microstructural analysis

Celebrating the 50th Birthday of Microanalysis

Sia Afshari, Global Marketing Manager, EDAX

The Microscopy & Microanalysis (M&M) Conference is celebrating 50 years of microanalysis at this year’s meeting in St. Louis next week. There is an entire session (A18.3) dedicated to the 50-year anniversary and the historical background of microanalysis from several different perspectives.

My colleague, Dr. Patrick Camus will be presenting the history of EDAX in his presentation, “More than 50 Years of Influence on Microanalysis” at this session and this is a must see for everyone who is at all interested in the historical development and advances in microanalysis!

Looking back at some of the images in the field of microscopy and seeing how far we have come from static spectrum collection to the standardless quantification of complex materials makes me wonder (in a good way!), about the future and especially about the technical possibilities in microanalysis.

Figure 1. Nuclear Diodes EDAX System Interfaced to Cambridge Stereoscan Scanning Electron Microscope – circa 1968

Pat will be describing the evolution of the company from Nuclear Diodes (1962) through EDAX International (1972) and purchase by Philips (1974) to acquisition by Ametek in 2001. Many accomplished microanalysts have been part of the EDAX team along the journey and have contributed enormously to the technical development of microanalysis. The advancements which have been made to date and those which will continue in the future would have not been possible without the dedication and hard work of all these pioneers in this field.

Figure 2. EDAX Element Silicon Drift Detector on a Scanning Electron Microscope – 2017.

At EDAX, which happens to be older than 50 years, I have been honored to meet some of the pioneers of microanalysis. I extend my gratitude to all those whose work has made it possible for us to enjoy the level of sophistication achieved today and we hope to continue their innovative tradition!

Please click here for more information on EDAX at M&M 2017.

XRF: Old Tech Adapting to New Times

Andrew Lee, Senior Applications Engineer, EDAX

X-rays were only discovered by Wilhelm Roentgen in 1895, but by the early 1900’s, research into X-rays was so prolific that half the Nobel Prizes in physics between 1914 to 1924 were awarded in this relatively new field. These discoveries set the stage for 1925, when the first sample was irradiated with X-rays. We’ve immortalized these early founders by naming formulas and coefficients after them. Names like Roentgen and Moseley seem to harken back to a completely different era of science. But here we are today a century later, still using and teaching those very same principles and formulas when we talk about XRF. This is because the underlying physics has not really changed much, and yet, XRF remains as relevant today as it ever was. You can’t say that for something like telephone technology.

XRF has traditionally been used for bulk elemental analysis, associated with large collimators, and pressed pellet samples. For many decades, these commercial units were not the most sophisticated instruments (although Apollo 15 and 16 in 1971 and 1972 included bulk XRF units). Modern hardware and software innovations to the core technique have allowed XRF to adapt to its surroundings in a way, becoming a useful instrument in many applications where XRF previously had little to offer. Micro-XRF was born this way, combining the original principles with newer hardware and software advancements. In fact, micro-XRF is included on the new NASA rover, scheduled for launch to Mars in 2020.

Biological/life sciences is one of those fields where possibilities are now opening as XRF technology progresses. A great example that comes to mind for both professional and personal reasons is the study of neurodegenerative diseases. Many such diseases, such as Parkinson’s, Alzheimer’s, and amyotrophic lateral sclerosis (ALS), exhibit an imbalance in metal ions such as Cu, Fe, and Zn in the human body. While healthy cells maintain “metal homeostasis”, individuals with these neurodegenerative diseases cannot properly regulate, which leads to toxic reactive oxygen species. For example, reduced Fe and Cu levels can catalyze the production of hydroxyl radicals which lead to damaged DNA and cell death. Imaging the distribution of biological metals in non-homogenized tissue samples is critical in understanding the role of these metals, and hopefully finding a cure. The common language between the people who studied physics versus the people who studied brain diseases? Trace metal distribution!

