Optimization of High-Speed Channel for Signal Integrity With Deep Genetic Algorithm
Huan Huan Zhang, Zhao Sheng Xue, Xin Yi Liu, Ping Li, Lijun Jiang, Guang Shi
Abstract
A deep genetic algorithm (GA) is proposed to optimize the high-speed channel for signal integrity. In the traditional genetic algorithm-based high-speed channel optimization method, the eye height and eye width of the eye diagram are obtained by eye diagram simulation based on the full-wave algorithm, which is computationally expensive. In this letter, a deep neural network (DNN) is trained to predict the eye diagram information corresponding to a set of given design parameters of the high-speed channel. This DNN is embedded into the genetic algorithm to carry out the evaluation operation, which can greatly accelerate the evaluation process. A high-speed channel model is constructed to demonstrate the optimization capability and the benefit of the proposed method.