An Improved Small-Signal Equivalent Circuit for GaN High-Electron Mobility Transistors

Huang, Fuqiang; Tang, Xusheng; Wei, Zhaoxiang; Zhang, L. H.; Jiang, Nan

doi:10.1109/led.2016.2609462

Cited by 23 publications

(35 citation statements)

References 11 publications

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“…Now the parameter w can be completely described as a linear combination of training observations known as support vector expansion stated in (9). Then the function f(x) can be re-written in (10)…”

Section: Svm Regression-primal and Dual Formulamentioning

confidence: 99%

“…An increasing number of publications are testament to the appeal of machine learning in device modelling. It is, therefore, an emerging and appropriate alternate technique to the conventional equivalent circuit-based modelling methods [5][6][7][8][9][10][11][12][13][14] that can be used for modelling the microwave devices. It essentially aids in modelling the devices based on data approximation.…”

Section: Introductionmentioning

confidence: 99%

“…The machine learning techniques do not only provide the accuracy in approximating the characteristics of the device without any knowledge of device physics but also expedites the design optimisation process. On the other hand, the conventional small signal device modelling technique based on the extraction of parameters based on analytical methods, de-embedding and cold pinch-off condition, hybrid optimisation methods were highly time consuming as they require solutions of numerous complex equations [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

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Enabling the development of accurate intrinsic parameter extraction model for GaN HEMT using support vector regression (SVR)

Khusro¹,

Hashmi²,

Ansari³

2019

IET Microwaves, Antennas & Propagation

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This study employs support vector regression (SVR) to develop an accurate and reliable intrinsic parameter extraction model for gallium nitride (GaN) high electron mobility transistors (HEMT) using two different geometries of 2 × 200 μm and 4 × 100 µm. The key aspect of the proposed approach is the use of nonlinear Gaussian kernel to transform the input space into a high‐dimensional feature space. It then allows the application of learning technique to develop a reliable procedure for parameter extraction. The proposed extraction model of GaN HEMT has been developed for a broad range of frequency, from 1 to 18 GHz, with multi‐biasing sets for HEMTs of two different geometries. Moreover, the proposed model is made scalable in terms of geometry parameters and therefore can be used to predict the intrinsic parameters and enumerate scaling efficiency of GaN HEMTs by investigating the geometry parameters. A good agreement is observed between the measured S‐parameters and the proposed model for the complete frequency range. It is shown that the proposed approach is simple, novel and can be readily incorporated into computer‐aided design tool for an accurate and expedited design process of RF and microwave circuits.

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Section: Svm Regression-primal and Dual Formulamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Enabling the development of accurate intrinsic parameter extraction model for GaN HEMT using support vector regression (SVR)

Khusro¹,

Hashmi²,

Ansari³

2019

IET Microwaves, Antennas & Propagation

View full text Add to dashboard Cite

show abstract

“…[5][6][7][8][9][10] In this context, empirical modeling approach is very common for the development of small signal model using cold pinch off condition, deembedding structures, and analytical techniques. [11][12][13][14][15][16][17][18][19] It is imperative to note that an accurate and reliable small signal equivalent model will ensure the development of reliable large signal and noise models. [20][21][22][23][24][25][26] The design of circuits generally requires behavior of the transistor and in this context, the analytical techniques have been found to be too involved due to technology dependency, and complex parameter extraction procedure.…”

Section: Introductionmentioning

confidence: 99%

“…During design of high frequency circuits, it is desired to develop HEMT models, large or small signal, meeting the needs of specific applications . In this context, empirical modeling approach is very common for the development of small signal model using cold pinch off condition, deembedding structures, and analytical techniques . It is imperative to note that an accurate and reliable small signal equivalent model will ensure the development of reliable large signal and noise models …”

Section: Introductionmentioning

confidence: 99%

Small signal behavioral modeling technique of GaN high electron mobility transistor using artificial neural network: An accurate, fast, and reliable approach

Khusro

Husain

Hashmi

et al. 2019

Int J RF Microw Comput Aided Eng

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This article reports a comparative study of two artificial neural network structures and associated variants used to describe and predict the behavior of 2 × 200 μm2 GaN high electron mobility transistors (HEMTs), utilizing radiofrequency characterization. Two architectures namely multilayer perceptron and cascade feedforward, have been investigated in this work to develop the behavioral model. A study is conducted utilizing the two architectures, all trained using Levenberg‐Marquardt, in terms of accuracy, convergence rate, and generalization capability to develop the behavioral model of GaN HEMT. However, to ensure the robustness of the model, accuracy, convergence rate, time elapsed, and generalization capability of the proposed model is also tested under couple of training algorithms, activation functions, number of hidden layers and neuron embedded inside it, methods for initialization of weights and bias and certain other vital parameters playing vital role in influencing the model accuracy and effectiveness. An excellent agreement found between measured S‐parameters and the proposed model proves the effectiveness of the proposed approach and excellent prediction ability for a sweeping multibias set and broad frequency range of 1 to 18 GHz. Moreover, a very good generalization capability is also recorded under variation of crucial parameters of GaN HEMT‐based neural model.

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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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An Improved Small-Signal Equivalent Circuit for GaN High-Electron Mobility Transistors

Cited by 23 publications

References 11 publications

Enabling the development of accurate intrinsic parameter extraction model for GaN HEMT using support vector regression (SVR)

Enabling the development of accurate intrinsic parameter extraction model for GaN HEMT using support vector regression (SVR)

Small signal behavioral modeling technique of GaN high electron mobility transistor using artificial neural network: An accurate, fast, and reliable approach

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

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