A few years ago, I had the opportunity to analyze a few slices of diseased human tissue in the EDAX Orbis micro-XRF (Figure 1 and 2), working towards proving this concept. Although the results were not conclusive either way, it was still very interesting to be able to detect and see the distribution of trace Cu near the bottom edge of the tissue sample. XRF provided unique advantages to the analysis process, and provided the necessary elemental sensitivity while maintaining high spatial resolution. This potential has since been recognized by other life science applications, such as mapping nutrient intake in plant leaves or seed coatings.

Figure 1. Stitched montage video image of the diseased human tissue slice, with mapped area highlighted in red. Total sample width ~25 mm.

Figure 1. Stitched montage video image of the diseased human tissue slice, with mapped area highlighted in red. Total sample width ~25 mm.

Figure 2. Overlaid element maps: Potassium{K(K) in green} and Copper {Cu(K) in yellow} from mapped area in Figure 1, showing a clear area of higher Cu concentration. Total mapped width ~7.6 mm.

Figure 2. Overlaid element maps: Potassium{K(K) in green} and Copper {Cu(K) in yellow} from mapped area in Figure 1, showing a clear area of higher Cu concentration. Total mapped width ~7.6 mm.

Sometimes, the application may not be obvious, or it may seem completely unrelated. But with a little digging, common ground can be found between the analysis goal and what the instrument can do. And if the technology continues to develop, there seems to be no limit to where XRF can be applied, whether it be outwards into space, or inwards into the human biology.

Journey of Learning: Teaching Yourself the Power of EBSD

Shawn Wallace – Applications Engineer, EDAX

The joy of learning is sadly something that many people forget about and some never really feel. One of the things I like to keep in mind when I am learning something new is that learning is usually not a eureka moment, but a process of combining concepts and ideas already known, to reach a new solution or idea. The reason I was thinking about learning as a process is because recently I found myself forgetting that. A customer sample came in that was, for EBSD, hard in every way: Difficult crystal system/orientation, sample prep issues, poor diffractor. With all those factors, the sample was putting up a fight and winning, mainly because I allowed it to. I had tried all my normal tricks and was not making much headway. I knew the sample was analyzable, but I was not treating the process as a personal learning opportunity, instead I was treating it as a fight that I had to win. I was quickly bouncing from potential solution to potential solution and trying them, without spending much time on thinking what would be best to try and how to tackle the problem as a problem, and not a challenge. I didn’t even frame it that way in my own head until a week later when I was visiting a customer site to do some training.

During the training session, a sample came up with a very different set of problems, but still ones that were stymieing us as we sat at the microscope. I found the user resorting to what I had done previously; just try this and see if it works, without thinking about what the best course of action was. As I sat there, I told them to take a step back and evaluate what the issue was and how we could use our knowledge of all the functions available to us in the TEAM™ software and/or our microscope to find a solution. We sat and talked about the issue and the user was able to come up with a game plan and try some things that would help him reach a solution or gain additional knowledge, aka LEARN. I learned that day – that I sometimes need to treat myself the way I would treat a user. There will always be cases when I don’t know the answer and I have to teach myself the solution.

That leads us to an open question. How do you learn EBSD as you go along? With that in mind, here at EDAX we are going to start a new series of blog posts to discuss the basics of EBSD, from pattern formation, the Hough Transform, and finally indexing. More importantly, I hope to touch on how to troubleshoot issues using your newfound understanding of these concepts and tie the entire processes together as they all play off each other.

My final goal is get your creative juices flowing to dive deeper into understanding the kind of questions that EBSD can answer, and how that, in the end, can provide you with an incredible understanding of your analysis challenges and ultimately a solution to the problem. EBSD is one of the most powerful analytical techniques that I know. It can answer the simple questions (what phase is my sample?) to the incredibly complex (if I squeeze my sample this way, which grains will tend to deform first?). As your knowledge grows, EBSD is one step ahead of you, egging you on to learn more and more. I hope to be your guide on this Journey of Learning. I think I will learn quite a bit too.

