Padé-Approximation-Based Behavioral Modeling

Cai, Jialin; King, Justin; Merrick, Brian M.; Brazil, T.J.

doi:10.1109/tmtt.2013.2287677

Cited by 38 publications

(37 citation statements)

References 9 publications

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“…Other approaches [14]- [16] make use of the LSNA or NVNA frequency-domain data to identify behavioral models. Obviously, approaches which rely directly on time-domain data require that the time-domain waveforms generated by a nonlinear device should be accurately measured implying the acquisition of a sufficient number of harmonic frequencies.…”

mentioning

confidence: 99%

Millimeter-Wave FET Nonlinear Modelling Based on the Dynamic-Bias Measurement Technique

Avolio

Raffo

Angelov

et al. 2014

IEEE Trans. Microwave Theory Techn.

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In the paper, the nonlinear model of a microwave transistor is extracted from large-signal measurements acquired under “dynamic-bias” operation. Specifically, the transistor is driven by low-frequency large signals while a high-frequency tickle is applied on top of them. The low-frequency large signals, along with the dc bias voltages, set the large-signal operating point which represents a dynamic-bias condition for the device under test. Thanks to this technique, one can get at once and separately the nonlinear currents and charges of the transistor as a result of a very few nonlinear measurements. Additionally, the proposed technique allows one to accurately reconstruct the time-domain waveforms at the device-under-test terminals while the frequency of the tickle can be set as high as the bandwidth of today’s vector calibrated nonlinear measurement systems (i.e., 67 GHz). The approach, which is general and independent of device technology, is applied on a 0.15- m GaAs pHEMT specifically designed for resistive cold-FET mixer applications

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mentioning

confidence: 99%

Millimeter-Wave FET Nonlinear Modelling Based on the Dynamic-Bias Measurement Technique

Avolio

Raffo

Angelov

et al. 2014

IEEE Trans. Microwave Theory Techn.

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“…Various different modeling approaches have been developed, for example, references [11][12][13][14][15][16][17] , most of which can be clearly categorized as either behavioral models, or equivalent circuit models. Considering first behavioral modeling methods, several key frequency-domain approaches have been reported in the literature, which are capable of reasonably accurate predictions.…”

Section: Introductionmentioning

confidence: 99%

“…Some examples include models based on conventional approximations, [11][12][13][14] such as polynomials [11][12][13] or rational functions. 14 However, all these models exhibit a functional dependence on the dominant incident wave(s), which is typically implemented using spline functions to enable interpolation between extraction points. The main restriction of the full black-box-based model is that it is difficult to understand the physical mechanisms of the device since the model lacks any physical correspondence to the real device.…”

Section: Introductionmentioning

confidence: 99%

Bayesian inference-based small-signal modeling technique for GaN HEMTs

Cai

King

et al. 2018

Int J RF Microw Comput Aided Eng

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A new modeling methodology for gallium nitride (GaN) high‐electron‐mobility transistors (HEMTs) based on Bayesian inference theory, a core method of machine learning, is presented in this article. Gaussian distribution kernel functions are utilized for the Bayesian‐based modeling technique. A new small‐signal model of a GaN HEMT device is proposed based on combining a machine learning technique with a conventional equivalent circuit model topology. This new modeling approach takes advantage of machine learning methods while retaining the physical interpretation inherent in the equivalent circuit topology. The new small‐signal model is tested and validated in this article, and excellent agreement is obtained between the extracted model and the experimental data in the form of dc I–V curves and S‐parameters. This verification is carried out on an 8 × 125 μm GaN HEMT with a 0.25 μm gate feature size, over a wide range of operating conditions. The dc I–V curves from an artificial neural network (ANN) model are also provided and compared with the proposed new model, with the latter displaying a more accurate prediction benefiting, in particular, from the absence of overfitting that may be observed in the ANN‐derived I–V curves.

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“…Another implementation of the PHD model has been proposed that uses multi-dimensional Fourier-series coefficients to model the output spectral components of the system [3] [4]. A third approach uses a non-polynomial (fractional) describing function known as Padé approximation as the basis for the frequency-domain model [5].…”

Section: Introductionmentioning

confidence: 99%

Time-invariant behavioral modeling for harmonic balance simulation based on waveform shape maps

Amini

Boumaiza

2015

2015 IEEE MTT-S International Conference on Numerical Electromagnetic and Multiphysics Modeling and Optimization (NEMO)

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A new time-domain time-invariant periodic nonlinear behavioral model for multi-port systems is formulated from first principles in this paper. Unlike frequency-domain behavioral models like the Poly-Harmonic Distortion (PHD) model that describe the output frequency spectral components of the system, this black-box approach provides a time-invariant map of the discrete-time representation of the input waveform shape to the discrete-time representation of the output waveform shape, resulting in a reduction of the number of describing functions compared to a frequency domain description of the system. A two-port transistor behavioural model implementation for harmonic balance simulation is presented. The validation results show that the proposed model is capable of modeling the steady-state periodic output of multi-port nonlinear timeinvariant systems and has potential to assist in the computeraided design of RF systems with nonlinear components.

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Padé-Approximation-Based Behavioral Modeling

Cited by 38 publications

References 9 publications

Millimeter-Wave FET Nonlinear Modelling Based on the Dynamic-Bias Measurement Technique

Millimeter-Wave FET Nonlinear Modelling Based on the Dynamic-Bias Measurement Technique

Bayesian inference-based small-signal modeling technique for GaN HEMTs

Time-invariant behavioral modeling for harmonic balance simulation based on waveform shape maps

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