The History of Jitter (Part IV)

Figure 1: An example of a time-interval error track

In the previous installment in this series on the history of jitter, we'd reached the cusp of the new millennium. The in-vogue methodology for jitter analysis of the day was using edge crossing-point data in the form of a histogram and fitting Gaussian functions to the tails of the plot. But tail fitting, as we well know, isn't for the faint of heart. How would test methodologies move forward to surmount that hurdle?

The History of Jitter (Part III)

Figure 1: Latching a signal at the outermost of the blue

hash marks results in a BER of 10^-3, while latching it

at the innermost hash marks yields a BER of 10^-12

If you've been keeping track of our history of jitter, we left off in Part II in the late 1990s, by which time bit-error rates (BER) had become a predominant statistic for quantifying jitter. That was subsequently refined into thinking in terms of BER as a function of jitter.

The History of Jitter (Part II)

Figure 1: An example of using histograms to plot
the statistical distribution of edge arrival times

Resuming our review of the history of jitter and the evolving response to it, we'd arrived at the late 1990s, when more sophisticated analysis methods were necessary to get a good handle on jitter. In particular, statistical analysis came onto the scene. Statistics are a great tool for analyzing phenomena such as jitter that change more as you look at them harder.

The Components of Total Jitter

Figure 1: An overview of the jitter hierarchy,
or "jitter tree," showing the various elements
that make up total jitter

In an earlier post, we began looking at the topic of jitter, a topic of keen interest to anyone working with high-speed serial communications or the components of such a system, including transmitters, receivers, and data channels. To gain an understanding of jitter, an important first step is getting to know a little about the various categories that comprise total jitter.

Check Constellation Diagrams for Digital Data Integrity

Data-communication systems that rely on quadrature signal generation to phase-encode data can run into a number of signal-corrupting snags. These can include things like Gaussian noise, non-coherent single-frequency interference, phase noise, and attenuation in the channel and/or receiver, to name a few. But did you know that you can use your digital oscilloscope to diagnose problems like these?