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Published November 1, 2005 | public
Journal Article Open

Data-dependent jitter in serial communications


We present a method for predicting data-dependent jitter (DDJ) introduced by a general linear time-invariant LTI system based on the system's unit step response. We express the exact DDJ of a first-order system and verify the validity of the solution experimentally. We then propose a perturbation technique to generalize the analytical expression for DDJ. We highlight the significance of the unit step response in characterizing DDJ and emphasize that bandwidth is not a complete measure for predicting DDJ. We separate the individual jitter contributions of prior bits and use the result to predict the DDJ of a general LTI system. In particular, we identify a dominant prior bit that signifies the well-known distribution of deterministic jitter, the two impulse functions. We also show a jitter minimization property of high-order LTI systems. We verify our generalized analytical expression of DDJ for several real systems including an integrated CMOS 10-Gb/s trans-impedance amplifier by comparing the theory and measurement results. The theory predicts the jitter with as low as only 7.5% error.

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"©2005 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE." Manuscript received April 1, 2005; revised July 7, 2005. This work was supported in part by the National Science Foundation and by the Lee Center for Advanced Networking, California Institute of Technology. The authors acknowledge Prof. H. Hashemi, University of Southern California, Los Angeles, A. Farajidana, California Institute of Technology, Pasadena, and M. Sharif, California Institute of Technology, for valuable discussions. The authors also thank A. Komijani and A. Natarajan, both of the California Institute of Technology, for providing feedback on this paper's manuscript.


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August 22, 2023
August 22, 2023