Testing at MultiGbps Rates

302 0740-7475/04/$20.00 © 2004 IEEE Copublished by the IEEE CS and the IEEE CASS IEEE Design & Test of Computers THE RECENT DEPLOYMENT of gigabit-per-second (Gbps) serial I/O interconnects aims at overcoming data transfer bottlenecks resulting from the limited ability to increase chip pin counts in parallel bus architectures. Gigabit-per-second data rates in today’s asynchronous I/O interconnects introduce new signal integrity issues. The traditional measure of a communication link’s performance has been its associated bit error rate (BER), which is the ratio of the number of bits received in error to the total number of bits transmitted. When data rates increase, jitter magnitude and signal amplitude noise must decrease to maintain the same BER. As data rates exceed 1 Gbps, a slight increase in jitter or amplitude noise has a far greater effect on the BER. Specifying jitter and noise simply through peak-to-peak or root-mean-square (RMS) values is inadequate and insufficiently accurate.1 Peak-to-peak value is sample-size dependent and is inaccurate in the presence of random noise because, by definition, random noise is unbounded. A peak-to-peak random jitter (RJ) measurement is ambiguous without an established boundary condition. Conversely, describing total jitter (TJ) simply by an RMS value is inaccurate in the presence of nonrandom noise. This is because a deterministic jitter (DJ) probability density function (PDF) can take any form and might have little correlation with a DJ RMS value. An RMS value is only valid for describing a Gaussian distribution such as RJ. Moreover, a simple RMS or peak-to-peak number cannot sufficiently describe the characteristics of different types of jitter. Overall, we need more-accurate jitter and noise models to allow better predictions and characterizations of devices subject to jitter effects. One difficulty with jitter analysis is identifying the different jitter components contributing to TJ. Deconvolution algorithms such as the TailFit algorithm can separate TJ into its random and deterministic components.2-4 Another method uses a real-time sampling oscilloscope to capture the timing information of each edge transition in a data stream. Proper techniques can then extract jitter parameters directly from the acquired data set. We can further decompose DJ to model the different impacts of its subcomponents on link performance. This article presents models that allow for such further jitter decomposition.