An improved small signal model of <scp>InP HBT</scp> for millimeter‐wave applications

Zhang, Ao; Gao, Jianjun

doi:10.1002/mop.32876

Cited by 2 publications

(2 citation statements)

References 9 publications

(14 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, A. Zhang improved the extraction method of small-signal equivalent circuit models for InP HBT [22], which can be applied in millimeter waves and effectively improves the accuracy of S-parameters. Also improving the small-signal equivalent circuit model of HBT, L. Cheng proposed a rigorous and systematic analysis-based parameter extraction technique [23], which can achieve high accuracy.…”

Section: Introductionmentioning

confidence: 99%

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

Lin

Wang

Wu³

2023

IEEE Access

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

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

Lin

Wang

Wu³

2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Many researchers made contributions to HBT modeling methods [6][7][8][9][10]. Some existing traditional modeling techniques such as equivalent circuit modeling match HBT data by manually adjusting the parameters, which are heavily based on trial-and-error process [11].…”

Section: Introductionmentioning

confidence: 99%

Analytical Neuro-space Mapping Technology for Heterojunction Bipolar Transistors Modeling

Yan,

Li,

et al. 2023

PIER M

View full text Add to dashboard Cite

An analytical modeling method for heterojunction bipolar transistor (HBT) is proposed in this paper. The new neuro-space mapping (Neuro-SM) model applied to DC, small signals and large signals simultaneously consists of two mapping networks, which provide the additional degrees of freedom. Sensitivity analysis expressions are derived to accelerate the training process. When the nonlinearity of device is high, or the response of the model is complex, the weights in the proposed model are automatically adjusted to address the accuracy limitations. The proposed modeling method is verified by measured HBT examples in DC, small signals and large signals Harmonic Balance (HB) simulation. The modeling experiments of the measured HBT demonstrate that the errors of the proposed Neuro-SM model are less than 2% by matching combined DC, small-signal S-parameters and large-signal HB data, which are less than the errors of the traditional Neuro-SM model and the coarse model. The proposed analytic Neuro-SM model fits the response of the fine model well.

show abstract

An improved small signal model of InP HBT for millimeter‐wave applications

Cited by 2 publications

References 9 publications

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

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

Analytical Neuro-space Mapping Technology for Heterojunction Bipolar Transistors Modeling

Contact Info

Product

Resources

About