuit elements, and boosting the model accuracy in reproducing the S-parameter measurements. However, these benefits come at the cost of higher computational complexity, more time consuming, and higher risk of obtaining unphysical values of the circuit elements.…”

Section: Introductionmentioning

confidence: 99%

“…Over the last half century or so, many efforts have been devoted to the equivalent‐circuit extraction for modeling the scattering S‐parameter measurements of active electron devices 1–21 . Although the frequency‐dependent behavior of the scattering parameters can be easily and quickly reproduced by using alternative representations (e.g., artificial neural networks 22–26 ), the calculation of a small‐signal equivalent‐circuit model is essential as this circuit can be utilized as a foundation for developing large‐signal 27–32 and noise 33–38 models.…”

Section: Introductionmentioning

confidence: 99%

Equivalent‐circuit extraction for gallium nitride electron devices: Direct versus optimization‐empowered approaches

Jarndal

Crupi

Alim

et al. 2022

Int J Numerical Modelling

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This work focuses on the equivalent‐circuit modeling of microwave field‐effect transistors. Although the purely direct approach allows obtaining a good prediction accuracy in a straightforward way without the need of any optimization, optimization algorithms are often used to achieve an improved accuracy at the cost of a higher modeling complexity. The pros and cons of empowering the direct approach with optimization algorithms are discussed by focusing the analysis on a 1000‐μm periphery gallium nitride high‐electron‐mobility transistor on silicon carbide substrate. The comparison between the two different approaches is performed through an extensive analysis of the frequency‐dependent behavior of the small‐signal characteristics.

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“…32,33 Thus, some other new modeling methods have been tried for RF and microwave device modelling, such as support vector regression (SVR) method. 31,32,[34][35][36] In Reference 35, a new dynamic behavioral model for RF PAs based on SVR technique is provided. Later in Reference 36, the SVR is employed to build a behavioral model for GaN HEMT.…”

mentioning

confidence: 99%

Analysis and modeling of the kink effect in S22 based on support vector machine for GaN HEMTs

Zhu

Geng

Cai

et al. 2022

Int J Numerical Modelling

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In this work, the kink effect (KE), typically visible in S22, is analyzed and modeled. Two different modeling techniques: equivalent circuit modeling (ECM) method and machine learning method which based on support vector regression (SVR) technique are presented and compared, when applied to the S22 behavior of a Gallium Nitride (GaN) high electron mobility transistor (HEMT). The device under test (DUT) has a width of 8 Â 125 μm, with a gate feature size of 0.25 μm. The proposed method identifies the effect that the bias voltage and extrinsic elements have on the S22 kink shape. Additionally, compared to ECM, the SVR model attains a superior fitting accuracy across the complete frequency band.

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Piecewise Small Signal Behavioral Model for GaN HEMTs based on Support Vector Regression

Cited by 11 publications

References 7 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

Equivalent‐circuit extraction for gallium nitride electron devices: Direct versus optimization‐empowered approaches

Analysis and modeling of the kink effect in S22 based on support vector machine for GaN HEMTs

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