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Understanding the microstructure of a material enables a link to be established between its properties, material processing and final properties. Understanding microstructure includes understanding the compounds or phases present in the material.

EBSD is applied to identify major and minor phases within a material. This can include the identification of intermetallic phases, secondary phases and precipitates within a processed material and the identification of mineral assemblages in naturally occurring material. 

In addition to identifying an unknown phase, another benefit of EBSD is to visualise the spatial distribution of these phases. This can be very important, for example; when investigating the occurrence of secondary phases either at grain boundaries or within grains.

Typically EBSD can differentiate between different crystallographic phases, and EDS can show chemical composition. When the results from these two systems are combined, using a sophisticated analysis system it is possible to use these tools to identify and separate unknown phases or compounds.

Phase identification (Identifying an unknown phase)

If the EBSD and EDS systems are integrated and installed in a suitable geometry on the SEM, it is possible to place the beam on each of the potential phases and simultaneously acquire an EBSP and an EDS spectrum. The system can search the available crystal structure databases for entries that match the chemistry of the phase, determined from EDS quantification. A short list of candidate phases is returned. This list is used for indexing the EBSP and the phase is identified.

Figure 1 A high temperature steel contains second phase particles, accurate phase identification is required to investigate this sample.

figure 1a

(a) A secondary electron image shows the unknown particles.  Oxford Instrument AZtec was used to perform Phase Identification. Simultaneous EBSP and X-ray spectrum were collected from the large dark contrasting particle.

figure 1b

(b) The EBSP with a solution overlaid. Following the Phase identification procedure, the particle was identified as AlN phase with a hexagonal structure.

Phase discrimination (Separating phases)

When a list of known phases in the material is available, EBSD can be applied to separate or differentiate these phases. As such the spatial distribution and fraction of individual phase can be identified.

The Kikuchi pattern is generated as the electron beam interacts with the crystal structure in the surface of the sample. As such the EBSP carries information about the crystal structure that generated it.

By analysing the EBSP, we can get information not just about the orientation of the crystal but also information that makes it possible to distinguish between different crystal structures, and hence phases.

Figure 2 Figure 2 EBSD map data was collected from the same region shown in Figure 1.

figure 2a

(a) EBSD phase map shows all the phases are accurately discriminated. Matrix Iron bcc is in red, Cr15.8Fe7.42C6 is in blue and AlN is in yellow;

figure 2b

(b) An EBSP from matrix with solution overlaid;

figure 2c

(c) An EBSP from Cr15.8Fe7.42C6 particle with solution overlaid;

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(d) An EBSP from AlN particle with solution overlaid.

Click here for more details of this application.

Typically EBSPs are analysed by looking at the angles between the detected bands. If only this information is used, then it is possible to distinguish between phases that have different unit cell structures as long as the difference is significant and results in a difference in the interplanar angles.

If more advanced routines are used to extract further information from the EBSPs (as example band width) then it becomes possible to also distinguish phases which have the same crystal structure but different lattice parameters. This approach is however limited as it needs the difference in the EBSPs and thereby in lattice parameters to be large enough, in order for it to be reliably detected.

Figure 3 Pt-Ni interface from the central electrode of an automotive spark plug. Platinum and Nickel have the same crystal structure with only 10% difference in lattice parameter and therefore can be difficult to differentiate on the basis of traditional EBSD. By using the band width the AZtec system is able to differentiate them.

figure 3a

(a) Pt and Ni have the same crystal structure with only 10% difference in lattice parameter.

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(b) Phase map overlaid onto the electron image. This map is processed using the traditional routine indexing algorithm to solve the patterns. This map shows no differentiation of the Pt or Ni, but an arbitrary solving of the pixels.

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(c) A phase map of the same area with the two phases clearly differentiated; this is achieved by grouping the two phases and sorting the solutions using the pattern band width.

figure 3d

(d) The corresponding X-ray maps collected simultaneously with the EBSD data, illustrate the concentration of these elements in the two phases.

