EBSD

Home Sweet Home

Dr. René de Kloe, Applications Specialist, EDAX

This last year has been different in many ways, both personally and at work. For me, it meant being in the office or working from home instead of being out and about and meeting customers and performing operator schools in person. This does not exactly mean that things are quieter, though! At home, I got confronted with lots of little maintenance things in and around my house that otherwise somehow manage to escape my attention. At work, lots of things vying for my attention have managed to land on my desk.

The upside is that with almost everything now being done through remote connections. I get to sit more at the microscope in the lab to work on customer samples, collect example datasets, perform system tests, and also practice collecting data on difficult samples so that I can support our customers better. To do that, I have the privilege of being able to choose which EBSD detector I want to mount, from the fast Velocity to the familiar Hikari to the sensitive Clarity Direct Electron System. But how do I decide what samples to use for such practice sessions?

A common garden snail (Cornu aspersum) and an empty shell used for the analysis.

Figure 1. A common garden snail (Cornu aspersum) and an empty shell used for the analysis.

In the past, I wrote about my habit of occasionally going “dumpster diving” to collect interesting materials (well, to be honest, I try to catch the things just before they land in the dumpster). That way, I have built up a nice collection of interesting alloys, rocks, and ceramics to keep me busy. But this time, I wanted to do something different, and an opportunity presented itself when I was working on a fun DIY project, a saddle stool for my daughter. On one of the days that I was shaping wood in my garden for the saddle-support, I noticed some garden snails moving about leisurely. These were the lucky chaps (Figure 1). While we occasionally feel the need to redecorate our walls to get a change of view, the snail’s home remains the same and follows him wherever he goes; sounds great! No need to do any decoration or maintenance, and always happy at home!

But all kidding aside, I have long been interested in the structure of these snail shells and have wanted to do microstructural analysis on one. So, when I found an empty shell nearby belonging to one of its cousins that had perished, I decided to try to do some Scanning Electron Microscope (SEM) imaging and collect Electron Backscatter Diffraction (EBSD) data to figure out how the shell was constructed. The fragility of the shell and especially the presence of organic material in between the carbonate crystals that make up the shell makes them challenging for EBSD, so I decided to mount my Clarity Detector and give it a very gentle try.

The outer layer that contains the shell’s color was already flaking off, so I had nice access to the shell’s outer surface without the need to clean or polish it. And with the Gatan PECS II Ion Mill that I have available, I prepared a cross-section of a small fragment. I was expecting a carbonate structure like you see in seashells and probably all made of calcite, which is the stable crystal form of CaCO3 at ambient temperatures. What I found was quite a bit more exotic and beautiful.

In the cross-section, the shell was made up of multiple layers (Figure 2). First, on the inside, a strong foundation made of diagonally placed crossed bars, then two layers of well-organized small grains, was topped by an organic layer containing the color markings.

A PECS II milled cross-section view of the shell with different layers. The dark skin on the top is the colored outer layer.

Figure 2. A PECS II milled cross-section view of the shell with different layers. The dark skin on the top is the colored outer layer.

At the edge of the PECS II prepared cross-section, a part of the outer shell surface remained standing, providing a plan view of the structure just below the surface looking from the inside-out. In the image (Figure 3), a network of separated flat areas can be recognized with a feather-like structure on the top, which is the colored outer surface of the shell. An EDS map collected at the edge suggests that the smooth areas are made up of Ca-rich grains, which you would expect from a carbonate structure. Still, the deeper “trenches” contain an organic material with a higher C and O content, explaining why the shell is so beam-sensitive.

A plan view SEM image of the structure directly below the colored surface together with EDS maps showing the C, O, and Ca distribution.

Figure 3. A plan view SEM image of the structure directly below the colored surface together with EDS maps showing the C (purple), O (green), and Ca (blue) distribution.

The EBSD data was collected from the outer surface, where I could peel off the colored organic layer. This left a clean but rough surface that allowed successful EBSD mapping without further polishing.

My first surprise here was the phase. All the patterns that I saw were not of calcite but aragonite (Figure 4). This form of calcium carbonate is stable at higher temperatures and forms nacre and pearls in shells in marine and freshwater environments. I was not expecting to see that in a land animal.

Figure 4. An aragonite EBSD pattern and orientation determination.

Figure 4. An aragonite EBSD pattern and orientation determination.

The second surprise was that the smooth areas that you can see in Figure 3 are not large single crystals but consist of a very fine-grained structure with an average grain size of only 700 nm (Figure 5). The organic bands are clearly visible by the absence of diffraction patterns – the irregular outline is caused by projection due to the surface topography.

Image Quality (IQ) and aragonite IPF maps of the outer surface of the shell. The uniform red color and (001) pole figure indicate a very strong preferred crystal orientation.

Figure 5. Image Quality (IQ) and aragonite IPF maps of the outer surface of the shell. The uniform red color and (001) pole figure indicate a very strong preferred crystal orientation.

After this surface map, I wanted to try something more challenging and see if I could get some information on the crossbar area underneath. At the edge of the fractured bit of the shell, I could see the transition between the two layers with the crossbars on the left, which were then covered by the fine-grained outer surface (Figure 6).

An IQ map of the fracture surface. The lower left area shows the crossbar structure, then a thin strip with the fine-grained structure, and at the top right some organic material remains.

Figure 6. An IQ map of the fracture surface. The lower left area shows the crossbar structure, then a thin strip with the fine-grained structure, and at the top right some organic material remains.

Because the fractured sample surface is very rough, EBSD patterns could not be collected everywhere. Nevertheless, a good indication of the microstructure could be obtained. The IPF map (Figure 7) shows the same color as the previous map, with all grains sharing the same crystal direction pointing out of the shell.

