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Weapons Of Noise Detection
Noise is a perennial problem in electronic circuits, but the design challenges it presents change year to year. Here's a look at some new plans of attack.
Date Posted: February 01, 2007 12:00 AM
EXTRACTING BER
The essential element for BER calculations is time interval error (TIE), the difference between
data edges and edges of the recovered
clock. Measuring the TIE histogram lets you determine the likelihood of a jitter
value exceeding a given maximum.
To obtain BER, the data sample's TIEs
are presented as a histogram of TIE value versus the number of occurrences of
that value. The objective is to determine
the probability that a data transition
occurs simultaneously with the sampling
of data. The histogram yields the conditional probability of a data edge occurring at a given time within a bit period, given that the data is sampled at that
time. A bathtub curve shows this relationship graphically ().
There's a catch here. Systems typically
specify bit error rates in the 10–12 range. It takes a lot of edges to measure events
with probabilities down to one in
10–12—too many to acquire and store on
a contemporary instrument. That necessitates extrapolation of the histogram
from a smaller set of measurements.
EXTRACTING BER
The essential element for BER calculations is time interval error (TIE), the difference between
data edges and edges of the recovered
clock. Measuring the TIE histogram lets you determine the likelihood of a jitter
value exceeding a given maximum.
To obtain BER, the data sample's TIEs
are presented as a histogram of TIE value versus the number of occurrences of
that value. The objective is to determine
the probability that a data transition
occurs simultaneously with the sampling
of data. The histogram yields the conditional probability of a data edge occurring at a given time within a bit period, given that the data is sampled at that
time. A bathtub curve shows this relationship graphically ().
There's a catch here. Systems typically
specify bit error rates in the 10–12 range. It takes a lot of edges to measure events
with probabilities down to one in
10–12—too many to acquire and store on
a contemporary instrument. That necessitates extrapolation of the histogram
from a smaller set of measurements.