LeCroy DDA 7 Zi series, Operator'S Manual

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Operator's Manual
WP700Zi-OM-E-RevA
426
Figure 5-41. Logarithm of the measured TIE histogram superimposed on the extrapolated curve (in
yellow). The extrapolation uses a quadratic curve fit to the histogram bins at the extremes.
A smaller data set is extrapolated in order to estimate the data for the larger sample size. The extrapolation of the
measured histogram of TIE values uses the random nature of jitter at the extremes of the histogram to extrapolate
the bins below the 10th percentile and above the 90th percentile. The central part of the distribution is dominated
by deterministic jitter, while the extremes are entirely random. The bins in these ranges behave as a normal
distribution as the jitter range is increased, that is, their populations fall as exp(-t2). Taking the logarithm of the
histogram makes this relationship quadratic, so the extrapolation is simply a quadratic curve fit to the extremes of
the log of the jitter histogram. The extrapolated histogram is used to compute the total jitter curve described above
and is normalized so that the sum of the populations of all of the bins is one. The integrals described above are
implemented by summing the extrapolated histogram bins.
Separating Rj and Dj – Two Methods
The total jitter curve is the basis for estimating the magnitude of Rj and Dj. Since the total jitter curve is derived
directly from the signal under test, its value is the most accurate representation of the jitter for a given bit error
rate. There are basically two ways of separating the random and deterministic jitter. The first method, which
models the growth of total jitter as BER is decreased, leads to the effective jitter parameters Rje and Dje. These
values are effective in the sense that they provide an equivalent total jitter model for low bit error rates. Starting
with the total jitter curve, the growth in the total jitter as a function of decreasing BER is plotted. The curve