It’s a zoo in there!

Dr. René de Kloe, Applications Specialist, EDAX

For most of us EBSD users, our day to day experience is with metals, ceramics, or perhaps rocks. For man-made materials, analysis allows us to characterise the microstructure so that we can finetune the processing or fabrication of a material for a specific application. Another common use of EBSD data is for failure analysis where the crystallographic information can be coupled to external characterisation data and deformation structures such as cracks, welds, or ductile deformation features.

Figure 1. IPF map of partially recrystallized steel (left); IQ map of quartzite rock from the Pilbara region in Australia (right).

For natural materials like rocks, the questions start to get a bit trickier as we typically do not know exactly how a rock has come to exhibit the structures that it has. In combination with other tools, EBSD can then be an invaluable tool to add crystallographic and phase information to the puzzle. This allows researchers to piece together the deformation, temperature, and pressure history of the rock. This way tiny samples can provide insight in processes on a global scale like mountain building and the motion of the continents.

A third group of materials that gets a bit less attention in EBSD analysis are biominerals, materials that are formed with a certain degree of biological control to become part of an organism. In these biomaterials, the question is not how we have produced it, or how it could be finetuned to its intended application. Here the question is how biological processes have been able to optimise a material to such a remarkable degree and the EBSD analysis is used to try to understand the biological use and control of crystallisation. Unfortunately, we rarely get to look at structures that are produced by living organisms, except possibly fossils. One of the reasons that “fresh” biomineral structures are rarely studied with EBSD is that they often contain an organic fraction that makes electron microscopy samples susceptible to beam damage. To analyse such materials, the researcher must be very careful. A single pass with the electron beam is often all you get as the structure is easily damaged. In fossilised remains of animals, the organic component has been lost or replaced by solid crystals which make its analysis somewhat easier. For example, in recent years, papers have been published on crystalline lenses in the eyes of long extinct trilobites which were formed of calcite [1] and EBSD has also been used to estimate which areas of dinosaur eggs are most likely to represent the original microstructure such that the isotope ratios from these grains can be used to estimate the crystallisation temperature of the eggs [2].

A bit closer to us is perhaps the analysis of hydroxyapatite in bones. In the SEM image this cross section of a bone consists of a fibrous framework with brighter areas containing individual hydroxyapatite grains. What is not clear from such an image is if the grain orientations in these areas are all identical or perhaps exhibit random orientation. EBSD analysis clearly shows that the apatite grains occur in small clusters with similar IPF colours or equivalent orientations, which indicates that these smaller clusters are connected in the 3rd dimension in the material.

Figure 2. BSE image cross-section of bone (left); Hydroxyapatite IPF map on a single hydroxyapatite region in bone (right).

The recent introduction of the easy recording of all EBSD patterns during a scan and performing NPAR (neighbour pattern averaging and reindexing) during EBSD post-processing have allowed dramatic improvements in the analysis of beam sensitive materials. You still have to use gentle beam currents and relatively low kV to obtain the EBSD patterns. These patterns are then very noisy and the initial maps often show poor indexing success rates, but once these have been collected you are free to find the optimum way to analyse these patterns for the best possible results. For example, beam sensitive materials like the aragonite in the nacre of shells can be successfully analysed.

Figure 3. Calcite-aragonite transition the inside of a shell: original measurement (left); after NPAR reprocessing (right).

The aragonite-calcite phase map above on the left shows the initial results of an EBSD map of the inner surface of a shell over a transition zone from the calcite “framework” on the right to the smooth nacre finish on the left of the analysis area. Directly at the interface the EBSD pattern quality is so poor that it is difficult to interpret the microstructure. The phase map on the right is after NPAR reprocessing. Now the poorly indexed zone at the transition is much narrower and the map clearly shows how the aragonite starts growing in between the calcite pillars, then forms a thin veneer on top of the calcite until it gets thick enough to create euhedral planar crystals that form the smooth nacre surface at the inside of the shell.