Combined EBSD and EDS

In some instances phases are so crystallographically similar that EBSD alone cannot separate them. This phenomenon is seen in both material science and geological applications, an example will be the separation of the Muscovite and Biotite phases in a mylonitic metapelite sample.

Figure 4 Combined EDS and EBSD analysis of a geological sample. This area of the sample contains 3 mineral phases: Albite, Biotite and Muscovite mica. Biotite and Muscovite mica have very similar crystal structures and therefore can be difficult to differentiate on the basis of traditional EBSD.

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(a) A forescatter diode (FSD) image of the area of the sample being analysed.

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(b) EDS element maps. The highest concentration of Na is found in albite. The highest concentration of Al is found in muscovite. The highest concentration of Fe is found in biotite.

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(c) A phase map acquired using traditional EBSD (left). Due to their similar crystal structures, biotite and muscovite are poorly differentiated, resulting in a speckled appearance. A phase map acquired using the TruPhase routine (right) in AZtec where EDS data is collected simultaneously to EBSD data and used to inform the solution given for the EBSP. This method successfully differentiates the biotite and muscovite mica in the sample.

In these cases phase discrimination using both EDS and EBSD signal can be applied during mapping, where by combining both signals the phases in the sample can be separated.

However, this requires that both the EDS and EBSD data are collected simultaneously, so the data from the two techniques is synchronized.

Acquiring EDS and EBSD data at the same time during mapping can also be of benefit even if there isn’t a known phase separation problem. By having both chemical and structural information it is easier to verify the data and ensure that no phases has been missed or misidentified. It is possible offline to identify phases in the sample and also to reanalyse the dataset using analysis settings.

A grain is a three dimensional crystalline volume within a specimen that differs in crystallographic orientation from its surroundings but internally has little variation. Grain size is an important characteristic used in understanding the development, engineering and potential failure in materials. The mechanical and physical properties of metallic materials are often related to grain size e.g. via the Hall-Petch relationship where strength is inversely dependent on the square root of grain size. Electron backscatter diffraction (EBSD) is an ideal technique for this determining grain size, it offers microstructural characterisation including grain size, grain boundary characterisation and texture quantification.

Grain size and grain parameters

To accurately measure grain size, it is imperative that all of the grain boundaries are detected. Therefore the technique used must produce the highest degree of grain boundary delineation. Traditionally grain size was measured using light optical microscopy (LOM) and some of the grain size standards still reference this method. This optical technique often requires a chemical etching of the surface in order to highlight the grain boundaries. However, this etching can be influenced by the existing microstructure in the sample which can be problem for fine structured materials. In addition, as the trend is towards nano scale materials, there is a limit to the grain size which can be detected by LOM. Therefore, EBSD becomes the only viable alternative to measuring grain size. In addition EBSD can provide additional information about the microstructure in excess of that achievable with optical techniques.

To identify the grains based on EBSD requires the definition of a critical misorientation angle, so that all boundary segments with an angle higher than this defined critical angle are considered grain boundaries. By measuring the misorientation between all pixel pairs it is possible to identify the boundaries enclosing the individual grains. If this information is used with the phase information then it is possible to determine the grain size distribution for the individual phases within the sample.

Grain size is a key parameter effecting a materials properties.  However EBSD data provides much more information, so it is possible to extract grain specific parameters on both the morphology and orientation changes within the grains.

Reporting grain size information is described in ASTM standard (E2627) as a grain number. If the grain size information is to be determined accurately then even at the data acquisition stage it is important to have an idea about the grain size so the   data can be collected at a suitable resolution, such that the grains are well defined within the map. It is recommended to have at least 100 pixels within each grain, and to sample at least 500 grains for the grain size information is to be statistically meaningful.

Grain statistics, relating to entire data set or selected phases are generated from the grain measurement.

Figure 1 EBSD data from a single phase steel sample.

