Applications

Spherical Indexing

Will Lenthe, Principal Software Engineer, EDAX

Dictionary indexing compares experimental electron backscatter diffraction (EBSD) patterns against a dictionary of simulated patterns for each orientation on a uniform grid in orientation space [1,2]. Synthetic patterns are generated by rotating the Kikuchi sphere by the crystal orientation and projecting onto a plane using the experimental geometry. Comparison against a physics-based forward model gives excellent precision and noise tolerance at the cost of significant computational overhead. Spherical harmonic-based indexing uses the same Kikuchi sphere or ‘master pattern,’ but back projects experimental patterns onto the sphere instead. The orientation is indexed using the maximum spherical cross-correlation between the back-projected pattern and the Kikuchi sphere [3,4]. Mathematically, dictionary and spherical indexing are extremely similar, but the spherical approach is more numerically efficient since it can leverage fast Fourier transforms for the computations. In practice, spherical indexing provides similar precision [5] and noise tolerance to dictionary indexing but at much faster speeds.

A GPU implementation of spherical harmonic-based EBSD indexing implemented in OIM Analysis™ as part of the OIM Matrix module provides excellent indexing quality at hundreds or thousands of patterns per second. Here, we applied it to a range of scans to demonstrate the indexing quality and user parameters.

Spherical harmonic indexing has two parameters: bandwidth and grid size. Bandwidth is how far in frequency space to compute harmonics (analogous to a low pass filter on the EBSD pattern). Grid size is the correlation resolution with an Euler angle cube of (grid size)3 used for correlation (i.e., 0 – 360 for phi1, Phi, and phi2). In general, computation time scales with the number of Euler angle grid points, and a reasonable bandwidth is one less than half the grid size. For example, the following are some reasonable pairs of values:

BandwidthGrid Size
63128
95192
127256

Once the best Euler grid point (maximum cross-correlation) is selected, subpixel resolution can be achieved through a refinement step.

Ni Sequence

This dataset is a scan of the same region at different camera gains to intentionally produce corresponding sets of low and high-quality patterns.

Figure 1. Shows a) the result of indexing high-quality patterns, b) spherical harmonic indexing at a bandwidth of 63 and Euler grid of 1283 without refinement, and c) at a bandwidth of 63 with refinement.

Figure 1 shows a) the result of indexing high-quality patterns, b) spherical harmonic indexing at a bandwidth of 63 and Euler grid of 1283 without refinement, and c) at a bandwidth of 63 with refinement. Note that since grid point spacing is ~2.8° (360° / 128), the unrefined result has a stepped appearance due to the discrete orientation possibilities. After refinement, any orientation is possible, providing smooth results.

Figure 2. KAM maps are shown for the same region at a) 0°, b) 1°, and c) 2°.

In Figure 2, KAM maps are shown for the same region at a) 0°, b) 1°, and c) 2°. Notice that without refinement, there is no misorientation within a patch and a sharp spike between them. Even though both the Hough and refined spherical appear smooth, the slight orientation noise in the Hough indexing is visible using KAM.

Figure 3. With low-quality patterns, Hough indexing a) starts to fail, but b) spherical indexing still provides robust solutions and c) accurately captures continuous orientation gradients after refinement.

With low-quality patterns, Hough indexing a) starts to fail, but b) spherical indexing still provides robust solutions and c) accurately captures continuous orientation gradients after refinement (Figure 3).

Figure 4. a) bandwidths of 63, b) 95, and c) 127 are compared before (a – c) and after (d – f) refinement.

For very low-quality patterns, higher bandwidths may be required for better indexing results. In Figure 4, a) bandwidths of 63, b) 95, and c) 127 are compared before (a – c) and after (d – f) refinement. Note that the discrete steps in orientations before refinement become smaller with increased Euler angle grid resolution, but they refine to similar orientations. For all three bandwidths, the grid size is 2 * (bandwidth + 1).