Figure 4. Aragonite structure from pillars to nacre: original measurement (left); after NPAR reprocessing (right).

Figure 4 shows another shell structure which is now completely composed of aragonite. In cross section the structure resembles that of the calcite pillars with the nacre platelets on top, but the initial scans do not reveal any structure in the pillars. This could be taken as evidence that the crystal structure might be damaged and cannot be characterised properly using EBSD. However, after NPAR reprocessing the crystal structure of the pillars becomes clear and a feather-like microstructure is revealed.

These fascinating biological structures don’t appear often to the average materials scientist or geologist, but if you keep an open mind for unexpected structures you can still be treated to beautiful virtual creatures in or on your samples. For example, dirt is not always just in the way. Here it poses as a micron sized ground squirrel overlooking your analysis. And this magnetite duck is just flying into view over a glassy matrix.

Figure 5. Dirt patch in the shape of a ground squirrel (left); crystal orientation map of a magnetite duck flying through glass (right).

And what to think of these creatures, a zirconia eagle that is flying over a forest of Al2O3 crystals and this micron sized dinosaur that was lurking in a granite rock from the highlands of Scotland. Perhaps we finally found an ancestor of Nessie?

Figure 6. Zirconia EDS Eagle: in zirconia -alumina ceramic (left); on PRIAS bottom image (right).

Figure 7. Ilmenite-magnetite dinosaur in a granite rock.

It is clear that “biological” EBSD can occur in many shapes and sizes. Sometimes it is literally a zoo in there!

[1] Clare Torney, Martin R. Lee and Alan W. Owen; Microstructure and growth of the lenses of schizochroal trilobite eyes. Palaeontology Volume 57, Issue 4, pages 783–799, July 2014
[2] Eagle, R. A. et al. Isotopic ordering in eggshells reflects body temperatures and suggests differing thermophysiology in two Cretaceous dinosaurs. Nat. Commun. 6:8296 doi: 10.1038/ncomms9296 (2015).

Aimless Wanderin’? – Part One

Dr. Stuart Wright, Senior Scientist, EDAX

On a recent transatlantic flight I passed the time watching one of my favorite movies: Oh Brother! There are a lot of great quotable lines in this movie. One that seems appropriate for this blog entry is from the lead character: Ulysses Everett McGill

“Say, uh, any a you boys smithies? Or, if not smithies per se, were you otherwise trained in the metallurgic arts before straitened circumstances forced you into a life of aimless wanderin’?”

Source: Rudy Wenk

While, in theory, I am “trained in the metallurgic arts”, my travels sometimes feel like “aimless wanderin’” and sometimes my mind follows suit – especially on long flights. In this series of entries for the EDAX Blog, I would like to take you on some “wanderin’s” through some of the terminology, history and personalities surrounding EBSD. Let’s begin with “texture”.

My global wanderings aren’t always aimless and I often learn some interesting things. At some recent conferences, I saw several interesting textures measured using neutron diffraction; for example, works by Heinz-Günther Brokmeier, Sven Vogel, Raul Bolmaro and others. Generally, these textures were measured over large volumes, such as from a section of a pipe, or an entire automobile component. It struck me that the use of the word “texture” has evolved to mean different things to different people.

My source of most early historical texture knowledge is Rudy Wenk. Rudy informs me that he believes the first use of the word was in an 1833 textbook by a Belgian geologist – d’Halloy to describe a directional microstructure. This seems a little ironic now as geologists tend to use the term “fabric” to describe what a metallurgist would refer to as “texture” but the evolution of these terms has also seen some wanderin’ as described in section 6 in Chapter 1 of Rudy’s 1985 book, Preferred Orientation in Deformed Metal and Rocks: An introduction to Modern Texture Analysis. I had the great fortune of learning from Rudy during a short-course on texture held at BYU when I was an undergrad as well as during his visits to Los Alamos National Lab when I was a Post-Doc. I am excited for a symposium in his honor at this year’s edition of ICOTOM in St George, Utah.

