Title
Behavioral Modeling of Pre-emphasis Drivers Including Power Supply Noise Using Neural Networks
Abstract
This paper addresses the nonlinear behavioral modeling of pre-emphasis drivers including power supply noise. The proposed multiple-port model relies on the use of power-aware weighting functions that control the driver’s output stage to model the pre-emphasis behavior with non-ideal power supply accurately. The weighting functions are implemented using feed-forward neural networks (FFNNs), and the dynamic memory characteristics of driver’s ports are captured using recurrent neural networks (RNNs). Practical industrial driver example demonstrates that the proposed modeling method offers good accuracy, flexibility and significant simulation speed-up to facilitate signal integrity and power integrity analysis without compromising intellectual property (IP).
Year
DOI
Venue
2019
10.1109/LASCAS.2019.8667589
2019 IEEE 10th Latin American Symposium on Circuits & Systems (LASCAS)
Keywords
Field
DocType
Integrated circuit modeling,Power supplies,Load modeling,Power transmission lines,Driver circuits,Recurrent neural networks
Weighting,Computer science,Behavioral modeling,Signal integrity,Power integrity,Recurrent neural network,Electronic engineering,Control engineering,Electric power transmission,Emphasis (telecommunications),Artificial neural network
Conference
ISSN
ISBN
Citations 
2330-9954
978-1-7281-0453-9
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Huan Yu14613.63
Jaemin Shin200.68
Tim Michalka301.01
Mourad Larbi401.35
madhavan swaminathan510824.63