GaN FET Load-Pull Data in Circuit Simulators: a Comparative Study

Avolio, Gustavo; Raffo, Antonio; Marchetti, Mauro; Bosi, Gianni; Vadalà, Valeria; Vannini, G.

doi:10.23919/eumic.2019.8909451

Cited by 12 publications

(11 citation statements)

References 6 publications

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“…This task results in fast modeling while protecting satisfied accuracy. In [69], NN is studied on gallium nitride (GaN) field-effect transistor (FET) to import load-pull data in a circuit simulator. Load-pull measurements are essential data in designing circuits such as power amplifiers.…”

Section: Neural Network Techniquementioning

confidence: 99%

Optimization techniques for analog and RF circuit designs: an overview

Kouhalvandi

Ceylan²,

Özoğuz

2020

Analog Integr Circ Sig Process

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Optimizations have gained much consideration from the researchers working in the domains of analog and radio frequency (RF), recently. Dealing with highly nonlinear behavior of active components and aiming to meet design specifications are common issues in all nonlinear circuit designs. As a consequence and accordingly, many studies have been conducted on diverse optimization methods and algorithms for tackling the design problems and meeting optimal solutions with high accuracy. The main purpose of this article is to provide a comprehensive and systematic literature review for the optimization approaches applied by the researchers for designing various analog and microwave circuits. We focus on considering the existing optimization methods from the newly published optimization methods in the last decade. Thus, this study can guide and enlighten complementary metal-oxide-semiconductor analog and RF microwave circuit designers to consider optimization methods commonly used in both areas and to expand conventional performance figures used in their area.

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Section: Neural Network Techniquementioning

confidence: 99%

Optimization techniques for analog and RF circuit designs: an overview

Kouhalvandi

Ceylan²,

Özoğuz

2020

Analog Integr Circ Sig Process

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“…However, a number of key issues exist with the ANN-based method. For example, in general ANNs lack the capability to extrapolate beyond their measured range, although some more recent work 23 suggests that careful choice of modeling domain can help to alleviate this problem. In addition, the nonlinear formulation of ANN-based functions precludes the guarantee of always finding the global optimal solution.…”

Section: Introductionmentioning

confidence: 99%

Modified small‐signal behavioral model for GaN HEMTs based on support vector regression

Geng

Cai

King

et al. 2021

Int J RF Microw Comput Aided Eng

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A novel, modified small signal behavioral modeling methodology for gallium nitride (GaN) high electron-mobility transistors (HEMTs), based on the support vector regression (SVR) technique, is presented in this paper. Compared with the classic equivalent small-signal circuit model, the proposed model adds an error correction technique, which is developed using SVR techniques. The proposed model maintains accuracy across a broad frequency range, from 1 GHz to 10 GHz. The verification is performed through comparisons with measured multibias S-parameter data performed on an 8 Â 125 μm GaN HEMT device with a 0.25 μm gate feature size. The modified model shows a significant improvement in S-parameter prediction accuracy when compared with the classic equivalent circuit modeling approach.

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“…On one hand, they require a considerable amount of measurements to be accurate within a given extraction area and those measurements must be stored in memory during simulation. On the other hand, they also reveal poor extrapolation capabilities, as demonstrated in [9]. These drawbacks promote the usage of equation-based approaches that can accurately interpolate the data and reduce the required measurements.…”

mentioning

confidence: 99%

“…The most well-known equation-based behavioral models in the literature can be divided into poly-harmonic distortion (PHD) models [10], Padé-approximation-based formulations [11], and artificial neural networks (ANNs) [12]. The PHD model is a black-box, frequency-domain, modeling technique based on the idea of extending S-parameters for large-signal conditions [9]. Over time, several behavioral formulations based on this approach have been proposed, such as the X-parameters [10] and the Cardiff model [13], [14].…”

mentioning

confidence: 99%

“…The previously mentioned model implementations rely on polynomial forms in which the number of parameters grows exponentially with the number of inputs and with the polynomial order, a problem known as the Curse of Dimensionality [18]. In the ANN case, the number of parameters only grows either linearly or quadratically [5], reducing the model complexity since it is able to represent complex functions with a low number of coefficients, [5], [9], [19]. In the active device modeling framework, the sigmoid is one good example of an activation function normally used for the neurons, as its graphic shape resembles the typical transistor current source or the PA's gain compression characteristics.…”

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confidence: 99%

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New Transistor Behavioral Model Formulation Suitable for Doherty PA Design

Louro

Belchior

Barros

et al. 2021

IEEE Trans. Microwave Theory Techn.

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This work presents a new artificial neural network (ANN) model formulation for RF high-power transistors which includes the S-parameters of the active device. This improves the small-signal extrapolation capability, and the OFFstate impedance approximation, making it suitable for Doherty power amplifier (DPA) design. This extrapolation capability plays a key role in the correct Doherty load modulation prediction, since, at low power levels, the peaking PA is subjected to active loads that cannot be synthetized with a passive load-pull system, forcing the model to extrapolate. Thus, the proposed model formulation is able to solve the issues that are normally observed when ANN-based models are used in complex PA architectures as the Doherty PA. To validate the proposed behavioral model, a 700-W asymmetrical LDMOS DPA, centered at 1.84 GHz, was simulated and measured. Index Terms-Artificial neural network (ANN), behavioral model, Doherty, load modulation, passive load-pull, power amplifier. I. INTRODUCTION T HE Doherty power amplifier (DPA) is, nowadays, the wireless base-station workhorse in what RF signal amplification is concerned [1]-[4]. Accurate nonlinear models for the state-of-the-art high-power transistors are very difficult to obtain mostly because of the thermal issues and the distributed nature of these devices [5]. Therefore, the conventional Doherty design process is normally based on load-pull and S-parameter measurements. From these measurements, the optimal power load (Z pwr) is determined and the optimal efficiency termination for a particular VSWR, defined on this chosen power load, is selected. This VSWR imposes the back-off Manuscript

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GaN FET Load-Pull Data in Circuit Simulators: a Comparative Study

Cited by 12 publications

References 6 publications

Optimization techniques for analog and RF circuit designs: an overview

Optimization techniques for analog and RF circuit designs: an overview

Modified small‐signal behavioral model for GaN HEMTs based on support vector regression

New Transistor Behavioral Model Formulation Suitable for Doherty PA Design

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