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).

With Great Data Comes Great Responsibility

Matt Nowell, EBSD Product Manager, EDAX

First, I have to acknowledge that I stole the title above from a tweet by Dr. Ben Britton (@BMatB), but I think it applies perfectly to the topic at hand. This blog post has been inspired by a few recent events around the lab. First, our data server drives suffered from multiple simultaneous hard drive failures. Nothing makes you appreciate your data more than no longer having access to it. Second, my colleague and friend Rene de Kloe wrote the preceding article in this blog, and if you haven’t had the opportunity to read it, I highly recommended it. Having been involved with EBSD sample analysis for over 20 years, I have drawers and drawers full of samples. Some of these are very clearly labeled. Some of these are not labeled, or the label has worn off, or the label has fallen off. One of these we believe is one of Rene’s missing samples, although both of us have spent time trying to find it. Some I can recognize just by looking, others need a sheet of paper with descriptions and details. Some are just sitting on my desk, either waiting for analysis or around for visual props during a talk. Here is a picture of some of these desk samples including a golf club with a sample extracted from the face, a piece of a Gibeon meteorite that has been shaped into a guitar pick, a wafer I fabricated myself in school, a rod of tin I can bend and work harden, and then hand to someone else to try, and a sample of a friction stir weld that I’ve used as a fine grained aluminum standard.

Each sample leads to data. With high speed cameras, it’s easier to collect more data in a shorter period of time. With simultaneous EDS collection, it’s more data still. With things like NPAR™, PRIAS™, HR-EBSD, and with OIM Analysis™ v8 reindexing functionality, there is also a driving force to save EBSD patterns for each scan. With 3D EBSD and in-situ heating and deformation experiments, there are multiple scans per sample. Over the years, we have archived data with Zip drives, CDs, DVDs, and portable hard drives. Fortunately, the cost for storage has dramatically decreased in the last 20+ years. I remember buying my first USB storage stick in 2003, with 256 MB of storage. Now I routinely carry around multiple TBs of data full of different examples for whatever questions might pop up.

How do we organize this plethora of data?
Personally, I sometimes struggle with this problem. My desk and office are often a messy conglomerate of different samples, golf training aids (they help me think), papers to read, brochures to edit, and other work to do. I’m often asked if I have an example of one material or another, so there is a strong driving force to be able to find this quickly. Previously I’ve used a database we wrote internally, which was nice but required all of us to enter accurate data into the database. I also used photo management software and the batch processor in OIM Analysis™ to create a visual database of microstructures, which I could quickly review and recognize examples. Often however, I ended up needing multiple pictures to express all the information I wanted in order to use this collection.


To help with this problem, the OIM Data Miner function was implemented into OIM Analysis™. This tool will index the data on any given hard drive, and provide a list of all the OIM scan files present. A screenshot using the Data Miner on one of my drives is shown above. The Data Miner is accessed through this icon on the OIM Analysis™ toolbar. I can see the scan name, where it is located, the date associated with the file, what phases were used, the number of points, the step size, the average confidence index, and the elements associated with any simultaneous EDS collection. From this tool, I can open a file of interest, or I can delete a file I no longer need. I can search by name, by phase, or by element, and I can display duplicate files. I have found this to be extremely useful in finding datasets, and wanted to write a little bit about it in case you may also have some use for this functionality.

Cleaning Up After EBSD 2016

Matt Nowell, EBSD Product Manager, EDAX

I recently had the opportunity to attend the EBSD 2016 meeting, the 5th topical conference of the Microanalysis Society (MAS) in a series on EBSD, held this year at the University of Alabama. This is a conference I am particularly fond of, as I have been able to attend and participate in all 5 of these meetings that have been held since 2008. This conference has grown significantly since then, from around 100 participants in 2008 to around 180 this year. This year there were both basic and advanced tutorials, with lab time for both topics. There have also been more opportunities to show live equipment, with demonstrations available all week for the first time. This is of course great news for EDAX, but I did feel a little badly that Shawn Wallace, our EBSD Applications guru in the US, had to stay in the lab while I was able to listen to the talks all week. For anyone interested or concerned, we did manage to make sure he had something to eat and some exposure to daylight periodically.

This conference also strongly encourages student participation, and offers scholarships (I want to say around 70) that allow students to travel and attend this meeting. It’s something I try to mention to academic users all the time. I’m at a stage in my career now that I am seeing that people, who were students when I trained them years ago, are now professors and professionals throughout the world. I’ve been fortunate to make and maintain friendships with many of them, and look forward to seeing what this year’s students will do with their EBSD knowledge.

There were numerous interesting topics and applications including transmission-EBSD, investigating cracking, both hydrogen and fatigue induced, HR-EBSD, nuclear materials (the sample prep requirements from a safety perspective were amazing), dictionary-based pattern indexing, quartz bridges in rock fractures, and EBSD on dinosaur fossils. There were also posters on correlation with Nanoindentation, atom probe specimen preparation, analysis of asbestos, ion milling specimen preparation, and tin whisker grain analysis. The breadth of work was great to see.

