Originally posted Dec 20, 2013
The power of engineering analysis and creativity
We’ve been fighting the good fight for better measurements all year long, and now the holiday season—perhaps with a work break!—is upon us. It’s a good time to take a slightly wider view of the challenges and opportunities that face us, with an eye toward improvements in the new year.
As engineers, we want to do more than just make the best of limited choices. We seek deeper understanding and chances to add our own insight and skill, aiming to actually improve the available choices. Let me illustrate the difference with a couple of diagrams.
First, let’s take a look at what some call the “designer’s holy triangle.”
This classic triad expresses the tension and interaction between three factors characterizing many projects, products, or problem solutions. (Image from Wikimedia Commons)
The traditional interpretation of this figure is as a Venn diagram with no central overlap and thus a forced choice constrained to any two of the three attributes. It’s a triangle that doesn’t seem too “holy” to me, and I’d argue it may be a perspective-limiting view. It’s useful as a reality check and a reminder to be realistic; however, for me (and maybe you) it fails to inspire innovative solutions.
For a few years I’ve been thinking instead along the lines of the alternative triad below.
This alternative triad uses information rather than performance as a third element in the measurement process. It symbolizes how measurements may be improved by treating information as an input to the measurement process and not only an output.
I may have made this up myself, as I can’t find earlier examples. However, if you can shed any light, please do so in the comments. I believe I started with the well-known example of cost-time-performance tradeoffs and altered one part to better reflect what I’ve learned.
What exactly have I learned? And what’s different about this triangle? Mainly it’s the general concept of added information as applied to the tradeoffs in making measurements.
A straightforward example is a traditional spectrum measurement. Time is measurement time.Information received from the measurement process can be quantified in terms of accuracy, dynamic range, signal/noise ratio, frequency resolution, measurement variance, and so on. Costcontains many items: equipment cost, the cost to make an individual measurement, the cost to measure a DUT, or perhaps the cost of writing the code needed to produce the required result.
Thus a typical approach to equate information with performance and then to improve performance in one of several ways:
- Improve signal/noise ratio, accuracy, variance, and frequency resolution by using a narrower RBW and consequently longer measurement time.
- Improve measurement performance by using a more expensive analyzer with inherently better accuracy, dynamic range, sensitivity and (perhaps) measurement speed.
- Improve (i.e., lower) cost by sacrificing performance for a given measurement time or tolerating a longer measurement time for a given performance level.
These optimizing approaches, however, may miss a fundamental opportunity. If instead you view information as an input to the process rather than only an output, you open the door to an expanded and improved set of tradeoffs. It’s a way to take advantage of your knowledge, insight and creativity to expand the otherwise-constrained envelope of tradeoffs. Here are three examples:
- Improve measurement time by using segmented sweeps or list sweeps to measure only frequencies of interest.
- Improve signal/noise ratio and measurement variance by using coherent (time) averaging on repetitive signals (a powerful and under-appreciated technique and subject of a future post).
- Improve measurement cost by using a passive pre-filter or internal analyzer stage to remove known large signals in the measurement span. Removing large signals can allow use of lower input attenuation and reduce the required analyzer performance (such as sensitivity or dynamic range) and thus solution cost.
In each case, you’re adding information to the measurement process and improving the landscape of tradeoffs you face. Many times, these improvements can be obtained at little or no incremental cost. There are countless potential examples that share the approach of adding information to the process rather than seeing information only as a process output.
You’re probably already doing this instinctively. However, it can be useful to take a more deliberate approach to determining all the different types of information you can add to improve your choices. You can explicitly collect what you know about the measurement or device involved, including your priorities and measurement options. You might also ask yourself what measurement process outputs you can do without. Each of these factors can point to potential improvements.
Continuing the spectrum analysis example, your analyzers may also be adding information that improves measurement speed and accuracy. For example, some recent-generation analyzers with digital IF filters use precise data about the repeatable dynamics of these filters to sweep them from three to six times faster than equivalent analog filters while maintaining specified accuracy and resolution. Because the dynamic effects of sweeping are well-corrected, information is added to the measurement process to yield faster measurements with no extra costs.
Another example of adding information is the sweep speed enhancement technology recently added to Agilent X-Series signal analyzers such as the PXA. Sophisticated RBW filters are matched to the sweeping LO of the analyzer, enabling sweep speeds up to 50 times faster than before. And because the improvement involves enhanced information processing, it’s simply a software upgrade for some existing analyzers.
Like knowledge, information is power you can use to make things better.