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(a) Grain map showing grains in random colour. Grains were detected using grain detection angle of 10 degree and minimum 100 pixels within a grain. 1378 grains are detected with mean grain diameter of 25.5μm.

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(b) Grain details and statistic summary.

In addition to morphological measurements the grain detection also offers quantitative data of orientation variation within each grain. This can also offer insight when investigating the impact of materials processing, as shown in the example of a shot peened Al.

Figure 2 Grain Orientation Spread (GOS) map of a shot peened aluminium in cross section. This valuable primary strain analysis tool reveals grains that show the most deformation - it illustrates their spatial distribution and numerical prevalence. It is a whole grain classification tool: for each grain in the mapped area, GOS measures the degree of orientation change between every pixel in the grain and the grain's average orientation. The grain is then coloured by the average measurement for all of its constituent pixels. Grains with a higher level of strain, measured by the internal degree of lattice rotation, are coloured at the yellow - red end of the rainbow colour scale shown in the histogram. In this example, the grains exhibiting higher levels of strain are concentrated near the surface, within a damage zone extending to approximately 150μm below the surface.

figure 2a

Grain Orientation Spread (GOS) map of a shot peened aluminium in cross section.

figure 2b

The corresponding misorietation distribution histogram.

Grain boundary

In grain boundary engineering it can be important to enhance or reduce the relative abundance of certain grain boundary types in order to optimise the properties of the final material. EBSD is well suited to extract this type of information as it gives both statistical and spatial information about the grain boundaries.

Grain boundaries can be identified through a misorientation distribution plot. Here the plot will have a distinct peak if there are many grain boundaries with the same misorientation angle. This method is typically used to get a quick overview of the boundary occurrences in the sample.

Similarly, generating a map which shows the spatial distribution of the grain boundaries can provide additional microstructural information. A typical example, shown below, includes a map showing low angle boundaries (highlighting the substructure of individual grains) combined with high angle boundaries (defining the actual grain structure).

Figure 3 Grain boundary from a steel sample determined by use of EBSD. The sample contains Ferrite (white) and Austenite (red) phases.

figure 3a

(a) Grain boundary positions of ferrite phase superimposed on the phase map. Grain boundaries between 2-10 degrees are in green (low angle boundaries), higher than 10 degrees are in black (high angle boundaries). It illustrates the grain structure and substructure of individual grains in ferrite.

figure 3b

(b) The corresponding misorientation distribution plot shows the frequency of grain boundaries in Ferrite.

When the misorientation angle is calculated it is also possible to calculate the misorientation axis. This means that EBSD data can be used to identify specific boundaries defined not just by a misorientation angle but by a combination of misorientation angle and misorientation axis. The most common example is the identification of twin boundaries, which are a subset of the coincident site lattice (CSL) boundaries.

CSL boundaries

CSL boundaries are special boundaries which fulfil the coincident site lattice criterias whereby the lattices are sharing some lattice sites. CSLs are characterized by Σ where Σ is the ratio of the CSL unit cell compared to the standard unit cell. Two examples of CSL relationships are shown below.

Figure 4 (a) The sigma 3 boundary (twin boundary) is a 60° rotation about the [111] direction.
(b) The sigma 5 boundary is a 36.9° rotation about the [100] direction.

figure 4a

CSL boundaries typically have a significant impact on the material properties, which means that from a materials engineering viewpoint it is important to determine both the ratio of CSL boundaries and their distribution within the material. An example from twinned steel is shown below.

Figure 5 CSL boundary data measured by EBSD for a steel sample.

figure 5a

(a) Pattern quality map for the steel sample;

figure 5b

(b) Coincident site lattice (CSL) boundary positions superimposed on the previous pattern quality image;

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(c) The boundaries are colour coded by CSL type as shown in the histogram of CSL found.

