New features

My New Lab Partner

Matt Nowell, EBSD Product Manager, EDAX

It has been an exciting month here in our Draper Utah lab, as we have received and installed our new FEI Teneo FEG SEM. We are a small lab, focusing on EBSD development and applications, and without a loading dock, so timing is critical when scheduling the delivery. So, 3 months ago, we looked at the calendar to pick a day with sunshine and without snow. Luckily, we picked well.

Figure 1: Our new SEM coming off the truck.

Figure 1: Our new SEM coming off the truck.

Once we got the new instrument up and running, of course the next step was to start playing with it. This new SEM has a lot more imaging detectors than our older SEM, so I wanted to see what I could see with it. I chose a nickel superalloy turbine blade with a thermal barrier coating, as it had many phases for imaging and microanalysis. The first image I collected was with the Everhart-Thornley Detector (ETD). For each image shown, I relied on the auto contrast and brightness adjustment to optimize the image.

Figure 2: ETD image

Figure 2: ETD image

With imaging, contrast is information. The contrast in this image shows phase contrast. On the left, gamma/gamma prime contrast is visible in the Nickel superalloy while different distinct regions of the barrier coating are seen towards the right. The next image I collected was with the Area Backscatter Detector (ABS). This is a detector that is positioned under the pole piece for imaging. With this detector, I can use the entire detector, the inner annular portion of the detector, or any of three regions towards the outer perimeter of the detector.

Figure 3: ABS Detector image.

Figure 3: ABS Detector image.

I tried each of the different options, and I selected the inner annular ring portion of the detector. Each option provided similar contrast as seen in Figure 3, but I went with this based on personal preference. The contrast is like the ETD contrast is Figure 2. I also compared with the imaging options using the detector in Concentric Backscatter (CBS) mode, where 4 different concentric annular detectors are available.

Figure 4: T1 Detector (a-b mode).

Figure 4: T1 Detector (a-b mode).

My next image used the T1 detector, which to my understanding is an in-lens detector. In this mode, I selected the a – b mode, so the final image is obtained by subtracting the image from the b portion of the detector from the a portion of the detector. I selected this image because the resultant contrast is reversed from the first couple of images. Here phases that were bright are now dark, and detail within the phases is suppressed.

Figure 5: T2 Detector.

Figure 5: T2 Detector.

My final SEM image was collected with the T2 detector, another in-lens detector option. Here we see the same general phase contrast, but the contrast range is more limited and the detail within regions is again suppressed.

I have chosen to show this set of images to illustrate how different detectors, and their positioning, can generate different images from the area, and that the contrast/information obtained with each image can change. Now I have done a cursory interpretation of the image contrast, but a better understanding may come from reading the manual and knowing the effects of the imaging parameters used.

Figure 6: Always Read the Manual!

Figure 6: Always Read the Manual!

Of course, I’m an EBSD guy, so I also want to compare this to what I can get using our TEAM™ software with Hikari EBSD detectors. One unique feature we have in our software is PRIAS™, which uses the EBSD detector as an imaging system. With the default imaging mode, it subsets the phosphor screen image into 25 different ROI imaging detectors, and generates an image from each when the beam is scanned across the area of interest. Once these images are collected, they can be reviewed, mixed, added, subtracted, and colored to show the contrast of interest, similar to the SEM imaging approach described above.

The 3 most common contrasts we see with PRIAS™ are phase, orientation, and topographic. To capture these, we also have a mode where 3 pre-defined regional detectors are collected during EBSD mapping, and the resulting images available with the EBSD (and simultaneous EDS) data.

Figure 7: PRIAS™ Top Detector Image.

Figure 7: PRIAS™ Top Detector Image.

The first ROI is positioned at the top of the phosphor screen, and the resulting phase contrast is very similar to the contrast obtained with the ETD and ABS imaging modes on the SEM.

Figure 8: PRIAS™ Center Detector Image.

Figure 8: PRIAS™ Center Detector Image.

The second ROI is positioned at the center of the phosphor screen. This image shows more orientation contrast.

Figure 9: PRIAS™ Bottom Detector Image.

Figure 9: PRIAS™ Bottom Detector Image.

The third ROI is positioned at the bottom of the phosphor screen. This image shows more topographical contrast. All three of these images are complementary, both to each other but also to the different SEM images. They all give part of the total picture of the sample.

Figure 10: Defining Custom ROIs in PRIAS™.

Figure 10: Defining Custom ROIs in PRIAS™.

With PRIAS™ it is also possible to define custom ROIs. In Figure 10, 3 different ROIs have been drawn within the phosphor screen area. The 3 corresponding images are then generated, and these can be reviewed, mixed, and then selected. In this case, I selected an ROI that reversed the phase contrast, like the contrast seen with the T1 detector in Figure 4.

