High-Speed Channel Modeling With Machine Learning Methods for Signal Integrity Analysis

In this work, machine learning methods are applied to high-speed channel modeling for signal integrity analysis. Linear, support vector, and deep neural network (DNN) regressions are adopted to predict the eye-diagram metrics, taking advantage of the massive amounts of simulation data gathered from...

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Bibliographic Details
Published in:IEEE transactions on electromagnetic compatibility 2018-12, Vol.60 (6), p.1957-1964
Main Authors: Lu, Tianjian, Sun, Ju, Wu, Ken, Yang, Zhiping
Format: Article
Language:English
Subjects:
Artificial intelligence
Artificial neural networks
Channels
Circuit design
Computational modeling
Computer simulation
Deep neural networks (DNNs)
Design parameters
eye diagram
High speed
high-speed channel
Integrated circuit modeling
Machine learning
Neural networks
Predictions
Predictive models
Regression analysis
Regression models
Signal integrity
Support vector machines
support vector regression (SVR)
Training
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Summary:In this work, machine learning methods are applied to high-speed channel modeling for signal integrity analysis. Linear, support vector, and deep neural network (DNN) regressions are adopted to predict the eye-diagram metrics, taking advantage of the massive amounts of simulation data gathered from prior designs. The regression models, once successfully trained, can be used to predict the performance of high-speed channels based on various design parameters. The proposed learning-based approach saves complex circuit simulations and substantial domain knowledge altogether, in contrast to alternatives that exploit novel numerical techniques or advanced hardware to speed up traditional simulations for signal integrity analysis. Our numerical examples suggest that both support vector and DNN regressions are able to capture the nonlinearities imposed by transmitter and receiver models in high-speed channels. Overall, DNN regression is superior to support vector regression in predicting the eye-diagram metrics. Moreover, we also investigate the impact of various tunable parameters, optimization methods, and data preprocessing on both the learning speed and the prediction accuracy for the support vector and DNN regressions.
ISSN:0018-9375
1558-187X
DOI:10.1109/TEMC.2017.2784833