Run the Automated Map

Once all of the previous steps have been carried out it is now time to collect the crystallographic orientations. Once the run is started the software will collect an EBSD pattern at each pixel, detect the bands in the EBSD pattern, calculate the best fit to the band positions using the candidate crystal structures, calculate the unit cell orientation, save the information and move to the next pixel and repeat this series. Even though modern EBSD systems are capable of running 100–1000 patterns each second, larger maps may consist of more than a million pixels and thus require hours or days to collect. A map of 2000 × 1000 pixels taken at a setting that allows 100 patterns to be collected and analyzed each second will require nearly 6 h to complete. Faster acquisition rates are available but at the expense of orientation accuracy or noise. Quite often it is most efficient to run these longer acquisitions overnight when the SEM is not being utilized anyway. These long acquisition times put extra emphasis in the microscope’s environment in order that sample drift due to temperature changes or other disturbances are minimized. It is also quite useful to post a note on the operating panel of the SEM so that the next user does not disturb an ongoing acquisition or assume that the microscope has been left in a safe condition with respect to the inserted EBSD cameras.

29.2.6\ Display of the Acquired Data

EBSD is somewhat unique in analytical techniques as there are so many ways to present the collected data in meaningful ways. Of course, everyone likes to produce beautiful color maps of microstructures, but in reality it is not just the images that are important but the crystallographic data that is contained in every point in an image that is important. In order to get that data, one must begin to use and understand crystallographic representations of the sample that are not simple images. Although it is beyond the scope of this chapter to describe every possible way that EBSD data can be displayed it is important to at least introduce these and how they might be utilized (Randle and Engler 2000).

Quite often, the first map that EBSD practitioners display is called a band contrast or an image quality map or other names depending on the particular vendor involved. These images are most commonly shown as gray-scale images where the gray level is scaled by some measure of the quality of the pixel by pixel EBSD pattern. The sharper or more defined the pattern is the higher the gray level in the image. These images can be striking representations of the microstructure of the sample and can reveal microstructural details not clearly visible in either light-optical microscope images or secondary or backscattered electron images in the SEM.

One of the most common ways to display EBSD orientation data is with orientation maps. More accurately these are called inverse pole figure maps with respect to a specific

physical direction. In order to understand inverse pole figure maps it is important to understand what are pole figures and inverse pole figures and how to interpret them.

Pole figures are used to answer the question, Where does a particular crystallographic direction or plane fall in space relative to some arbitrary physical sample direction or plane? Pole figures have been used for many years and are very common in the preferred orientation or texture literature. In many cases a single pole figure does not provide sufficient information and additional pole figures of other crystallographic directions are required. A pole figure is simply a stereogram with the axes defined by the external reference frame. It is common for evaporated or deposited thin films to have a specific crystallographic direction parallel to the film growth direction. .  Figure 29.17 is a series of pole figures from an evaporated Au thin film. In this example we show the [111] and the [110] pole figures. For the <111 > pole figure we note that there is a large number of poles plotted in the center of the pole figure. This shows that many of the pixels in the data set have the <111 > direction aligned with the sample normal. There are additional rings also in the <111 > pole figure. These are a result of there being more than one <111 > type direction in a cubic crystal (in fact there are actually four of these present). In the <110 > pole figure we see that there is a number of poles in the center of the pole figure indicating that there are a number of measured pixels with <110 > directions parallel to the sample normal direction.

Inverse pole figures are used to answer the question, What crystallographic poles or planes are preferentially parallel or perpendicular to a specific sample direction? We usually again display these with respect to the physical axes of the microscope or the sample as described for the pole figures. For inverse pole figures we plot all of the directions that are pointing in a specific direction of the sample. Inverse pole figures are extremely useful for samples where there are specific axes of the sample that have a preferred crystallographic direction. .  Figure 29.18 is an example of inverse pole figures plotted for the same Au-thin film shown in .  Fig. 29.17. It is often helpful to show all three orthogonal directions so that the preferred texture can be visualized. Note that the inverse pole figures of .  Fig. 29.18 show exactly the same information that is shown in .  Fig. 29.17. It is sometimes helpful to first plot the inverse pole figures as they will give an indication of the samples texture without plotting pole figures of many different directions. The Z or surface normal direction of the inverse pole figures of .  Fig. 29.18 is the one that carries the most information about the sample. Here we see the high density of pixel orientations that are clustered around the <111 > and less so around the <110 > directions.

