Non Linear RF Device Characterization in Time Domain using an Active Loadpull Large Signal Network Analyzer

Ducatteau, D.; Werquin, M.; Grimbert, B.; Morvan, E.; Théron, D.; Gaquière, Christophe

doi:10.1109/imtc.2007.379387

Cited by 6 publications

(2 citation statements)

References 11 publications

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“…An active loadpull large-signal network analyzers (LSNA) up to 50 GHz [11] was used to measure RF power performances up to 40 GHz. Figure 5 depicts the on-wafer continuous-wave (CW) power sweep at 18 GHz performed on a 0.2 × 200 µm 2 AlN/GaN-on-Si HEMT and DHFET.…”

Section: Resultsmentioning

confidence: 99%

Toward highly scaled AlN/GaN-on-Silicon devices for millimeter wave applications

Medjdoub

Tagro

Grimbert

et al. 2013

Int. J. Microw. Wireless Technol.

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farid medjdoub, yoann tagro, bertrand grimbert, damien ducatteau and nathalie rolland In this work, the possibility of achieving GaN-on-Si devices for millimeter wave applications operating at high bias is demonstrated. It is shown that highly scaled AlN/GaN-on-Si double heterostructure enables us to significantly improve electron confinement under high electric field as compared to single heterostructure while delivering high carrier density (.2 × 10 13 cm 22 ). Subsequently, trapping effects can be minimized resulting in the highest GaN-on-Si output power density up to 40 GHz and at a drain bias of 15 V together with a record f max close to 200 GHz. At higher bias, infrared camera analysis clearly shows that these devices are mainly limited by self-heating effects. Furthermore, low noise figure has been assessed on this heterostructure, promising integration of cost effective low noise and high power millimeter wave amplifiers.

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Section: Resultsmentioning

confidence: 99%

Toward highly scaled AlN/GaN-on-Silicon devices for millimeter wave applications

Medjdoub

Tagro

Grimbert

et al. 2013

Int. J. Microw. Wireless Technol.

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“…In order to obtain the incident and scatter waves of devices stated in (3), calibrated measurements, which use a NVNA with active load-pull capabilities is implemented. 54 Figure 8 shows the topology graph of the nonlinear measurement bench. A keysight-N5247 PNA-X is taken as the main receiver of the test system to receive the A qn , B pm .…”

Section: Getting Fine Modelmentioning

confidence: 99%

Knowledge‐based neural network with Bayesian optimization for efficient nonlinear RF device modeling

Wang

Xie

et al. 2023

Int J Numerical Modelling

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This paper proposes a novel approach for modeling the nonlinear behavior of microwave devices, with a particular focus on addressing the challenging problem of modeling compound semiconductor transistors under large‐signal excitation. The approach utilizes a knowledge‐based neural network (KBNN) to construct a frequency‐domain behavioral model of the transistor, which maps incident and scattered waves from a coarse model to a fine model in cases where the coarse model fails to match the behavior of a new device. This neural network‐based method aims to achieve better alignment between the extracted model parameters obtained from the coarse model and the behavior of the fine model. To improve mapping efficiency and accuracy, Bayesian optimization techniques are employed to automatically adjust the hyper parameters in the KBNN within a custom‐defined hyper parameter space. The proposed method uses simulation and measurement of a 0.25 um GaN HEMT device operating at 8 GHz, with VGS at −2 V and VDS at 18 V. Furthermore, the proposed model exhibits good interpolation capability at different input power levels, indicating its broad applicability in the design of high‐speed device models. Specifically, the accuracy of the simulation data and test data reached −53.83 and −51.68 dB, respectively, with the test data used in model training being less than 7.5% of the simulation data.

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Characterization of Carbon Nanotube Field Effect Transistors using an active load pull LSNA setup

Gaquière

Curutchet

Théron

et al. 2008

2008 71st ARFTG Microwave Measurement Conference

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Non Linear RF Device Characterization in Time Domain using an Active Loadpull Large Signal Network Analyzer

Cited by 6 publications

References 11 publications

Toward highly scaled AlN/GaN-on-Silicon devices for millimeter wave applications

Toward highly scaled AlN/GaN-on-Silicon devices for millimeter wave applications

Knowledge‐based neural network with Bayesian optimization for efficient nonlinear RF device modeling

Characterization of Carbon Nanotube Field Effect Transistors using an active load pull LSNA setup

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