Serial Data Analysis in M1™

"One thing that you may not be aware of - on the day we got M1 loaded and running on the scope, we learned more about our jitter components than in three months of testing using other instruments and approaches. There were clear periodic jitter components, and they could be easily tracked to their sources by matching jitter frequencies to chip operations and power fluctuations." - M1 Customer

Rj/Dj Capabilities / Rigor

RjDjCapabilitiesM1 includes statistical jitter decomposition (called SEEj) . SEEj has a number of important characteristics:

  • Error calibration – M1™ uses a neural network approach to calibrate errors out of the result across an enormous jitter space. SEEj has a typical error of less than 5% or .002UI (0.0005UI for Rj) across the vast majority (> 90%) of jitter space (see below for specifics).
  • Calibration domain – The error calibration of SEEj has been performed for magnitudes of each of the types of jitter that are larger than the user is likely to be working with. Most other approaches have validated across a small number of cases closely clustered in one very small region of jitter space.
  • Instrument noise calibration – M1 can calibrate instrument noise effects out of the measurement results.
  • ConvergenceConvergence/divergence indication – While there's barely awareness in the marketplace that convergence is an issue with all statistical jitter decomposition methods, SEEj will tell you when it has converged or diverged.  Convergence/divergence can occur when non-stationarity occurs in the signal behavior.
  • Specify typical error across full operating range – Anyone can download an active datasheet called the Data-jitter Performance Explorer utility (DPE) from ASA’s website. DPE will describe for you the typical performance of M1 Waveform Tools™ for the kinds of jitter you are working with.
  • Correlation – M1 is the only tool that provides you with one calibrated/validated RjDj algorithm (one answer) for any of your scope platforms. It's impossible to get results that can be compared across scopes with any other product.

We have a video showing SEEj's performance.  Watch Video

Eye-diagram / Mask Tools

MaskMargin M1 includes eye-diagram/mask tools. You can view data in an eye diagram with mask testing capability. The mask tool also computes margin information to let you know how close your signal is coming to a violation of the mask.