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.
Recommended Citation
H. H. Zhang et al., "Optimization Of High-Speed Channel For Signal Integrity With Deep Genetic Algorithm," IEEE Transactions on Electromagnetic Compatibility, vol. 64, no. 4, pp. 1270 - 1274, Institute of Electrical and Electronics Engineers, Aug 2022.
The definitive version is available at https://doi.org/10.1109/TEMC.2022.3161298
Department(s)
Electrical and Computer Engineering
Keywords and Phrases
Deep neural network (DNN); genetic algorithm; high-speed channel; signal integrity
International Standard Serial Number (ISSN)
1558-187X; 0018-9375
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Aug 2022