EBSD provides information from the sample surface. However, there is often a requirement to get the same type of information from a 3D volume, in order to study grain structures, grain size and interface boundaries. Depending on the size of the volume of interest, this can be achieved in several ways: For large scale features it is possible to use mechanical sectioning techniques to expose a new surface at different depths of the sample, and to analyse at these different depths.  For small scale features, it is not practical to take the sample out of the SEM chamber and then reposition it to collect more data. A solution is to use an SEM combined with a focused ion beam (FIB-SEM), where the ion column is used to mill away the surface between each EBSD map.

This process is typically automated. To achieve this the sample must be placed in a geometry suitable for both milling, with the ion beam parallel to the sample surface and for EBSD data collection (Figure 1).  Depending on the setup it can involve moving the sample automatically between these two working geometries to have an optimum geometry for both, on other setups it is done without moving the sample and having one geometry which allows milling as well as EBSD data acquisition.

By repeating the process of acquiring an EBSD dataset and exposing a new surface, it is possible to generate a 3D representation of the microstructure within the analysed volume. To get good data resolution, it can be necessary to collect data from many tens of slices (potentially more than a hundred).

An example from a Cu dataset collected with a step size of 0.2 mm in the x, y and z directions is shown in Figure 2. Here, this technique is used to present and study interfaces between grains as well as provide information about the grain size and shape. 

Figure 1 Schematic showing an example of the EBSD geometry for a FIB-SEM used for 3D-EBSD analysis.

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Figure 2 Example shows 3D EBSD analysis through a Cu sample;
A) Reconstructed and processed 3D orientation map;
B) Cross-sections in X, Y and Z planes;
C) A single grain selected and highlighted.

figure 2a

The data collected with EBSD contains a wealth of sample information which can be processed using a suite of analytical tools to visualise and represent microstructure at the micro and nano scale.

Interrogating the crystallographic orientation and phase information acquired with EBSD can then be processed to deliver information about the sample, which can be linked to the materials processing history and likely performance. Examples include

Grain Size

The mechanical and physical properties of metallic materials are closely related to grain size e.g. through the Hall- Petch relationship, where strength is inversely dependent to the square root of grain size [1]. Electron backscatter diffraction (EBSD) on a Scanning Electron Microscope (SEM) is the ideal technique for determining grain size. 

A grain is a three-dimensional crystalline volume within a sample that differs in crystallographic orientation from its surroundings but internally has little variation. Grains are identified by defining a critical misorientation angle and grain boundaries are ‘pixel pairs’ which have a misorientation higher than the critical angle. Once individual grains are detected a statistical overview of the grains in the sample can be presented, coupled with a grain map illustrating the individual grains. This data can be linked to the phases present in the map. 

Figure 1 EBSD data from a single phase steel sample.

figure 1a

(a) Grain map showing grains in random colour. Grains were detected using grain detection angle of 10 degree and minimum 100 pixels within a grain. 1378 grains are detected with mean grain diameter of 25.5μm.

figure 1b

(b) Grain details and statistical summary.

In addition, grain measurement parameters can be used to visualise microstructure in a grain measurement map. This representation will highlight, for example, larger grains, or those of a specific size or shape.

Figure 2 EBSD data from a steel sample. The microstructure is not homogeneous, with a range of grain  size and shape.. The grain aspect ratio varies between 1 to 34.

figure 2a

(a) A grain map shows the grain structure in the entire sample;

figure 2b

(b) A grain map highlights those grains with grain aspect ratio higher than 6.

Grain Boundary Characterisation

The interface between two grains in a polycrystalline material creates a grain boundary. Grain boundaries will influence the properties of the material: typically grain boundaries are a site for the initiation of corrosion and also for the precipitation of new phases from the solid. They are also important in the mechanisms of creep. Grain boundaries can also be beneficial, and disrupt the movement of dislocations through a material, so reducing grain size, and increasing boundaries, is a way to improve mechanical strength. Techniques such as grain boundary engineering (GBE) are applied to improve material properties. As such it is important to identify and characterise different boundary types, and to understand the impact on material behaviour.

