How Many Electrons Do You Need For An EBSD Pattern?

Matt Nowell, EBSD Product Manager, EDAX

I always liked the commercial that asked,” How many licks does it take to get to the center of a Tootsie Pop?”. I like contests where you estimate the number of M&Ms in a jar. Taking the concept away from delicious treats and moving towards something more technical, I’ve also enjoyed looking at the number of grains we need to measure with EBSD to get a good idea of the texture of a material.

Recently I’ve been working with our new Clarity™ Direct Electron Detector for EBSD. It’s the first commercial EBSD direct detector and will be launching soon. Traditionally, EBSD patterns are captured when the diffracted electrons strike a phosphor screen, where energy is converted into light photons, which are focused through a lens onto an imaging sensor, where the light photons are then converted back to electrons. However, a direct electron detector is just that, it captures the diffracted electrons directly. This allows us to count the electrons in an EBSD pattern directly.

EBSD pattern collected with Clarity™ with an average of 5,000 electrons per pixel.

Figure 1. EBSD pattern collected with Clarity™ with an average of 5,000 electrons per pixel.

Take the EBSD pattern collected from a nickel superalloy using the Clarity™ shown in Figure 1. For an EBSD pattern like this, remember that it has been background corrected to flat-field the image and improve the contrast. This is because the actual live EBSD pattern does not have a uniform intensity across the sensor, as shown in Figure 2. In this example, a background collected while imaging many grains was collected and subtracted from the live signal to produce the image in Figure 1. The background image has the spatial information for a specific orientation removed, while retaining the overall intensity gradient that is a function of the material of interest and the sample geometry. Note that the Clarity™ uses four direct electron detectors that are coupled together. The cross-hair image visible in Figure 2 shows the location of the seams between the detectors. These can be masked out of the image if desired but are quickly minimized with this background correction.

EBSD pattern from Figure 1 prior to background correction.

Figure 2. EBSD pattern from Figure 1 prior to background correction.

For Figure 1, a pixel at the center of the signal intensity contained approximately 10,000 electrons, and the average counts for all pixels was approximately 5,000 electrons. After background subtraction, I drew a line across the image, and the intensity profile across this line is shown in Figure 3. This profile shows that the final processed EBSD pattern has a dynamic range of about 1,700 electrons.

Line profile across the EBSD pattern in Figure 1 showing the dynamic range of the EBSD signal.

Figure 3. Line profile across the EBSD pattern in Figure 1 showing the dynamic range of the EBSD signal.

EBSD pattern with an average of 10 electrons per pixel.

Figure 4. EBSD pattern with an average of 10 electrons per pixel.

Now seeing that I could count the number of electrons in an EBSD pattern, I wanted to know how many I needed to get a usable EBSD pattern. I could decrease the exposure time, decrease the beam current, or do both. In this case, I continually decreased the exposure time to find where the EBSD pattern indexing started to fail. Figure 4 shows an EBSD pattern where the maximum number of electrons is 20 and the average number of electrons is 10. Even with this small amount of a signal, I was still able to index it with a confidence index of 0.92 and a fit of 0.6°, which indicates a good orientation solution. Talk about doing a lot with a little. This performance is enabled by the single electron sensitivity and zero readout noise of the detector, which makes this camera very exciting for low beam dose applications for beam-sensitive materials. I look forward to sharing more later.

Indexing solution for the pattern in Figure 4 with a confidence index of 0.92.

Figure 5. Indexing solution for the pattern in Figure 4 with a confidence index of 0.92.

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!

Shelf Life

Dr. Bruce Scruggs, XRF Product Manager, EDAX

Recently, we had a customer request to see a demonstration on the Orbis micro-XRF system. As we talked about what they would like to see, he mentioned that he had made some test XRF measurements on table salt, and he couldn’t measure the iodine content. I agreed to measure the iodine content in table salt. Initially, I thought this would be a very straightforward exercise, as table salt is just NaCl with some iodine added, but this was anything but straightforward.

The iodization of salt in the United States began about a century ago. Iodine is an important micronutrient for thyroid gland health. Certain portions of the American population had diets deficient in iodine and the iodization of table salt was chosen as a method to increase the level of iodine in the average American diet. The salt iodization process was inexpensive; salt does not spoil and estimates of table salt consumption were available.

