Fast and Precise High-Speed Channel Modeling and Optimization Technique Based on Machine Learning

This letter proposes a fast and precise high-speed channel modeling and optimization technique based on machine learning algorithms. Resistance, inductance, conductance, and capacitance (RLGC) matrices of a high-speed channel are precisely modeled by design-of-experiment method and artificial neural...

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Bibliographic Details
Published in:IEEE transactions on electromagnetic compatibility 2018-12, Vol.60 (6), p.2049-2052
Main Authors: Kim, Heegon, Sui, Chunchun, Cai, Kevin, Sen, Bidyut, Fan, Jun
Format: Article
Language:English
Subjects:
Artificial intelligence
Artificial neural network (ANN)
Artificial neural networks
channel modeling and optimization
Computer simulation
Crosstalk
Data models
genetic algorithm (GA)
Genetic algorithms
High speed
Inductance
Latin-hypercube sampling (LHS)
Machine learning
Modelling
Neural networks
Numerical models
Optimization
Optimization techniques
Resistance
Symmetric matrices
Training data
Transmission line matrix methods
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Summary:This letter proposes a fast and precise high-speed channel modeling and optimization technique based on machine learning algorithms. Resistance, inductance, conductance, and capacitance (RLGC) matrices of a high-speed channel are precisely modeled by design-of-experiment method and artificial neural network. In addition, an optimal channel design, which achieves minimum channel loss and crosstalk, is investigated within short time by a genetic algorithm. The performance of the proposed technique is validated by simulations up to 20 GHz.
ISSN:0018-9375
1558-187X
DOI:10.1109/TEMC.2017.2782704