Fast and accurate modeling of embedded passives in multi-layer printed circuits using neural network approach

被引:11
|
作者
Zhang, QJ [1 ]
Yagoub, MCE [1 ]
Ding, X [1 ]
Goulette, D [1 ]
Sheffield, R [1 ]
Feyzbakhsh, H [1 ]
机构
[1] Carleton Univ, Dept Elect, Ottawa, ON K1S 5B6, Canada
来源
52ND ELECTRONIC COMPONENTS & TECHNOLOGY CONFERENCE, 2002 PROCEEDINGS | 2002年
关键词
D O I
10.1109/ECTC.2002.1008174
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
In this paper, we present a new approach to modeling of high-frequency effects of embedded passive components in multilayer printed circuits based on artificial neural networks. The training data are generated by electromagnetic simulators, e.g., Ansoft-HFSS and Sonnet-Lite software. The models are trained to learn the S-parameters of the embedded passives versus physical and geometrical parameters. The models are fast and represent the EM based information of the components. They can be used for efficient design of high-frequency circuits and systems.
引用
收藏
页码:700 / 703
页数:2
相关论文
共 50 条
  • [31] Carbonate texture identification using multi-layer perceptron neural network
    Fociro, Oltion
    Fociro, Ana
    Muci, Redi
    Skrame, Klodian
    Pekmezi, Jeton
    Mezini, Mario
    OPEN GEOSCIENCES, 2023, 15 (01):
  • [32] Human Gait Recognition using Neural Network Multi-Layer Perceptron
    Mohammed, Faisel Ghazi
    Eesee, Waleed Khaled
    JOURNAL OF MECHANICS OF CONTINUA AND MATHEMATICAL SCIENCES, 2019, 14 (03): : 234 - 244
  • [33] The weak signal measurement by using multi-layer feedforward neural network
    Tao, WZ
    Liu, XQ
    ICEMI '97 - CONFERENCE PROCEEDINGS: THIRD INTERNATIONAL CONFERENCE ON ELECTRONIC MEASUREMENT & INSTRUMENTS, 1997, : 426 - 429
  • [34] Extraction of voltage harmonics using multi-layer perceptron neural network
    Tumay, Mehmet
    Meral, M. Emin
    Bayindir, K. Cagatay
    NEURAL COMPUTING & APPLICATIONS, 2008, 17 (5-6): : 585 - 593
  • [35] Cluster Membership of Galaxies Using Multi-Layer Perceptron Neural Network
    Hashimoto, Yasuhiro
    Liu, Cheng-Han
    UNIVERSE, 2022, 8 (07)
  • [36] Depth estimation for deformable object using a multi-layer neural network
    Zhu, Hangwei
    Wang, Hesheng
    Chen, Weidong
    Wu, Ruimin
    2017 IEEE INTERNATIONAL CONFERENCE ON REAL-TIME COMPUTING AND ROBOTICS (RCAR), 2017, : 477 - 482
  • [37] A multi-layer neural network approach for the stability analysis of the Hepatitis B model
    Farhan, Muhammad
    Ling, Zhi
    Shah, Zahir
    Islam, Saeed
    Alshehri, Mansoor H.
    Antonescu, Elisabeta
    COMPUTATIONAL BIOLOGY AND CHEMISTRY, 2024, 113
  • [38] Neural network approach for via minimization in multi-layer VLSI/PCB routing
    Zhejiang Univ, Hangzhou, China
    Tien Tzu Hsueh Pao/Acta Electronica Sinica, 1998, 26 (02): : 20 - 24
  • [39] FMLGLN: Fast Multi-layer Graph Linear Network
    Zhu, Zonghai
    Xing, Huanlai
    Xu, Yuge
    ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2024, 127
  • [40] MULTI-LAYER NETWORK APPROACH FOR MODELING GENERAL COMBINED-MODE TRIPS
    Wu, Z. X.
    Ye, H. S.
    Sun, M.
    Lam, William H. K.
    TRANSPORTATION AND THE ECONOMY, 2005, : 268 - 277