An IPF map showing the crystal direction perpendicular to the shell surface. All grains share the same color indicating that the [001] axes are aligned.

Figure 7. An IPF map showing the crystal direction perpendicular to the shell surface. All grains share the same color indicating that the [001] axes are aligned.

But looking at the in-plane directions showed a very different picture (Figure 8). Although the sample normal direction is close to [001] for all grains, the crystals in the crossbar structure are rotated by 90° and share a well-aligned [100] axis with the two main directions rotated by ~30° around it.

An IPF map along Axis 2 showing the in-plane crystal directions with corresponding color-coded pole figures.

Figure 8. An IPF map along Axis 2 showing the in-plane crystal directions with corresponding color-coded pole figures.

Detail of the IPF map of the crossbar area with superimposed crystal orientations.

Figure 9. Detail of the IPF map of the crossbar area with superimposed crystal orientations.

I often have a pretty good idea of what to expect regarding phases and microstructure in manufactured materials. Still, I am often surprised by the intricate structures in the smallest things in natural materials like these snail shells.

These maps indicate a fantastic level of biogenic crystallographic control in the snail shell formation. First, a well-organized interlocked fibrous layer with a fixed orientation relationship is then covered by a smooth layer of aragonite islands, bound together by an organic structure, and then topped by a flexible, colored protective layer. With such a house, no redecoration is necessary. Home sweet home indeed!

Disoriented

Dr. Stuart Wright, Senior Scientist, EDAX

Of all the papers I’ve written, my favorite title I’ve managed to sneak past the editors and reviewers is “Random thoughts on non-random misorientation distributions.” The paper is a write-up of a presentation I gave at a celebration of Professor David Dingley’s contributions to EBSD, which was held as a special version of the annual Royal Microscopy Society EBSD meeting at New Lanark in Scotland. It was a fun meeting as several of David’s former Ph.D. students shared some great stories and pictures of David, and the talks were a little less formal than usual, which led to some interesting discussions.

There are many terms used to describe the difference in crystallographic orientation between two crystal lattices: misorientation, disorientation, orientation difference, misorientation angle, minimum misorientation angle, grain boundary character, intercrystalline interface. One can get a bit “disoriented” trying to sort out all these different terms. Unfortunately, I am at fault for some of the confusion as I have tended to use the different terms loosely in my presentations and papers. But I am not the only one; I have seen some wandering in the definition of some of these terms as different researchers have followed up on the work of others. I will not pretend to be rigorous in this blog, but let me see if I can help sort through the different terms.

My first exposure to the idea of misorientation was from Bunge’s classic book Texture Analysis in Materials Science from 1969. I was first introduced to the book when I joined Professor Brent Adam’s Lab in 1985. We called it the “Red Bible,” as we had a very well-worn copy in the lab. We were even lucky enough to have Peter Morris with us at the time, who translated the book from German to English (a herculean task for a non-German speaker without modern tools like Google Translate). On page 44 of this book, you will find the following:

If two adjacent grains in a grain boundary have orientations g1 and g2, the orientation difference is thus given by:

g = g2 g1-1                                                                   (3.12)

This looks like a relatively simple expression, and we have generally calculated it using orientations described as matrices, and thus the result ∆g would also be a matrix. But the most common description of this orientation difference given in the literature would be an axis-angle pair. Any two crystals have at least one axis in common. A rotation about that axis will bring the two crystal lattices into coincidence.

Axis-angle description of misorientation.

Figure 1. Axis-angle description of misorientation.

While the equation above seems simple, we need to remember that, due to crystal symmetry, there are multiple symmetrically equivalent descriptions of the orientations g1 and g2. We can term the symmetry operators Li. These are the elements of the crystallographic point group symmetry for the crystals in question. For example, for a cubic crystal, there will be 24 symmetry elements. Since there are 24 symmetric equivalents for g1 and 24 for g2 that means there will be 576 symmetric equivalents for ∆g. In the expression below, the apostrophe denotes symmetrically equivalent.

g’12 = Lig2∙(Lig1)-1

As an example, here is a list for a random axis angle pair assuming cubic crystal symmetry: 12° @ 〈456〉. Note that the notation 〈uvw〉 denotes the family of crystal directions and [uvw] denotes a single crystal direction. Once again, for cubic symmetry, there are (in general) 24 [uvw] directions in the 〈uvw〉 family of directions (note in general there are 24 directions in the family, i.e. [123], [132], [-123], [-132], …. but this can be reduced for families where multiplicity plays a role, such as 〈00w〉 or 〈uuw〉…).

AngleAxisAngleAxis
12.00(4 5 6)124.26(139 132 170)
82.16(2 18 155)125.80(118 121 148)
83.62(20 4 157)131.85(44 43 45)
85.06(4 45 325)169.37(2 161 177)
95.94(33 3 262)170.34(235 6 265)
97.23(4 20 177)171.30(2 149 172)
98.51(62 7 617)171.80(10 8 167)
108.17(39 38 40)173.17(8 12 167)
114.78(137 173 177)174.54(12 10 167)
116.39(130 103 136)178.07(25 196 221)
117.99(137 177 181)179.03(188 26 207)
122.71(149 153 192)179.03(155 18 179)

So, this is a list of symmetrical misorientations given as axis-angle pairs. The minimum rotation angle in this set is the disorientation. But, you will also see the disorientation called the orientation distance (Bunge equation 2.123), rotation angle and misorientation angle (OIM), minimum misorientation angle, as well as simply the misorientation, orientation difference, grain boundary angle, . For a little comic relief at intense EBSD workshops, I have often said that I prefer the term misorientation because disorientation is what we tend to feel at the end of the day of lectures. I give Professor Marc De Graef credit for helping me finally get these terms straight. So, now I can retire that joke that probably never really translated very well into different languages anyway.