Figure 5. 4. a) Raw pattern and b) NPAR pattern using Hough indexing and c) raw pattern and d) NPAR pattern using spherical indexing with a bandwidth of 127.

With spherical indexing integrated into OIM Analysis, existing image processing algorithms can be used for especially difficult patterns. At extremely high noise levels, Hough indexing cannot index any points, and the spherical indexing begins to fail for some points. NPAR trades spatial resolution for pattern quality by averaging each pattern with its neighbors. The improved patterns can be indexed reliably by both methods but Hough indexing struggles with the resulting overlap patterns near grain boundaries (Figure 5).

Hot Rolled Mg

Figure 6. Hough indexing struggles to index when pattern quality is reduced by a) high deformation, but b) spherical indexing is robust against significantly degraded pattern quality. Note that the d) spherical indexing confidence index strongly correlates with c) image quality but is high even in some regions with extremely low IQ.

Hough indexing struggles to index when pattern quality is reduced by a) high deformation, but b) spherical indexing is robust against significantly degraded pattern quality. Note that the d) spherical indexing confidence index strongly correlates with c) image quality but is high even in some regions with extremely low IQ (Figure 6).

Rutile

Figure 7. Excellent results are possible even with a single pattern center used for the entire dataset. Vignetting is visible in a) an IPF+IQ map of Hough indexing with a fixed pattern center. The field is flat over the entire area for b) an IPF+CI map of spherical indexing with a fixed pattern center.

Spherical indexing can use a unique pattern center for each point at no extra cost for large fields of view. Excellent results are possible even with a single pattern center used for the entire dataset, as shown in Figure 7. 

Deformed Duplex Steel

Figure 8. Phase discrimination depends on the similarity of the phases with a two-phase steel. In addition to the quality in orientation results with d – f) spherical indexing vs. a – c) Hough indexing, b – c & e – f) phase discrimination is improved with spherical BCC and FCC iron well separable.

Spherical indexing can be applied to multiple phases in the same way as any other indexing technique. Phase discrimination depends on the similarity of the phases with a two-phase steel shown in Figure 8. In addition to the quality in orientation results with d – f) spherical indexing vs. a – c) Hough indexing, b – c & e – f) phase discrimination is improved with spherical BCC and FCC iron well separable. Real space refinement may be required for particularly difficult cases in addition to the spherical harmonic refinement shown.

Figure 9. a) spherical CI + IPF shows similar trends as b) Hough IQ + IPF.

Again, spherical indexing’s confidence index correlates well with pattern quality. In Figure 9, a) spherical CI + IPF shows similar trends as b) Hough IQ + IPF.

References

  1. Callahan, P. G., & De Graef, M. (2013). Dynamical electron backscatter diffraction patterns. Part I: Pattern simulations. Microscopy and Microanalysis, 19(5), 1255-1265.
  2. Callahan, P. G., & De Graef, M. (2013). Dynamical electron backscatter diffraction patterns. Part I: Pattern simulations. Microscopy and Microanalysis, 19(5), 1255-1265.
  3. Lenthe, W. C., Singh, S., & De Graef, M. (2019). A spherical harmonic transform approach to the indexing of electron backscattered diffraction patterns. Ultramicroscopy, 207, 112841.
  4. Hielscher, R., Bartel, F., & Britton, T. B. (2019). Gazing at crystal balls: Electron backscatter diffraction pattern analysis and cross-correlation on the sphere. Ultramicroscopy, 207, 112836.
  5. Sparks, G., Shade, P. A., Uchic, M. D., Niezgoda, S. R., Mills, M. J., & Obstalecki, M. (2021). High-precision orientation mapping from spherical harmonic transform indexing of electron backscatter diffraction patterns. Ultramicroscopy, 222, 113187.

Simulate them!

