Machine Learning for high speed channel optimization

-- Design of printed circuit board (PCB) stack - up requires the consideration of characteristic impedance, insertion loss and crosstalk. As there are many parameters in a PCB stack - up design, the optimization of these parameters needs to be efficient and accurate. A le ss optimal stack - up would lead to expensive PCB material choices in high speed designs. In this paper, a n efficient global optimization method using parallel and intelligent Bayesian optimization is proposed for the stripline design . In high speed system design, optimizing printed circuit board (PCB) stack - up is playing a more and more important role in design stage.