Structural determination of multilayered large-signal neural-network HEMT model

Shirakawa, K.; Shimizu, Masahiko; Okubo, Naofumi; Daido, Y.

doi:10.1109/22.721137

Cited by 41 publications

(24 citation statements)

References 11 publications

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“…It was previously demonstrated that neural networks can provide a large-signal description from the small-signal dependence of linear elements with the bias voltages [8] or from pulsed measurements [9]. In this work, neural networks are used to match, simultaneously, the nonlinear I/V characteristic in each bias point, based on DC or pulsed measurements and its derivative parameters up to the 3 rd order [10].…”

Section: Introductionmentioning

confidence: 99%

Neural network modeling of microwave FETs based on third-order distortion characterization

Giannini

Colantonio

Orengo

et al. 2006

Int J RF and Microwave Comp Aid Eng

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A new method for characterization of HEMT distortion parameters, which extracts the coefficents of a Taylor series expansion of Ids(Vgs, Vds), including all cross-terms, is developed from low-frequency harmonic measurements. The extracted parameters will be used either in a Volterra series model around a fixed bias point for 3rd-order characterization of small-signal Ids nonlinearity, or in a large-signal model of Ids characteristic, where its partial derivatives are locally characterized up to the 3rd order in the whole bias region, using a novel neural-network representation. The two models are verified by one-tone and two-tone intermodulation distortion (IMD) tests on a PHEMT device

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Section: Introductionmentioning

confidence: 99%

Neural network modeling of microwave FETs based on third-order distortion characterization

Giannini

Colantonio

Orengo

et al. 2006

Int J RF and Microwave Comp Aid Eng

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show abstract

“…This kind of model has the potential to enhance and automate new device model development even if the device theory/equations are not available by computerised training with measured device characteristics. Neural Networks have been used in transistor modelling to replace empirical equations or table based models in frequency domain equivalent circuits [3,4]. Neural Networks for time domain modelling have been imple- mented in the form of a black box amplifier to model existing simulation results [5], the aim being to increase simulation speed.…”

Section: Introductionmentioning

confidence: 99%

Neural network based time domain modelling of 0.18 μm MOSFETs

Toner¹,

Alam²,

Fusco³

et al. 2002

Micro & Optical Tech Letters

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In this paper a new time domain based neural network model of a 0.18 μm RF MOSFET will be demonstrated. The model consists of only three intrinsic non‐linear current sources, each being modelled by a neural network derived from large signal time domain voltage/current waveform measurements at the intrinsic device terminals. This approach negates the traditional method of multi‐bias S‐parameter measurement providing a fast and accurate method of modelling the transistor under large signal conditions. Full verification of the model will be provided against an 80 μm/0.18 μm MOSFET operating at 2.4 GHz. © 2002 Wiley Periodicals, Inc. Microwave Opt Technol Lett 35: 203–206, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.10558

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“…uniroma2.it. circuit, giving physical insight into device behavior [1,2,3]. As operating frequency increases, the accurate characterization of passive and active devices for MMIC simulation becomes more and more difficult.…”

Section: Introductionmentioning

confidence: 99%

Small-signal and large-signal modeling of active devices using CAD-optimized neural networks

Giannini¹,

Leuzzi²,

Orengo³

et al. 2001

Int J RF and Microwave Comp Aid Eng

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Artificial neural networks (ANNs) are presented for the technologyindependent modeling of active devices in MMICs. ANNs trained with S-parameter and DC measurements over the entire bias and frequency operational band are used for the small-signal bias-dependent modeling of a low-noise GaAs HEMT device, without the need of the equivalent circuit parameter extraction. ANNs are also used within the large-signal model of a power MESFET device, modeling the drain-source current I ds and charges Q g and Q d obtained from integration of their partial derivatives. After training and testing, the ANN models have been implemented as two-port networks into a microwave circuit simulator. This enabled the ANN models to be used in the design, analysis, and optimization of microwave/mm-wave circuits. Improved techniques in network building to provide not only accurate but also fast simulation models have been applied.

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Structural determination of multilayered large-signal neural-network HEMT model

Cited by 41 publications

References 11 publications

Neural network modeling of microwave FETs based on third-order distortion characterization

Neural network modeling of microwave FETs based on third-order distortion characterization

Neural network based time domain modelling of 0.18 μm MOSFETs

Small-signal and large-signal modeling of active devices using CAD-optimized neural networks

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