Modified
            <scp>small‐signal</scp>
            behavioral model for
            <scp>GaN HEMTs</scp>
            based on support vector regression

Geng, Mingqiang; Cai, Jialin; King, Justin; You, Bin; Su, Jiangtao; Li, Jun; Sun, Lingling; Cao, Wenhui; Pan, Mian

doi:10.1002/mmce.22774

Cited by 6 publications

(3 citation statements)

References 31 publications

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“…As a result, various modeling approaches have been developed in recent years [15]- [25], including physical base models [15], equivalent circuit models [16]- [18], behavioral models [19]- [22] and combinatorial models [23]- [25]. The present work focuses on modeling GaN HEMT technology using a behavior-based approach.…”

Section: Introductionmentioning

confidence: 99%

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Accurate and Effective Nonlinear Behavioral Modeling of a 10-W GaN HEMT Based on LSTM Neural Networks

2023

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This paper presents a novel nonlinear behavioral modeling methodology based on long-shortterm memory (LSTM) networks for gallium nitride (GaN) high-electron-mobility transistors (HEMTs). There are both theoretical foundations and practical implementations of the modeling procedure provided in this paper. To determine the most appropriate optimizer algorithm for the model presented in this work, four different optimization algorithms are examined. The results of both simulation and experimental validation are provided based on a 10-W GaN HEMT device. According to the developed investigation, the model is capable of extrapolating and interpolating over multiple input power levels and frequencies, including linear, weakly nonlinear, and strongly nonlinear areas. The analysis of the simulated and measured results shows that the developed model has superior performance also when considering the DC drain current (Ids.). Compared with the existing support vector regression (SVR) based model and the Bayesian based model, the proposed approach shows a significantly improved extrapolation capability.INDEX TERMS behavioral modeling, black box model, gallium nitride (GaN), high-electron-mobility transistor (HEMT), long-short term memory (LSTM), microwave frequency, power transistor.

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Section: Introductionmentioning

confidence: 99%

“…The model is capable of excellent interpolation. Small-signal and large-signal behavioral models based on support vector regression (SVR) were implemented in [25] and [37], and both showed accurate prediction performance.…”

Section: Introductionmentioning

confidence: 99%

Accurate and Effective Nonlinear Behavioral Modeling of a 10-W GaN HEMT Based on LSTM Neural Networks

2023

Self Cite

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“…Importantly, models which are comfortably incorporated into Computer-Aided Design (CAD) tools are vital [8]. The Equivalent Circuit (EC) and the behavioral modeling techniques are the most frequently used for the development of device models [9]- [11]. The EC methods extract the parameters based on the measurement set, nevertheless, keeping the correspondence with the physics of the device alive [12].…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Investigation of ANN Algorithms Implemented in MATLAB, Python, and R for Small-Signal Behavioral Modeling of GaN HEMTs

Husain,

Kadirbay,

Jarndal

et al. 2023

IEEE J. Electron Devices Soc.

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Artificial Neural Network (ANN) is frequently utilized for the development of behavioral models of Gallium Nitride (GaN) High Electron Mobility Transistors (HEMTs). However, exhaustive investigation concerning the ANN algorithms implemented in major programming platforms for small-signal behavioral models of GaN HEMTs is generally not available. To fill this void, this paper carefully examines and evaluates ANN algorithms implemented in MATLAB, Python and R software environments for the development of accurate and efficient GaN HEMTs modelling. At first, the ANN based models are developed using MATLAB, Python's major frameworks namely Keras, PyTorch and Scikit-learn, and R's ANN framework namely H2O to model the GaN devices. Thereafter, an in-depth analysis is carried out to comprehend the usefulness of each framework in different application scenarios. At last, a detailed evaluation of the developed models in terms of generalization capability, training and prediction speed, seamless integration with the standard circuit design tool advanced design system, and of the development environments in respect of support and documentation, user-friendly interface, ease of model development, openaccess and cost is carried out.

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A reliable parameter extraction method for the augmented GaN high electron mobility transistor small‐signal model

Zhai

Cai

Zhang

et al. 2022

Int J RF Mic Comp-Aid Eng

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An improved GaN high electron mobility transistor (HEMT) small-signal equivalent model is proposed. To model the effects of extrinsic parameters that vary with the bias voltage, R L and L ds are added to the model. Moreover, substrate-related parameters (R sub and C sub ) are used to model the substrate effect. In order to avoid damaging the gate under high forward gate pressure conditions, an accurate parasitic parameter extraction method is proposed, which improves the reliability and validity of the parameter extraction. The S-parameters of the proposed model are in good agreement with the measured S-parameters in the range of 0.5-20.5 GHz.

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

Cited by 6 publications

References 31 publications

Accurate and Effective Nonlinear Behavioral Modeling of a 10-W GaN HEMT Based on LSTM Neural Networks

Accurate and Effective Nonlinear Behavioral Modeling of a 10-W GaN HEMT Based on LSTM Neural Networks

Comprehensive Investigation of ANN Algorithms Implemented in MATLAB, Python, and R for Small-Signal Behavioral Modeling of GaN HEMTs

A reliable parameter extraction method for the augmented GaN high electron mobility transistor small‐signal model

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