One topic in particular was the concept of cleaning up EBSD data. EBSD data clean up must be used carefully. Generally, I use a Grain CI Standardization routine, and then create a CI >0.1 partition to evaluate the data quality. This approach does not change any of my measured orientations, and gives me a baseline to evaluate what I should do next. My colleague Rene uses this image, which I find appropriate at this stage:

Figure 1: Cleanup ahead.

Figure 1: Cleanup ahead.

The danger here, of course, is that further cleanup will change the orientations away from the initial measurement. This has to be done with care and consideration. I mention all this because at the EBSD 2016 meeting, I presented a poster on NPAR and people were asking about the difference is between NPAR and standard cleanup. I thought this blog would be a good place to address the question.

With NPAR, we average each EBSD pattern with all of the neighboring patterns to improve the signal to noise ratio (SNR) of the averaged pattern prior to indexing. Pattern averaging to improve SNR is not new to EBSD, we used this with analog SIT cameras years ago, but moved away from it as a requirement as digital CCD sensors improved pattern quality. However, if you are pushing the speed and performance of the system, or working with samples with low signal contrast, pattern averaging is useful. The advantage of the spatial averaging with NPAR is that one does not have the time penalty associated with collecting multiple frames in a single location. A schematic of this averaging approach is shown here:

Figure 2: NPAR.

Figure 2: NPAR.

As an experiment, I used our Inconel 600 standard (nominally recrystallized), and found a triple junction. I then collected multiple patterns from each grain with a fast camera setting with corresponding lower SNR EBSD pattern. Representative patterns are shown below.

Figure 3: Grain Patterns.

Figure 3: Grain Patterns.

Now if one averages patterns from the same grain with little deformation, we expect SNR to increase and indexing performance to improve. Here is an example from 7 patterns averaged from grain 1.

Figure 4: Frame Averaged Example.

Figure 4: Frame Averaged Example.

That is easy though. Let’s take a more difficult case, where with our hexagonal measurement grid averaging kernel, we have 4 patterns from one grain and 3 patterns from another. The colors correspond to the orientation maps of the triplet junction shown below.

Figure 5: Multiple Grains

Figure 5: Multiple Grains.

In this case, the orientation solution from this mixed averaged pattern was only 0.1° from the pattern from the 1st grain, with this solution receiving 35 votes out of a possible 84. What this indicated to me was that 7 of the 9 detected bands matched this 1st grain pattern. It’s really impressive what the triplet indexing approach accomplishes with this type of pattern overlap.

Finally, let’s try an averaging kernel where we have 3 patterns from one grain, 2 patterns from a second grain, and 2 patterns from a third grain, as shown here:

Figure 6: Multiple Grains.

Figure 6: Multiple Grains.

Here the orientation solution misoriented 0.4° from the pattern from the 1st grain, with this solution receiving 20 votes out of the possible 84. This indicates that 6 of the 9 detected bands matched this 1st grain pattern. These example do show that we can deconvolute the correct orientation measurement from the strongest pattern within a mixed pattern, which can help improve the effective EBSD spatial resolution when necessary.

Now, to compare NPAR to traditional cleanup, I then set my camera gain to the maximum value, and collected an OIM map from this triple junction, with an acquisition speed near 500 points per second at 1nA beam current. I then applied NPAR to this data. Finally, I reduced the gain and collected a dataset at 25 points per second at the same beam current as a reference. The orientation maps are shown below with corresponding Indexing Success Rates (ISR) as defined by the CI > 0.1 fraction after CI Standardization. This is a good example of how clean up can be used to improve the initial noisy data, as NPAR provides a new alternative with better results.

Figure 7: Orientation Maps.

Figure 7: Orientation Maps.

We can clearly see that the NPAR data correlated well with the slower reference data with the NPAR data collected ≈ 17 times faster than the traditional settings.

Now let’s see how clean up (or noise reduction, although I personally don’t like this term as often we are not dealing with noise-related artifacts) compared to the NPAR results. To start, I used the grain dilation routine in OIM Analysis, which first determines a grain (I used the default 5° tolerance angle and 2 pixel minimum defaults), and then expands that grain out by one step per pass. The results from a single pass, a double pass, and dilation to completion (when all the grains are fully grown together) are shown below. If we compare this approach with the NPAR and As-Collected references, we see that dilation cleanup has brought the 3 primary grains into contact, but a lot of “phantom” artifact grains with low confidence index are still present (and therefore colored black).

Figure 8: Grain Dilation.

Figure 8: Grain Dilation.

The other clean up routine I will commonly use is the Neighbor Orientation Cleanup routine, which in principle is similar to the NPAR neighbor relation approach. Here, instead of averaging patterns spatially, from each measurement point we compare the orientation measurements of all the neighboring points, and if 4 of the 6 neighbors have the same orientation, we change the orientation of the measurement point to this new neighbor orientation. Results from this approach are shown here.