Some weeks before the customer demo, I bought some iodized table salt from the local grocery store. The ingredients list showed iodine in the form of potassium iodide at about 45 ppm iodine. This concentration was consistent with my web searches. I pressed a pile of salt grains onto a piece of carbon tape and measured it with the Orbis system using a 2 mm spot size (the system was equipped to measure down to a 30 μm spot size, small enough for individual grains, but I wanted to avoid any potential issues with grain to grain variations). It was easy enough and I could measure the I(L) lines with I(Lα) at 3.937 keV (Figure 1).

(A): Salt spectrum with peak deconvolution, not including I(L) series; Fig 1(B): The same salt spectrum as in (A) with peak deconvolution including I(L) series.

Figure 1. (A) Salt spectrum with peak deconvolution, not including I(L) series. (B) The same salt spectrum as in (A) with peak deconvolution including I(L) series.

Some weeks later, during the actual customer demonstration, we measured a variety of customer supplied samples and the customer asked to measure table salt near the end of the demo. I put my table salt sample into the Orbis and was astonished to find that the iodine signal disappeared (Figure 2). Peak fitting and quantification results showed no detectable iodine. After a discussion with the customer, I began to suspect that the salt iodization level was not stable, given that solid I2 is known to undergo sublimation at room temperature. I spoke to the customer again and in his previous attempts, he measured table salt (from shakers) in the company cafeteria. I often wonder how long that salt has been in the shaker!

The same salt sample, as Figure 1, measured on the Orbis a few weeks later without the presence of iodine.

Figure 2. The same salt sample, as Figure 1, measured on the Orbis a few weeks later without the presence of iodine.

Further web searches indicated that indeed, the iodization level of salt has a certain shelf life depending on many factors, including temperature, humidity, impurities in the salt, the chemical form of the iodine bearing additives, and product packaging. For example, potassium iodide is oxidized by contact with oxygen and atmospheric moisture and the resulting iodine then undergoes sublimation. In various regions of the world, iodized table salt is formulated to improve its shelf life with regard to iodine retention based on the characteristics of the table salt and the general environment, e.g., desert, tropical. Based on this loss mechanism, I suspect that there must also be a significant loss of iodine during cooking depending on whether salt is added while cooking or directly applied before consuming.

In my case, the iodine level had dropped below detectable limits in about three weeks of being left out on the table. The grains of salt ranged in size from about 100 – 500 μm in characteristic dimensions, and I was curious to what characteristic depth XRF was measuring. Was there possibly any iodine left in the largest crystals? This depth can be estimated based on the fluorescent signal energy as the exciting X-ray energy always has to be greater than the fluoresced photons (The physics are a bit different for electron excitation where the answer is determined by electron penetration depth into the sample).

XRF measurement depth can be estimated from the Beer-Lambert equation for the absorption and transmission of light:

Equation 1

Equation 1.

The mass absorption coefficient (MAC) describes how readily the I(Lα) signal line at 3.937 keV will be absorbed by the NaCl matrix. It can be described as follows:

Equation 2

Equation 2.

For NaCl, we have two MACs describing how Na and Cl each absorb the 3.937 keV photon. The easiest way to get the full matrix MAC is to back-calculate it from the Beer-Lambert equation and any web-based calculator describing X-ray absorption/transmission characteristics modeling the fluoresced photon traversing the sample matrix to the detector. I prefer the website, http://henke.lbl.gov/optical_constants/filter2.html. By inputting the sample matrix formula (including trace elements if desired), and an arbitrary path length, one can get the calculated result for I/Io and then rearrange Equation 1 to solve for the NaCl matrix MAC by inputting the previously used path length and the known density of table salt. The result is: μNaCl(3.937 keV) ~ 540 cm2/g.

Rearranging Equation 1, one can solve for the signal path length through the sample traversed by the fluoresced photon to the detector as a function of I/Io:

Equation 3

Equation 3.

The XRF Emission Depth, D, would typically be defined as normal to the sample surface, and you should also consider the take-off angle (TOA) of the detector defined from the sample surface, as shown in Equation 4.

Equation 4

Equation 4.

Table 1 shows the XRF Emission Depth as a function I/Io with a nominal detector TOA of 50ᵒ.

