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Calibration Data: How to Use What You Know

Blog Post created by benz on Oct 13, 2016

Originally posted Aug 24, 2015

 

How well do you know what you know, and how well do you need to know it anyway?

We choose, purchase and maintain test equipment because we want answers: how big, how pure, how fast, how wide, and so on. The answers are essential to our success in design and manufacturing, but they come at a cost. Therefore, we want to make the most of them, and I have written previously about improving measurements by adding information.

There are many ways to add information, including time averaging of repetitive signals and subtracting known noise power from a measurement. I’ve recently discussed using the information from periodic calibration of individual instruments as a way to get insight into the likely—as opposed to the specified—accuracy for actual measurements. If you’re paying for calibration and the information gathered during the process, it’s wise to make the most of it. Here’s an example, from calibration, of the measured frequency response of an individual PXA signal analyzer:

Frequency response of one PXA signal analyzer as measured during periodic calibration. The measured performance and measurement uncertainty are shown in comparison to the warranted specification value.

Frequency response of one PXA signal analyzer as measured during periodic calibration. The measured performance and measurement uncertainty are shown in comparison to the warranted specification value.

In the cal lab, this analyzer is performing much better than its hard specs, even after accounting for measurement uncertainty. That’s not surprising, given that the specs must account for environmental conditions, unit-to-unit variation, drift, and our own measurement uncertainty.

Of course, if you’re using this particular instrument for a similar measurement in similar conditions, it’s logical to expect that flatness will be closer to the measured ±0.1 dB than to the specified ±0.35 dB. How can we take advantage of this extra performance?

Not surprisingly, the answer depends on a number of factors, many specific to your situation. I’ll offer a few thoughts and guidelines here, gathered from experts at Keysight.

Begin by understanding your measurement goals and responsibilities. You may be looking for a best estimate rather than a traceable result to use in the design phase, knowing the ultimate performance will be verified later by other equipment or methods. In this situation, the minimum and maximum uncertainty values shown above (dotted red lines) might lead you to comfortably expect ±0.15 dB flatness.

On the other hand, you may be dealing with the requirements and guidelines in standards documents such as ISO17025, ANSI Z540.3 and ILAC G8. While calibration results are relevant, relying on them is more complicated than using the warranted specs. The calibration results apply only to a specific instrument and measurement conditions, so equivalent instruments can’t be freely swapped. In addition, you must also explicitly account for measurement conditions rather than relying on the estimates of stability and other factors that are embedded in Keysight’s spec margins.

These factors don’t rule out using calibration results in calculating total measurement uncertainty and, in some cases, it may be the most practical way to achieve the lowest levels of measurement uncertainty—but using them can complicate how you verify and maintain test systems. You’ll want to identify the assumptions inherent in your methods and have a process to verify them, to avoid insidious problems.

Measurement uncertainty is not the only element of test plan design, and calibration results can help in other ways. Consider the measured and specified values for displayed average noise level (DANL) in the following graph.

The actual and specified average noise levels of a PXA signal analyzer are shown over a range of 3.6 to 50 GHz. Where measurement speed is a consideration, the actual DANL may be a better guide than the specifications in optimizing settings such as resolution bandwidth.

The actual and specified average noise levels of a PXA signal analyzer are shown over a range of 3.6 to 50 GHz. Where measurement speed is a consideration, the actual DANL may be a better guide than the specifications in optimizing settings such as resolution bandwidth.

In this example the actual DANL is 5 to 10 dB better than specified, and this has implications for the test engineer. When measuring low-level signals or noise, it’s necessary to select an RBW narrow enough to reduce the noise contributed by the signal analyzer. Narrow RBWs can lead to slow measurements, so there’s a real benefit to understanding the actual noise level as a way to use RBWs that are as wide—and therefore as fast—as possible.

When your measurements and test plans are especially demanding, it makes sense to use all the information available. Guardbanding is part of a Keysight calibration service that includes the most complete set of calibration results such as those above. For easy access to calibration results without tracking paper through your organization, you can use the free Infoline service that comes with all calibrations.

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