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

Abstract

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.