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).

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).


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!

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.

Intelligent Use of IQ

Dr. Stuart Wright, Senior Scientist, EDAX

You don’t have to be a genius with a high IQ to recognize that IQ is an imperfect measure of intelligence much less EBSD pattern quality.

A Brief History of IQ
At the time we first came up with the idea of pattern quality, we were very focused on finding a reliable (and fast) image processing technique to detect the bands in the EBSD patterns. Thus, we were using the term “image” more frequently than “pattern” and the term “image quality” stuck.  The first IQ metric we formulated was based on the Burn’s algorithm (cumulative detected edge length) that we were using to detect the bands in the patterns in our earliest automation work1.

We presented this early work at the MS&T meeting in Indianapolis in October 1991. Niels Krieger-Lassen showed some promising band detection results using the Hough Transform2. Even though the Burn’s algorithm was working well we thought it would be good to compare it to the Hough Transform approach.  During that time we decided to use the sum of the Hough peak magnitudes to define the IQ when using the Hough Transform3. The impetus for defining an IQ was to compare how well the Hough Transform approach performed versus the Burn’s algorithm as a function of pattern quality. In case you are curious, here is the result. Our implementation of the Hough transform coupled with the triplet indexing routine clearly does a good job at indexing patterns of poor quality. Notice the relatively small IQ Hough-based values; this is because in this early implementation the average intensity of the pattern was subtracted from each pixel. This step was later dropped, probably simply to save time, which was critical when the cycle time was about four second per pattern.


After we did this work we thought it might be interesting to make an image by mapping the IQ value to a gray scale intensity at each point in a scan. Here is the resulting map – our first IQ map (Hough based IQ).

Not only did we explore ways of making things faster, we also wanted to improve the results. One by-product of those developments was that we modified the Hough Transform to be the average of the detected Hough peak heights instead of the sum. A still later modification was to use the number of peaks requested by the user, instead of the number of peaks detected. This was done so that patterns, where only a few peaks were found, did not receive unduly high IQ values.

The next change came not from a modification in how the IQ was calculated, but from the introduction of CCD cameras with 12 bit dynamic range which dramatically increased the IQ values.
In 2005 Tao and Eades proposed using other metrics for measuring IQ4. We implemented these different metrics and compared them with our Hough based IQ measurement in a paper we published in 20065. One of the main conclusions of that paper was that while for some very specific instances the other metrics had some value, our standard Hough based IQ was the best parameter for most cases. Interestingly, exploring the different IQ values was the seed for our PRIAS6 ideas but that is another story. Our competitors use other measures of IQ, but unfortunately these have not been documented – at least to my knowledge.

Factors Influencing IQ
While we have always tried to keep our software chronologically compatible, the IQ parameter has evolved and thus comparing absolute IQ values from data sets obtained using older versions of OIM with results obtained using new ones is probably not a good idea. Not only has the IQ definition evolved but so has the Hough Transform. In fact, since we created the very first IQ maps we realized that while the IQ maps are quite useful they are only quantitative in the sense of relative values within an individual dataset. We have always cautioned against using absolute IQ values of a method for comparing different datasets. In part, because we know a lot of factors affect the IQ values:

  • Camera Settings:
    • Binning
    • Exposure
    • Gain
  • SEM Settings
    • Voltage
    • Current
  • Hough Transform Settings
    • Pattern Size
    • Mask Size
    • Number of peaks
    • Secondary factors (peak symmetry, min distance, vertical bias,…)
  • Sample Prep
  • Image Processing

In developing the next version of OIM we thought it might be worthwhile revisiting the IQ parameter as implemented in our various software packages to see what we could learn about the absolute value of IQ.  In that vein, I thought it would be particularly interesting to look at the Mask Size and the Number of Peaks selected.  To do this, I used a dataset where we had recorded the patterns. Thus, we were able to rescan the dataset using different Hough settings to ascertain the impact of these settings on the IQ values. I also decided to add some Gaussian noise7 to the patterns to see what effect the noise had on the Hough settings.

It would be nice to scale the peak heights with the mask size. However, the “butterfly” masks have negative values in them, making it quite difficult to scale to the weighting of the individual elements of the convolution masks. In the original 7×7 mask we selected the individual components so that the sum would equal zero, to provide some inherent scaling. However, as we introduced other mask sizes this became increasingly difficult, particularly with the smaller masks (intended primarily for more heavily binned patterns).  Thus, we expected the peak heights to be larger for larger masks simply due to the number of matrix components. This trend was confirmed and is shown using the red curves in the figure below.  It should be noted that the smaller mask was used on a 48×48 pixel pattern, the medium on a 96×96 and the larger on a 192×192 pixel pattern.

We also decided to look at the effect of the number of peaks selected. It is assumed that, as we include more peaks we expect the pattern quality to decrease, as the weaker peaks will drive the average Hough peak heights down. This trend was also confirmed as can be seen by the blue curves in the figure.

Image3While these results went as expected, it can be harder to predict the effects of various image processing routines on IQ. The following plot shows the effect of various image processing routines on the IQ values. Perhaps someone with higher IQ could have predicted these results but to me the trends were not all expected. Of course, we usually apply image processing to improve indexing not IQ.