Once we are familiar with the concepts of pole figures and inverse pole figures, we can then go ahead and understand inverse pole figure maps which are one of the more common ways that EBSD orientation data is shown. An inverse pole figure map extends the idea of an inverse pole

figure. This demonstrates that the majority of the grains have a <111 > direction parallel to the sample surface normal

Pole figure

[Au_20kv_5kx_280pa_r: Iron fcc (m3m) Complete data set 39083 data points Equal Area projection Upper hemisphere

X0

Pole figure

[Au_20kv_5kx_280pa_r: Iron fcc (m3m) Complete data set 39083 data points Equal Area projection Upper hemisphere

X0

[Au_20kv_5kx_280pa_r: Iron fcc (m3m) Complete data set 39083 data points Equal Area projection Upper hermisphere

[Au_20kv_5kx_280pa_r: Iron fcc (m3m) Complete data set 39083 data points Equal Area projection Upper hermisphere

111

. Fig. 29.18  Inverse pole figures that show the same data as the pole figures in .  Fig. 29.9. The three directions X, Y, and Z (parallel to the samples surface normal) are shown. The high density of poles plotted near the <111 > apex of the stereographic triangle indicates that many of the measured pixels had <111 > parallel to the sample surface normal

direction. The X and Y pole figures show where the other <111 > poles plot. The smaller density of poles near the <110 > apex of the triangle indicates that there are a few pixels with a <110 > direction parallel to the sample surface normal

figure to an image. In order to do this we must first assign a color to each direction in the inverse pole figure stereographic triangle. A common way this is done is shown in

.  Fig. 29.19. Inverse pole figure maps are then plotted by mapping the orientation of each pixel onto the color key shown in .  Fig. 29.19. .  Figure 29.20 is an inverse pole figure map produced from the same data that is shown as pole

figures (.  Fig. 29.17) or inverse pole figures (.  Fig. 29.18). When inverse pole figure maps are displayed it is important to always include the reference direction, otherwise it is difficult or impossible for the viewer to make sense of the information shown. .  Figure 29.20 is an inverse pole figure map with respect to the surface normal direction or the Z direction. Thus, the map is mostly blue, indicating that most of the

grains have an <111 > direction normal to the sample surface. There are also some areas that are green in the map and these areas have an <110 > direction parallel to the surface normal. The inverse pole figure map with respect to the X direction looks totally different to the Z inverse pole figure map. Both of these are shown in .  Fig. 29.20.

IPF colouring Iron fcc

29.2.7\ Other Map Components

Once the orientations of the pixels in an array are known, it is now possible to add additional information to the maps. There are many possible components the can be plotted based on EBSD data. One of the easiest components to add is that of grain boundaries. Grain boundaries are the planes (which intersect the planar surface as lines) that separate two regions of different crystallographic orientations. It is fairly trivial to calculate the change in orientations between two pixels. If we do this for an entire map we can then plot lines where the difference in orientations between two adjacent pixels exceeds a predefined limit. It is typically assumed that a grain boundary is represented by a change in orientation that exceeds 10°.

.  Figure 29.20 has black lines plotted where the change in orientation exceeds this limit and thus the map shows the size of individual grains even though they are of consistent color due to the grain orientations with respect to the sample normal.

