Teledyne LeCroy SDA III-CompleteLinQ - Manual (Page 56)

SDAIII-CompleteLinQ Software
2. Subtracting the noiseless version of the pattern (iterated as necessary), from the original waveform
returns a noise waveform without data-dependent effects that cause relatively large variations in
amplitude from unit interval to unit interval. When retaining only the values of this waveform at a
selected position in each unit interval (the Sampling Phase), a waveform that contains the noise seen
by a receiver that samples or strobes each unit interval is created. This is the RnBUnTrack waveform,
which contains random and bounded uncorrelated noise, and which has data dependent effects
(which are highly deterministic) removed. The data can also be histogrammed to form the RnBUnHist
histogram. RUnBUnTrack and RnBUnHist can be used to understand sources of noise that can cause
bit errors, and can be enabled for display via the Noise Track and Noise Histogram dialogs. These
results are also the source data for the calculation of the noise measurements that are enabled in the
Noise Measurements dialog. The next steps in the measurement of these parameters follow closely
the algorithm that is used to measure and breakdown horizontal jitter.
3. The selection for the model used to calculate the jitter results is used in the noise measurements. In
the dual-Dirac spectral methods, the FFT of the RnBUnTrack is taken to create the RnBUnSpect spec-
trum. Peaks in this spectrum above a calculated noise floor are associated with periodic noise, and
are removed and used to calculate a value for Pn. The remaining spectrum is used to form a raw
value of Rn by integrating (square root of the sum of the magnitudes squared). In the Rj Direct
method, this raw value (RnRaw) becomes the value of Rn itself. In the Rj+Dj CDF Fit method, the raw
value is used as a sigma value for fitting the tails of the RnBUn histogram.
4. The distribution of data dependent “noise” that was removed in the time domain by subtracting to
form the RnBUn trace is convolved with RnBUnHist to form an overall probability density function
(PDF). This is done independently for both the high and low levels, resulting in two PDFs .Integrating
these functions gives cumulative probability density functions, or CDFs for high and low levels. The
inner half of these distributions are taken and put together in order to form a CDF that provides infor-
mation about the total noise that contributes to eye closure and bit errors.
5. In the Rj+Dj CDF Fit method, the CDF is then fit to the dual-Dirac equation Tn = alpha(BER) Rn + Dn,
using 4 values of BER about the user’s selected BER value to return values for Rn and Dn. In the Rj
Direct method, this fit is performed with the constraint that Rn = RnRaw.
6. When using the NQ-scale method, the tails of RnBUnHist are fitted to two Gaussian distributions that
can have both different sigmas and different populations. The data dependent noise is convolved in
as described above, and Tn, Rn and Dn is determined as described above for the Rj+Dj CDF Fit
method.
7. Pn is determined by taking the peaks found in step 3 above, and taking the iFFt. The peak-to-peak
amplitude resulting waveform is the periodic noise.
8. ISIn is determined by analyzing the distribution of voltages at the sampling phase, for the high and
low levels separately.
9. Tn is used to calculate an estimate of the eye opening at BER. The measurement Eye Height @ BER
(EH(BER)) is the Eye Amplitude-Tn.
10. Eye width @ BER (EW(BER)) is calculated by subtracting Tj from the unit interval width.
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