Figure 11: PRIAS™ Center Image with EDS Bland Map (Red-Ni, Blue – Al, Green-Zr)

Figure 12: PRIAS™ Center Image with Orientation Map (IPF Map Surface Normal Direction).

figure-12a

Of course, the PRIAS™ information can also be directly correlated with the EDS and EBSD information collected during the mapping. Figure 11 shows an RGB EDS map while Figure 12 shows an IPF orientation map (surface normal direction with the corresponding orientation key) blended with the PRIAS™ center image. Having this available adds more information (via contrast) to the total microstructural characterization package.

I look forward to using our new SEM, to develop new ideas into tools and features for our users. I imagine a few new blogs posts should come from it as well!

Considerations for your New Year’s Resolutions from Dr. Pat

Dr. Patrick Camus, Director of Research and Innovation, EDAX

The beginning of the new calendar year is a time to reflect and evaluate important items in your life. At work, it might also be a time to evaluate the age and capabilities of the technical equipment in your lab. If you are a lucky employee, you may work in a newly refurbished lab where most of your equipment is less than 3 years old. If you are such a fortunate worker, the other colleagues in the field will be envious. They usually have equipment that is much more than 5 years old, some of it possibly dating from the last century!

Old Jalopy circa 1970 EDAX windowless Si(Li) detector circa early 70’s

In my case, at home my phone is 3 years old and my 3 vehicles are 18, 16, and 3 years old. We are definitely evaluating the household budget this year to upgrade the oldest automobile. We need to decide what are the highest priority items and which are not so important for our usage. It’s often important to sort through the different features offered and decide what’s most relevant … whether that’s at home or in the lab.

Octane Elite Silicon Drift Detector 2017 Dr. Pat’s Possible New Vehicle 2017

If your lab equipment is older than your vehicles, you need to determine whether the latest generation of equipment will improve either your throughput or the quality of your work. The latest generations of EDAX equipment can enormously speed up throughput and the improve quality of your analysis over that of previous generations – it’s just a matter of convincing your boss that this has value for the company. There is no time like the present for you to gather your arguments into a proposal to get the budget for the new generation of equipment that will benefit both you and the company.
Best of luck in the new year!

Adding a New Dimension to Analysis

Dr. Oleg Lourie, Regional Manager A/P, EDAX

With every dimension, we add to the volume of data, we believe that we add a new perspective in our understanding and interpretation of the data. In microanalysis adding space or time dimensionality has led to the development of 3D compositional tomography and dynamic or in situ compositional experiments. 3D compositional tomography or 3D EDS is developing rapidly and getting wider acceptance, although it still presents challenges such as the photon absorption, associated with sample thickness and time consuming acquisition process, which requires a high level of stability, especially for TEM microscopes. After setting up a multi hour experiment in a TEM to gain a 3D compositional EDS map, one may wonder Is there any shortcut to getting a ‘quick’ glimpse into 3-dimensional elemental distribution? The good news is that there is one and compared to tilt series tomography, it can be a ‘snapshot’ type of the 3D EDS map.

3D distribution of Nd in steel.

3D distribution of Nd in steel.

To enable such 3D EDS mapping on the conceptual level we would need at least two identical 2D TEM EDS maps acquired with photons having different energy – so you can slide along the energy axis (adding a new dimension?) and use photon absorption as a natural yardstick to probe the element distribution along the X-ray path. Since the characteristic X-rays have discrete energies (K, L, M lines), it might work if you subtract the K line map from the L line or M line map to see an element distribution based on different absorption between K and L or M line maps. Ideally, one of EDS maps should be acquired with high energy X-rays, such as K lines for high atomic number elements, and another with low energy X-rays where the absorption has a significant effect, such as for example M lines. Indeed, in the case of elements with a high atomic number, the energies for K lines area ranged in tens of keV having virtually 0 absorption even in a thick TEM sample.

So, it all looks quite promising except for one important detail – current SDDs have the absorption efficiency for high energy photons close to actual 0. Even if you made your SDD sensor as large 150 mm2 it would still be 0. Increasing it to 200 mm2 would keep it steady close to 0. So, having a large silicon sensor for EDS does not seem to matter, what matters is the absorption properties of the sensor material. Here we add a material selection dimension to generate a new perspective for 3D EDS. And indeed, when we selected a CdTe EDS sensor we would able to acquire X-rays with the energies up to 100 keV or more.

To summarize, using a CdTe sensor will open an opportunity for a ‘snapshot’ 3D EDS technique, which can add more insight about elemental volume distribution, sample topography and will not be limited by a sample thickness. It would clearly be more practical for elements with high atomic numbers. Although it might be utilized for a wide yet selected range of samples, this concept could be a complementary and fast (!) alternative to 3D EDS tomography.