Dr. Chang Lu, Application Specialist, Gatan & EDAX

In early 2022, Gatan and EDAX completed the integration, and our new division was named Electron Microscope Technology (EMT). As an EMT application scientist on the China applications team, I am responsible for almost all the Gatan and EDAX products for Northern China, on both Scanning Electron Microscopy (SEM) and Transmission Electron Microscopy (TEM) platforms. Therefore, I work with diversified products and diversified user groups that focus on different subject matters. In the first half of this year, I found that the data analysis software from EMT Gatan’s DigitalMicrograph® (DM) and EDAX’s OIM Analysis™ are not completely isolated, but in many cases, they can cooperate with each other to help our customers.

For instance, DM can do a series of electron microscopy-related data processing. For some energy dispersive spectroscopy (EDS) mapping data from the minor content, there are various methods to achieve smoothing and enhance the contrast. While in the MSA panel, the principal component analysis (PCA) function can be helpful in terms of high-resolution EDS mapping. However, in today’s EDAX blog, I will talk a little bit more about one feature in OIM Analysis that could potentially benefit a lot of Gatan camera users.

In northern China, there are a group of Gatan users who are focused on nanoscale phases and grains in the TEM. In most scenarios, they heavily employ electron diffraction or bright field imaging to make judgments. However, it is really difficult to determine the unknown (unidentified but has a known x-ray diffraction (XRD) pattern and chemical composition, so there is a potential for it) phase by simply relying on the minor changes of grayscale bright field images. You may say diffraction could help. Yes, a clean, beautiful diffractogram of a particular crystal direction can be helpful. But, no, you need to find the zone axis carefully. If this unknown phase has a crystal structure of low symmetry, most of the time, the effort will be in vain. Generally speaking, the Difpack tool in the DM software could help in determining d-spacing and angles, however, it is not intuitive enough to know the sample at first sight.

The solution is pattern simulation with OIM Matrix™. At first, I noticed this feature because it helped an EDAX user who was studying strains. It can easily export a theoretical Kikuchi pattern for a specific sample orientation with zero stress. Then one day, I had a sudden thought during my morning shower. Maybe I can change the acceleration voltage to 200 kV (typical for TEM), and the sample tilt angle to 0° (make it flat). After entering a specific orientation, we can get a Kikuchi pattern under TEM conditions! For example, take the simulated pattern from NdCeB. With Kinematic Color Overlay, we can also find out what crystal plane corresponds to a specific Kikuchi line. Now, when we start changing the zone axis in an unidentified sample, we can first simulate several orientations and compare them with what we see under TEM. In this way, the process of finding the Kikuchi pole turns out to be very convenient.

Figure 1. A simulated pattern from NdCeB using OIM Matrix.

Now, when some Gatan users bring in some “weird” unidentified samples and say they want to find various zone axis for doing diffractions. I don’t worry about it. I think from a problem-solving point of view, the powerful software from both Gatan and EDAX, like the integration of two companies, can also be combined to solve complex and difficult problems for our customers in the future.

模拟他们!

Dr. Chang Lu, Application Scientist, Gatan & EDAX

2022年初,Gatan和EDAX这两家公司完成了整合,我们的部门更名为Electron Microscope Technology (EMT)。作为EMT中国团队的应用技术支持,我负责中国北方区扫描电镜(SEM)还有透射电镜(TEM)上Gatan还有EDAX的几乎全部产品。产品多样,服务的用户群体也多样。慢慢地在工作过程中,我就发现两边的数据分析软件(Gatan的Digital Micrograph以及EDAX的Orientation Imaging Microscopy)其实并不是完全分开的,很多时候它们可以在不同程度上相互支持,互通互联。

比如说,Gatan公司在TEM平台上知名的DigitalMicrograph(DM)软件。DM可以处理一系列的电镜相关的表征数据。对于部分信号较弱的EDAX能谱结果,我们也可以把数据载入,通过DM内置的图像平滑,互相关算法以及主成分分析(PCA)进行数据的优化处理。但是我今天更想要分享的是EDAX Orientation Imaging Microscopy(OIM)软件在TEM平台上的一些应用。

