Accurate Small-Signal Model Extraction for pHEMT on GaAs

Wang, Lei; Xu, Ruimin; Yan, Bo

doi:10.1007/s10762-007-9288-7

Cited by 4 publications

(3 citation statements)

References 6 publications

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“…In each generation, the distances between chromosomes are measured and used to terminate the algorithm if these distances are very close. Work in [48] uses T Pop criterion for termination by adding distances among individuals and making sure it is smaller than a predefined threshold. The other two classes are T Bud and T SFB .…”

Section: Related Workmentioning

confidence: 99%

Evolutionary Algorithms Enhanced with Quadratic Coding and Sensing Search for Global Optimization

Hedar

Deabes

Almaraashi

et al. 2020

MCA

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Enhancing Evolutionary Algorithms (EAs) using mathematical elements significantly contribute to their development and control the randomness they are experiencing. Moreover, the automation of the primary process steps of EAs is still one of the hardest problems. Specifically, EAs still have no robust automatic termination criteria. Moreover, the highly random behavior of some evolutionary operations should be controlled, and the methods should invoke advanced learning process and elements. As follows, this research focuses on the problem of automating and controlling the search process of EAs by using sensing and mathematical mechanisms. These mechanisms can provide the search process with the needed memories and conditions to adapt to the diversification and intensification opportunities. Moreover, a new quadratic coding and quadratic search operator are invoked to increase the local search improving possibilities. The suggested quadratic search operator uses both regression and Radial Basis Function (RBF) neural network models. Two evolutionary-based methods are proposed to evaluate the performance of the suggested enhancing elements using genetic algorithms and evolution strategies. Results show that for both the regression, RBFs and quadratic techniques could help in the approximation of high-dimensional functions with the use of a few adjustable parameters for each type of function. Moreover, the automatic termination criteria could allow the search process to stop appropriately.

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Section: Related Workmentioning

confidence: 99%

Evolutionary Algorithms Enhanced with Quadratic Coding and Sensing Search for Global Optimization

Hedar

Deabes

Almaraashi

et al. 2020

MCA

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“…Before calculating the intrinsic parameters, we subtracted the extracted extrinsic parameters from the measured Sparameters of the MHEMTs by using a simple topology in the ADS schematic window as shown in figure 9. The intrinsic parameters of the 30 μm × 2 MHEMT biased at V GS of −0.7 V and V DS of 1.5 V were calculated in our measurement frequency range by using the intrinsic Y-parameter expressions [7,[14][15][16][17][18]. The extracted values of all intrinsic parameters are listed in table 2.…”

Section: Extraction Proceduresmentioning

confidence: 99%

Millimeter-wave small-signal modeling with optimizing sensitive-parameters for metamorphic high electron mobility transistors

Moon

Baek

et al. 2010

Semicond. Sci. Technol.

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In this paper, we present a simple and reliable technique for determining the small-signal equivalent circuit model parameters of the 0.1 μm metamorphic high electron mobility transistors (MHEMTs) in a millimeter-wave frequency range. The initial eight extrinsic parameters of the MHEMT are extracted using two S-parameter (scattering parameter) sets measured under the pinched-off and zero-biased cold field-effect transistor conditions by avoiding the forward gate biasing. Furthermore, highly calibration-sensitive values of the R s , L s and C pd are optimized by using a gradient optimization method to improve the modeling accuracy. The accuracy enhancement of this procedure is successfully verified with an excellent correlation between the measured and calculated S-parameters up to 65 GHz.

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“…Because of its high power handling capabilities, GaN based pHEMTs have attracted increasing interests [4][5]. Owing to its large band-gap, GaN is particularly suitable for high frequency, high power and high temperature applications [6][7]. In addition, AlGaN/GaN material is suitable for applications in the transmission and distribution of electric power [8][9].…”

Section: Introductionmentioning

confidence: 99%

Microwave Analysis for Intrinsic and Extrinsic Characteristics of AlGaN/GaN pHEMT

Ramnish¹,

Arya²,

Ahlawat³

2014

IJAST

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Accurate Small-Signal Model Extraction for pHEMT on GaAs

Cited by 4 publications

References 6 publications

Evolutionary Algorithms Enhanced with Quadratic Coding and Sensing Search for Global Optimization

Evolutionary Algorithms Enhanced with Quadratic Coding and Sensing Search for Global Optimization

Millimeter-wave small-signal modeling with optimizing sensitive-parameters for metamorphic high electron mobility transistors

Microwave Analysis for Intrinsic and Extrinsic Characteristics of AlGaN/GaN pHEMT

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