I/Io [%] Path Length, x [μm] Emission Depth, D [μm]
10 20 15
1 39 30
0.1 59 45

Table 1. XRF Emission Depth as a function of the signal transmission ratio, I/Io.

The definition of the characteristic XRF path length and emission depth is somewhat arbitrary, as it depends on the value assigned to the signal transmission ratio, I/Io. Typically, the characteristic path length is defined as the length over which 99% of the signal is absorbed. Hence:

Equation 5

Equation 5.

It is interesting to note from Table 1, that at 50% of the critical emission depth, the XRF signal is undergoing 90% absorption.

Coming back to the original analysis, it is possible that iodine was still present at the core of the larger 500 μm grains of salt. Further analyses could be done on cross-sectioned grains or pulverized grains to make that determination. It would be possible to measure cross-sectioned grains of NaCl using the 30 μm spot size on the Orbis to study how readily iodine is lost as a function of depth into the NaCl grain, but that is a study for another day.

Colorful Language

Dr. Stuart Wright, Senior Scientist EBSD, EDAX

As some of you may know, I dabble in woodworking. Over the years, I’ve built several things for our home. I’m embarrassed to admit that when things don’t go right on these projects, I’ve also been known to use some colorful language to express my frustration. I’m a little prouder that color maps have been the language of EBSD since the inception of the automated systems. Figure 1 shows one of the first color maps I created with the help of Karsten Kunze, who was a Post-Doc at Yale University while I was working on my PhD. The colors are associated with prominent peaks in the ODF. Namely the ideal copper orientation {112}〈111〉 in blue and its statistically symmetric variant (i.e. arising from the processing symmetry – rolled sheet in this case) in red, the ideal brass orientation {011}〈211〉 in green and its statistical variant in orange. The ability to illuminate the crystallographic orientation aspects of the microstructure using such color maps took off quickly. I’ve always thought such maps have an aesthetic beauty to them. As the New Mexico based artist, Georgia O’Keeffe said “I found I could say things with color… that I couldn’t say any other way.”

Orientation distribution function (ODF) plotted in Euler space and an orientation map from rolled aluminum (November 1991). Colors correspond to the copper and brass texture α-fiber components for rolled FCC materials.

Figure 1. Orientation distribution function (ODF) plotted in Euler space and an orientation map from rolled aluminum (November 1991). Colors correspond to the copper and brass texture α-fiber components for rolled FCC materials.

I remember a conversation in the lab with Karsten and my advisor, Professor Brent Adams, arguing whether we could see some patterns in the arrangement of the different “colored” orientations. We found each of us were predisposed to seeing patterns for certain colors over other colors. This led to one more chapter in my PhD thesis focused on orientation correlation. In this chapter, I tried to confirm the presence of patterns in the arrangement of orientations within the microstructure with some statistical rigor. In the end, there didn’t appear to be much correlation for any of the colors.

There has been some recent work on improving color mapping. There are two parts to this, to try and get (1) more “perceptually uniform” color maps1 and (2) better color maps for showing crystallographic orientation particularly for low symmetry materials2,3. I’ve implemented these ideas into OIM Analysis™ v8.5. This version will hopefully be ready early this year – we are currently in the testing/bug fixing phase. The reason for “perceptually uniform” color maps (PUCM) is that we can see variations within some colors but not others. For example, Figure 2 shows a GROD map for a steel sample after 10% tensile strain. The mapping has been done using our standard “rainbow” color gradients and with a PUCM version of the rainbow gradient.

Grain Reference Orientation Deviation (GROD) maps for steel tensile specimen deformed in-situ. The first row is displayed using the standard OIM “rainbow” color gradient and the second using a perceptually uniform color map (PUCM). The first column of maps are GROD-angle maps, the second column of maps are GROD-angle maps overlaid on gray scale image quality (IQ) maps.

Figure 2. Grain Reference Orientation Deviation (GROD) maps for steel tensile specimen deformed in-situ. The first row is displayed using the standard OIM “rainbow” color gradient and the second using a perceptually uniform color map (PUCM). The first column of maps are GROD-angle maps, the second column of maps are GROD-angle maps overlaid on gray scale image quality (IQ) maps.