One more note on terminology. A grain boundary is a five-parameter entity: three for the misorientation and two to describe the orientation of the boundary plane.

5D Grain Boundary Character.

Figure 2. 5D Grain Boundary Character.

This five-dimensional entity is now often referred to as the Grain Boundary Character (Rohrer) but has also been termed the Intercrystalline Interface Structure (Adams). In the past and in OIM Analysis, the Grain Boundary Character Distribution or GBCD refers to the distribution of grain boundaries across three classifications, low-angle random boundaries, high-angle random boundaries, and “special” (generally CSL) boundaries. As a side note, Grain Boundary Character has been called a “full” or “complete” description of a grain boundary, but this is a bit of an overreach. There are still other parameters associated with a grain boundary that may be just as important as these five, for example, curvature, faceting, chemical composition.

It should be noted that we can calculate the misorientation between two crystals of different symmetry and get a nice, neat axis-angle pair.

Picture4

However, the concept of coincidence is not as clear as for two crystals of the same symmetry, as illustrated in the schematic shown in Figure 3. Nonetheless, this terminology (and its corresponding mathematical methods) can be helpful when analyzing the orientation relationships associated with phase transformations.

Misorientation between a hexagonal and cubic crystal.

Figure 3. Misorientation between a hexagonal and cubic crystal.

I hope this brief discussion has helped “orient” you in the right direction. I know I am now trying to be more careful in using these terms, which will probably result in a few changes in our user interface for a future version of OIM to reflect this.

References

Wright, SI (2006) Random thoughts on non-random misorientation distributions. Materials Science and Technology 22: 1287-1296.

Bunge, HJ (1969) Mathematische Methoden der Texturanalyse. Akademie-Verlag: Berlin.

Beladi H, Nuhfer NT, and Rohrer GS (2014) The five-parameter grain boundary character and energy distributions of a fully austenitic high-manganese steel using three dimensional data. Acta Materialia 70:281-289

Zhao J, Koontz JS, and Adams BL, 1988. Intercrystalline structure distribution in alloy 304 stainless steel. Metallurgical Transactions A, 19:1179-1185.

Boxes

Dr. Stuart Wright, Senior Scientist, EDAX

It has been a tough year for all of us – at times, I get cabin fever and feel boxed-in. The recent holiday break was a pleasant diversion. Even though we weren’t able to gather like we usually do, we did get to spend some time with a couple of our grandkids. As we opened gifts, per the usual stereotype, our youngest grandson had more fun playing with the boxes than the toys in them! Since today’s blog is on boxes, Figure 1 shows a picture of our granddaughter atop an old toy box. Yes, she is more than willing to pose for the camera.

My granddaughter atop a toy box I built many years ago.

Figure 1. My granddaughter atop a toy box I built many years ago.

So why the picture of a toy box? That toy box is 32 years old and has a tie-in to the development of EBSD (since I am getting older, I’m allowed to be a bit nostalgic.)

I joined Professor Brent Adams’ group as an undergrad at BYU in 1985. Brent was working on the orientation coherence function (OCF) at the time, which is a statistical description of crystallographic orientation arrangement within a polycrystalline microstructure. One of the Ph.D. students, T. T. Wang, went off to what was then the Alcoa Technical Center to make orientation measurements using selected area diffraction – a painstakingly slow process. He returned with a large set of Euler angles and a box of micrographs with numbered spots to indicate where the orientation measurements were from. My assignment was to digitize those micrographs – both to manually point-and-click each grain vertex and to write software to use those vertices to reconstruct and visualize the digital microstructure. Figure 2 shows one example from the set of 9 section planes. The entire set contained 5,439 grains and 21,221 boundaries. It was a lot of tedious work.

Digitized microstructure from aluminum tubing for work reported in B. L. Adams, P. R. Morris, T. T. Wang, K. S. Willden and S. I. Wright (1987). “Description of orientation coherence in polycrystalline materials.” Acta Metallurgica 35: 2935-2946.

Figure 2. Digitized microstructure from aluminum tubing for work reported in B. L. Adams, P. R. Morris, T. T. Wang, K. S. Willden and S. I. Wright (1987). “Description of orientation coherence in polycrystalline materials.” Acta Metallurgica 35: 2935-2946.

When Brent saw David Dingley’s presentation on EBSD at ICOTOM in 1987, he got very excited as he realized how much it could help with our group’s data collection needs. We got the first EBSD system in the US shortly after ICOTOM. It was installed on an old SEM in the botany department. The system was all computer-controlled, but it still required a user to manually (with the mouse) identify zone axes in each EBSD pattern to be indexed. It was a huge step forward for our research group. Brent quickly envisioned a fully automated system for site-specific orientation measurements. In 1988, Professor Adams moved to Yale University. I was fortunate to be invited to be a member of the research team that accompanied him. My wife and I boxed up our belongings and moved our small family of four from Utah to Connecticut for our new adventure.

The first few weeks at Yale were spent cleaning out an old laboratory space (some items even went to the Yale museum) in preparation for receiving our new CamScan SEM and the next generation EBSD system from David Dingley. When the SEM boxes arrived at the lab, we were very excited to see the microscope uncrated and installed. It was great to have our own microscope to work with, and we waited in eager anticipation for David’s arrival to install the new EBSD system. Unfortunately, I don’t have many photos from the Yale lab, but Figure 3 shows one with three of my colleagues in front of the SEM.

Brent Adams, John Hack, and Karsten Kunze in the SEM lab at Yale.

Figure 3. Brent Adams, John Hack, and Karsten Kunze in the SEM lab at Yale.

After everything was installed, there were a lot of wooden boards left over from all the crates in which the equipment was shipped. Being the stereotypical poor-starving student, I saw the wood as an opportunity. I diverted the bigger pieces of wood to my car instead of the dumpster and took them to our apartment. It was enough wood to build a toy box for each of our two kids (Figure 4).