With Great Data Comes Great Responsibility

Matt Nowell, EBSD Product Manager, EDAX

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

fig-1_modified
Each sample leads to data. With high speed cameras, it’s easier to collect more data in a shorter period of time. With simultaneous EDS collection, it’s more data still. With things like NPAR™, PRIAS™, HR-EBSD, and with OIM Analysis™ v8 reindexing functionality, there is also a driving force to save EBSD patterns for each scan. With 3D EBSD and in-situ heating and deformation experiments, there are multiple scans per sample. Over the years, we have archived data with Zip drives, CDs, DVDs, and portable hard drives. Fortunately, the cost for storage has dramatically decreased in the last 20+ years. I remember buying my first USB storage stick in 2003, with 256 MB of storage. Now I routinely carry around multiple TBs of data full of different examples for whatever questions might pop up.

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

blog-fig-3_modified

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

“It’s not the size of the dog in the fight, it’s the size of the fight in the dog.” (Mark Twain)

Dr. Oleg Lourie, Senior Product Manager, EDAX

San Javier, Spain, October 18, 2015: Airbus A400M airlifter escorted by Sains Patulla Aguila squad on their 30th anniversary celebration event.

Many of us like to travel and some people are fascinated by the view of gigantic A380’ planes slowly navigating on tarmac with projected gracious and powerful determination. I too could not overcome a feel of fascination every time I observed these magnificent planes, they are really – literally big..  The airline industry however seems to have a more practical perspective on this matter – the volume of the A380s purchase is on decline and according to the recent reports Airbus is considering reducing their production based on growing preference towards smaller and faster airplanes. Although the connection may seem slightly tenuous,  in my mind I see a fairly close analogy to this situation in EDS market, when the discussion comes to the size of EDS sensors.

In modern microanalysis where the studies of a compositional structure rapidly become dependent on a time scale, the use of the large sensors can no longer be a single solution to optimize the signal. The energy resolution of an EDS spectrometer can be related to its signal detection capability, which determines the signal to noise ratio and as a result the energy resolution of the detector. Fundamentally, to increase signal to noise ratio one may choose to increase signal, or number of counts, or as alternative to reduce the noise of the detector electronics and improve its sensitivity. The first methodology, based on larger number of counts, is directly related to the amount of input X-rays determined by a solid angle of the detector, and/or the acquisition time. A good example for this approach would be a large SDD sensor operating at long shaping times. A conceptually alternative methodology, would be to employ a sensor with a) reduced electronics noise; and b) having higher efficiency in X-ray transmission, which implies less X-ray losses in transit from sample to the recorded signal in the spectra.

Using this methodology signal to noise ratio can be increased with a smaller sensor having higher transmissivity and operating at higher count rates vs larger sensor operating at lower count rates.

To understand the advantage of using a small sensor at higher count rates we can review a simple operation model for SDD.  A time for a drift of the charge generated by X-ray in Si body of the sensor can be modeled either based on a simple linear trajectory or a random walk model. In both cases, we would arrive to approximate l~√t dependence, where l is the distance traveled by charge from cathode to anode and t is the drift time. In regard to the sensor size this means that a time to collect charge from a single X-ray event is proportional to the sensor area. As an example, a simple calculation with assumed electron mobility of 1500 cm2/V-1s and bias 200 V results in 1 µs drift time estimate for 100 mm2 and 100 ns drift time for 10 mm2 sensors. This implies that in order to collect a full charge in a large sensor the rise time for preamplifier needs to be in the range of 1 µs vs 100 ns rise time that can be used with 10 mm2 sensor.  With 10 times higher readout frequency for 10 mm2 sensor it will collect equivalent signal to a 100 mm2 sensor.

What will happen if we run a large sensor at the high count rates? Let’s assume that a 100mm2 sensor in this example can utilize the 100 ns rise time. In this case, since the rise time is much shorter than the charge drift time (~1 µs), not all electrons, produced by an X-ray event, will be collected. This shortage will result in an incomplete charge collection effect (ICC), which will be introducing artifacts and deteriorating the energy resolution. A single characteristic X-ray for Cu (L) and Cu Kα will generate around 245 and 2115 electrons respectively in Si, which will drift to anode, forced by applied bias, in quite large electron packets.  Such large electron packets are rapidly expanding during the drift with ultimately linear expansion rate vs drift time. If the rise time used to collect the electron packet is too short, some of the electrons in the packet will be ‘left out’ which will result in less accurate charge counting and consequently less accurate readout of the X-ray energy. This artifact, called a ‘ballistic deficit’ (BD), will be negatively affecting the energy resolution at high count rates. It is important to note that both ICC and BD effects for the large sensors are getting more enhanced with increasing energy of the characteristic X-rays, which means the resolution stability will deteriorate even more rapidly for higher Z elements compare to the low energy/light elements range.