You will notice in the color gradient that it is difficult to see subtle color variations for blue, green and red in the standard rainbow gradient, whereas the PUCM color gradient shows a more consistent variation in the colors across the full range of colors. The variations at the top of the color scales are 10%. From a purely aesthetic point of view, I like the vibrancy of the colors in our standard rainbow color mapping. However, I can also see that the standard color gradient can be somewhat misleading as to the degree of color variation. I noted in my thesis, “This visualization of the microstructure is a useful technique for coupling the morphological and orientation aspects of microstructure into a discrete picture. … Orientation correlation calculations can be made to statistically quantify this apparent structure. This is discussed in the following chapter.” In other words, the colors are nice and helpful. They can serve as a guide for further quantitative analysis, but without that subsequent chapter on the “further quantitative analysis”, our microstructure characterization report will end up as a picture book for the coffee table.

Here is another way to look at the benefits of perceptually uniform color mapping. Figure 3 shows a series of IPF maps where I have rotated the data to show the large grain at the center in several ideal orientations. The top row uses our standard color triangle, whereas the bottom row uses the PUCM color triangle. Note that the subtle color variations in the large grain are more evident in some colors than in other colors.

Inverse Pole Figure (IPF) maps for the same grain in seven different orientations displayed using the standard OIM color mapping scheme (top row) and the PUCM scheme (bottom row).

Figure 3. Inverse Pole Figure (IPF) maps for the same grain in seven different orientations displayed using the standard OIM color mapping scheme (top row) and the PUCM scheme (bottom row).

It is interesting to note that, in general, the PUCM maps show less color variation than our standard IPF maps. Also note that the degree of color variation is more consistent across the full range of colors. Consistency is good, but the results are not as dramatic as I had originally anticipated. PUCM does not alleviate the need to extend our analysis beyond pretty pictures to a thorough quantitative examination of the orientation data.

For the lower symmetries, the PUCM color mapping schemes are quite different from our standard color mapping schemes and include both white and black to extend the available color palette. As can be seen in Figure 4, for rhodochrosite, the extended PUCM color palette works well for standalone IPF maps, but does not work as well when the IPF maps are overlaid on a gray scale map, such as an IQ map. So once again, the PUCM maps have some advantages and disadvantages over the standard maps. Certainly, having more views of the same data is helpful. But another chapter on quantitative analysis is needed.

IPF maps for a mineral specimen containing rhodochrosite, barite, quartz and pyrite. The left column of maps are displayed using the standard OIM color mapping scheme and the right column using the PUCM scheme. The top row of maps are standalone IPF maps, the second row of maps are IPF maps overlaid on gray scale IQ maps.

Figure 4. IPF maps for a mineral specimen containing rhodochrosite, barite, quartz and pyrite. The left column of maps are displayed using the standard OIM color mapping scheme and the right column using the PUCM scheme. The top row of maps are standalone IPF maps, the second row of maps are IPF maps overlaid on gray scale IQ maps.

A phase map for the rhodochrosite mineral specimen.

Figure 5. A phase map for the rhodochrosite mineral specimen.

OK, probably a few too many references about the need to go beyond pictures to quantitative analysis, so let me end on a fun anecdote about color from the “Microscale Texture of Materials” Symposium at the joint ASM/TMS Meeting in Cincinnati, OH in October 1991. At this meeting, I gave our first presentation on results obtained using fully automate EBSD. My presentation was short leaving time for an energetic discussion. One point of discussion was the bit depth needed for image processing to detect the bands in the patterns. My advisor, Professor Adams, said something along the lines of “the human eye can only differentiate 32 colors” (he meant 32 gray levels). I remember Professor Dr. Robert Schwarzer chimed in after Brent with “I don’t know about the American eye, but the European eye can certainly see more than 32 colors!” Of course, we all got a big laugh 😊. Perhaps my European friends can get by with the color maps and can skip the extra “chapter” on quantitative analysis!

1 Peterkovesi.com/projects/colourmaps/index.html
2 G. Nolze and R. Hielscher (2016) “Orientations–perfectly colored” Journal of Applied Crystallography, 49: 1786-1802.
3 William C. Lenthe & Marc De Graef (2018), “Perceptually Uniform Color Maps for the Disk, Sphere, and Ball”, preprint

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.

