Nonlinear AlGaN/GaN HEMT Model Using Multiple Artificial Neural Networks

被引：0
|
作者：
Barmuta, P. [1 ]
Plonski, P. [1 ]
Czuba, K. [1 ]
Avolio, G. [2 ]
Schreurs, D. [2 ]
机构：
[1] Warsaw Univ Technol, Warsaw, Poland
[2] Katholieke Univ Leuven, Leuven, Belgium
来源：
2012 19TH INTERNATIONAL CONFERENCE ON MICROWAVE RADAR AND WIRELESS COMMUNICATIONS (MIKON), VOLS 1 AND 2 | 2012年
关键词：
artificial neural network; temperature; GaN HEMT; nonlinear model; MESFET;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
In this work, a complete nonlinear-transistor-model extraction-method is described. As a case study, the AlGaN/CaN High Electron Mobility Transistor manufactured on SiC substrate is modeled. The parasitic components model is proposed, and its extraction results are presented. Low- and high-frequency large-signal measurement data are involved in order to produce multiple artificial neural networks. The network topologies of multilayer perceptron networks are established automatically. A complete learning procedure using back propagation algorithm is described. A good agreement between the measurement data and the model has been observed.
引用
收藏
页码：462 / 466
页数：5
相关论文
共 50 条
  • [21] Nonlinear model predictive control of a multiscale thin film deposition process using artificial neural networks
    Kimaev, Grigoriy
    Ricardez-Sandoval, Luis A.
    CHEMICAL ENGINEERING SCIENCE, 2019, 207 : 1230 - 1245
  • [22] Enhancement of Sensitivity in AlGaN/GaN HEMT Based Sensor Using Back-Barrier Technique
    Jarndal, Anwar
    Arivazhagan, L.
    Almajali, Eqab
    Mahmoud, Soliman
    Majzoub, Sohaib
    Bonny, Talal
    IEEE SENSORS JOURNAL, 2022, 22 (16) : 15742 - 15749
  • [23] Accurate statistical extraction of AlGaN/GaN HEMT device parameters using the Y-function
    Kammeugne, R. Kom
    Leroux, C.
    Cluzel, J.
    Vauche, L.
    Le Royer, C.
    Krakovinsky, A.
    Gwoziecki, R.
    Biscarrat, J.
    Gaillard, F.
    Charles, M.
    Bano, E.
    Ghibaudo, G.
    SOLID-STATE ELECTRONICS, 2021, 184
  • [24] Using chaotic artificial neural networks to model memory in the brain
    Aram, Zainab
    Jafari, Sajad
    Ma, Jun
    Sprott, Julien C.
    Zendehrouh, Sareh
    Pham, Viet-Thanh
    COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION, 2017, 44 : 449 - 459
  • [25] A New GaN HEMT Nonlinear Model for Evaluation and Design of 1-2 Watt Power Amplifiers
    Marcoux, Nick L.
    Fisher, Christopher J.
    White, Doug
    Lachapelle, John
    Palacios, Tomas
    Saadat, Omair
    Sonkusale, Sameer
    2012 IEEE 55TH INTERNATIONAL MIDWEST SYMPOSIUM ON CIRCUITS AND SYSTEMS (MWSCAS), 2012, : 53 - 56
  • [26] Modeling of a 20 W GaN HEMT Using QPZD Model
    Yu, Xiuling
    Mao, Shuman
    Xie, Xiaoqiang
    Xu, Yuehang
    2020 IEEE MTT-S INTERNATIONAL CONFERENCE ON NUMERICAL ELECTROMAGNETIC AND MULTIPHYSICS MODELING AND OPTIMIZATION (NEMO 2020), 2020,
  • [27] GaN HEMT Small-Signal Modelling: Neural Networks versus Equivalent Circuit
    Marinkovic, Z.
    Crupi, G.
    Caddemi, A.
    Markovic, V.
    2017 IEEE 30TH INTERNATIONAL CONFERENCE ON MICROELECTRONICS (MIEL), 2017, : 153 - 156
  • [28] Tailoring composite materials for nonlinear viscoelastic properties using artificial neural networks
    Xu, Xianbo
    Elgamal, Mariam
    Doddamani, Mrityunjay
    Gupta, Nikhil
    JOURNAL OF COMPOSITE MATERIALS, 2021, 55 (11) : 1547 - 1560
  • [29] Investigation of Multiple Models of Artificial Neural Networks
    Verma, Monika
    Singh, Shikha
    Agrawal, Krishna Kant
    PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON INTELLIGENT SUSTAINABLE SYSTEMS (ICISS 2017), 2017, : 1062 - 1067
  • [30] Ultrahigh Sensitive Mercury Ion Detector Using AlGaN/GaN HEMT-Based Sensor and System
    Mishra, Shivanshu
    Kachhawa, Pharyanshu
    Jain, Amber Kumar
    Kishore, Kaushal
    Chaturvedi, Nidhi
    MICRO AND NANOELECTRONICS DEVICES, CIRCUITS AND SYSTEMS, 2023, 904 : 351 - 357
12345