Figure 1: Comparative Resolution at MnKa (eV).

Figure 1: Comparative Resolution at MnKα (eV) *

As the factual illustration to this topic, the actual SDD performance for sensors with different areas is shown in the Fig. 1. It displays the effect of the acquisition rates on the energy resolution for the EDS detectors having different sensors size and electronics design. Two clear trends can be observed – a rapid energy resolution deterioration with increase of the sensor size for the traditional electronics design; and much more stable resolution performance at high count rates for the sensor with new CMOS based electronics. In particular, the data for Elite Plus with 30 mm2 sensor shows stable resolution below 0.96 µs shaping time, which corresponds to >200 kcps OCR.

In conclusion, conceptually, employing a smaller sensor with optimized signal collection efficiency at higher count rates does offer an attractive alternative to acquiring the X-ray signal matching the one from large area sensors, yet combined with high throughput and improved energy resolution. Ultimately, the ideal solution for low flux applications will be a combination of several smaller sensors arranged in an array, which will combine all the benefits of smaller geometry, higher count rates, higher transmissivity and maximized solid angle.

* SDD performance data courtesy of the EDAX Applications Team.

Cleaning Up After EBSD 2016

Matt Nowell, EBSD Product Manager, EDAX

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

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

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

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

Figure 1: Cleanup ahead.

Figure 1: Cleanup ahead.

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

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

Figure 2: NPAR.

Figure 2: NPAR.

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

Figure 3: Grain Patterns.

Figure 3: Grain Patterns.

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

Figure 4: Frame Averaged Example.

Figure 4: Frame Averaged Example.

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

Figure 5: Multiple Grains

Figure 5: Multiple Grains.

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

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

Figure 6: Multiple Grains.

Figure 6: Multiple Grains.

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

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

Figure 7: Orientation Maps.

Figure 7: Orientation Maps.

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

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

Figure 8: Grain Dilation.

Figure 8: Grain Dilation.

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

Figure 9: Neighbor Orientation Correlation.

Figure 9: Neighbor Orientation Correlation.

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

Figure 10: NPAR Cleanup.

Figure 10: NPAR Cleanup.

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

Return Ticket from the East Coast to East Asia

Dr. Jens Rafaelsen, Applications Engineer, EDAX

Figure 1

As I write this I am on my way back to the US after having spent the past week in Singapore with my schedule filled with meetings and training sessions with both local microscope vendors and for customers, and discussions with the EDAX sales and applications people from China, India and Singapore. A good amount of time was spent discussing detector specifics and how to really make the advantages of our silicon nitride window and Elite detectors shine, but there was also general knowledge transfer and comparison between the challenges that we see in the different regions.

Singapore is definitely a change from the east coast of the United States, with the tropical climate and architecture including a sky-rise hotel with a ship parked on top, buildings with the exterior designed to look like the shell of the Durian fruit, or giant steel tree structures in the middle of the city park. But it is also a central hub where we have one of our regional offices and a state that invests heavily in the knowledge industry.
Figure 2
While the primary reason for my trip was the training of our local team and introduction of new and up-coming projects and software features, I also wanted to gather input and knowledge to bring back to our main office in Mahwah. Often we get so used to what we see every day that we forget that there’s a whole world out there. What we in the US think should be the major focus can be of less interest in other regions and vice versa. One of the things I learned was that the Asia/Pacific region sees a larger proportion of operators being technicians with limited insight into the advantages and limitations of the technique, than we usually do in the US and Europe. At the same time the microscope vendors were impressed with the level of analysis and how powerful the TEAM™ software is. These are things that we will have to take into consideration for future development, making it easier for novice users to apply the flexibility and power of the software but still allowing our advanced users access to all the bells and whistles that we have to offer.

Although we have conference systems, phone meetings and e-mail, there’s definitely something to be said for meeting face to face. The discussions and interactions flow much more easily when we can actually point to the same thing on the screen, draw on a piece of paper or just chat over coffee. Of course it can be a little overwhelming to come back to the hotel after a long day and find an overflowing inbox when you open the computer (not to mention getting calls at 3 AM from people who aren’t aware that you are travelling), but this is easily compensated by the experience of the culture, local food, and the chance to catch up with colleagues. Who knew that fried fish skin with salted egg goes so well with a cold beer?

With my Singapore trip over, I am making my way through the 24-hour travel back to the US and I have time to contemplate the experiences and discussions that I have had during the past week. There’s plenty of data to analyze, ideas for new software features, and input from microscope vendors to consider, but all that will have to wait. For now, it’s time to catch some sleep, try to get back on east coast time and maybe not worry about the line at immigration and New York traffic till I actually have to deal with it!