在我服务的客户群体中,有一些用户尤其关注一些纳米级的物相和晶粒。这时候在TEM平台,我们往往需要使用到电子衍射的手段来对样品进行判断。然而,很多时候单单依靠透射电镜下灰度值衬度的变化,一些微弱的物相的改变很难被发觉。同时,一张干净,漂亮的来自特定晶向的电子衍射,往往需要我们对样品旋转晶带轴。虽然我们可以沿着特定的菊池线去找菊池极,但是这条菊池线对应什么晶面?菊池极对应哪个晶带轴?我们往往需要在Difpack工具里面标定晶格间距和角度,再比对样品材料不同晶面和夹角才有可能清楚,这很麻烦。

我第一次注意到OIM中的Pattern Simulation功能是因为帮助某个研究应力应变的老师输出无应力的特定取向的标准菊池花样图。然后我注意到,其实我是可以对应更改电镜的加速电压,样品倾斜角度还有取向来得到一系列的菊池花样,比如下图这个200 kV加速电压下得到的NdCeB 样品的模拟花样。我们可以对左下角的orientation参数进行修改,得到一系列模拟出来的菊池花样。通过Kinematic Color Overlay,我们还可以知道对应的菊池线对应的是什么晶面。现在,当我们处于未知状态开始转晶带轴的时候我都会首先模拟几个相似的取向,进行对比。这样一来,我沿着那个晶面(菊池线)在找哪个晶带轴(菊池极)都变得异常清楚,这非常方便。

图 1. NdCeB 使用 OIM Matrix。

现在,当Gatan的用户再拿来一个“奇奇怪怪”的样品说要找到特定晶带轴做衍射,我也不像以前担心怎么搞了。需要什么,我们就在EDAX OIM中先模拟出来一个,对着这个模拟的图,旋转,调整晶面,然后在TEM电子衍射过程中去找。我想从解决问题的角度来讲,Gatan和EDAX丰富的软件资源,就像我们这两家公司的合并一样,未来也可以合并解决客户复杂和困难的问题。

Inflation Got You Down?  

Matt Chipman, Sales Manager – Western U.S., EDAX and Gatan

I recently watched a local news story about inflation in consumer goods. The reporter wanted to know if the dollar store could save you money on groceries. The general answer was perhaps on some items, but it wasn’t significant. However, it was interesting to see how some stores focus on a perceived value instead of a real value to its consumer. First, the dollar store raised its starting price from $1.00 to $1.25. Then they used odd-sized packages that were not equivalent to regular grocery store items, making a direct comparison difficult and offering minimal to no real savings. Finally, the dollar store’s selection was very limited so you may end up back at the regular grocery store for anything other than packaged goods.

So, what does this have to do with the microanalysis business? Well, I believe it’s important to look at the big picture with real, tangible benefits that can impact your research. By offering both EDAX and Gatan products, there are more opportunities to combine different technologies to enable unique analyses that can provide a tremendous value to your material studies.

One great example is the quantification of lithium on a scanning electron microscope. By uniting Gatan’s low-kV OnPoint™ Backscattered Electron Detector with EDAX’s Octane Elite Super EDS Detector, this one-of-a-kind analysis is now possible, surpassing what can be done by either technique alone.

Figure 1. The lithium mapping from joint characterization of the EDAX Octane Elite EDS Detector and Gatan OnPoint BSE Detector.

Not to forget, we’ve also been combining the strengths of the Gatan DigitalMicrograph® Software with the EDAX EDS detector technology for TEMs. I believe we are just beginning to scratch the surface of creative things we can do by joining microanalysis systems and techniques. I love discussing creative ways my customers can coalesce microanalysis techniques to do something new.

Figure 2. Multimodal data acquisition of EELS and EDS data combines the chemical sensitivity of EELS with the broad compositional mapping of EDS. Pictured – STEM EELS/EDS mapping of vertical channel 3D NAND acquired with DigitalMicrograph software.

