An improved GaN P-HEMT small-signal equivalent circuit with its parameter extraction

Zhang, Jincan; Liu, Min; Liu, Bo; Wang, Jinchan

doi:10.1016/j.mejo.2021.105042

Cited by 10 publications

(13 citation statements)

References 27 publications

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“…In the worst‐case scenario, at the device's pinch‐off instant, a maximum MSE of 0.07 and maximum MAE of 0.01 was recorded. This is because the equivalent circuit of the GaN HEMT demonstrates a depleted channel, and an interplay between the depletion capacitances and parasitics on the S‐parameters become dominant 94–96 . Figure 9A,C reveals the MSE, and Figure 9B,D shows the MAE in prediction using MLP‐RF regressor based analysis for sample A and sample B, when the complete scenario of 13 inputs is considered, which may be accounted for the fact that the drain rails are grounded (i.e., V DS = 0 V), and the device exhibits a significantly lower value of drain current.…”

Section: Resultsmentioning

confidence: 99%

“…This is because the equivalent circuit of the GaN HEMT demonstrates a depleted channel, and an interplay between the depletion capacitances and parasitics on the S-parameters become dominant. [94][95][96] Figure 9A,C reveals the MSE, and Figure 9B,D shows F I G U R E 7 Symbol: Actual (simulated) and line: Predicted values for S-parameters (S11, S12, S21, and S22) for: Sample A: at…”

Section: S-parameter and Current Gain Estimationmentioning

confidence: 99%

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Multilayer perceptron–random forest based hybrid machine learning–neural network model for GaN high electron mobility transistor's parameter estimations

Mishra

Raut

Sehra

et al. 2022

Int J RF Mic Comp-Aid Eng

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Section: Resultsmentioning

confidence: 99%

Section: S-parameter and Current Gain Estimationmentioning

confidence: 99%

Multilayer perceptron–random forest based hybrid machine learning–neural network model for GaN high electron mobility transistor's parameter estimations

Mishra

Raut

Sehra

et al. 2022

Int J RF Mic Comp-Aid Eng

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“…In order to calculate the parameters of intrinsic components, the calculation formulas are shown in () 17,19,38

A goodbreak= \frac{ω {|Y_{gs}|}^{2}}{Imag ((), Y_{gs})} goodbreak= \frac{1}{R_{gsf}^{2} C_{gs}} goodbreak+ ω^{2} C_{gs}

\begin{matrix} trueleft \\ B = ω \cdot Real (\frac{Y_{gs}}{Imag (Y_{gs})}) \\ = \frac{R_{gsf} + R_{normali} + ω^{2} R_{gsf}^{2} C_{gs}^{2} R_{normali}}{R_{gsf}^{2} C_{gs}} = \frac{R_{gsf} + R_{normali}}{R_{gsf}^{2} C_{gs}} + ω^{2} C_{gs} R_{normali} \end{matrix}

G_{m} goodbreak= goodbreak- \frac{Imag ((), Y_{21} goodbreak- Y_{12})}{\sin ((), ωτ)} goodbreak= \frac{Real ((), Y_{21} goodbreak- Y_{12})}{\cos ((), ωτ)}

normalτ goodbreak= \frac{\tan^{- 1} ((), \frac{- Imag (Y_{21} - Y_{12})}{Real (Y_{21} - Y_{12})})}{ω}

…”

Section: Extraction Of Intrinsic Parametersmentioning

confidence: 99%

“…Anwar Jarndal et al 14–17 proposed the 22‐element GaN HEMT small‐signal circuit model which takes into account the effects. However, reference 17 does not consider the effect of parasitic parameters, which vary with the bias voltage 18–20 . The method used in reference 20 adds R L and L ds to the intrinsic circuit, which effectively solves the problem of bias dependence.…”

Section: Introductionmentioning

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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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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“…[2][3][4] The purpose of this article is to develop accurate multi bias SSM from the S-parameters measurement of GaAs HEMTs, which is highly beneficial for the development and improvement of circuit design and manufacturing process. In the past decades, various extraction techniques, including numerical optimization method [5][6][7] and direct analytical method [8][9][10] have been developed. Although the numerical optimization method is very accurate, it depends on the initial values of the parameters and has relatively low convergence.…”

Section: Introductionmentioning

confidence: 99%

Small‐signal modeling of microwave transistors using radial basis function artificial neural network‐comparison of different methods for spread constant determined

Yan

et al. 2022

Int J RF Mic Comp-Aid Eng

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This article presents a high‐precision modeling method to build a small signal model of GaAs pseudomorphic high electron mobility transistor (pHEMT) by using a radial basis function artificial neural network (RBF ANN). Both the RBF ANN Method 1 that uniformly distributed spread constant (SC) in the given range and Method 2 that increasingly changed SC with a chosen step have been modeled in this work. Compared with the l RBF ANN Method 1, the RBF ANN method 2 can automatically obtain the optimal ANN corresponding to the SC. The RBF ANN method 2 is developed for S‐parameters model and equivalent circuit parameters (ECPs) model of HEMTs. S‐parameters model establishes S‐parameters versus bias, temperature and frequency, while the ECPs model establishes ECPs versus bias and temperature. To validate the capability of the RBF ANN in small signal modeling of GaAs pHEMTs, measured and modeled data of S‐parameters model and ECPs model of a 4 × 75 μm gate width, 0.15 μm gate length GaAs pHEMT are compared, and very good agreement is achieved up to 50 GHz. For the S‐parameters model, the average error of the method 2 is improved by about 20% at the temperature of −20°C, 25°C, and 85°C. For the ECPs model, the average error of the method 2 is 80% higher than that of the method 1.

show abstract

An improved GaN P-HEMT small-signal equivalent circuit with its parameter extraction

Cited by 10 publications

References 27 publications

Multilayer perceptron–random forest based hybrid machine learning–neural network model for GaN high electron mobility transistor's parameter estimations

Multilayer perceptron–random forest based hybrid machine learning–neural network model for GaN high electron mobility transistor's parameter estimations

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

Small‐signal modeling of microwave transistors using radial basis function artificial neural network‐comparison of different methods for spread constant determined

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