Posts Tagged metrology
Improvements in machining precision, testing and simulation make the use of aspheres available to improve optical system performance.
Most lenses are spherical, in that each curved surface is some part of a sphere (usually a big radius compared to the lens glass diameter). Lately we’ve been working on some systems that require the use of lenses that have an ‘aspheric’ curve. These are more unusual, but if you can solve a problem that is otherwise unsolvable, ‘unusual’ is a good answer. Ok, maybe since I’m the electronics guy, I’m impressed with the precision of these optics and their measurement – I think you’ll be too, when you look into it.
We’ve found some references about designing and testing these asphere elements. Start with the article by Jay Kumler, and then read the other two about some fancy gear to test these aspheres.
Jay Kumler, Designing and Specifying Aspheres for Manufacturability, by Jay Kumler of Jenoptik-Inc
Interferometric Measurement of Rotationally Symmetric Aspheric Surfaces, by Michael Kuechel of Zygo
Subaperture stitching interferometry of high-departure aspheres by incorporating configurable null optics, by Andrew Kulawiec, Markus Bauer, Gary DeVries, Jon Fleig, Greg Forbes,
Dragisha Miladinovic, Paul Murphy of QED Technologies.
The measurement of light is complicated by a variety of units and concepts that are not used in other fields. For example, the ‘light level’ could be measured in units appropriate to the sensitivity of our eyes (lux), or by the power level (Watts) – but that’s confounded by the wavelength (nano-meters, but sometimes Angstroms) and you need to think in steradians, etendue must be conserved … you get the idea.
We’ve written about some of these issues in earlier posts, but this is one big, complete reference manual – a kind of ‘everything you wanted to know about light, but were afraid to ask’ – and it’s from NIST. They call it a ‘Self-Study Manual’ and it’s a clearly written tutorial on optical radiometry.
And it’s a free download. Enjoy. The test is Tuesday.
The official title is The Self-Study Manual on Optical Radiation Measurements, edited by Fred Nicodemus
We notice the assertion that A/D converter quantization noise is equal to ADU/SQRT(12), where ADU is the quantization unit or LSB. We saw this in Hobbs’ excellent book Building Electro-Optical Systems, Making It All Work.
So, we decided to derive this. Took us a while to get the ‘trick’, and to remember how to perform calculus, to get that pesky root-mean-squared function.
Think of the quatization error as a sawtooth function that repeats. Then work out the RMS noise of that sawtooth wave (it happens to be the same as a triangle wave). And, yes, it does work out to that value.
Now the next part is Hobbs’ assertion that this quantization noise is not a Gaussian distribution. Get to work.
For both a clinical test microscope, and a home theater HDTV projection display, the light from the source must be quite uniform.
To test some non-imaging illumination optics, we set up our digital camera, and wrestled with the RAW data files from the camera. Most cameras have some ability to ‘see’ infra-red, so we can also test the pattern from the remote control output, or for other purposes.
Careful consideration of all the elements of a system’s design can lead you to some very improved performance. Imagine improving a benchtop NMR system by making it 60 times lighter (120kg to 2kg), 40 times smaller, and yet 60 times more sensitive!
This article, from the IEEE Journal of Solid State Circuits (Vol. 44, No. 5, May 2009), shows an excellent example of how this occurs.
link to IEEE abstract of ‘CMOS RF Biosensor Utilizing Nuclear Magnetic Resonance’ by Sun, Liu, Lee, Weissleder, and Ham
I recommend reading the article – it’s very well written, it describes how NMR works, and it details their systems approach to their improved design. Much can be learned here. The use of a resonant circuit for gain (they call it ‘passive amplification’) is detailed in Figure 8 of the article. (It reminded me of the old ‘regenerative’ type radio receivers, back when a vacuum tube had a power gain of about 12).
Put another way, this article shows that the ‘building block’ approach, when off-the-shelf 50 Ohm compatible RF modules are used, makes it easy to build a system that works – but that it leaves out some great performance improvements that are only possible when you analyze the basic system operation and theory. The design improves when you ask questions like ‘why 50 Ohms’ or ‘where does that noise originate and how can I maximize the signal’ and ‘how can I make this work with a much smaller and lighter magnet’? The article also answers ‘now that I can use a small magnet, can I make a custom CMOS IC that performs the RF detection, and seriously reduce system cost and size’?
Buying as much stuff off the shelf is not bad – it’s a great way to get a proof of principle working FAST, and it demonstrates that an idea or technique can work. Nothing says ‘success’ like working hardware – it allows the investors, managers and engineers to breathe easier.
But that extra performance gain from really digging into the details of how things work can pay off – in this case, it changes a benchtop lab instrument into a battery operated portable clinical test platform – this opens new opportunities and situations where this NMR system can be utilized.
It’s easy to confuse the units of LED light output. Steradians, luminous intensity, etc.
Here’s a link to an application note that explains these well, written by C. Richard Duda of UDT (now part of OSI Inc.). Apertures, intentional and otherwise, are discussed, along with typical test configurations.
Please tell us if the link gets broken!
Lately we’ve been able to use our digital camera to perform some nice measurements, through the help of a program called ImageJ.
It’s free, was developed at NIH, is open-source, it has a ton of features and plug-ins, and you can write scripting macros, etc etc. It was developed so that the scientific community would have an open standard to process images. (Without an open standard for image number crunching, there’s no good way to independently reproduce an experiment that makes heavy use of images and image processing.)
You can read about it here at Wikipedia:
It’s available here:
We were turned onto this image analysis program by a couple of our clients. We recommend it. Today the cool thing was to separate the RGB channels, and allow us to ‘see’ an IR LED without being confused by the camera’s ‘grey scale’ clipping algorithm. Very nice.
This tech note was motivated by the question – how does the response of our eyes
differ from the response of a CCD camera sensor.
Using the data of a particular Hammamatsu CCD camera as an example,
we compared how silicon ‘sees’ to the photopic eye response
and compared both to a Planck black-body curve of a light at a particular
We don’t know what those lumps are in that CCD response curve – maybe some
strange reflection interference??
If you know – tell us!
Color temperature is based upon the idea of a Planck black-body radiator.
Here’s a Tech Note that shows how our eyes respond to the Planck Black-Body radiator.
For a lamp filament at a certain ‘color temperature’ there’s a curve of how our eyes
respond to the lamp. Pete put this into a MathCAD model, and there’s a pdf here
that shows off a few nice graphs.