I hope we can all figure out ways to get a real, noticeable value from the equipment we purchase during this time of inflation. I hope to hear ideas from some of you as you tell me about the needs of your laboratories.

EDAX and Gatan Bring You Lithium

Dave Durham, Sales Manager – U.S. Western, EDAX

It has been interesting to recently witness EDAX and Gatan working together to combine the technologies in our portfolios. Although technically, Gatan was acquired by AMETEK back in late 2019, it seems like 2021 has been a year where the integration of our two companies has really begun to hit its stride.

For example, I’ve seen how Gatan’s ion polishing instruments can dramatically improve indexing success for EDAX’s Electron Backscatter Diffraction (EBSD) users compared to the conventional methods for sample preparation. And I’ve seen EDAX’s Elite T Energy Dispersive Spectroscopy (EDS) System undergo a tremendous workflow improvement and ease-of-use overhaul with the implementation of Gatan’s Microscopy Suite user interface. It has been great stuff!

However, the most recent integration between our two companies is truly groundbreaking, and I’m thrilled to see what it will do to enhance the research being done in its field.

Hopefully, you’ve already seen the news mentioned on our website. For the first time, we’ve been able to perform quantitative mapping of lithium in the Scanning Electron Microscope (SEM) by combining the power of EDAX and Gatan detectors and software! These breakthrough results will enable a new level of lithium research that was previously never possible with the SEM.

Figure 1. EDAX and Gatan bring you lithium.

So who cares about lithium? Everyone should. Lithium compounds and alloys are very important materials with significant commercial value. The compounds are being implemented into lightweight structural alloys in the aerospace and automotive industries. They’re also utilized in lithium-ion batteries for small electronic devices and vehicles. Many governments worldwide have proposed plans to reduce dependence on legacy energy sources, which makes further development of lithium-based technologies critical to the adoption of these plans. This means significant investments are currently being made in R&D, failure analysis, and quality control of these materials.

Figure 2. (left) Lithium-ion battery cross-section prepared by Ilion II broad beam argon milling system. (right) EBSD IQ + orientation map revealing the microstructure of the heat-affected zone in a lightweight structural alloy.

So what are the issues with lithium? While electron microscopy and EDS are already essential characterization tools in this industry, there is a distinct inability to map lithium distribution in the SEM. This has presented a significant obstacle, holding back research on these tools. EDS is typically a valuable technique for material characterization, with high sensitivity and spatial resolution to allow for quantitative analysis on a wide range of sample types. But it is not possible to identify lithium in commercially important materials by EDS because:

  1. There is no guarantee that lithium X-rays will be produced from the sample. The X-ray energy and the number of photons produced from the specimen depend on the lithium bonding state. So, even if you have lithium in your sample, it does not mean that lithium X-rays will be generated.
  2. Even if a sample does generate lithium X-rays, they are easily absorbed back into the sample itself, contamination or oxidation, or by the EDS detector window before they can even reach the EDS detector.

Indeed, specialized windowless EDS detectors can detect lithium, but these have drawbacks that impede their practicality and largescale adoption. Even on samples that have a high lithium fluorescence, these special detectors have a limit of detection of about 20 wt %. This is equivalent to about half of the atoms in the sample being lithium, which restricts analysis to only metallic lithium or simple lithium compounds that may not be relevant to advanced lithium research or applications.

And having a specialized windowless EDS system potentially introduces a slew of operational issues/limitations with the detector that aren’t present with a “standard” windowed EDS system. It also restricts the detector’s utility on non- lithium -research-based applications in the lab.

So what have EDAX and Gatan done? We have solved these issues by using a patent-pending technique called the Composition by Difference Method. In this method, we quantify the backscattered electron signal to determine the mean atomic mass for all elements in a particular area of a sample. And from the same region, we collect the EDS signal to quantify the non-lithium elements. From that information, we have two data points that tell us the actual mean atomic mass from the region and a calculated value based on the EDS results — when they don’t agree with one another, it tells us we are missing something in the EDS data. That something we’re missing is lithium.