Building a toy box with my kids while at Yale.

Figure 4. Building a toy box with my kids while at Yale.

A picture of the SEM at BYU (Brent returned to BYU after I graduated in 1992 and brought the system with him back to BYU) can be seen in Figure 5. Note all the boxes surrounding the instrument. In the very first system, instead of controlling the SEM beam, we moved the sample under a stationary beam using piezoelectric stages. In this photo, the camera was fixed so that it was always inserted into the microscope chamber, so there wasn’t a box to control the slide yet. Eventually, the stages were replaced with beam control, the SEM image could be viewed live on the workstation monitor, the camera was controlled through the computer, the image processing was done in the computer, the camera slide was controlled in software until we reached the modern, streamlined systems we are accustomed to today.

Photo of the first fully automated EBSD system in a lab at BYU (originally at Yale but later moved to BYU).

Figure 5. Photo of the first fully automated EBSD system in a lab at BYU (originally at Yale but later moved to BYU).

The old SEM was scrapped several years ago, but the two toy boxes are still in use and filled with “stuffies” as my granddaughter likes to say. So, just like the presents under the Christmas tree, the SEM boxes are still providing entertainment long after the toys they once held have been recycled into new ones 😊.

Care and Upkeep of Your Standards

Shawn Wallace, Applications Engineer, EDAX

As I prepared for some analytical work yesterday, I had to repolish a standard block. This made me think about how important these little blocks are and how often they are not cared for properly. With that in mind, I thought it might be useful to pass on some little nuggets of information I have gathered over the years from many sources.

The most important thing about caring for a block is knowing what is in it. Standard blocks can be purchased as a whole or personally made. No matter what, you need to know what you have! To do so, you should keep several copies of the following for every standard you have:

  • Optical light images of the whole block
  • SEM Montage image of the whole block (BSE and SE)
  • Individual image of each standard material
  • Composition of each standard material with sources
  • Notes on each standard

Each of our standard blocks has a name and a duplicate document. This packet has optical, BSE, and SE images of the standard. This allows us to quickly find the standard we want while having all the information easily accessible in hand.

Figure 1. Each of our standard blocks has a name and a duplicate document. This packet has optical, BSE, and SE images of the standard. This allows us to quickly find the standard we want while having all the information easily accessible in hand.

Each of these above items is important. You want to keep both a visual record of your standards, a record of what it is and the condition that it is in, to allow you to track any issues that may pop up (Figure 1). Therefore, having a note section is important. You may find that one of the areas of your standard gives anomalous values and should be avoided. You want to make sure this information is easily accessible to everyone that uses the standard. I suggest scanning and keeping electronic copies in a shared folder on your desktop.

Besides the documentation aspect of care, physical care is just as critical. Most commercial standard blocks come pre-polished and carbon-coated. Over time, both of those will degrade and need to be redone. Usually, the carbon coating damages first, but you also need to check for burn marks and other beam damage done to the standard material itself. When repolishing and recoating, I usually do a solid 10 minute repolish with diamond paste. This removes enough material to eliminate the carbon coating and get new clean, undamaged surfaces while not change the physical appearance all that much. I try my best to avoid using an Al-based polishing material, as they tend to stick around too much and can interfere with my analysis on elements I use. With carbon-based polishing material, it is much easier to see the effects of the carbon. In the end, I do not tend to do quant work on carbon that much, while I often try to quantify aluminum. Whatever you do, document what was done. It can help you both head off and understand issues that may present.

While physically handling your sample, it shouldn’t need to be said, but you should never be touching your sample with ungloved hands. Your oils are bad for both the SEM cleanliness and the sample cleanliness. Avoid any sort of colloidal products with standards, as they do tend to flake with age. When not in use, samples should be held in a desiccator with good desiccant (Figure 3).

A good desiccator should have a rubber molding to help it hold a seal at a minimum. You should try to keep it under vacuum for the best results. While taking this picture, I noticed I should dry my desiccant or replace it. I have seen some users keep a small plastic bag of fresh desiccant in the desiccator as a quick visual reference.

Figure 3. A good desiccator should have a rubber molding to help it hold a seal at a minimum. You should try to keep it under vacuum for the best results. While taking this picture, I noticed I should dry my desiccant or replace it. I have seen some users keep a small plastic bag of fresh desiccant in the desiccator as a quick visual reference.

There are many other tips I can think of sharing, but to wrap it up, standards are valuable in our industry. A good, well cared for standard will last multiple careers while giving consistent results time after time. Take the time to keep your standards in the best condition, and they will repay your time spent on them tenfold.

Between the Lines

Dr. René de Kloe, Applications Specialist, EDAX

While I am testing new hardware and software versions, I use it as an opportunity to collect some data on unique materials. Testing detector speed or general software functionality is easiest on a simple material like an undeformed Ni or Fe alloy. But, I think it is a shame to perform longer duration tests on materials I have already seen many times before. For such occasions, I look through my collection of materials for something nice to map. During testing of the upcoming APEX™ 2.0 EBSD software, I collected a few larger scans on fossils that I had found during geological fieldwork and family holidays. This included large single-field scans and a Montage map, where we combine beam scans with stage movements for a large mosaic map.

Cross-section through a fossil crinoid stem and IPF on PRIAS™ center map of the fossil crinoid stem sample collected from the indicated area.

Figure 1. a) Cross-section through a fossil crinoid stem. b) IPF on PRIAS™ center map of the fossil crinoid stem sample collected from the indicated area.

For example, Figure 1a shows a cross-section through a fossil crinoid stem. At the edge, the lighter areas represent the structure of the organism, while the darker areas are later sedimentary infill.

