An Support Vector Regression Based Nonlinear Modeling Method for Sic Mesfet

Xu, Yuehang; Guo, Yunnchuan; Lei, Xia; Wu, Yunqiu

doi:10.2528/pierl07122102

Cited by 28 publications

(16 citation statements)

References 14 publications

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“…Accurate large signal model is crucial for GaN power devices and circuit design. Compared with physical based model [2] and table based empirical model [3], empirical large signal equivalent circuit model (LSECM) is more simple and easier to be implemented in microwave commercial simulation software, and has been widely used in GaAs and SiC based devices [4,5]. However, accurate LSECM for GaN based devices is still a major topic of discussion due to the existence of thermal effects and trapping related dispersion in GaN HEMT devices [2,3,6,7].…”

Section: Introductionmentioning

confidence: 99%

LARGE SIGNAL EQUIVALENT CIRCUIT MODEL FOR PACKAGE ALGaN/GaN HEMT

Sang¹,

Xu²,

Guo³

et al. 2011

PIER Letters

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Abstract-In this paper, a large signal equivalent circuit empirical model based on Anglov model for ceramic package high power AlGaN/GaN HEMT has been proposed. A temperature-dependent drain current model, including self-heating effect, has been presented, and good agreements are achieved between measurement results and calculated results at different temperatures. The nonlinear capacitance models are modeled directly by measured microwave scattering (S) parameters and multi-bias small signal equivalent circuit model (SSECM) of package device is also established based on the measurements. A power amplifier based on large size AlGaN/GaN HEMT with a total gate periphery of 36 mm has been designed by using the proposed model for validation purpose, and the simulation results fit the measurement results well at different temperatures.

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Section: Introductionmentioning

confidence: 99%

LARGE SIGNAL EQUIVALENT CIRCUIT MODEL FOR PACKAGE ALGaN/GaN HEMT

Sang¹,

Xu²,

Guo³

et al. 2011

PIER Letters

Self Cite

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“…Both of these learning machines are powerful, efficient, and robust in solving these problems. They are able to be trained from learning set and to generalize the target characterize accurately [6][7][8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

A Support Vector Machine for Identification of Monitors Based on Their Unintended Electromagnetic Emanation

Mo¹,

Jin-ling³

et al. 2013

PIER M

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Abstract-Electrical equipments usually radiate unintended emission which carries characteristic information when running, such as emanation from computers monitors, keyboards and other components, this emanation can be possibly used to reconstruct the source information. Most of the experiments related to this area are carried out inside a semi-anechoic chamber, and measurement out of it may not be considered to be optimal, because the data captured are usually not sufficient. Yet in this study, we take LCD monitors as typical examples and find that characteristics significantly differ between products, parameters such as the magnitude and spectrum were measured under normal environment. We take the PCB traces as antennas and acquire the raw signal directly near the antenna and extract the parameters to use as input to support vector machine (SVM) which was trained to identify the emanating source (LCD monitors). In this study, the method was tested using the emission captured from one Samsung (SyncMaster E1920) and two LG (L1753s) monitors, and a laptop (ACER Aspire 5542). The SVM was able to classify the source of signals with 98.9510% accuracy while using emission that captured from the running monitors.

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“…Typically, neural networks are applied to forming quasi-static modeling for multilayer cylindrical coplanar lines [10], calculation of the impedance of air-suspended trapezoidal and rectangular shaped microshield lines [11], non uniform antenna array synthesis [12], design of the coplanar waveguides combining fuzzy systems [13], passive dipole arrays with together genetic algorithm [14]. On the other-hand, support vectors have become a strong competent method to the neural networks by the typical applications on the linear- [15] and non linear modeling [16] of mesfets; modeling of the microwave devices such as microstrip antenna based on the experimental data [17], mim capacitors [18].…”

Section: Introductionmentioning

confidence: 99%

A Generalized Design Procedure for a Microwave Amplifier: A Typical Application Example

Güneş¹,

Bilgin²

2008

PIER B

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Abstract-In this work, a generalized procedure is carried out for the design of a microwave amplifier. First of all, the Performance Data Sheets (PDS) resulted from the active device characterization are used as Feasible Design Target Space (FDTS). Employing the PDS, the compatible (Noise F , Input VSWR V i , Gain G T ) is determined over the predetermined bandwidth B between f min and f max operation frequencies with the source Z S and load Z L terminations as the design target. In the design stage, the Simplified Real Frequency Technique (SRFT) is utilized in the scattering-parameter formulation of the front-and back-end matching two-ports to provide the source and load terminations to the transistor, respectively. As an application example, a novel high technology transistor is chosen and the design targets are determined using the PDSs of the device and its frontand back-end matching two-ports are characterized by the scatteringparameters using the novel SRFT for each design target. Furthermore, the performances of the resulted amplifier circuits are analyzed and compared with the simulated results.

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An Support Vector Regression Based Nonlinear Modeling Method for Sic Mesfet

Cited by 28 publications

References 14 publications

LARGE SIGNAL EQUIVALENT CIRCUIT MODEL FOR PACKAGE ALGaN/GaN HEMT

LARGE SIGNAL EQUIVALENT CIRCUIT MODEL FOR PACKAGE ALGaN/GaN HEMT

A Support Vector Machine for Identification of Monitors Based on Their Unintended Electromagnetic Emanation

A Generalized Design Procedure for a Microwave Amplifier: A Typical Application Example

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