Figure 3. Data from the OnPoint and the Octane Elite Super are combined and analyzed to quantify lithium.

By using this method, and specifically by combining the EDAX Octane Elite Super EDS Detector and the Gatan OnPoint Backscattered Electron Detector to collect these two signals, we can now generate lithium maps quantitatively with single-digit mass percentages of lithium with sub-micron spatial resolution. This accuracy has been verified to ~1 wt. % lithium by an external accredited laboratory using Glow-discharge Optical Emission Spectroscopy (GDOES).

Figure 4. Secondary electron image and elemental metal fraction maps (by wt. %) of the same region of the MgLiAl alloy; white pixels are regions excluded from the analysis due to the influence of topography (identified by arrows in the secondary electron image) shown here for clarity.

This is a cutting-edge capability in the SEM, and it is a huge opportunity for anyone wanting to discover where lithium exists in their specimens. Just to reiterate, this method does not use a specially designed EDS system for lithium detection! It uses EDAX’s standard (windowed) Octane Elite Super and Gatan’s OnPoint BSE detector, along with EDAX and Gatan software. Simply amazing!

Now that EDAX and Gatan have introduced the ability to provide quantitative lithium analysis, that is:

  • A substantial improvement in limits of lithium detection
  • Insensitive to the lithium bonding state
  • More tolerant to contamination and oxidation
  • Not limited to metallic materials or simple lithium compounds
  • Free from windowless detector-related limitations on the SEM

It seems that we have helped open an avenue for our customers to expand their lithium research beyond anything previously possible. We are truly beginning a very exciting new stage in lithium analysis, and I can’t wait to see how this new capability is used and what comes next!

You can find more information on this new development in our experiment brief.

Want a Free Set of Microanalysis Standards?

Dr. Shangshang Mu, Applications Engineer, EDAX

Modern EDS systems are capable of quantitative analysis with or without standards. Unlike standard-less analysis, the k-ratio is either calculated in the software or based on internal standards. For analysis with standards, it is measured from a reference sample with known composition under the same conditions as the unknown sample. As an applications engineer, sometimes users ask me where to order these standards. Usually, I point them to the vendors that manufacture and distribute reference standards where you can order either off-the-shelf or customized standard blocks. In addition to these commercial mounts, I always tell them that they can request a set of mineral, glass, and rare earth element phosphate standards from the National Museum of Natural History free of charge! These are very useful standards that I’ve seen widely used in not only the geoscience world but also in various manufacturing industries. These free standards are also great for those graduate students with limited budgets and ideal for practicing sample preparation (yes, I was one of them).

This set of standards is officially called the Smithsonian Microbeam Standards and includes 29 minerals, 12 types of glass, and 16 REE phosphates. You can find out more information about these standards and submit a request form by clicking on the link below:
https://naturalhistory.si.edu/research/mineral-sciences/collections-overview/reference-materials/smithsonian-microbeam-standards

I mentioned sample preparation earlier. Yes, you read that right. These standards come in pill capsules containing from many tiny grains to a few larger ones and you need to mount them on your own (Figure 1).

Figure 1. Grains in a pill capsule.

Since you can get the information such as the composition, locality, and references for each standard from the website, what I want to discuss in this blog post is how to prepare them properly for X-ray analysis. The first tricky thing is to get them out of the capsules. The grains in Figure 1 are almost the largest in this set and you won’t get too many of this size. Some of the grains are even too tiny to be seen at first glance. For the majority that are really tiny, you need to tap the capsule a couple of times to release the grains that get stick to the capsule wall, then you can open the capsule very carefully and let the grains slide out with a little tapping.

For mounting, the easiest way is to mount the standards in epoxy using a mounting cup and let it cure. I did this in a fancy way to make it look like a commercial mount (Figure 2). I ordered a 30 mm diameter circular retainer with 37 holes used by commercial mount manufacturers (Figure 3) and filled the holes with standards on my own. I must admit that the retainer is not cheap, but you can machine the mount by yourself or have a machine shop do it for you. In addition to looking pretty, the retainer ensures a good layout so you can quickly locate the standards you need during microanalysis, and you can mount the same type of standards on one block and get rid of the hassle of frequently venting and pumping the SEM chamber to switch standard blocks.