Those People and Things

Dr. Sophie Yan, Applications Engineer, EDAX

Click here to read the post in Chinese.

The end of the year is my conference season. I have been to various conferences since October and I have seen many new faces. Recently, I realized that several young people I trained have stepped into the electron microscopy and microanalysis world. Their reasons seemed to be similar: want a life like Sophie’s. I felt deeply honored but also frightened. Did I give the young people a good example or fantasy?

A couple of us who used to study and/or work at Shanghai Institute of Ceramics have organized an annual meetup at the Chinese Electron Microscopy Society Conference. This year the number of participants reached 19, indicating more and more people have joined this field. As time passes, I have been able to recognize some of the big names in microscopy, and I am overwhelmed at how quickly young scientists have become those big names. Indeed, when more and more new faces have become major players in this field, it indicates the prosperity of this field. I am very fortunate to be a witness of this booming industry.

Once at SEMICON, a participant from Taiwan couldn’t believe my decision to step out after he/she realized that I was no longer in the semiconductor industry. At that time, I didn’t care about his/her words, but right now I figured out why he/she felt so sorry for me. It is very fortunate to love a job you choose. The semiconductor industry was a little down at the time I left, but it has been developing incredibly fast afterwards and I have found the job I love. It is so good to see that my breakup with semiconductor made both of us happy.

Mr. Yang from The University of Science and Technology Beijing (the author of the first Chinese EBSD book you’re supposed to read in China) used to tell me that “I felt you have been attending a lot of conferences and got much more resources than other people.” So I really got lucky.

My EBSD mentor, European applications specialist, René de Kloe, has traveled all around the world. He is very knowledgeable and humble but shows his expertise when questioned. He always promptly and fully replies to my emails and is always ready to help. Although we meet often, every time I am impressed by his expertise and like him more.

Dr. Sophie Yan and Dr. Stuart Wright

Dr. Sophie Yan and Dr. Stuart Wright

EDAX EBSD experts at a meeting in Draper, UT.

EDAX EBSD experts at a meeting in Draper, UT.

And Dr. Stuart Wright, he is a legend in the EBSD world. His name appears in textbooks and references to all kinds of EBSD papers. He took René and I to the west coast of the United States the first time I met him. René said that his toes finally touched the water of the Pacific Ocean again and for the first time in 3 years. He said that his feet high fived each other from the last time he dipped his feet in Tokyo Bay. In 2017, ICOTOM was held in the small city where Stuart lives. As a conference organizer, he took care of everything by himself. That was the most successful conference that considered both academic atmosphere and hospitality. (Well, I must attend the next ICOTOM in Osaka in September 2020).

With lots of luck, I have been to many places and gotten in touch with big names in this field. The cost is I travel more than 50%, on mainly domestic trips with more than 100,000 kilometers every year. I have seen everything there is to see in the Beijing and Shanghai airports. In contrast, the streets of every city look common to me. A kind of common that you can’t figure out their meanings at a glance.

Beijing Daxing International Airport

The new Beijing Daxing International Airport opened in September 2019.

But when people ask what exactly is EDAX’s direct electron detection? I can finally calm down and keep the conversation going, although I just know a little about it. René and Stuart patiently explained it to me when I knew nothing about it, and now it is my turn to spread the word. This is a brand-new field, and EDAX is the first player. What can we do with direct electron detection? Just wait and see. For a sneak preview, take a look at René’s recent webinar, “Direct Electron Detection with Clarity™ – Viewing EBSD Patterns in a New Light”.

Look Closer

Dr. René de Kloe, Applications Specialist, EDAX

All our senses are aimed at observation. We feel, see, hear, smell and taste things to experience the world around us. We are relying on our senses to make many of our day-to-day decisions and choices. And especially in the upcoming holiday season, shops and companies in the business of selling things cleverly use shiny advertisements, brochures, fragrances, and unbeatable product descriptions to entice us to select their wares. All the time hoping that we will succumb to our senses that focus on the superficial appearance of products before thinking things through.

We must be very careful not to let this very successful marketing strategy subconsciously guide us when analyzing materials as well. During our work as microscopists we are continuously selecting samples, cutting and preparing them to expose a feature of interest, and then choosing the analytical tool and actual analysis area. How sure can we be that we really get representative and objective information?