I was first introduced to the term texture in 1985 by Peter Morris, who was a visiting researcher at BYU working with Professor Brent Adams. At the time, I was employed by a Professor in the Physics Department, Dorian Hatch, to track down papers in the library (long before libraries went digital and on-line search and retrieval tools were available). I was a junior Mechanical Engineering student but had become a bit disenchanted with my coursework. I expressed to Dorian my frustration and that I was considering switching my major (Dorian was one of my leaders in our local church congregation when I was a teenager and was very helpful in offering good advice to a young university student). He recommended I go and visit with a new Professor in Mechanical Engineering named Brent Adams. When I knocked on Brent’s office door he was busy and recommended I speak with Peter. I still remember being completely lost as Peter tried to talk to me about which kind of mathematical functions would be appropriate to describe the r-dependence of the Two-Point Orientation Coherence function. Luckily, Brent popped in before I left Peter’s office completely befuddled; he brought things down a little closer to my level (if you can imagine Brent doing such a thing) and introduced me to texture. Brent was looking for someone with programming skills which I happened to have and so I joined his research team. (I got to know Peter better as part of Brent’s team particularly on a long drive from Provo, Utah to Santa Fe, New Mexico for ICOTOM 8. At one point in the drive I thought I would try out my German on Peter but was very surprised to learn that he didn’t speak German – remarkable, because if you dig out a copy of Bunge’s Texture Analysis in Materials Science you will note it was translated from German to English by Peter).

My personal introduction to texture was through the ODF or Orientation Distribution Function (another odd description as in the formal statistical sense it is actually a density function as opposed to a distribution function) per Bunge (“Zur Darstellung allgemeiner Texturen”, Zeitschrift der Metallkunde, 56, 872-874 (1965)):

“Die Orientierungsvertailung oder Textur eines polykristallinen Materials wird charakterisiert durch den Volumenateil derjenigen Kristalle, deren Orientierung zwischeng g and g + dg liegt.”

My best attempt at a translation is “the orientation distribution or texture of polycrystalline materials is characterized through the volume fraction of the constituent crystals, with orientations lying between g and dg.”

Bunge further explains in Chapter 4 of Rudy’s book entitled Preferred Orientation in Deformed Metal and Rocks: An Introduction to Modern Texture Analysis (1985):

“The texture is thus, per definition, the orientation distribution of all crystals present in the sample irrespective of their arrangement in the sample. Since the texture is defined as a statistical quantity, the sample must at least be big enough, compared to the grain size, to allow a statistically significant description. This, in turn, depends on the degree of relevance required. If we have a sample much bigger that what is required by statistical relevance, then it may be divided into volume elements V big enough to allow the statistical description of the texture. The texture can then be measured in each of these volumes elements separately. If the textures of all volume elements of the big sample are statistically identical, then the big sample is said to have a homogeneous texture. If we speak about he the texture of a material without further specification, the homogeneity is assumed. In may important cases, however, the textures of the volume elements are not the same. Such textures are called inhomogeneous, and the definition of the term “texture” become more complex (e.g., Bung, 1982c).”

In the world of EBSD, we measure textures on surfaces. We hope this is representative of the volume but oft times we know it is not. For instance, consider the following (111) pole figure measured from the surface of an aluminum sheet. It has some of the characteristics we expect for a rolled fcc material but does not exhibit the symmetry we would expect for the texture through the volume of the sheet.