This is beautifully visible in the 2.1 x 1.7 mm IPF on PRIAS™ center map, where the biomineral structure appears smooth and fine-grained. In contrast, the infill is more equiaxed and shows topography due to compositional differences (Figure 1b).

Another beautiful scan was collected while I was trying out the new APEX™ 2.0 EBSD Montage map wizard. This wizard allows easy pre-imaging of the entire scan field to set the actual scan area. With the wizard, setting up such a large, 18 million point, 30-field Montage map over a 1.3 x 7 mm area can be done in a few minutes.

Calcite rock sample with fossils and EBSD Montage map of one of the nummulite fossils.

Figure 2. a) Calcite rock sample with fossils. b) EBSD Montage map of one of the nummulite fossils.

We collected these two scans on calcite rocks for which you can simply load the appropriate crystal structure. But collecting data is not always that easy, especially if you are not sure what phase(s) you have in your sample. And ultimately, EBSD data collection is based on pattern analysis and then matching the detected bands against a lookup table. In most cases, you can just search the included EDAX structure file database that contains close to 500 phases and covers most commonly studied materials, such as the calcite used for the scans above.

But where do these files come from? Partly, they are a result of our combined legacy. Over the years, we have seen many materials and often painstakingly identified which bands to select to get reliable indexing results. Nowadays, you can create phase files directly using atomic and crystallographic information. However, you can continue to extract the majority of “new” phase files from XRD databases, such as the AMCS, ICSD, or ICDD PDF databases. These databases contain 10’s to sometimes 100’s of thousands of phase descriptions that are based on XRD measurements. The XRD data shows which lattice planes are effective X-ray diffractors, and are also useful to construct a structure file for electron diffraction patterns.

Indexed olivine EBSD pattern.

Figure 3. Indexed olivine EBSD pattern.

And there the fun starts. Often there are multiple possibilities for phases or minerals (e.g., solid solution series) available in the database. Which one to select? And in many cases, there is no single-phase file that matches the pattern exactly. There are always bands that do not get labeled or are shown in the overlay that are not visible in the real pattern. This is due to the differences between X-ray and electron diffraction intensities or simply incomplete database entries. Time for some human intervention. The APEX™ EBSD software contains advanced tools to modify and optimize the reflector tables of imported or calculated structure files. First, the color-coding itself. All bands are labeled with a color that corresponds to the IPF color triangle, so equivalent lattice planes get identical colors. This allows a visual inspection if bands that are designated with the same color also appear identical.

IPF color triangle.

Figure 4. IPF color triangle.

Then there is a band ID tool to help identify non-labeled bands in the diffraction patterns. When a pattern appears correctly indexed, but a number of bands are not labeled, the user can draw a line on the missing band. The software then shows which lattice plane corresponds to that band and also indicates all crystallographic equivalent planes. When it is still difficult to identify the correct indexing solution, it can be beneficial to bypass the Hough band detection and instead manually draw the bands for indexing. A good trick for low symmetry crystals is only to select the thinnest bands. These correspond to the lattice planes with the largest d-spacings and should be the important low-index crystallographic planes. By excluding the (often) large number of bands with similar bandwidths, you reduce the number of options and more quickly land at the best matching orientation or phase.

Manual Band Selection tool.

Figure 5. Manual Band Selection tool.

When a solution is found that matches the thin bands, you can start drawing in the other ones. When drawing a band, the software automatically shows where all the crystallographic equivalent planes should be. If a line is drawn where no band is present, you have the wrong candidate, and you need to look further. If all the indicated bands match in appearance and width, you can enable the reflector. This way, it is easy to interactively generate a matching phase file. Just keep in mind that when you have optimized a structure file to a pattern, it is a good idea to find some more patterns from that phase (if necessary, just rotate the sample to get a different orientation) and verify that all the bands in the other patterns are also properly identified. This is especially important for low symmetry materials where few lattice planes are equivalent.

Band optimization sequence on an EBSD pattern from W2C. The initial reflector table (a) misses a number of strong bands. Manually selecting a band (b) shows which reflector this is and where the crystallographic equivalent bands should be. This can be repeated (c) until all clear bands have been labeled.

Figure 6. Band optimization sequence on an EBSD pattern from W2C. The initial reflector table (a) misses a number of strong bands. Manually selecting a band (b) shows which reflector this is and where the crystallographic equivalent bands should be. This can be repeated (c) until all clear bands have been labeled.

Although it can be rewarding to identify a new phase and optimize the structure file to allow for EBSD mapping of a new and interesting material, I would like to end with a word of warning. When you are working with a good pattern and successfully identify the phase and orientation, it is very tempting to keep looking for bands and completely fill the pattern with everything you can see. But that is often a bad idea, as the weaker bands will typically not get selected by the Hough transformation on the poorer patterns that are used during indexing. Enjoy playing with the materials and structure files, but don’t overdo it.

Diffraction pattern with all visible bands enabled for indexing.

Figure 7. Diffraction pattern with all visible bands enabled for indexing.

How to Get a Good Answer in a Timely Manner

Shawn Wallace, Applications Engineer, EDAX

One of the joys of my job is troubleshooting issues and ensuring you acquire the best results to advance your research. Sometimes, it requires additional education to help users understand a concept. Other times, it requires an exchange of numerous emails. At the end of the day, our goal is not just to help you, but to ensure you get the right information in a timely manner.

For any sort of EDS related question, we almost always want to look at a spectrum file. Why? There is so much information hidden in the spectrum that we can quickly point out any possible issues. With a single spectrum, we can quickly see if something was charging, tilted, or shadowed (Figure 1). We can even see weird things like beam deceleration caused by a certain imaging mode (Figure 2). With most of these kinds of issues, it is common to run into major quant related problems. Any quant problems should always start with a spectrum.

Figure 1. The teal spectrum shows a strange background versus what a normal spectrum (red) should look like for a material.