Figure 2. Examples of commercial mounts.

 

Figure 3. 30 mm diameter circular retainer with 37 holes.

To prevent the tiny grains from moving and floating up when pouring the epoxy mix, I placed the retainer upside down and pressed it onto a piece of sticky tape (Figure 4a) and positioned the grains on the sticky surface of the tape within the holes. When tapping the capsule to let the grains slide out and fall into the hole, the other holes were covered to prevent contamination (Figure 4b). These holes are small in diameter and pouring the epoxy mix directly will trap air bubbles in the hole to separate the grains from the epoxy mix. To overcome this problem, I filled up the hole by letting the epoxy mix drip down very slowly along the inner surface of the hole.

Figure 4. Positioning grains within the holes of the retainer.

For general grinding, I start with wet 240 grit SiC sandpaper with subsequent use of 320, 400, 600, 800, and 1,200 grit wet SiC sandpapers. But coarser grits can grind off tiny grains in this case, so I would recommend starting with a relatively fine grit based on the sizes of the grains you receive and always use a light microscope or magnifier to check the grinding. For polishing abrasive, I used 1 micron and 0.3 micron alumina suspensions on a polishing cloth. For the grains used as standards or quantification in general, the surface needs to be perfectly flat. However, the napped polishing cloth tends to abrade epoxy and the grains at different rates, creating surface relief and edge rounding, especially on tiny grains. To mitigate this effect, the polishing should be checked under a light microscope constantly and stopped as soon as the scratches are removed. A vibratory final polishing with colloidal silica is optional. Followed by ultrasonic cleaning and carbon coating, the standard mount is ready to use.

Note that commercial mount manufacturers may prepare standards individually (especially for metal standards) and insert them into the holes from the back of the retainer and fasten them with retaining rings (Figure 5a). A benefit of this approach is that the standards on the mount are changeable, so you can load all the standards you need on one mount before microanalysis. I used to make several individual mounted standards that can fit into the retainer (Figure 5b) but this process is very time consuming and much trickier to keep the small surface flat during grinding and polishing.

Figure 5. a) The back of a commercial metal standard mount. b) A tiny cylindrical mount that can fit into the retainer holes.

This is definitely a good set of standards to keep in your lab. With EDAX EDS software, in addition to quantification with these standards, you can also use them to create a library and explore the Spectrum Matching feature. The next time you want to quickly determine the specific type of a mineral, you can simply collect a quick spectrum and click the “Match” button, and the software will compare the unknowns to the library you just created.

Between the Lines

Dr. René de Kloe, Applications Specialist, EDAX

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

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

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

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

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

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

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

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

Figure 3. Indexed olivine EBSD pattern.

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

Figure 4. IPF color triangle.

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

Figure 5. Manual Band Selection tool.

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

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

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

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

How to Get a Good Answer in a Timely Manner

Shawn Wallace, Applications Engineer, EDAX

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

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

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

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.

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.

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.

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!

Is It Worth The Salt?

Felix Reinauer, Applications Specialist, EDAX

When you are in Sweden at Scandem 2019 it is the perfect time to order SOS as an appetizer or for dinner. It is made of smör, ost and sill (butter, cheese and herring) served together with potatoes. Sometimes the potatoes need a little bit of improvement in taste. It is very easy to take the salt mostly located on all tables and salt them. Doing that I thought about how easy it is to do this today and what am I really pouring on my potatoes?

