Name
Abstract | ||
|---|---|---|
Determination of the data dependent jitter and its effect on the eye diagram is a challenging task in modern high-speed links; therefore, novel statistical approaches are required to expedite this task. Most of the current methods for jitter estimation are only applicable to linear systems, while nonlinear components play an essential role in the high-speed link response. Therefore, this paper introduces a new data dependent jitter estimation approach by using stochastic analysis. In this approach generalized Polynomial Chaos theory is utilized, where linear regression is used to create surrogate models for the link. Statistics of the output signal and jitter calculation are then directly obtained from these models. Two numerical examples are provided to evaluate the efficiency and accuracy of the proposed approach showing good match with the traditional transient eye analysis with good speedup. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TEST.2018.8624875 | 2018 IEEE International Test Conference (ITC) |
Keywords | Field | DocType |
Bit error rate,jitter,machine learning,modeling high-speed links,polynomial chaos theory | Nonlinear system,Linear system,Computer science,Stochastic process,Algorithm,Polynomial chaos,Electronic engineering,Jitter,Data-dependent jitter,Speedup,Linear regression | Conference |
ISSN | ISBN | Citations |
1089-3539 | 978-1-5386-8383-5 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Majid Ahadi Dolatsara | 1 | 0 | 1.01 |
| Huan Yu | 2 | 46 | 13.63 |
| Jose Ale Hejase | 3 | 0 | 1.01 |
| Wiren D. Becker | 4 | 2 | 1.54 |
| madhavan swaminathan | 5 | 108 | 24.63 |