(111) pole figures from two samples of rolled aluminum. Left: recent EBSD measurements on the surface of a sample. Right: X-Ray measurements from the cross-section (this pen plot is from my M.S. Thesis which formed the basis of the paper S. I. Wright and B. L. Adams (1990) An Evaluation of the Single Orientation Method for Texture Determination in Materials of Moderate Texture Strength”, Textures and Microstructures 12, 65-76.

Could the lack of symmetry be due to a lack of statistics – i.e. the volume element investigated is too small? I don’t believe so as the average grain size for this material is approximately 25 microns (always a bit tricky to estimate in deformed materials with elongated grains and with a well-defined subgrain structure) and the step size was 4µm. The scan area was 2.1 x 1.6 mm (~250,000 orientation measurements) and thus, approximately 6900 grains were sampled. In addition, the pole figure is fairly symmetric horizontally. Rather, I assume the lack of vertical symmetry in the pole figure comes from a texture gradient from the surface to the center of the sheet. So rather than calling this a texture in the classic volumetric sense it would be more correct to add “surface” as a qualifier – i.e. a surface texture.

One concern I have, is the use of the term micro-texture. I understand the point, it is the texture measured at the “micro-scale” – in the language of the quote from Bunge, a volume element at the micro-scale. But, if the area contains just a few grains, is it really a “texture”? That isn’t to say we can’t learn a lot from such measurements but, in my mind, the term texture has a statistical component to it in terms of the number of grain orientations sampled. For example, consider the following texture measurements from the same sample. Each measurement contains approximately 250,000 EBSD measurements of orientation but the step sizes are 4µm, 400nm, 40nm and 4nm. Clearly, as the sampled area becomes smaller and smaller, the measured texture becomes less and less representative of the sample as a whole. Actually, it is remarkable that the fcc rolling texture is recognizable in all but the 4nm step size. At the smallest step size, the “texture” contains just 3 grains and thus the oscillations around the major peaks arising from the spherical harmonics used to calculate the texture are relatively prominent.

(111) pole figures and orientation maps from the surface of rolled aluminum sheet from EBSD measurements at step sizes of 4µm, 400nm, 40nm and 4nm each with just over 250,000 orientation measurements.

My concern is not enough to protest the use of the word micro-texture as I think most who use the term understand the implications, but as a community we need to be aware of sampling and statistical reliability as we draw conclusions from our EBSD measurements so that our scientific wanderin’s don’t become aimless but, to quote another classic movie, “stay on target”.

(Stay tuned for some thoughts on the term “meso-texture” 😊)

My New Lab Partner

Matt Nowell, EBSD Product Manager, EDAX

It has been an exciting month here in our Draper Utah lab, as we have received and installed our new FEI Teneo FEG SEM. We are a small lab, focusing on EBSD development and applications, and without a loading dock, so timing is critical when scheduling the delivery. So, 3 months ago, we looked at the calendar to pick a day with sunshine and without snow. Luckily, we picked well.

Figure 1: Our new SEM coming off the truck.

Figure 1: Our new SEM coming off the truck.

Once we got the new instrument up and running, of course the next step was to start playing with it. This new SEM has a lot more imaging detectors than our older SEM, so I wanted to see what I could see with it. I chose a nickel superalloy turbine blade with a thermal barrier coating, as it had many phases for imaging and microanalysis. The first image I collected was with the Everhart-Thornley Detector (ETD). For each image shown, I relied on the auto contrast and brightness adjustment to optimize the image.

Figure 2: ETD image

Figure 2: ETD image

With imaging, contrast is information. The contrast in this image shows phase contrast. On the left, gamma/gamma prime contrast is visible in the Nickel superalloy while different distinct regions of the barrier coating are seen towards the right. The next image I collected was with the Area Backscatter Detector (ABS). This is a detector that is positioned under the pole piece for imaging. With this detector, I can use the entire detector, the inner annular portion of the detector, or any of three regions towards the outer perimeter of the detector.

Figure 3: ABS Detector image.

Figure 3: ABS Detector image.

