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Grasping jitter separation

29 Nov 2013  | Brig Asay

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One of the most widely used terms amongst engineers who design digital data links is jitter. Whether you design a new board or validate an old board, you'll run have to confront jitter and its components. The problem is how to separate jitter into random and deterministic components. Knowing those components can uncover the sources of jitter.

Jitter is simply the "wiggle" or time variation of digital clock's edge that each transition makes. That difference in timing is called time interval error (TIE). I'll use that as the basis for this discussion.

In an ideal world, rise and fall times would be infinitely fast and every edge of a digital stream would consistently fall the exact same time away from the last. The end result would be no jitter, no wiggling, and no eyes diagrams that suffer from being closed or close to closed. Unfortunately it is not a perfect world, and what once was conceived as an infinitely fast edge, becomes slower as trade-offs occur as data rates increase.

A signal often must travel through inexpensive PCB material. That, combined with other factors, causes a signal to lose amplitude. Plus, a signal can couple with other signals, causing it to move from a perfectly timed edge to an edge that is now moving from the clock. That's the "wiggling" that figure 1 shows. Because of these real-world conditions, digital eye diagrams will close, making it harder for a receiver to distinguish between a logic 1 and a logic 0.

Figure 1: Jitter occurs because the time between edges in a digital data stream will vary.

Jitter separation lets you learn if the components of jitter are random or deterministic. That is, if they are caused by crosstalk, channel loss, or some other phenomenon. Separating jitter enables engineers to better understand the systematic problems of their devices and to quickly find solutions to adjust for any errors in them. The jitter-separation concept seems simple enough. In an ideal world, all jitter separation techniques would work the same and give exactly the same answers. All the tools are looking at the same jitter.

Unfortunately this is not always the case. In fact, "answers" for different jitter separations can vary widely. The problem has become prevalent enough, that compliance tools can ensure that all designers use standardised separation techniques. What seems like a simple problem that can solved fairly simply is complicated by the fact the separation tool's answers will vary by test-and-measurement vendor and instrument.

So how does an engineer know which answer is correct? This is where science meets art and the user must use the tools available to him (the science) to decide which answer best represents what he or she is debugging (the art).

Jitter separation challenges
One reason that answers vary from instrument to instrument is that there is often a limited amount of information available to do the separation. A reasonable analogy is that of solving a linear system of equations. It's well known you must have as many independent equations as there are unknowns. If there are too few equations, or if the equations aren't all linearly independent, then you can't get a unique answer. One way to get around this problem is to impose additional constraints or assumptions onto the system, which acts like adding more independent equations. This new system is then uniquely solvable. Solving for the unknown values of the different jitter components is a similar problem, and typically software packages must impose additional constraints, or models, in order to obtain a unique, repeatable answer.

The most fundamental separation is between components that are deterministic (deterministic jitter, or DJ), and those that are random (random jitter, or RJ). Sometimes these categories are defined as components that have bounded histograms and those that have unbounded histograms, but are still referred to as DJ and RJ. The concept of determining which jitter components are random in versus deterministic may seem simple, but the actual process is difficult. To separate jitter, an algorithm typically will need to not only separate random from deterministic jitter, but also identify classes of deterministic and random jitter. I'll limit my focus primarily to the separation of deterministic and random jitter.

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