Figure 1. The teal spectrum shows a strange background versus what a normal spectrum (red) should look like for a material.

This background information tells us that the sample was most likely shadowed and that rotating the sample to face towards the detector may give better results.

Figure 2. Many microscopes can decelerate the beam to help with imaging. This deceleration is great for imaging but can cause EDS quant issues. Therefore, we recommend reviewing the spectrum up front to reduce the number of emails to troubleshoot this issue.

Figure 2. Many microscopes can decelerate the beam to help with imaging. This deceleration is great for imaging but can cause EDS quant issues. Therefore, we recommend reviewing the spectrum up front to reduce the number of emails to troubleshoot this issue.

To save the spectrum, right-click in the spectrum window, then click on Save (Figure 3). From there, save the file with a descriptive name, and send it off to the applications group. These spectrum files also include other metadata, such as amp time, working distance, and parameters that give us so many clues to get to the bottom of possible issues.

Figure 3. Saving a spectrum in APEX™ is intuitive. Right-click in the area and a pop-up menu will allow you to save the spectrum wherever you want quickly.

Figure 3. Saving a spectrum in APEX™ is intuitive. Right-click in the area and a pop-up menu will allow you to save the spectrum wherever you want quickly.

For information on EDS backgrounds and the information they hold, I suggest watching Dr. Jens Rafaelsen’s Background Modeling and Non-Ideal Sample Analysis webinar.

The actual image file can also help us confirm most of the above.

Troubleshooting EBSD can be tricky since the issue could be from sample prep, indexing, or other issues. To begin, it’s important to rule out any variances associated with sample preparation. Useful information to share includes a description of the sample, as well as the step-by-step instructions used to prepare the sample. This includes things like the length of time, pressure, cloth material, polishing compound material, and even the direction of travel. The more details, the better!

Now, how do I know it is a sample prep problem? If the pattern quality is low at long exposure times (Figure 4) or the sample looks very rough, it is probably related to sample preparation (Figure 4). That being said, there could be non-sample prep related issues too.

Figure 4. This pattern is probably not indexable on its own. Better preparation of the sample surface is necessary to index and map this sample correctly.

Figure 4. This pattern is probably not indexable on its own. Better preparation of the sample surface is necessary to index and map this sample correctly.

For general sample prep guidelines, I would highly suggest Matt Nowell’s Learn How I Prepare Samples for EBSD Analysis webinar.

Indexing problems can be challenging to troubleshoot without a full data set. How do I know my main issues could be related to indexing? If indexing is the source, a map often appears to be very speckled or just black due to no indexing results. For this kind of issue, full data sets are the way to go. By full, I mean patterns and OSC files. These files can be exported out of TEAM™/APEX™. They are often quite large, but there are ways available to move the data quickly.

For the basics of indexing knowledge, I suggest checking out my latest webinar, Understanding and Troubleshooting the EDAX Indexing Routine and the Hough Parameters. During this webinar, we highlight attributes that indicate there is an issue with the data set, then dive into the best practices for troubleshooting them.

As for camera set up, this is a dance between the microscope settings, operator’s requirements, and the camera settings. In general, more electrons (higher current) allow the experiment to go faster and cover more area. With older CCD based cameras, understanding this interaction was key to good results. With the newer Velocity™ cameras based on CMOS technology, the dance is much simpler. If you are having difficulty while trying to optimize an older camera, the Understanding and Optimizing EBSD Camera Settings webinar can help.

So how do you get your questions answered fast? Bury us with information. More information lets us dive deeper into the data to find the root cause in the first email, and avoids a lengthy back and forth exchange of emails. If possible, educate yourself using the resources we have made available, be it webinars or training courses. And always, feel free to reach out to my colleagues and me at edax.applications@ametek.com!

What a Difference a Year Makes

Jonathan McMenamin, Marketing Communications Coordinator, EDAX

EDAX is considered one of the leaders in the world of microscopy and microanalysis. After concentrating on advancements to our Energy Dispersive Spectroscopy (EDS) systems for the Scanning Electron Microscope (SEM) over the past few years, EDAX turned its attention to advances in Electron Backscatter Diffraction (EBSD) and EDS for the Transmission Electron Microscope (TEM) in 2019.

After the introduction of the Velocity™ Plus EBSD camera in June 2018, which produces indexing speeds greater that 3,000 indexed points per second, EDAX raised the bar further in 2019. In March, the company announced the arrival of the fastest EBSD camera in the world, the Velocity™ Super, which can go 50% faster at 4,500 indexed points per second. This was truly a great accomplishment!

EBSD orientation map from additively manufactured Inconel 718 collected at 4,500 indexed points per second at 25 nA beam current.

EBSD orientation map from additively manufactured Inconel 718 collected at 4,500 indexed points per second at 25 nA beam current.

Less than three months later, EDAX added a new detector to its TEM product portfolio. The Elite T Ultra is a 160 mm2 detector that offers a unique geometry and powerful quantification routines for comprehensive analysis solutions for all TEM applications. The windowless detector’s geometric design gives it the best possible solid angle to increase the X-ray count rates for optimal results.

EDAX Elite T Ultra EDS System for the TEM

EDAX Elite T Ultra EDS System for the TEM.

Just before the annual Microscopy & Microanalysis conference, EDAX launched the OIM Matrix™ software module for OIM Analysis™. This new tool gives users the ability to perform dynamic diffraction-based EBSD pattern simulations and dictionary indexing. Users can now simulate EBSD patterns based on the physics of dynamical diffraction of electrons. These simulated patterns can then be compared to experimentally collected EBSD patterns. Dictionary indexing helps improve indexing success rates over standard Hough-based indexing approaches. You can watch Dr. Stuart Wright’s <a href=”https://youtu.be/Jri181evpiA&#8221; target=”_blank”>presentation from M&M</a> for more information.