I tried each of the different options, and I selected the inner annular ring portion of the detector. Each option provided similar contrast as seen in Figure 3, but I went with this based on personal preference. The contrast is like the ETD contrast is Figure 2. I also compared with the imaging options using the detector in Concentric Backscatter (CBS) mode, where 4 different concentric annular detectors are available.

Figure 4: T1 Detector (a-b mode).

Figure 4: T1 Detector (a-b mode).

My next image used the T1 detector, which to my understanding is an in-lens detector. In this mode, I selected the a – b mode, so the final image is obtained by subtracting the image from the b portion of the detector from the a portion of the detector. I selected this image because the resultant contrast is reversed from the first couple of images. Here phases that were bright are now dark, and detail within the phases is suppressed.

Figure 5: T2 Detector.

Figure 5: T2 Detector.

My final SEM image was collected with the T2 detector, another in-lens detector option. Here we see the same general phase contrast, but the contrast range is more limited and the detail within regions is again suppressed.

I have chosen to show this set of images to illustrate how different detectors, and their positioning, can generate different images from the area, and that the contrast/information obtained with each image can change. Now I have done a cursory interpretation of the image contrast, but a better understanding may come from reading the manual and knowing the effects of the imaging parameters used.

Figure 6: Always Read the Manual!

Figure 6: Always Read the Manual!

Of course, I’m an EBSD guy, so I also want to compare this to what I can get using our TEAM™ software with Hikari EBSD detectors. One unique feature we have in our software is PRIAS™, which uses the EBSD detector as an imaging system. With the default imaging mode, it subsets the phosphor screen image into 25 different ROI imaging detectors, and generates an image from each when the beam is scanned across the area of interest. Once these images are collected, they can be reviewed, mixed, added, subtracted, and colored to show the contrast of interest, similar to the SEM imaging approach described above.

The 3 most common contrasts we see with PRIAS™ are phase, orientation, and topographic. To capture these, we also have a mode where 3 pre-defined regional detectors are collected during EBSD mapping, and the resulting images available with the EBSD (and simultaneous EDS) data.

Figure 7: PRIAS™ Top Detector Image.

Figure 7: PRIAS™ Top Detector Image.

The first ROI is positioned at the top of the phosphor screen, and the resulting phase contrast is very similar to the contrast obtained with the ETD and ABS imaging modes on the SEM.

Figure 8: PRIAS™ Center Detector Image.

Figure 8: PRIAS™ Center Detector Image.

The second ROI is positioned at the center of the phosphor screen. This image shows more orientation contrast.

Figure 9: PRIAS™ Bottom Detector Image.

Figure 9: PRIAS™ Bottom Detector Image.

The third ROI is positioned at the bottom of the phosphor screen. This image shows more topographical contrast. All three of these images are complementary, both to each other but also to the different SEM images. They all give part of the total picture of the sample.

Figure 10: Defining Custom ROIs in PRIAS™.

Figure 10: Defining Custom ROIs in PRIAS™.

With PRIAS™ it is also possible to define custom ROIs. In Figure 10, 3 different ROIs have been drawn within the phosphor screen area. The 3 corresponding images are then generated, and these can be reviewed, mixed, and then selected. In this case, I selected an ROI that reversed the phase contrast, like the contrast seen with the T1 detector in Figure 4.

Figure 11: PRIAS™ Center Image with EDS Bland Map (Red-Ni, Blue – Al, Green-Zr)

Figure 12: PRIAS™ Center Image with Orientation Map (IPF Map Surface Normal Direction).

figure-12a

Of course, the PRIAS™ information can also be directly correlated with the EDS and EBSD information collected during the mapping. Figure 11 shows an RGB EDS map while Figure 12 shows an IPF orientation map (surface normal direction with the corresponding orientation key) blended with the PRIAS™ center image. Having this available adds more information (via contrast) to the total microstructural characterization package.