In theory, if all the settings are the same, then the absolute value of the IQ for a matrix of samples should be meaningful. However, it would be rare to use the same settings (Camera, SEM, sample prep,…) for all materials in all states (e.g. deformed vs recrystallized). In fact this is one of the challenges of doing in-situ EBSD work for either a deformation experiment or a recrystallization/grain growth experiment – it is not always easy to predict how the SEM parameters or camera settings need to change as an in-situ experiment progresses. In addition, any changes made to the hardware generally mean that changes to the software are needed as well. Keeping everything constant is a lot easier in theory than it is in practice.

In conclusion, the IQ metric is “relatively” straightforward, but it must “absolutely” be used with some intelligence.☺

1. S.I. Wright and B.L. Adams (1992) “Automatic Analysis of Electron Backscatter Diffraction Patterns”  Metallurgical Transactions A 23, 759-767.
2. K. Kunze, S.I. Wright, B.L. Adams and D.J. Dingley  (1993) “Advances in Automatic EBSP Single Orientation Measurements” Textures and Microstructures 20, 41-54.
3. N.C. Krieger Lassen, D. Juul Jensen and K. Conradsen (1992) “Image processing procedures for analysis of electron back scattering patterns” Scanning microscopy 6,  115-121.
4. X. Tap and A. Eades (2005) “Errors, artifacts, and improvements in EBSD processing and mapping” Microscopy and Microanalysis 11, 79-87.
5. S.I. Wright and M.M Nowell (2006) “EBSD Image Quality Mapping” Microscopy and Microanalysis 12, 72-84.
6. S. I. Wright, M. M. Nowell, R. de Kloe, P. Camus and T. M. Rampton  (2015) “Electron Imaging with an EBSD Detector” Ultramicroscopy 148, 132-145.
7. S I. Wright, M. M. Nowell, S. P. Lindeman, P. P. Camus, M. De Graef and M. Jackson (2015) “Introduction and Comparison of New EBSD Post-Processing Methodologies”  Ultramicroscopy 159, 81

What’s in Your EBSD Pattern?

Dr. Travis Rampton, Applications Engineer EDAX

When collecting EBSD data it is important to optimize the detector/camera to obtain the desired information which is usually focused on crystal orientation. However, many factors affect the creation of an EBSD pattern beyond orientation. Some of these are demonstrated in Figure 1. In this blog post we will examine some of those factors. In doing so we will also take advantage of PRIAS imaging to illustrate a few of the different effects.

Examples of EBSD patterns collected under varied conditions. The pattern on the left represents the ideal pattern, while the one in the middle is a mixed pattern and the pattern on the right is an unprocessed image.

Figure 1: Examples of EBSD patterns collected under varied conditions. The pattern on the left represents the ideal pattern, while the one in the middle is a mixed pattern and the pattern on the right is an unprocessed image.

A list of a few of the most important factors that affect EBSD patterns is given here. The author recognizes that all effects may not be accounted for and invites your additions in the comments section of this blog.

  • SEM kV and beam current
  • EBSD camera parameters (gain, exposure, image processing)
  • Sample/detector geometry
  • Material density
  • Surface structures (topography, defects, quality)
  • Crystal structure/orientation
  • Interaction volume
  • Magnetic domains

Not only do the listed factors affect entire EBSD patterns, but some manifest more apparently in certain regions of the image. This is often best manifested in geological samples containing topography, atomic differences, and orientation contrast (see Figure 2).

Figure 2: (Left) PRIAS image taken from the top of the EBSD detector, showing surface topography; (middle) PRIAS image taken from the center for the detector, showing orientation contrast; (right) PRIAS image taken from the bottom of the detector, showing some atomic difference.

Figure 2: (Left) PRIAS image taken from the top of the EBSD detector, showing surface topography; (middle) PRIAS image taken from the center for the detector, showing orientation contrast; (right) PRIAS image taken from the bottom of the detector, showing some atomic difference.

Some of the factors that affect EBSD patterns are apparent enough that they can be seen by eye; others are so subtle that they require more sensitive techniques. The final example that will be shown in this post is of magnetic domains. The effect of magnetic domains is not visible by just looking at the EBSD patterns, however, PRIAS imaging makes this effect visible. For this example we will look at a grain oriented electrical steel. Figure 3 shows distinct magnetic regions especially when compared to the SEM image.

Figure 3: (left) SEM image taken of steel and (right) PRIAS image taken from the left side of the detector showing magnetic domains.

Figure 3: (left) SEM image taken of steel and (right) PRIAS image taken from the left side of the detector showing magnetic domains.

The images in Figure 3 give one view of the magnetic domains. An additional set of views is seen in the full 5 x 5 array of PRIAS images shown in Figure 4. A close inspection of all 25 images reveals several varying structures. The differences between all of the ROIs is not fully understood at this time and is the subject of an ongoing study.

Figure 4: 5 x 5 grid of PRIAS images taken from grain oriented electrical steel. Each ROI image shows different structure.

Figure 4: 5 x 5 grid of PRIAS images taken from grain oriented electrical steel. Each ROI image shows different structure.

These examples represent just a few of the factors that affect the formation of an EBSD pattern. Often these effects can be seen in the patterns alone, but other times PRIAS imaging is required for clear visualization.  While EBSD is a reliable method for measuring crystallographic orientation and phase information there is often much more information in the EBSD pattern. So I pose the question, what’s in your EBSD pattern?