An accurate and simplified small signal parameter extraction method for GaN HEMT

Khusro, Ahmad; Hashmi, Mohammad S.; Ansari, Abdul Quaiyum; Mishra, Aditya Dev; Tarique, Mohammad

doi:10.1002/cta.2622

Cited by 27 publications

(18 citation statements)

References 28 publications

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“…In order to increase attention for high‐temperature and high‐power applications at high frequencies, the kink effects are investigated in the output reflection coefficient (S 22 ) and the short‐circuit current gain (h 21 ) to be shown how these phenomena affect the applications of electronic devices . At first, the S parameters are analyzed completely that S 11 and S 22 on Smith charts and S 21 and S 12 on polar plots are reported . The S parameters are investigated from 1 KHz to 100 GHz at V DS = 20 V and V GS = −1.5 V and as it is shown that the kink effect has the impressive influence on all the S parameters in Figure .…”

Section: Resultsmentioning

confidence: 99%

An AlGaN/GaN HEMT by a reversed pyramidal channel layer: Investigation and fundamental physics

Jaghargh

Orouji

2020

Int J Numerical Modelling

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In this paper, an AlGaN/GaN HEMT with a reversed pyramidal channel layer (RPC‐HEMT) is proposed. The main purpose of the paper is an increase in the breakdown voltage of a conventional HEMT (C‐HEMT) to be suitable in high‐power applications. In the RPC‐HEMT, a reverse pyramidal channel layer in the high electric field traps the energetic carriers that cause the transistors to break. The steps decrease the carrier concentration across the two‐dimensional electron gas (2DEG) channel layer, especially under the gate contact. Therefore, the critical electrical field reduces by the uniform division of carrier mass and improves the breakdown voltage of the RPC‐HEMT by 32%. Besides, the maximum output power density of the new transistor improves compared with the conventional one. In addition, the radio frequency (RF) characteristics such as the cutoff frequency and maximum oscillation frequency of the RPC‐HEMT improve by the decline in the gate‐source capacitance. This article provides kink effects with the output reflection coefficient (S22) and the short‐circuit current gain (h21) to show how this anomalous phenomenon affects high‐frequency performance of devices. Finally, the proposed method makes the RPC‐HEMT appropriate for high‐power and high‐frequency applications.

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Section: Resultsmentioning

confidence: 99%

An AlGaN/GaN HEMT by a reversed pyramidal channel layer: Investigation and fundamental physics

Jaghargh

Orouji

2020

Int J Numerical Modelling

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“…|y meas − y predicted | y meas × 100. (19) The calculated MRE shows very less error within the range of training frequency (upto 13 GHz), and a rise in spike from 2-5% at the the start of extrapolation set before the error starts decreasing and curve settles down. Altogether, with the mean relative error varying between 1.5-3.5% for extrapolation set shows a very promising accuracy of the proposed model.…”

Section: Model Validationmentioning

confidence: 85%

“…This section investigates the reliability and effectiveness of the trained model by subjecting the model to novel set of inputs for broad frequency range from 1-18 GHz (with the frequency extrapolation) including noise and without noise utilizing one-third of measured data reserved for testing and validation purpose. To study the accuracy of the model more precisely, the prediction ability of the proposed model is enumerated in terms of mean relative error (MRE), expressed in (19), where y meas is the measured value and y predicted is the predicted value of the response variable of the proposed model. The relative error is plotted against the whole frequency range for four different set of multi-bias and different operating region of the transistor under noiseless condition as depicted in Fig.…”

