An Artificial Neural Network-Based Electrothermal Model for GaN HEMTs With Dynamic Trapping Effects Consideration

Huang, Andong; Zheng, Zhong; Wu, Wen; Guo, Yong‐Xin

doi:10.1109/tmtt.2016.2586055

Cited by 114 publications

(57 citation statements)

References 34 publications

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“…The activation function is represented here also by tanh . This function is also consistent with behavior of the drain current and it can accurately describe its ohmic‐saturation transition and pinch‐off (turn‐on) nonlinearities . In this model, the self‐heating regenerative process was taken in to account by feeding the drain current back as a third input.…”

Section: Temperature Dependent Large‐signal Modelmentioning

confidence: 99%

Neural network electrothermal modeling approach for microwave active devices

Jarndal

2019

Int J RF Microw Comput Aided Eng

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This article presents an artificial neural network (ANN) approaches for small‐ and large‐signal modeling of active devices. The small‐signal characteristics were modeled by S‐parameters based feedforward NN models. The models have been implemented to simulate the bias, frequency and temperature dependence of measured S‐parameters. Feedback NN based large‐signal model was developed and implemented to simulate the drain current and its inherent thermal effect due to self‐heating and ambient temperature. Both small‐ and large‐signal models have been validated by measurements for 100‐μm and 1‐mm GaN high electron mobility transistors and very good agreement was obtained.

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Section: Temperature Dependent Large‐signal Modelmentioning

confidence: 99%

Neural network electrothermal modeling approach for microwave active devices

Jarndal

2019

Int J RF Microw Comput Aided Eng

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“…An increase in the published literature is itself an evidence of the significance of machine learning techniques in device modeling . artificial neural network (ANN), one of the learning techniques, has been emerged as a powerful tool for device behavioral characterization and modeling and accommodates all the features of the machine learning . The advantage of ANN is that it can model highly nonlinear complex relations without even requiring explicit mathematical representations.…”

Section: Introductionmentioning

confidence: 99%

“…[30][31][32][33] artificial neural network (ANN), one of the learning techniques, has been emerged as a powerful tool for device behavioral characterization and modeling and accommodates all the features of the machine learning. [34][35][36][37][38][39][40][41][42][43][44] The advantage of ANN is that it can model highly nonlinear complex relations without even requiring explicit mathematical representations. ANN is generally based on the concept of empirical risk minimization; therefore, local minima can also appear instead of global minima in some cases.…”

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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“…14,15 Furthermore, many of the reported approaches addressed the extraction methodology based on de-embedding structures, cold FET condition, and neural network to model GaN devices. [16][17][18][19][20][21][22][23][24] In these reports, one of the extraction procedures was based on a two-step optimization process. [16][17][18][19][20][21] In such techniques, first the starting values were found either from measurements or using genetic algorithm, and then simplex algorithm technique was employed to find the optimal values for each element.…”

Section: Introductionmentioning

confidence: 99%

An accurate and simplified small signal parameter extraction method for GaN HEMT

Khusro

Hashmi

Ansari

et al. 2019

Circuit Theory & Apps

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Summary In this paper, development of a small signal model for 2 × 200 μm GaN HEMT based on the conventional 20‐element model is presented. The proposed model presents a direct parameter extraction algorithm, instead of the hybrid optimization approach, that provides simplification, accuracy, and less computational complexity. The extrinsic elements are extracted using a modified cold pinch‐off condition while discarding the unwanted forward biasing of the gate. The negative drain to source capacitance Cds is also observed in the ohmic region (for smaller VDS). An excellent agreement found between the measured and modeled data for a wide range of frequencies and bias values shows the effectiveness of the proposed approach. The proposed modeling technique is validated with a good agreement between the achieved bias dependency of intrinsic parameter values and the respective theoretical parameter values.

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An Artificial Neural Network-Based Electrothermal Model for GaN HEMTs With Dynamic Trapping Effects Consideration

Cited by 114 publications

References 34 publications

Neural network electrothermal modeling approach for microwave active devices

Neural network electrothermal modeling approach for microwave active devices

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

An accurate and simplified small signal parameter extraction method for GaN HEMT

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