Extrapolation of Load-Pull Data: A Novel Use of GAN Artificial Intelligence Image Completion

Egbert, Austin; Baylis, Charles; Marks, Robert J.

doi:10.1109/tmtt.2022.3209700

Cited by 9 publications

(2 citation statements)

References 18 publications

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“…In order to observe the error situation more clearly, the absolute errors of S11, S12, S21 and S22 are calculated separately and the error curves are displayed. The absolute errors can be calculated from equation (12):…”

Section: Frequencymentioning

confidence: 99%

“…In recent years, artificial neural networks have attracted wide attention due to the strong nonlinear mapping capabilities, no need to deeply understand the internal structure of modeling objects, and fast simulation speed [12][13][14][15][16]. Thus, S-parameter modeling technology based on artificial neural network has attracted attention, and a series of studies have been carried out [12][13][14][15][16]. In 2019, A. Jarndal used ANN to model the S-parameters of Ga N HEMT [17], which has good convergence and can fit the S-parameters well.…”

Section: Introductionmentioning

confidence: 99%

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GRNN-Based Scattering Parameter Modeling Investigation for HBT at Different Temperature

Lin

Wang

Wu³

2023

IEEE Access

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In this paper, the scattering parameter (S-parameter) modeling method for heterojunction bipolar transistor (HBT) at different temperatures is investigated. The S-parameters of HBT at different temperatures are randomly divided into training and test sets, which are modeled by radial basis function (RBF) neural network and general regression neural network (GRNN), respectively. Then, the fitting results of the two models in predicting S-parameter are displayed. The experimental show that the fitting results of RBF neural network are good, but the fitting errors of some data are existed. Meanwhile most of the data predicted by GRNN can achieve ideal fitting. In addition, the absolute error curves of the two models show that the error curve of RBF neural network is more volatile, while the error curve of GRNN has a small fluctuation range, which can predict the Sparameters more stably. Finally, the mean square error (MSE) of RBF neural network prediction model and GRNN prediction model are 8.6178e-04 and 3.1041e-4, respectively. It is proved that GRNN has a better modeling effect for HBT S-parameter at different temperatures. Therefore, the proposed modeling method can accurately characterize the S-parameter of HBT at different temperatures.INDEX TERMS Scattering parameter, HBT modeling, RBF neural network, GRNN.

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Section: Frequencymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

GRNN-Based Scattering Parameter Modeling Investigation for HBT at Different Temperature

Lin

Wang

Wu³

2023

IEEE Access

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Transistor modeling based onLM‐BPNNandCG‐BPNNfor theGaAs pHEMT

Lin,

Yang,

Yang

et al. 2024

Int J Numerical Modelling

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In order to address the challenges of complex process and low precision in traditional device modeling, double hidden layer back propagation neural network (BPNN) are trained using the conjugate gradient (CG) algorithm and the Levenberg–Marquardt (LM) algorithm, the CG‐BPNN and LM‐BPNN models of small signal for gallium arsenide (GaAs) pseudomorphic high electron mobility transistor (pHEMT) are obtained and analyzed here. At first, the scattering parameters (S‐parameters) of GaAs pHEMT are divided into training set and test set randomly. Experimental results show that the CG‐BPNN model is better than another S‐parameters when predicting ImS12 with mean square error (MSE) of 7.6632e‐06, while LM‐BPNN model predicts ImS12 with MSE of 2.4672e‐06. Meanwhile, the MSE of CG‐BPNN model is higher than LM‐BPNN model when predicting all the S‐parameters. In addition, it shows a smaller fluctuation range for the error curve of LM‐BPNN model, which is more stable than the CG‐BPNN model. Therefore, the double hidden layer LM‐BPNN model is the better choice to characterize the small signal of GaAs pHEMT.

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Comparative Analysis of Deep Neural Networks for Gallium Nitride High Electron Mobility Transistor Load Pull Data Reconstruction

Alleman,

Prigent,

Courreges

2024

2024 10th International Conference on Control, Decision and Information Technologies (CoDIT)

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Extrapolation of Load-Pull Data: A Novel Use of GAN Artificial Intelligence Image Completion

Cited by 9 publications

References 18 publications

GRNN-Based Scattering Parameter Modeling Investigation for HBT at Different Temperature

GRNN-Based Scattering Parameter Modeling Investigation for HBT at Different Temperature

Transistor modeling based onLM‐BPNNandCG‐BPNNfor theGaAs pHEMT

Comparative Analysis of Deep Neural Networks for Gallium Nitride High Electron Mobility Transistor Load Pull Data Reconstruction

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