Generating a map representation of grain boundaries is powerful when visualising microstructure. Different boundary types are identified by misorientation between the two grains. Typically low angle boundaries or subgrain boundaries are those with a misorientation less than 5 degrees. High angle grain boundaries have a larger misorientation generally greater than 10 degrees. In addition, special boundaries or twin boundaries occur where the crystal lattices share a fraction of the sites on either side of the boundary. These boundaries are called coincident site lattices (CSL), and are denoted by Σ, where Σ is the ratio of the size of the CSL unit cell to the standard unit cell.  

A ‘twin limited’ microstructure, i.e. a microstructure composed entirely of special grain boundaries and triple junctions is highly resistant to intergranular degradation. These different grain boundary types are readily identified and displayed with using EBSD.

Figure 3 EBSD data from a coarse grain solar cell, area of 2.7cm by 8.2cm. The aim here is to correlate grain boundary type and distribution with sample carrier life.

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(a) EBSD IPF Z map denoting the grain orientation.

figure 3b

(b) Grain boundary map illustrates the large grain size and dominance of CSL boundaries.  High angle (>10°) boundaries are in black,  Σ3 boundaries are in red, Σ9 boundaries are in blue, Σ27 boundaries are in green.

Phase Distribution and Fraction

The identification and distribution of different phases is another important application of EBSD. Phase distribution is   represented on a map, with a measure of phase fraction. A phase map is powerful in representing the spatial distribution of phases, for example useful in determining precipitates formation at grain boundaries.

Figure 4 EBSD data of a duplex steel sample showing phase distribution and fraction.

figure 4a

(a) EBSD phase map of a duplex steel sample. The microstructure contains austenite (red) and ferrite (blue) phases; it also includes intermetallic precipitation of both sigma (yellow) and chi (green) phases. These intermetallics are significant as they will degrade mechanical and corrosion properties of the material. Therefore it is important to identify them, determine their distribution and phase fraction.

figure 4b

(b) The corresponding phase fraction quantified from EBSD data.

EBSD Pattern Quality

The EBSD pattern quality parameter assigns a number to the degree of sharpness or band definition in the EBSP. Therefore the pattern quality is influenced by several factors: phase, orientation, contamination, sample preparation and the local crystalline perfection.

Figure 5 A pattern quality map for a Titanium (Ti6Al4V) sample.
Dark areas are indicative of poorer pattern quality and light areas of higher pattern quality.

figure 5a

Pattern quality maps will often reveal features invisible in the electron image such as grains, grain boundaries, internal grain structure and surface damage such as scratches. The pattern quality map is therefore very useful both during the analysis of the data and as a simple tool for checking the sample before and during analysis – in terms of focus and drift.

Orientation Data

In many materials grains do not have a completely random orientation distribution. When orientation is not random the material is said to have preferred orientation or texture. The individual crystal orientation measurements collected by EBSD can be used to show the crystallographic textures developed in the sample. The orientation information acquired from multiple points within each phase enables a statistical check whether that phase has a preferred orientation. This can be achieved by studying orientation maps or by creating pole figures.

Orientation Maps

The orientation data collected with an EBSD system is spatially displayed in either an Euler Map or a series or inverse pole figure (IPF) maps. The Euler maps give a basic presentation of microstructure. The IPF maps uses the colour from the IPF colour key, in this case the colour assigned is based on the measured orientation and the selected viewing direction. This map is good at representing preferred orientation (or texture), seen as similar or single colours in the map. The orientation data displayed in a map makes it easy to visualise and extract information about how a specific texture is spatially distributed.

Figure 6 A leaded brass sample continaing three phases: lead, α brass and β brass. EBSD illustrates phase distribution and associated texture development following thermal recovery.

figure 6a

(a) Phase map. α brass in yellow, β brass in blue and lead in red.

figure 6b

(b) IPF-Z map of all phases.

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(c) IPF- Z map of β brass, dominant green colour illustrates a preferred orientation present in β brass.