Dictionary indexing flow chart and conventional indexing results compared with dictionary indexing results for a nickel sample with patterns collected in a high-gain/noisy condition.

Dictionary indexing flow chart and conventional indexing results compared with dictionary indexing results for a nickel sample with patterns collected in a high-gain/noisy condition.

EDAX has several exciting product announcements on the way in early 2020. We have teased a two of these releases, APEX™ Software for EBSD and the Clarity™ Direct Electron Detector. APEX™ EBSD will give users the ability to characterize both compositional and structural characteristics of their samples on the APEX™ Platform. It gives them the ability to collect and index EBSD patterns and EBSD maps, as well as allow for simultaneous EDS-EBSD collection. You can learn more about APEX™ EBSD in the September issue of the Insight newsletter and in our “APEX™ EBSD – Making EBSD Data Collection How You Want It” webinar.

EBSD of a Gibeon Meteorite sample covering a 7.5 mm x 6.5 mm area using ComboScan for large area analysis.

EBSD of a Gibeon Meteorite sample covering a 7.5 mm x 6.5 mm area using ComboScan for large area analysis.

The Clarity™ is the world’s first commercial direct electron detector (DeD) for EBSD. It provides patterns of the highest quality and sensitivity with no detector read noise and no distortion for optimal performance. The Clarity™ does not require a phosphor screen or light transfer system. The DeD camera is so sensitive that individual electrons can be detected, giving users unprecedented performance for EBSD pattern collection. It is ideal for analysis of beam sensitive samples and potential strain applications. We recently had a webinar “Direct Electron Detection with Clarity™ – Viewing EBSD Patterns in a New Light” previewing the Clarity™. You can also get a better understanding of the system in the December issue of the Insight newsletter or the .

EBSD pattern from Silicon using the Clarity™ detector.

EBSD pattern from Silicon
using the Clarity™ detector.

All this happened in one year! 2020 looks to be another great year for EDAX with further improvements and product releases to offer the best possible tools for you to solve your materials characterization problems.

Intersections

Dr. Stuart Wright, Senior Scientist EBSD, EDAX

The city has recently started burying a pipe down the middle of one of the roads into my neighborhood. There were already a couple of troublesome intersections on this road. The construction has led to several accidents in the past couple of weeks at these intersections and I am sure there are more to come.

A question from a reviewer on a paper I am co-authoring got me thinking about the impact of intersections of bands in EBSD patterns on the Hough transform. The intersections are termed ‘zone axes’ or ‘poles’ and a pattern is typically composed of some strong ones where several high intensity bands intersect as well as weak ones where perhaps only two bands intersect.

To get an idea of the impact of the intersections on the Hough transform, I have created an idealized pattern. The intensity of the bands in the idealized pattern is derived from the peaks heights from the Hough transform applied to an experimental pattern. For a little fun, I have created a second pattern by blacking out the bands in the original idealized pattern, leaving behind only the intersections. I created a third pattern by blacking out the intersections and leaving behind only the bands. I have input these three patterns into the Hough transform. As I expected, you can see the strong sinusoidal curves from the pattern with only the intersections. However, you can also see peaks, where these sinusoidal curves intersect and these correspond (for the most part) to the bands in the pattern.

In the figure, the middle row of images are the raw Hough Transforms and the bottom row of images are the Hough Transforms after applying the butterfly mask. It is interesting to note how much the Hough peaks differ between the three patterns. It is clear that the intersections contribute positively to finding some of the weaker bands. This is a function not only of the band intensity but also the number of zone axes along the length of the band in the pattern.

Eventually the construction on my local road will be done and hopefully we will have fewer accidents. But clearly, intersections are more than just a necessary evil 😊

Hats Off/On to Dictionary Indexing

Dr. Stuart Wright, Senior Scientist EBSD, EDAX

Recently I gave a webinar on dynamic pattern simulation. The use of a dynamic diffraction model [1, 2] allows EBSD patterns to be simulated quite well. One topic I introduced in that presentation was that of dictionary indexing [3]. You may have seen presentations on this indexing approach at some of the microscopy and/or materials science conferences. In this approach, patterns are simulated for a set of orientations covering all of orientation space. Then, an experimental pattern is tested against all of the simulated patterns to find the one that provides the best match with the experimental pattern. This approach does particularly well for noisy patterns.

I’ve been working on implementing some of these ideas into OIM Analysis™ to make dictionary indexing more streamlined for datasets collected using EDAX data collection software – i.e. OIM DC or TEAM™. It has been a learning experience and there is still more to learn.

As I dug into dictionary indexing, I recalled our first efforts to automate EBSD indexing. Our first attempt was a template matching approach [4]. The first step in this approach was to use a “Mexican Hat” filter. This was done to emphasize the zone axes in the patterns. This processed pattern was then compared against a dictionary of “simulated” patterns. The simulated patterns were simple – a white pixel (or set of pixels) for the major zone axes in the pattern and everything else was colored black. In this procedure the orientation sampling for the dictionary was done in Euler space.
It seemed natural to go this route at the time, because we were using David Dingley’s manual on-line indexing software which focused on the zone axes. In David’s software, an operator clicked on a zone axis and identified the <uvw> associated with the zone axis. Two zone axes needed to be identified and then the user had to choose between a set of possible solutions. (Note – it was a long time ago and I think I remember the process correctly. The EBSD system was installed on an SEM located in the botany department at BYU. Our time slot for using the instrument was between 2:00-4:00am so my memory is understandably fuzzy!)