Section: Model Validationmentioning

confidence: 99%

A Generic and Efficient Globalized Kernel Mapping-Based Small-Signal Behavioral Modeling for GaN HEMT

et al. 2020

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The work reported in this paper explores a novel Particle Swarm Optimization (PSO) tuned Support Vector Regression (SVR) based technique to develop the small-signal behavioral model for GaN High Electron Mobility Transistor (HEMT). The proposed technique investigates issues such as kernel selection and model optimization usually encountered in the application of SVR to model the GaN based HEMT devices. Here, the PSO algorithm is utilized to find the optimal hyperparameters to minimize the fitness function. To enumerate the efficiency and the generalization capability of the predictors, the performance of the model is investigated in terms of mean square error (MSE) and mean relative error (MRE). A very good agreement is found between the measured S-parameters and the proposed model for multi-biasing sets over the complete frequency range of 1 GHz-18 GHz. The proposed technique is even used to test the frequency extrapolation capability of the model. A comparative analysis indicates that the proposed PSO-SVR predictor achieves significantly improved computational efficiency and the overall prediction accuracy. To demonstrate the ready usefulness of the modeling approach, the developed model has been incorporated in CAD environment using MATLAB Cosimulation in ADS Ptolemy. Subsequently, the small-signal stability analysis is performed and gain of a power amplifier configuration designed using the proposed GaN HEMT model is determined.

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“…Next, de‐embedded S ‐parameters of the HEMT device is delivered to a small‐signal modeling process to investigate the influence of residual error on model parameters extraction. The modeling process is quite similar with well‐developed method for HEMT devices in literature, but it starts simply from intrinsic topology of the small‐signal model illustrated in Figure . Y ‐parameters of the intrinsic equivalent circuit can be simply expressed as

y_{11} = Y_{GS} + Y_{GD}

y_{12} = - Y_{GD}

y_{21} = \frac{g_{m} \exp ((), - italicjωτ)}{1 + italicjω C_{gs} R_{i}} - Y_{GD}

y_{22} = Y_{DS} + Y_{GD}

where Y GS , Y GD , and Y DS are passive admittances of corresponding branch,

Y_{GS} = \frac{1}{R_{italicgs}} + \frac{ω^{2} C_{gs}^{2} R_{i} + italicjω C_{gs}}{1 + ω^{2} C_{gs}^{2} R_{i}^{2}}

Y_{GD} = \frac{1}{R_{italicgd}} + \frac{ω^{2} C_{gd}^{2} R_{j} + italicjω C_{gd}}{1 + ω^{2} C_{gd}^{2} R_{j}^{2}}

Y_{}

…”

Section: De‐embedding Errors and Its Influence On Model Extractionmentioning

confidence: 99%

A de‐embedding method with matrix rectification and influences of residual errors on model parameters extraction of InP HEMTs

Ding

et al. 2020

Int J RF Microw Comput Aided Eng

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As the cutoff frequency of InP HEMTs enters the terahertz band, high frequency measurement and modeling techniques in hundreds of gigahertz become urgent needs for further millimeter monolithic integrated circuits design. We proposed a new de‐embedding method linking device measurements and modeling based on full EM simulation data acquired from HFSS and advanced design system (ADS). The simulation results for passive dummy structures are well consistent with experiments, and the de‐embedding method is proved very effective for a resistive passive device with high distributed embedding surroundings in frequency range below 40 GHz. Based on these experimental facts, the EM simulations were extended up to 300 GHz and corresponding de‐embedding deviation was further investigated. Results show that the proposed de‐embedding method has very high accuracy in the whole frequency region with a maximum S‐parameters deviation of only 2.58%. However, further analysis proves that the small residual errors still significantly affect extracted small signal model parameters of InP HEMTs especially for transit time τ. Thus, further improvements on de‐embedding accuracy or careful considerations of more error functions in modeling process are necessary for obtaining physically meaningful model parameters.

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An accurate and simplified small signal parameter extraction method for GaN HEMT

Cited by 27 publications

References 28 publications

An AlGaN/GaN HEMT by a reversed pyramidal channel layer: Investigation and fundamental physics

An AlGaN/GaN HEMT by a reversed pyramidal channel layer: Investigation and fundamental physics

A Generic and Efficient Globalized Kernel Mapping-Based Small-Signal Behavioral Modeling for GaN HEMT

A de‐embedding method with matrix rectification and influences of residual errors on model parameters extraction of InP HEMTs

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