I look forward to using our new SEM, to develop new ideas into tools and features for our users. I imagine a few new blogs posts should come from it as well!

The Hough Transform – An Amazing Tool.

Shawn Wallace, Applications Engineer, EDAX

Part of my job is understanding and pushing the limits of each part of our systems. One of the most fundamental parts of the EBSD system is the Hough Transform. The Hough Transform role is finding the lines on an EBSD pattern. This is the first step in indexing a pattern (Fig. 1). If this step is not consistent, the quality of any indexing and any derivative data is questionable. A normal user does not really need to understand all the intricacies of every part of the system, but it still is worthwhile to understand how your data and data quality can be affected.

Figure 1: On the left are the overlaid lines found via the Hough Transform. On the right is the Indexed solution overlaid based on the Hough. The quality of the indexed solution is based on the quality of the Hough.

Figure 1: On the left are the overlaid lines found via the Hough Transform. On the right is the Indexed solution overlaid based on the Hough. The quality of the indexed solution is based on the quality of the Hough.

With that in mind, I ran an experiment on a steel sample to see how far the Hough could be pushed and still give consistent indexing. For this experiment, I used our Hikari Super at a series of different binnings between its native resolution of 640X480 Pixels at 1×1 binning down to 35×26 pixels at 18×18 binning. All pixel resolutions are noted in Table 1. I kept my Hough Settings and beam settings consistent. My only other variable was exposure to get the camera to be equally saturated at around 0.85 saturation.

I expected the lower binning Patterns to be consistent and they were (Fig. 2). All three Euler Angles between the 1×1, 2×2, 4×4, and 8×8, were within 0.4 degrees of each other. Pushing the camera and the Hough even further really surprised me though.

Figure 2: Indexed Pattern for the lower binning showed a remarkable consistency in indexing.

Figure 2: Indexed Pattern for the lower binning showed a remarkable consistency in indexing.

Figure 3: The indexing results still held their consistency even for highest binning settings used.

Figure 3: The indexing results still held their consistency even for highest binning settings used.

I expected some drop off with the consistency of the orientation when I dropped my binning to 10×10, 16×16, and even 18×18 and it did not fully materialize (Fig. 3). The range did broaden in the Euler Angles, specifically ᶲ₂’s range increased to 3 degrees, but that is change of <1% given the entire range for ᶲ₂ is 360 degrees. Table 1 shows the data is the raw form. Overall, the data is great, from low to high binning with minimal loss in in our indexing metrics (CI and Fit) and consistency in Euler Angles except for the 18×18 binning. That is where we have found our limit, specifically when it comes to indexing metrics. We see a sharp drop off in the CI. The pixilation of the pattern has gotten to a point where it is difficult to find a unique solution. This drop off is why we tell our customer that 16×16 is the limit of binning they should use for reliable, high quality data.

Table 1. Indexing Metrics and Euler Angles for all data points.

Table 1. Indexing Metrics and Euler Angles for all data points.

With all that said, most EBSD work is not on a single orientation, but a map. Does this hold true on a map? It does. In Figure 4 and Figure 5, we can see the mapping results for 2×2 binning and 10×10 binning. Both indexed at 99.9% with their average CI’s being 0.89 and 0.84 respectively, with very little change in orientations. This level of data quality across binnings is why EDAX uses the Hough. It is an amazing little tool.

Figure 4. This map was taken at 2x2 binning. Internal deformation of the grains is visible, with inclusions between relatively undeformed.

Figure 4. This map was taken at 2×2 binning. Internal deformation of the grains is visible, with inclusions between relatively undeformed.

Figure 5. This map was taken at 10x10 binning in approximately the same area as Figure 4. Again, internal deformation is showed in the larger grain, while the inclusions are undeformed.

Figure 5. This map was taken at 10×10 binning in approximately the same area as Figure 4. Again, internal deformation is showed in the larger grain, while the inclusions are undeformed.