<scp>Equivalent‐circuit</scp> extraction for gallium nitride electron devices: Direct versus <scp>optimization‐empowered</scp> approaches

Jarndal, Anwar; Crupi, Giovanni; Alim, Mohammad Abdul; Vadalà, Valeria; Raffo, Antonio; Vannini, G.

doi:10.1002/jnm.3008

Cited by 3 publications

(3 citation statements)

References 45 publications

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“…Several transistor models are available for power transistors, including physics‐based models, 14–16 equivalent circuit models, 17–23 and behavioral models 24–31 . Typically, the first two models are used for nonlinear circuit designs, as they provide acceptable results when devices are subjected to extreme operating conditions.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, GaN-based power transistors have received a widespread attention for developing RFPAs due to their ability to generate high power densities. [4][5][6][7][8][9][10][11][12][13] Several transistor models are available for power transistors, including physics-based models, [14][15][16] equivalent circuit models, [17][18][19][20][21][22][23] and behavioral models. [24][25][26][27][28][29][30][31] Typically, the first two models are used for nonlinear circuit designs, as they provide acceptable results when devices are subjected to extreme operating conditions.…”

Section: Introductionmentioning

confidence: 99%

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Incorporating DC bias voltage in poly‐harmonic distortion modeling for RF power GaN transistors

Cheng,

Tang,

Marinković

et al. 2024

Int J Numerical Modelling

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This paper presents a novel poly‐harmonic distortion (PHD) model that incorporates the DC input and output bias voltages using Gaussian process regression (GPR). Simulation tests were conducted using a 10‐W gallium nitride (GaN) HEMT transistor from Wolfspeed, and the model implementation test was performed in the Keysight Advanced Design System environment. The results showed that the GPR‐based PHD model exhibited good performance in predicting both fundamental and harmonic behaviors over a wide range of bias variations with significant advantages over basic linear regression methods. Additionally, the model accurately predicted load‐pull simulations. The measurement test was conducted using a 6‐W GaN device, and the results showed a mean error of 2.22% and 4.54% for the fundamental and second harmonic of the reflected wave, respectively.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Incorporating DC bias voltage in poly‐harmonic distortion modeling for RF power GaN transistors

Cheng,

Tang,

Marinković

et al. 2024

Int J Numerical Modelling

Self Cite

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“…The effectiveness of these methods, however, depends on the quality of the measurements, extraction procedures, optimization techniques, and assumptions that are made. The existing closed‐form equations from both ECM and PM, while suitable for many existing devices, are not always appropriate for newer device technologies 25–27 …”

Section: Introductionmentioning

confidence: 99%

Comparative analysis of nonlinear behavioral models for GaN HEMTs based on machine learning techniques

Liu,

Cai

2023

Int J Numerical Modelling

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A variety of novel behavioral modeling techniques have been reported to accurately capture the nonlinear characteristics of GaN devices. For the purpose of describing GaN HEMT large‐signal behavior, machine learning‐based approaches have been proposed. There is, however, a lack of comparison and analysis of their performance with different machine learning techniques in these studies. An examination of machine learning‐based large‐signal modeling techniques is presented in this article. To develop large‐signal models of GaN HEMT, a range of commonly used modeling techniques such as artificial neural networks (ANN), random sample consistency (RANSAC), support vector regression (SVR), Gaussian process regression (GPR), decision trees (DTs), and genetic algorithm‐assisted ANN (GA‐ANN) are described and employed. Afterwards, integrated modeling techniques such as Bootstrap aggregation (BA), random forests (RF), AdaBoost, and gradient tree boosting (GTB) are reviewed and tested for their capabilities in developing GaN HEMT LSM. The hyperparameters of each built model are modified using the random search optimization (RSO) method, and five‐fold cross‐validation is used during validation. In order to identify optimal algorithms for nonlinear behavioral modeling of GaN devices, the model prediction results are comprehensively analyzed.

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Comprehensive Investigation and Comparative Analysis of Machine Learning-Based Small-Signal Modelling Techniques for GaN HEMTs

Husain

Hashmi

Ghannouchi

2022

IEEE J. Electron Devices Soc.

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A number of machine learning (ML) algorithm based small signal modeling of Gallium Nitride (GaN) High Electron Mobility Transistors (HEMTs) have been reported in literature. However, these techniques rarely provide any inkling about their suitability in modeling GaN HEMTs under varied operating conditions. In this context, this paper thoroughly investigates various ML based techniques and identifies their suitability for specific application scenarios. At first, an array of commonly employed modeling techniques based around Artificial Neural Network, RANdom SAmple Consensus, Support Vector Regression, Gaussian Process Regression, Decision Tree, and Genetic algorithm assisted Artificial Neural Network are used for development of modeling framework to exploit the bias, frequency and geometry dependence on S-parameter based outputs. Subsequently, the ensemble techniques namely Bootstrap aggregating, Random Forests, Extremely Randomized Trees, AdaBoost, Gradient Tree Boosting, Histogram-based Gradient Boosting, and Extreme Gradient Boosting are also explored to understand the capability of these algorithms in the development of GaN HEMT small signal models. Thereafter, an exhaustive analysis of bias and variance is carried out to figure out the most appropriate algorithms for specific applications. The discrepancies during model development are removed by tuning the hyperparameters of the respective models using Random search optimization with 5-fold cross validation technique. Post tuning, the models are evaluated in terms of generalization capability, Advanced Design System compatibility, computational efficiency, training and simulation time, models' capacity and parameters'tuning time.

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Equivalent‐circuit extraction for gallium nitride electron devices: Direct versus optimization‐empowered approaches

Cited by 3 publications

References 45 publications

Incorporating DC bias voltage in poly‐harmonic distortion modeling for RF power GaN transistors

Incorporating DC bias voltage in poly‐harmonic distortion modeling for RF power GaN transistors

Comparative analysis of nonlinear behavioral models for GaN HEMTs based on machine learning techniques

Comprehensive Investigation and Comparative Analysis of Machine Learning-Based Small-Signal Modelling Techniques for GaN HEMTs

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