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

  • Kai Siang Ooi
  • Chun Lei Kong
  • Chan Hong Goay
  • Nur Syazreen Ahmad
  • Patrick GohEmail author
Original Article


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.


Crosstalk Microstrip Multilayer perceptron Stripline 



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

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  • Kai Siang Ooi
    • 1
  • Chun Lei Kong
    • 2
  • Chan Hong Goay
    • 2
  • Nur Syazreen Ahmad
    • 2
  • Patrick Goh
    • 2
    Email author
  1. 1.Keysight TechnologiesBayan LepasMalaysia
  2. 2.School of Electrical and Electronic EngineeringUniversiti Sains MalaysiaNibong TebalMalaysia

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