Accurate modeling of pHEMT output current derivatives over a wide temperature range

Zhu, Yi; Ma, Jan; Fu, Haipeng; Zhang, Qi‐Jun; Cheng, Qian‐Fu; Lin, Qian

doi:10.1002/jnm.2185

Cited by 8 publications

(4 citation statements)

References 23 publications

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“…On the other hand, electromagnetic (EM) simulators or multiphysics simulators, which support temperature-dependent modeling, are often based on complex calculations, thereby making the simulations and optimizations of the circuits containing the devices whose characteristics are temperature-dependent time-consuming. There have been many attempts to develop alternative models and modeling approaches dealing with the temperature dependence of the behavior of different microwave circuits and devices [ 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 ]. Among them, ANNs have been proved to be a very efficient solution to develop such models, either purely based on ANNs or combined with existing models.…”

Section: Methodsmentioning

confidence: 99%

Development and Validation of an ANN-Based Approach for Temperature-Dependent Equivalent Circuit Modeling of SAW Resonators

et al. 2023

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In this paper, a novel approach is proposed for modeling the temperature-dependent behavior of a surface acoustic wave (SAW) resonator, by using a combination of a lumped-element equivalent circuit model and artificial neural networks (ANNs). More specifically, the temperature dependence of the equivalent circuit parameters/elements (ECPs) is modeled using ANNs, making the equivalent circuit model temperature-dependent. The developed model is validated by using scattering parameter measurements performed on a SAW device with a nominal resonant frequency of 423.22 MHz and under different temperature conditions (i.e., from 0 °C to 100 °C). The extracted ANN-based model can be used for simulation of the SAW resonator RF characteristics in the considered temperature range without the need for further measurements or equivalent circuit extraction procedures. The accuracy of the developed ANN-based model is comparable to that of the original equivalent circuit model.

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Section: Methodsmentioning

confidence: 99%

Development and Validation of an ANN-Based Approach for Temperature-Dependent Equivalent Circuit Modeling of SAW Resonators

et al. 2023

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“…Many studies have been devoted to the analysis of the temperature effects on the performance of the HEMT devices, focusing on both GaAs [9][10][11][12][13][14][15][16][17][18] and GaN [18][19][20][21][22][23][24][25][26][27][28][29][30][31] semiconductor technologies. The present article is aimed at an experimental investigation of the behavior of various HEMT technologies in highand low-temperature conditions.…”

Section: Introductionmentioning

confidence: 99%

Measurement-based Investigation of the DC and RF Transconductance for Various HEMT Technologies in High-and Low-temperature Conditions

Alim

Jarndal

Gaquière

et al. 2021

Preprint

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The purpose of this experimental study is to evaluate quantitatively the impact of the temperature on the behavior of various high electron-mobility transistor (HEMT) technologies through the analysis of the DC and RF transconductance. The experimental data are reported for six different HEMT devices, in order to develop a comparative analysis based on various technologies, including gallium arsenide (GaAs) and gallium nitride (GaN) materials, matched and pseudomorphic HEMTs, single- (S-H) and double-heterojunction (D-H) HEMTs, and both virgin and multi-layer devices. The reported findings show that the impact of the ambient temperature on the HEMT behavior strongly depend on the tested technology and operating conditions. As a matter of fact, a higher temperature can lead to increased or degraded transconductance, depending on the device technologies and bias point. In the GaAs-based devices, an operating bias condition at which the DC and RF transconductance are temperature insensitive can be defined, owing to two-opposite temperature-dependent effects counteracting with each other.

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“…Artificial neural networks (ANNs) are a well‐established and very powerful mathematical tool, finding a variety of applications as a modeling tool in the field of RF and microwaves . One of the most attractive features of ANNs is their ability to learn and generalize from a set of training data, which is suitable to be exploited for building device models from the measured characteristics.…”

Section: Introductionmentioning

confidence: 99%

A review on the artificial neural network applications for small‐signal modeling of microwave FETs

Marinković

Crupi

Caddemi

et al. 2019

Int J Numerical Modelling

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The purpose of this paper is to provide a comprehensive overview of the fieldeffect transistor (FET) small-signal modeling using artificial neural networks (ANNs). To gain an in-depth insight into how to effectively develop an ANN model, we present a comparative study on the application of the ANNs for modeling the scattering (S-) parameters of a variety of FET technologies versus bias point, ambient temperature, and geometrical dimensions. As will be shown, the main challenge consists of identifying the most appropriate ANN model for the specific case under study. This is because the performance of an ANN-based model can vary significantly, depending especially on the choice of the model structure and the size and parameters of the chosen ANN. In addition, the choice of the model is related directly to the behavior of the FET characteristics, which might greatly depend on the selected device technology and operating conditions. The analysis of the present comparative study allows understanding how to properly construct ANN models to perform at their best for a successful FET modeling.

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Accurate modeling of pHEMT output current derivatives over a wide temperature range

Cited by 8 publications

References 23 publications

Development and Validation of an ANN-Based Approach for Temperature-Dependent Equivalent Circuit Modeling of SAW Resonators

Development and Validation of an ANN-Based Approach for Temperature-Dependent Equivalent Circuit Modeling of SAW Resonators

Measurement-based Investigation of the DC and RF Transconductance for Various HEMT Technologies in High-and Low-temperature Conditions

A review on the artificial neural network applications for small‐signal modeling of microwave FETs

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