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Crosstalk modeling in high-speed transmission lines by multilayer perceptron neural networks

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Abstract

Signal degradation due to crosstalk-related issues has become increasingly important particularly in high-speed signal transmissions. Conventional analysis of crosstalk requires a full electromagnetic modeling of the signal transmission path along with a time-domain transient simulation which is computationally demanding. In this work, we apply a multilayer perceptron neural network for crosstalk prediction in coupled transmission lines. The well-trained neural networks can be used to predict the time-domain crosstalk directly, thereby replacing complex circuit simulations. Numerical results show a high degree of generalization of the neural networks, which are able to produce accurate results and can be trained to include effects such as reflections and input mismatches.

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Acknowledgements

This work is supported by Universiti Sains Malaysia under the Research University (RUI) Grant (1001/PELECT/8014011).

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Authors and Affiliations

  1. Keysight Technologies, 11900, Bayan Lepas, Penang, Malaysia

    Kai Siang Ooi

  2. School of Electrical and Electronic Engineering, Universiti Sains Malaysia, 14300, Nibong Tebal, Penang, Malaysia

    Chun Lei Kong, Chan Hong Goay, Nur Syazreen Ahmad & Patrick Goh

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  1. Kai Siang Ooi
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  2. Chun Lei Kong
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  3. Chan Hong Goay
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  4. Nur Syazreen Ahmad
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  5. Patrick Goh
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Correspondence to Patrick Goh.

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Ooi, K.S., Kong, C.L., Goay, C.H. et al. Crosstalk modeling in high-speed transmission lines by multilayer perceptron neural networks. Neural Comput & Applic 32, 7311–7320 (2020). https://doi.org/10.1007/s00521-019-04252-3

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  • DOI: https://doi.org/10.1007/s00521-019-04252-3

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