Figure 9: Neighbor Orientation Correlation.

Figure 9: Neighbor Orientation Correlation.

Now of course the starting data is very noise, and was intentionally collected at higher speeds with lower beam currents to highlight the application of NPAR. With initial data like this, traditional clean up routines will have limitations in representing the actual microstructure, and this is why we urge caution when using these procedures. However, clean up can be used more effectively with better starting data. To demonstrate this, a single pass dilation and single pass of neighbor orientation correlation was performed on the NPAR processed data. These results are shown below, along with the reference orientation map. In this case, the low confidence points near the grain boundary have been filled with the correct orientation, and more of the grain boundary interface has been filled in, which would allow better grain misorientation measurements.

Figure 10: NPAR Cleanup.

Figure 10: NPAR Cleanup.

When I evaluate these images, I think the NPAR approach gives me the best representation relative to the reference data, and I know that the orientation is measured from diffraction patterns collected at or adjacent to each measurement point. I think this highlights an important concept when evaluating EBSD indexing, namely that one should understand how pattern indexing works in order to understand when it fails. Most importantly, I think (and this was also emphasized at the EBSD 2016 meeting) that it is good practice to always report what approach was used in measuring and presenting EBSD data to better interpret and understand the measurements relative to the real microstructure.

Old Dogs and New Tricks!

Matt Nowell, Product Manager EBSD, EDAX

This year, three of us in the EBSD development group (Stuart Wright, Scott Lindeman, and myself) celebrated 20 years at EDAX.  I consider myself quite fortunate to have gotten involved with EBSD so early in its commercial development, and it’s been exciting and rewarding to see its growth, both in terms of number of users but also in the wide array of applications.

However, there are still some characterization challenges that we continue to revisit.  One example is differentiating ferrite from martensite in different steel alloys.  This phase differentiation application is challenging because martensite is crystallographically only slightly distorted from the ferrite body-centered cubic cell, and that distortion will depend on carbon content and thermal processing history.  This makes it difficult to differentiate these phases directly via crystallographic structure measurements.  Because the martensitic phase is generally more strained, most differentiation work has focused on using the EBSD Image Quality value as the key differentiation metric [1-2].

Figure 1.

Figure 1.

As new features are developed, it is enjoyable to see where these features can be applied, and what benefits might be gained from them beyond what was initially envisioned.  One example is Neighbor Pattern Averaging and Reindexing or NPAR.  NPAR improves the signal to noise of an EBSD pattern by averaging each pattern with all the neighboring patterns, as shown in Figure 1.    NPAR was initially created as a method of successfully indexing some very noisy patterns we received from a customer, but we quickly found benefits trying this approach on a range of different materials and under different SEM operating conditions.  More details can be found in an earlier blog post at :

Figure 2a. Figure 2b.

Figure 2 shows EBSD Image Quality (IQ) maps collected on a dual phase ferritic-martensitic steel sample.  Fig 2a shows the IQ map collected under standard conditions, while Fig 2b shows the IQ map after NPAR processing.  It can easily be seen that the phase contrast has been increased after using NPAR.  This is because the quality of the EBSD pattern from the martensitic phase is lower due to the internal strain and not because of camera parameters.  This means that the spatial pattern averaging of NPAR does not improve the IQ values for the martensitic phase at the same rate as it does for the ferritic phase, hence increasing the phase contrast values.

Figure 3a. Figure 3b.

NPAR processing does have another effect that can be observed.  Using NPAR, orientation precision is improved through better Signal to Noise levels in the EBSD pattern, resulting in more precise band detection. Because of this effect, the average misorientation (as measured here with the Kernel Average Misorientation metric) measured within each martensitic grain is lower with NPAR processing.  The results with and without NPAR processing are shown in Figure 3.  While NPAR does improve indexing and orientation precision performance, this improvement reduces the effectiveness of the KAM value to differentiate these phases.  I think the fact that NPAR improves one indirect differentiation method while not improving another shows why this is a challenging characterization problem.

In the end, while NPAR does offer some improvements, we still have not found a fully satisfactory solution to the ferrite-martensite differentiation problem.  I look forward to continuing to work on this and other characterization problems as we continue with EBSD product development.

[1] Wilson, A. W., J. D. Madison and G. Spanos (2001). “Determining phase volume fraction in steels by electron backscattered diffraction.” Scripta Materialia 45(12): 1335-1340.
[2] Nowell, M. M., S. I. Wright and J. O. Carpenter (2009). A Practical investigation into Identifying and Differentiating Phases in Steel Using Electron Backscatter Diffraction. Materials Processing and Texture. A. D. Rollett. Hoboken, NJ, John Wiley & Sons: 285-292.

To learn more about NPAR click here to see our video overview.