In addition those points in the material which have a specific texture in an EBSD map can be identified. This is a useful tool in identifying reference or ideal textures in a sample, such as Cube, GOSS or Fibre.

Figure 7 A texture map from the β brass phase shown in figure 6. A colour scheme reflects how close the data is to the ideal orientation of <110> parallel to Z direction.

figure 7a
figure 7b

Pole Figures

Pole figures are also applied for displaying texture. They enable 3D orientation data to be plotted in 2D, by converting crystallographic directions into points. This is done automatically with modern EBSD systems, with the pole figure being created being determined by the crystal structure of the phase being displayed.

Figure 8 Pole figures of β brass phase shown in figure 6 & 7.
The clustering of the data illustrates a strong <110> fibre parallel to Z direction.

figure 8a

Internal Microstructure

The orientation data measured by EBSD can be processed to illustrate different aspects of material microstructure. There are many examples of this; probably one of the most frequently used in literature is the Kernel Average Misorientation map (KAM). This is a calculation of the average misorientation between each pixel and its nearest neighbours. This map is used to study subgrain structures, which are an indication of strain which has occurred. 

Figure 9 A Ni sheet used in the manufacture of gas pipelines is bent as part of the manufacturing process.  
The KAM Map indicates where there is a high level of misorientation in the sample, shown by the green / yellow colour.
It illustrates the areas of tensile and compressive strain.

figure 9a
figure 9b

There is a continuing drive to make nano scale materials, and nano scale components. This is driven by increased performance and efficiency. Materials with nano scale grains typically exhibit very different properties to a large grained bulk material. This is linked to the Hall-Petch relationship which predicts material strength to be inverse proportional to the square root of the grain size.

As grains and materials are engineered to be smaller and smaller it is increasingly important that we can characterise these materials on the nano scale. The requirement to improve the spatial resolution has an effect on the EBSD hardware as well as on how the samples needs to be prepared.

Improving the spatial resolution with bulk samples

Traditionally to achieve nano scale performance in the SEM, requires lower acceleration voltages (kV), smaller probe current and shorter working distance. To successfully collect EBSD under these conditions requires the detector position to be optimised for data acquisition at a short WD and ideally the detector needs to be optimised for sensitivity. This is because when the probe current or acceleration voltage is reduced, the intensity of the diffracted signal is similarly reduced. High detector sensitivity is required to compensate for the reduction in signal. If the detector is not sensitive enough then the acquisition speed will be significantly reduced.

EBSD Detector Sensitivity

The NordlysNano EBSD detector is an example of an EBSD detector optimised for sensitivity. It has a customized optics design optimising light throughput to the sensor. In addition the sensor has high quantum efficiency making it the detector of choice for analysis at low beam currents, of beam sensitive samples, and for the identification or discrimination of difficult phases.

This means that with the NordlysNano it becomes possible to collect mapping data at low kV without compromising the acquisition speed.

Click to see more information of NordlysNano Sensitivity.

Figure 1 Typical EBSPs of Iron Pyrite collected.

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(a) At 20kV excellent detail can be seen within the pattern.

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(b) At 5kV, the patterns remain clear and are readily indexed.

Figure 2 Higher spatial resolution requires low kV analysis.
The example of nanocrystalline Nickel displays small grains, in the order of 0.5μm, surrounded by much larger grains.
Raw data shown with 92% hit rate at 101Hz. Beam conditions 2nA at 5kV.

figure 2a

An EBSD study of mollusc shells illustrates the benefits of working at lower beam energies to both improving spatial resolution and to prevent beam damage on beam sensitive material.

Figure 3 EBSD was required from a Mollusc shell sample at low kV to reduce the beam damage and enhance the resolution using NordlysNano detector.
The sample contains Calcite and Aragonite layers. EBSD results shown here were from Aragonite layer.

figure 3a

(a) EBSD IPFZ coloured plus bandcontrast and grain boundary map from the Aragonite layer. Thick black lines represent grain boundaries >10o misorientation, while the thin black lines are >2o misorientation.

figure 3b

(b) A typical EBSP from the aragonite phase, acquired at 8 kV.

