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
  • [21] Fast and improved backpropagation learning of multi-layer artificial neural network using adaptive activation function
    Panda, Sashmita
    Panda, Ganapati
    EXPERT SYSTEMS, 2020, 37 (05)
  • [22] An approach to empirical Optical Character recognition paradigm using Multi-Layer Perceptorn Neural Network
    Abdullah-al-Mamun, Md.
    Alam, Tanjina
    2015 18th International Conference on Computer and Information Technology (ICCIT), 2015, : 132 - 137
  • [23] Multi-layer Neural Network for EMV Evaluation
    Ouerdi, Noura
    Hajji, Tarik
    Azizi, Abdelmalek
    Yahia, Amina
    EUROPE AND MENA COOPERATION ADVANCES IN INFORMATION AND COMMUNICATION TECHNOLOGIES, 2017, 520 : 549 - 557
  • [24] Optimizing Parameters of Multi-Layer Convolutional Neural Network by Modeling and Optimization Method
    Chou, Fu-, I
    Tsai, Yun-Kai
    Chen, Yao-Mei
    Tsai, Jinn-Tsong
    Kuo, Chun-Cheng
    IEEE ACCESS, 2019, 7 : 68316 - 68330
  • [25] Polyimide multi-layer printed circuit board with embedded WLP chip
    Micro Device Department, Electron Device Laboratory, Fujikura Ltd., 1440 Mutsuzaki, Sakura-shi, Chiba 285-8550, Japan
    J. Jpn. Inst. Electron. Packag., 2008, 4 (275-279):
  • [26] Fault diagnosis for photovoltaic inverters using a multi-layer neural network
    Noh M.-J.
    Bang J.
    Rhee J.-S.
    Cho P.-H.
    Kwon M.-H.
    Lim J.-G.
    Chun H.-J.
    Song J.-H.
    Transactions of the Korean Institute of Electrical Engineers, 2021, 70 (07): : 1056 - 1063
  • [27] Extraction of voltage harmonics using multi-layer perceptron neural network
    Mehmet Tümay
    M. Emin Meral
    K. Çağatay Bayindir
    Neural Computing and Applications, 2008, 17 : 585 - 593
  • [28] Detection of human activities using multi-layer convolutional neural network
    Essam Abdellatef
    Rasha M. Al-Makhlasawy
    Wafaa A. Shalaby
    Scientific Reports, 15 (1)
  • [29] Exploratory Test Oracle using Multi-Layer Perceptron Neural Network
    Makondo, Wellington
    Nallanthighal, Raghava
    Mapanga, Innocent
    Kadebu, Prudence
    2016 INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTING, COMMUNICATIONS AND INFORMATICS (ICACCI), 2016, : 1166 - 1171
  • [30] Automatic target recognition using a multi-layer convolution neural network
    Mirelli, V
    Rizvi, SA
    SIGNAL PROCESSING, SENSOR FUSION, AND TARGET RECOGNITION V, 1996, 2755 : 106 - 125