As part of the successful strategy for manufacturing optical transceivers, the test efficiency
must constantly improve, volumes go up, and there's always pressure to reduce cost. Agilent
has many ways to help you. This video will talk about the eye-mask hit ratio technique
and the auto mask margin feature found on the 86100 DCA. Historically, one or more samples
violating the eye mask indicates noncompliance independent of how many waveform samples have
been acquired. By design, this process captures the worst case performance of the transmitter
over a given observation time. However, this can lead to inconsistent mass margins. As
one set of samples may capture an extreme outlier, another set of samples may not. Mask
margins can vary significantly from test to test. Also, the more samples that are taken,
the more likely extreme outliers will be observed. Mask margin values are then very dependent
on sample size. The following measurements illustrate how the traditional all or nothing
approach to establishing a mask margin percentage varies from test to test. On a sample size
of 100,000, the mask is aligned and the margin calculated so that there are no hits. Repeating
this test four times gives four different results. When the sample size changes to 500,000,
then 1 million, and finally to 5 million, you can see that the results are very dependent
on the sample size. The more samples that are collected, the smaller the margin percentage
becomes. This all or nothing approach produces inconsistent mask margins and is very dependent
on the sample size. Recent changes to testing standards for mask margin percentage have
recognized that this no fail sample allowed approach gives inconsistent results and has
the potential to make good transmitters look bad simply because an extreme outlier was
observed. Since about 2004, many new industry standards allowed very small but greater than
zero proportion of the total waveform samples to violate the eye mask. This is commonly
known as the eye-mask hit ratio technique. The most common hit ratio is to allow 1 in
every 20,000 samples to violate the mask. For this hit ratio, as long as the sample
size is significantly larger than 20,000, susceptibility to outliers are dramatically
reduced and the mask margin results are very repeatable. For example, with a sample size
of 100,000, the mask dimensions can be expanded until you to 5 hits are observed. If we continue
to acquire samples at 1 million samples, we can expand the mask until up to 50 hits are observed.
In the following measurements, the hit ratio technique is used to determine the
margin percentage. Again, the sample size of 100,000 is chosen and repeated three times.
Each time the test is run, the mask's aligned for a target hit ratio of 1 in 20,000. The
results are more consistent from test to test compared with the traditional approach.
Increasing the sample size to 500,000, 1 million, and then 5 million do not change the margin percentage
and give results that are independent of sample size. It is not susceptible to extreme outliers
like the traditional no hit method. It is important to note that the mask margin value
is independent of the number of samples acquired even with a very large data set as one mask
hit does not lead to noncompliance. The mask hit ratio process also provides an important
opportunity to improve test efficiency. Smaller sample sizes requiring less test time yield
very accurate results. Now let's talk about the auto mask margin function. Eye masks are
used extensively to determine compliance to industry standards. It is common to expand
the size of the mask to determine not only mask compliance but also by what margin compliance
is achieved. One common yet tedious and time consuming method of determining the correct
mask margin is to sequentially increase the mask dimensions until one or more waveform
samples violates the expanded mask. The mask dimension is reduced to the largest size possible
with no mask violations. The 86100 has a convenient function that quickly calculates the mask
margin automatically based on the user-defined hit ratio. With the calculated auto margin
button, a remote command, the maximum mask margin is determined in one step almost instantaneously.
Here we see how just pressing the calculate auto margin button does a job quickly and
easily. In summary, what do these techniques buy us? The eye mask hit ratio technique allowed
us to reduce the sample size from 1 million to 100,000 knocking off over 25 seconds per
test. Not only was the test time dramatically reduced, but the consistency and accuracy
of the data is greatly improved. The calculate auto margin feature saved us 12 seconds per
test compared to manually adjusting the mask dimensions for the correct mask margin. For
more information on these subjects and to find other ways that the 86100 DCA can reduce
your cost of test, talk to your Agilent sales representative.