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.
(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.
(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.
(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;
(b) An EBSP from matrix with solution overlaid;
(c) An EBSP from Cr15.8Fe7.42C6 particle with solution overlaid;
(d) An EBSP from AlN particle with solution overlaid.
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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.
(a) Pt and Ni have the same crystal structure with only 10% difference in lattice parameter.
(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.
(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.
(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.
(a) A forescatter diode (FSD) image of the area of the sample being analysed.
(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.
(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.