Salt was very important in the past. In ancient times salt was so important that the government of Egypt and other countries setup salt taxes. Around 4000 years ago in China and during the Bronze age in Europe, people started to preserve food using brine. The Romains had soldiers guarding and securing the transportation of salt. Salt was as expensive as gold. Sal is the Latin word for salt and the soldiers used to get their salare. Today you still get a salary. Later ‘Streets of Salt’ were settled to guarantee safe transportation all over the country. As a result, cities along these roads got wealthy. Even cities, like Munich, were founded to make money with the salt tax. Salt even destroyed empires and caused big crises. Venice fought with Genoa over spices in the middle ages. In the 19th century soldiers were sent out to conquer a big mountain of salt of an Inconceivable value, lying along the Missouri River. We all know the history of India´s independence. Mohandas Gandhi organized a salt protest to demonstrate against the British salt tax. The importance of the word salt is also implemented in our languages, “Worth the salt”, “Salz in der Suppe” or “Mettre son grain de sel”.

The two principle ways of getting salt are from underground belts and from the sea. It can be extracted from underground either by mining or by using solution mining. Sea salt is produced in small pools which were filled up during high tide and water evaporates under sunny weather conditions. Two kinds of salt mining are done. Directly digging the salt out of the mountain, then dissolving it to clean it. Or hot water is directly used to dissolve the salt and then the brine is pumped up.

Buying salt today is no longer that expensive, dangerous or difficult. But now a new problem arises. I´m talking about salt for consumption, which usually means NaCl in nice white crystals. So, are there any advantages to using different kind of salts? If we believe advertisements or gourmets, it is important, where the salt we use came from and how it was produced. Today the most time-consuming issue is the selection of the kind of salt you want in the supermarket!

For my analysis I chose three kinds of salts from three different areas. The first question was, are the differences big enough to detect them using EDS or will the differences be related to minor trace elements which can only be seen in WDS. It was a surprise for me that the differences are that huge. I had a look at several crystals from one sample. Shown as examples are the typical analysis of the different compounds and elements for that provenance.

First looking at the mined salt. I selected a kind of salt from the oldest salt company in Germany established over 400 years ago. One kind from Switzerland manufactured in the middle of the Alpes and one from the Kalahari, to be as far away as possible from the others. The salt from Switzerland is the purest salt only containing NaCl with some minor traces. The German salt contains a bigger amount of potassium and the Kalahari salt a bigger amount of sulfur and oxygen (Figure 2.).

Figure 2.

Secondly, I was interested in the salt coming from the sea. I selected two types of salt from French coasts one from the Atlantic Ocean in Brittany and another one from the Mediterranean Sea. The third one came from the German coast at the Baltic Sea. The first interesting impression is that all the sea salt contains many more elements. The Mediterranean salt contains the smallest amount of trace elements. The salt from the Atlantic Ocean and the Baltic sea contains, besides the main NaCl, phases containing Ca, K, S, Mg and O. A difference in the two is the amount of Ca containing compounds (Figure 3.).

Figure 3.

Finally, I was interested in some uncommon types of salt. In magazines and television, experts often publish recipes with special types supposedly offering a special taste, or advertising offers remarkable new kinds of healthy salt. So, I was looking for three kinds which seem to be unusable. I found two, a red and a black colored, Hawaiian salt. The spectrum of the red salt shows nicely that Fe containing minerals cause the red color. Even titanium can be found and a bigger amount of Al, Si and O. The black salt contains mainly the same elements. Instead of Fe the high amount of C causes the black color. A designer salt is the Pyramid finger salt, which is placed on top of the meat to make it look nicer. Beside the shape, the only specialty is the higher amount of Ca, S and O (Figure 4).

Figure 4.

It was really interesting that salt is not even salt. As the shape of the crystals varies, so they differ in composition. In principle it is NaCl but contain more or less different kinds of compounds or even coal to color it. There are elements found in different amounts related to the type of salt and area it came from. These different salts are located in a few very small areas in and on the crystals.
And finally, I pour salt onto my potatoes and think, ok it is NaCl.

 

Hats Off/On to Dictionary Indexing

Dr. Stuart Wright, Senior Scientist EBSD, EDAX

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

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

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

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

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