. Fig. 29.19  Typical color key used for inverse pole figure maps. This color key is used to color each pixel in an image to produce an inverse pole figure map or image. Thus, if a pixel has a <001 > direction parallel to a specific direction then we use this inverse pole figure key to plot that pixel as red. Or conversely, if we observe a red pixel in an orientation map we know the orientation of that pixel is close to <100 > parallel to the plotted direction

29.2.8\ Dangers and Practice of “Cleaning”

EBSD Data

Many of the currently available software platforms for EBSD allow users to modify the inverse pole figure maps in order to improve their appearance only (Brewer and Michael 2010). There are always pixels in a map that are either not indexed due to pattern quality or many other reasons or that are mis-­ indexed due to some sort of symmetry issues or multiple possible solutions to the bands that are found. These cleaning routines normally perform two separate operations. The first step is to remove the mis-indexed pixels. These pixels usually

show up as single pixels surrounded by correctly indexed pixels. The software searches through the data and where there is a single pixel of a different orientation surrounded by pixels of a different but similar orientation, the single mis-­indexed pixel is replaced by the average of the surrounding orientations. The next step is to deal with the pixels that are not indexed. The same procedure is applied as with the single mis-indexed pixels in that kernel math is used. Each individual pixel has six nearest neighbors. If a cleaning procedure that requires six nearest neighbors to agree then the single un-indexed pixel is assigned the average orientation of it’s nearest neighbors. These procedures allow the user to select the number of nearest neighbors that must agree and using less than six nearest neighbors allows larger regions of pixels with no correct indexing to be replaced. Use of these procedures can be very dangerous and must be disclosed when inverse pole figure maps are published.

29.2.9\ Transmission Kikuchi Diffraction

in the SEM

One of the limitations of EBSD is that the resolution is compromised by the fact that the patterns are formed by backscattered electrons and that the sample is highly tilted leading to the previously mentioned fact that the resolution perpendicular to the tilt axis is much worse than that parallel to the tilt axis. The resolution can be greatly improved if the backscattered volume is reduced and the geometrical factors are reduced. One could image EBSD at reduced voltages to reduce the interaction volume, but this process has practical limitations related to the need for increased sample preparation quality. Also, improved EBSD cameras would be needed to take advantage of lower voltage operation. Lower voltage operation does nothing to reduce the geometrical effects.

Transmission Kikuchi diffraction (TKD, although some in the literature have referred to this method as t-EBSD, which is

the acronym for transmission electron backscattered diffraction, which is a rather non-physical description due to the inclusion of both transmission and backscattered in the name) is the transmission analog to EBSD is a way to achieve extremely high spatial resolution for crystallographic orientation or phase mapping (Keller and Geiss 2012; Trimby 2012). TKD is realized by using an electron-transparent sample, as in the transmission electron microscope, that is held at normal or nearnormal incidence with respect to the electron beam while a standard EBSD camera is placed at the exit surface of the sample, as shown in

.  Fig. 29.21. Here the electron beam accelerating voltage must be high and the sample must be thin in order for transmission of the electrons to occur. The maximum beam energy typically available in modern SEMs is 30 kV, which requires that the sample must be quite thin. The use of a thin sample limits the size of the beam interaction volume within the sample and immediately improves the spatial resolution of TKD. In addition, the fact that the sample may be oriented normal to the incident electron beam further improves the resolution to the point that 2-nm spatial resolution has been achieved in TKD. It is incredibly fortuitous that the TKD patterns are extremely similar in appearance to EBSD patterns, as shown in .  Fig. 29.22, and can be collected with the same cameras and the same analysis software as is used for EBSD. There are two main disadvantages to TKD. First, it may be difficult to produce appropriate thin samples. However, most laboratories will have access to a dual-plat- form FIB/SEM and thin samples prepared with FIB are perfectly adequate for TKD. The second disadvantage is that when a small pixel size is needed, it is difficult to map larger regions. For example, if a map step size of 4 nm is selected, a 1000 × 1000 pixel map will only cover 4× 4 μm. However, if orientation mapping of extremely fine grained material is needed, TKD may be the only way to achieve the resolution needed.

A typical TKD map acquired at 30 kV of a thinned sample of polycrystalline Si layers in a semiconductor device is shown in .  Fig. 29.23. This map was acquired with a step size

. Fig. 29.21  Detector and sample positioning for transmission Kikuchi diffraction. a The detector shown is an on-axis detector. Sample and detector positioned and inserted for TKD with an on-axis

detector. Note row of solid state detectors located on the detector for collecting transmission images of the sample. b Sample and detector arrangement for TKD with a conventional style EBSD detector