This has for the first time successfully shown low kV EBSD mapping of aragonite.

Click to read the full application note: Characterisation of a mollusc shell with low kV EBSD using AZtec HKL and Nordlys Nano.

Developments to improve spatial resolution - EBSD on electron transparent samples

The spatial resolution in conventional EBSD is inherently limited by the pattern source volume, to resolutions in the order of 25-100nm. This is insufficient to accurately measure truly nano structured materials (with mean grain sizes below 100 nm), as illustrated on the figure below.

Figure 4 Pattern Interaction volume modelled at 25kV from...

figure 4a

(a) Traditional EBSD with Bulk Ni sample, tilted at 70o and...

figure 4b

(b) Thin 50nm thick Ni sample at TKD geometry with 0o tilt. Red regions correspond to electrons which have more than 93% of incident beam energy. It shows reduced scattering and minimal beam broadening in transmission mode (TKD).

A new approach to SEM-based diffraction has received a lot of interest; it applies conventional EBSD hardware to an electron-transparent sample. The technique, referred to as transmission EBSD (t-EBSD: Keller and Geiss, 2012) or transmission Kikuchi diffraction (TKD: Trimby, 2012) has been proven to enable spatial resolutions better than 10 nm. This technique is ideal for routine EBSD characterisation of both nano structured and highly deformed samples.

TKD samples are prepared in the standard way for transmission electron microscopy (TEM). The sample thickness is critical: best results are achieved using relatively thin samples, in the range of 50 nm to 150 nm.

The samples are typically mounted horizontally in the SEM chamber, at a level above the top of the EBSD detector’s phosphor screen.

Figure 5 The geometry of a system set up for TKD. Electron transparent samples are mounted horizontally in the SEM chamber and positioned towards the top of the EBSD detector's phosphor screen.

figure 5a

The geometry for TKD allows a short working distance (e.g. 5-10 mm), depending on the position of the EBSD detector. This geometry maximises the opportunity to achieve the best spatial resolution by reducing the working distance as well as by reducing the sample tilt.

Figure 6 In this example a duplex stainless steel sample has been deformed at room temperature by high pressure torsion, resulting in significant grain size refinement and intragranular deformation. The ferrite (BCC) phase develops a final grain size below 100nm, whereas the austenite (FCC) is even finer grained, with high resolution TEM imaging indicating a mean grain size below 10nm. TKD mapping with a step size of 4nm was used to characterise the sample, with the results shown here.

figure 6a

(a) The pattern quality map shows clearly the fine grain size, with a few areas with significantly poorer quality patterns.

figure 6b

(b) The phase map shows that the poorer patterns are from areas of the FCC phase, in which the TKD technique can only resolve the larger grains.

figure 6c

(c) The cleaned orientation map illustrates the lack of texture in this sample, but also the deformation within the larger grains (>100 nm) exhibited by substantial intra-grain orientation variations.

Although the geometry when using TKD is very different from the geometry when using reflective EBSD, modern systems can deal with this. The most notable differences to consider when working with TKD patterns are:

  • Wider than normal bands at the lower part of the pattern
  • A non symmetric intensity across these wide bands
  • A pattern centre located above the screen

If these effects are not handled correctly then it can cause problems for the band detection, resulting in an associated drop in hit rate and reduced orientation accuracy. Modern systems are able to deal with these issues, making it possible to do TKD analysis using a conventional EBSD system, thereby improving the spatial resolution of the analysis.

Figure 7 A typical TKD pattern from Aluminium. Broad bands can be seen at the lower part of the pattern.
The intensity across these broad bands is non symmetric resulting in the band edges being very bright or very dark.

figure 7a

Click to read the full application note: TKD with AZtec - the application of EBSD to Nanoscale.

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