Dr. René de Kloe's PhD thesis

Dr. René de Kloe’s PhD thesis.

As a geologist, I was taught to take your distance from a rock outcrop and look it over before going into any detail, knowing that the context of your observations is crucial in your interpretation. Then I would go in close to look, feel, and yes sometimes actually taste the rock in order to try to identify what I was actually looking at and how the overall structure fit in the geological setting of the area.

Observing this distance is crucial for your understanding of structures, but in some cases, you cannot get out far enough to see the bigger picture and then you must make do with what you can see.

Perhaps an extreme example is what I did for my PhD research. I have studied the occurrence and distribution of nm-scale films of amorphous material along grain boundaries in experimentally deformed rocks that originate deep inside the Earth. In total I may have characterized a few cubic microns of material but based on that I tried to draw conclusions on the effects of these melt layers on the movements of entire continents!

In microanalysis, we are suffering from the same problem. Microscopy inherently means that you cannot look at the wider picture and when you are looking at extremely small-scale features, their size combined with a practical image resolution may limit the observable surface even further. And one of the most difficult questions you then must ask yourself before starting an analysis is, if the analysis area is representative. And that can be a really tricky question. How objective are we all when browsing the sample surface to find a spot to collect the data? Don’t we all tend to preferentially pick an area that looks promising? I am not so sure that that would always be the most representative region.

It is not that long ago that the acquisition limits in EDS and EBSD were caused by the detector technology. For EDS mapping, we were quite happy if you could collect your data with 50,000 input counts per second and a 50% dead time. This meant that when you were collecting a 512 x 400 pixel map where you wanted to have, say 1000 X-ray counts per pixel, it would take you a few hours. And after that time someone else would be hovering behind you, eager to use the microscope. This seriously limited the sample area that could be analyzed and as a researcher you needed to think carefully about your analysis strategy to get representative information.

Single field EDS map of FeSi sample with REE phases

Single field EDS map of FeSi sample with REE phases.

The area that can be analyzed has changed dramatically with the introduction of the latest EDS detector technology. These detectors are capable of processing more than two million input counts and get maximum throughputs of 850,000 counts per second. You can now get the same area analysis in a matter of minutes, which allows you to analyse more samples or simply more areas on your sample. Alternatively, you can choose to get a wider view and collect large area mosaic maps to minimise the risk of unintended preferential area selection and get more representative data.

120 field multi-field EDS map of an igneous rock showing merged ROI maps of Si (red), Fe (yellow), and O (green) on a backscatter SEM image. Total image resolution 6144 x 4000 points ~ 5.4 x 3.5 mm

120 multi-field EDS map of an igneous rock showing merged ROI maps of Si (red), Fe (yellow), and O (green) on a backscatter SEM image. Total image resolution 6144 x 4000 points ~ 5.4 x 3.5 mm.

A similar dramatic improvement has occurred in EBSD technology. When I started as EBSD application specialist at EDAX in 2001, my first EBSD demo system could collect at least two points per second when it was not raining and the moon was in the right quarter (or perhaps more realistically, if I was really lucky to have a good sample with strong patterns). The map below was one of my first maps that I collected when getting to know the system and I still use it today as an example to show different typical EBSD mapping features, such as grain boundaries, subgrain boundaries, twins, and slip planes. This map contains “only” 124,405 points but took an 8.5-hour overnight scan to collect.

EBSD IPF on IQ map of Ni alloy

EBSD IPF on IQ map of Ni alloy.

 

49 field EBSD comboscan IPF on PRIAS™ center map of an Fe alloy

49 multi-field EBSD comboscan IPF on PRIAS™ center map of an Fe alloy.

The same map today would take less than half a minute to collect with a Velocity™ EBSD detector. Or when you would like to take a little wider view you can combine beam and stage movements to collect a 2.5 million point scan of an entire sample in about 15 minutes.

These technological improvements allow you to be more efficient with your time and collect the same data much faster. But alternatively, it can effectively open our eyes and allow us to investigate much larger areas to see the bigger picture. Just be careful when you look at things from a bit further away, sometimes at the end of the day it may seem that these things start looking back at you!

Large area EDS map of FeSi sample with REE phases – look who’s watching!

Large area EDS map of FeSi sample with REE phases – look who’s watching!