One interesting thing of note in those early dictionary indexing experiments was that the maximum step size in the sampling grid of Euler space that would result in successful indexing was found to be 2.5°, quite similar to the maximum target misorientation for modern dictionary indexing. Of course, this crude sampling approach may have led to the lack of robustness in this early attempt at dictionary indexing. The paper proposed that the technique could be improved by weighting the zone axes by the sum of the structure factors of the bands intersecting at the zone axes.
However, we never followed up on this idea as we abandoned the template matching approach and moved to the Burn’s algorithm coupled with the triplet voting scheme [5] which produced more reliable results. Using this approach, we were able to get our first set of fully automated scans. We presented the results at an MS&T symposium (Microscale Texture of Materials Symposium, Cincinnati, Ohio, October 1991) where Niels Krieger-Lassen also presented his work on band detection using the Hough transform [6]. After the conference, we hurried back to the lab to try out Niels’ approach for the band detection part of the indexing process [7].
Modern dictionary indexing applies an adaptive histogram filter to the experimental patterns (at left in the figure below) and the dictionary patterns (at right) prior to performing the normalized inner dot-product used to compare patterns. The filtered patterns are nearly binary and seeing these triggered my memory of our early dictionary work as they reminded me of the nearly binary “Sombrero” filtered patterns– Olé!
We may not have come back full circle but progress clearly goes in steps and some bear an uncanny resemblance to previous ones. I doff my hat to the great work that has gone into the development of dynamic pattern simulation and its applications.

[1] A. Winkelmann, C. Trager-Cowan, F. Sweeney, A. P. Day, P. Parbrook (2007) “Many-Beam Dynamical Simulation of Electron Backscatter Diffraction Patterns” Ultramicroscopy 107: 414-421.
[2] P. G. Callahan, M. De Graef (2013) “Dynamical Electron Backscatter Diffraction Patterns. Part I: Pattern Simulations” Microscopy and Microanalysis 19: 1255-1265.
[3] S.I. Wright, B. L. Adams, J.-Z. Zhao (1991). “Automated determination of lattice orientation from electron backscattered Kikuchi diffraction patterns” Textures and Microstructures 13: 2-3.
[4] Y.H. Chen, S. U. Park, D. Wei, G. Newstadt, M.A. Jackson, J.P. Simmons, M. De Graef, A.O. Hero (2015) “A dictionary approach to electron backscatter diffraction indexing” Microscopy and Microanalysis 21: 739-752.
[5] S.I. Wright, B. L. Adams (1992) “Automatic-analysis of electron backscatter diffraction patterns” Metallurgical Transactions A 23: 759-767.
[6] N.C. Krieger Lassen, D. Juul Jensen, K. Conradsen (1992) “Image processing procedures for analysis of electron back scattering patterns” Scanning Microscopy 6: 115-121.
[7] K. Kunze, S. I. Wright, B. L. Adams, D. J. Dingley (1993) “Advances in Automatic EBSP Single Orientation Measurements.” Textures and Microstructures 20: 41-54.

Saying What You Mean and Meaning What You Say!

Shawn Wallace, Applications Engineer, EDAX

A recent conversation on a list serv discussed sloppiness in the use of words and how it can cause confusion. This made me consider that in the world of microanalysis, we are not immune. We are probably sloppiest with two particular words. They are resolution and phase.

Let us start with how we use the word phase and how phases are commonly defined in microanalysis. In Energy Dispersive Spectroscopy (EDS), we use phase for everything, for example, phase mapping, phase library. In Electron Backscatter Diffraction (EBSD), the usage is a little more straightforward.

So, what is a phase? Well to me, a geologist, a phase has both a distinct chemistry and a distinct crystal structure. Why does this matter to a geologist? Two different minerals with the same chemistry, but with different structures, can behave in very different ways and this gives me useful information about each of them.
The classic example for geologists is the Al2SIO5 system (figure 1). It has three members, Kyanite, Sillimanite, and Andalusite. They each have the same chemistry but different structures. The structure of each is controlled by the pressure and temperature at which the mineral equilibrated. Simple chemistry tells me nothing. I need the structure to tease out that information.

Figure 1. Phase Diagram of the Al2SiO5 system in geological conditions. Different minerals form at different pressures and temperatures, letting geologists know how deep and/or the temperature at which the parent rock formed.**

EDS users use the term phase much more loosely. A phase is something that is chemically distinct. Our phase maps look at a spectrum pixel by pixel and see how they compare. In the end, the software goes through the entire map and groups each pixel with like pixels. The phase library does chi squared fits to compare the spectrum to the library (figure 2).

Figure 2. Our Spectrum Library Match uses as Chi-squared fit to determine the best possible matches. This phase is based on compositional data, not compositional and structural data.

While the definition of phase is relatively straight forward, the meaning of resolution gets a little murkier. If you asked someone what the EDS resolution is, you may get different answers depending on who you ask. The main way we use the term resolution when talking about EDS is spectral resolution. This defines how tight the peaks in a spectrum are (figure 3).

Figure 3. Comparison of EDS vs. WDS spectral resolution. WDS has much higher resolution (tighter peaks) than EDS, but fewer counts and more set-up are required.

The other main use of resolution, in EDS is the spatial resolution of the EDS signal itself (figure 4). There are many factors which determine this, but the main ones are the accelerating voltage and sample characteristics. This resolution can go from nanometers to microns.

Figure 4. Distribution of the electron energy deposited in an aluminum sample (top row) and a gold sample (bottom row) at 15 kV (left column) and 5 kV (right column). Note the dramatic difference in penetration given by the right hand side scale bar.

The final use of resolution for EDS is mapping resolution. This is by far the easiest to understand. It is just the step size of the beam while you are mapping.

Luckily for us, the easiest way to find out what people mean when they use the terms resolution or phase, is just to ask. Of course, the way to avoid any confusion is to be as precise as possible with your choice of words. I resolve to do my part and communicate as clearly as I can!

** Source: Wikipedia