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Getting Started with Jitter: The Best Jitter Glossary I’ve Ever Written

Blog Post created by Daniel_Bogdanoff Employee on Sep 1, 2016

(Also the only Jitter Glossary I’ve ever written)

 

Does jitter have you all shook up? This quick overview should help ease those jitters (puns intended, sorry). Learning this list of key terms will give you the confidence you need to start tackling the jitter bugs in your design.

Buckle up! Here’s the exhaustive list of terms you need to know:

Jitter: Essentially a measurement of where your signal’s edges actually are compared to where you want them to be. If your edges are too far off, bad things happen. Really, really bad things. Or sometimes just marginally bad things. Bit errors, timing errors, the works. You can hope it’s just marginally bad, or you can use the right equipment and know for sure.

Jitterbug: An old-school dance.  Note: you don’t actually need to learn this to talk intelligently about jitter. It will probably just have the opposite effect.

Probability Distribution Function (PDF): Remember your statistics class in college? Me neither.  But, you’ll probably remember the term “bell curve” because that affected your grades. A bell curve is just one type of probability distribution function and is simply another way to describe a “normal” or “Gaussian” distribution. A PDF is simply a chart of possible values based on their likelihood of occurring. The x-axis represents a possible value (sometimes marked by standard deviations away from zero) and the y-axis represents the possibility of that value occurring. We use PDFs to visualize and interpret jitter measurements.

Gaussian Distribution: or “normal distribution,” it’s unbounded and continuous. That’s a fancy way of saying that basically any value is possible. But the farther away from the middle of the PDF you go, the less likely it is that that value will occur.

Random Noise: Also “random jitter,” is 100% random and has a Gaussian distribution. It’s caused by physics (yay science!) and has three components: thermal noise, shot noise (or Poisson noise if you’re a math major), and pink noise. If you want to geek out more on this, just look it up on Wikipedia. So, you expected your clock to have a 60 ns period? Well, because you can’t get rid of random noise (earplugs don’t help) you could end up with a rogue 500 ns period every once and a while.  But you probably won’t unless you have a few years to run the test. But you could. This is why we like to measure and analyze jitter! You can analyze jitter on your oscilloscope using histograms.

Histograms: A tool that visually describes how a signal varies over time.  Figure 1 shows a jitter histogram on the Keysight InfiniiVision 6000 X-Series oscilloscope.  Because it looks like a bell, you can say “That’s Gaussian!” (and get smarty-pants points from your cubicle-mate). Because there’s only one peak on the histogram, you can say “Psh, it’s only random jitter so there’s nothing we can do about it!” (and get double bonus smarty-pants points from your cubicle-mate). But, look at Figure 2.  That looks a little bit scarier. Because the histogram has two peaks it means that there’s “deterministic” jitter.

Figure 1: A histogram of a signal that just has random noise

 

Figure 2: a “Bimodal” histogram shows that there’s deterministic jitter

 

Deterministic Jitter (DJ): It’s not random.  It’s usually bounded, so it can’t go off to infinity even if it wants to. This is when it starts to get scary, because deterministic jitter is caused by system phenomena. Notice that there are two peaks with a random distribution around each of those peaks. Random and deterministic jitter are both in play here.  Deterministic jitter can be broken down into a few sub-categories:

Bounded Uncorrelated Jitter (BUJ): Gives engineers night terrors.  It’s bounded but isn’t really related to anything in that same system.  It could be something like cross talk or just interference from the wall.  (The wall? Yeah, there’s noise everywhere. Check out this awesome video: https://youtu.be/SJefUNAJZNA)

Data Dependent Jitter (DDJ): Can be one of two things.  The first is “duty cycle distortion” (DCD). This is when one bit value tends to have a longer period than the other (like when you can get one kid out of bed way easier than the other). The second is “Inter symbol interference” (ISI). This is caused by long strings of a single bit value. This is sort of like when you’ve been sitting too long in a weird position and one leg doesn’t work right when you get up and try to walk.

Periodic Jitter: can be correlated or uncorrelated, but is always periodic.  This means it’s pretty easy to identify like we’ve done in figure 2. Take your jitter measurement, and plot a trend of the measurement.  Then measure the frequency of the trend, and that will point you directly to the culprit (probably Professor Plum in the library with the candlestick).

“Whoa Daniel, that was too much at once. Remind me again how they all relate to each other?” I’m glad you asked; here’s a nice family tree (Figure 3).

Figure 3: Jitter and its components

Jitter Measurements: This probably doesn’t need defining; I just needed a segue. Ok, fine. Jitter measurements are measurements you make to get a better understanding of the jitter you’re dealing with. Here are a few jitter measurements you might care to make:

Time Interval Error: The mother of all jitter measurements. It’s usually measured as an RMS value and describes the difference between the ideal clock period and the actual clock period. Like I said, it’s the mother of all jitter measurements. You might think this is all you need to measure, but there are some other helpful measurements out there.

Period Jitter: Is usually measured as a peak-to-peak value, and yields the difference between the longest and shortest clock periods over a specified amount of time.

Cycle-to-Cycle Jitter: Is also usually measured as a peak-to-peak value, and is the maximum difference between adjacent clock periods. The longer you measure this, the larger it’ll get, so if you want to characterize this for posterity, use a set number of cycles that you measure. Basically, period jitter tells you how bad it is in the long run, and cycle-to-cycle jitter tells you how fast you are going to get there.

All of this should be enough to get you started if you want to measure (or just discuss) jitter. If your interest was piqued or you felt cheated because I didn’t talk about eye diagrams, clock recovery, or phase lock loops, check out this app note on Jitter Analysis written by Johnnie Hancock. It’s really good, but doesn’t have as many jokes. Although fewer jokes are probably a welcome relief by this point. You can also learn more about jitter  and jitter measurement tools at Keysight.com.

Thanks for reading! If I didn’t coax you into clicking that link (who reads app notes, right?) check out our YouTube channel.

Also, check out some of our other posts! We’ve talked about probing techniques with Kenny Johnson: Splurge, get an active probe and Measure ripple and noise on power supply voltage railsconfusion in Australia and normal triggering with Johnnie Hancock; signal modulation and DIY oscilloscope Bode Plots with Mike Hoffman and  measuring system bandwidth and measuring oscilloscope and probe bandwidth with Taku Furuta.

And of course, Melissa Spencer’s oscilloscope zombie apocalypse survival guide.

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