Crosstalk modeling in high-speed transmission lines by multilayer perceptron neural networks
- 6 Downloads
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.
KeywordsCrosstalk 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.
- 2.Deo M, Enabling next-generation platforms using Intel’s 3D system-in-package technology [White paper]. Retrieved from http://www.intel.com
- 8.Das D, Lee CSG (2018) Cross-scene trajectory level intention inference using Gaussian process regression and naive registration. Department of Electrical and Computer Engineering Technical Reports. Paper 491. Purdue UniversityGoogle Scholar
- 9.Dai F, Bao G, Su DL (2010) Crosstalk prediction in non-uniform cable bundles based on neural network. In: Proceedings of the 9th international symposium on antennas, propagation and EM theory. IEEE, Guangzhou, pp 1043–1046Google Scholar
- 10.Cannas B, Fanni A, Maradei F (2002) A neural network approach to predict the crosstalk in non-uniform multiconductor transmission lines. In: 2002 IEEE International Symposium on Circuits and Systems. IEEE, Phoenix, pp 573–576Google Scholar
- 20.Hsu KT, Guo WD, Shiue GH, Lin CM, Huang TW, Wu RB (2008) Design of reflectionless vias using neural network-based approach. IEEE Trans Adv Packag 31(1):211–218Google Scholar
- 25.Hagan MT, Demuth HB, Beale MH, Jesús OD (2014) Neural network design. Martin HaganGoogle Scholar
- 28.Nguyen T, Schutt-Aine JE (2018) A pseudo-supervised machine learning approach to broadband LTI macro-modeling. In: 2018 IEEE international symposium on electromagnetic compatibility and 2018 IEEE Asia-Pacific symposium on electromagnetic compatibility. IEEE, Singapore, pp 1018–1021Google Scholar
- 29.Nguyen T, Lu T, Sun J, Le Q, We K, Schut-Aine J (2018) Transient simulation for high-speed channels with recurrent neural network. In: IEEE 27th conference on electrical performance of electronic packaging and systems. IEEE, San Jose, pp 303–305Google Scholar