AZtec Tru- I The Most Accurate EBSD Results. Application Note

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1 Introduction Band detection and indexing are at the heart of good analysis. In this technical bulletin we illustrate how improving both has significantly enhanced the performance of the AZtec solution. Pattern Quality Pattern quality will impact the band detection, and therefore it is important that the best pattern quality is achieved. Unprocessed Kikuchi patterns, such as shown in figure 1, do not show strong diffraction contrast. In order to accurately identify the bands in a pattern a background removal is required. The aim of this background is to remove all contrast from the patterns, other than that related to diffraction, as shown in figure 2. Traditionally a static background is collected and applied to all patterns in an acquisition, this however has some limitations, in that when analysing multiphase materials where there is a significant density difference between the phases, using one single background will not work. Heavier atomic number phases will have a larger backscatter electron yield and therefore require more background removal than lighter atomic phases. In AZtec the dynamic background correction enables pattern by pattern contrast optimisation. This means that reasonable pattern quality can be achieved from multiphase materials. In addition the dynamic correction in AZtec masks any screen imperfections. Band detection Band detection is a fundamentally important aspect of and is critical in terms of data quality. Improvements in band detection can increase the percent of correctly indexed points, especially for materials which exhibit indistinct bands or have low quality patterns, such as those consisting of highly strained or partially recovered microstructures. Figure 1 Raw Pattern In AZtec a new automatic method to determine which detected bands should be used in the indexing has been implemented. This is based on the bands average intensity and the position of the band in the area of interest. The band detection routine now includes a weighting function, which makes the band detection more reliable. This weighting is based on band intensity and position of the band in the pattern. As a result higher priority is given to those bands at or near the centre of the area of interest. Figure 2 Background Corrected Pattern 1

2 This means that bands which are more reliably detected from the centre of the pattern are used in the indexing, thus making the indexing more robust. Figure 3 shows a pattern from an intermetallic phase in a stainless steel. This is a tetragonal structure, with large lattice parameters, and as a result the bands are narrow. Figure 4 shows eight bands detected from this pattern. The automatic algorithm has detected the broader bands closer to the centre in preference to the thinner bands at the pattern edge. However, in reality neither patterns nor band detection will always be perfect. Pattern quality may be poor, patterns may be overlapping, bands will be missed, and so indexing needs to be robust. The new algorithm has been developed so that it can cope with, or is tolerant to these potential issues. Indexing The indexing routine of an system is critical in achieving accurate data. Originally, the automated software routines were written to replicate what was done manually. As a result this technique was very sensitive to non fitting bands, as for example might occur at a grain boundary. In AZtec the indexing routine has been re-designed. It uses a new method called Class Indexing. This examines permutations of four bands and considers band coherence (where the measured bands and reference crystal reflectors are in agreement) and non-coherence (where the measured bands and the reference crystal reflectors are not in agreement). Figure 3 These four band combinations enable the indexing routine to break the solutions into smaller blocks which are then the foundations of the indexing. Using these four band combinations the routine is far more robust and can find the correct solution even when one or more bands are non-coherent. An example is shown below using a duplex steel sample, containing four phases; Iron FCC, Iron BCC, Sigma and Chi. Figure 4 An improvement in indexing rate of correctly solved points is illustrated in Figure 5. Figure 5a shows a phase map collected using the original indexing routines, i.e. using the mean angular deviation (MAD number) to rank the correct solution. Figure 5b shows the same data reanalysed using the class method. The same analysis (number of bands and reflectors conditions were used for both. 2

3 In Figure 5a there are areas where no solution is found, in the highlighted areas, resulting in a lower total hit rate (approx 85%). In Figure 5b the same areas can be seen to be indexed correctly as Sigma, and the total hit rate is higher. reduced bands is shown in Figure 6b, produces results comparable to those in Figure 5b. This illustrates that the number of bands and reflectors selected when indexing using the Class Method becomes far less critical, i.e. if too few or too many bands are selected the system can still deliver the correct indexing solution, thus improving ease of use of the system. Figure 5a Solutions Ranked Using MAD 12 Bands, Reflectors: Iron FCC=56; Iron BCC=43; Sigma=56; Chi=40. Figure 6a Solutions Ranked Using MAD 8 Bands, Reflectors: Iron FCC=56; Iron BCC=43; Sigma=56; Chi=40. Figure 5b Solutions Ranked using Class Indexing 12 Bands, Reflectors: Iron FCC=56; Iron BCC=43; Sigma=56; Chi=40. Figure 6a shows a phase map of the same area. Here the number of bands used has been reduced. While the Sigma phase is indexed correctly, there is misindexing with the Iron FCC phase indexed as Iron BCC. Reanalysis of this data using the Class Method, using the same number of Figure 6b Solutions Ranked using Class Indexing 8 Bands, Reflectors: Iron FCC=56; Iron BCC=43; Sigma=56; Chi=40. 3

4 Similar Crystal Structures There have traditionally always been difficulties when using to distinguish phases with similar crystal structures. One technique which can aid in identifying these phases is using the difference in lattice parameter (or band width). If this is greater than 10% then it is possible to use the difference in the band widths to sort these phases. processed using the routine indexing algorithm to solve the patterns. This map shows no differentiation of the Pt or Ni, but an arbitrary solving of the pixels. In AZtec it is now possible to group materials or phases with similar crystal structures together. This enables band width sorting for the grouped phases. The separation of platinum and nickel illustrates this functionality. These phases have the same crystal structure, shown in the table below, with only 14% difference in lattice parameter. Pt Ni Figure 7b Band Width Measure can Differentiate Between Ni and Pt. Cubic fcc Cubic fcc nm nm Space Group 255 Space Group 255 In this example a Pt Ni interface from the central electrode of an automotive spark plug was examined. The interface over the two phases was mapped; the resulting phase map was overlain onto the secondary electron image and is shown below. Figure 7a shows the phase map overlain onto the electron image. This map is Figure 7b shows a 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. The corresponding X-ray maps (Figures 7c & d) collected simultaneously with the data, illustrate the concentration of these elements in the two phases. Figure 7c Ni X-ray Map Figure 7d Pt X-ray Map Figure 7a Routine Indexing 4

5 Ni and Pt form a solid solution, and one will substitute into the lattice of the other. So as substitution occurs the lattice parameter will change with respect to concentration, as Pt is substituted into Ni the lattice parameter will increase. At the interface between the two materials as more substitution occurs it becomes more difficult to distinguish the difference in lattice parameter and at this interface region there is some mixing of blue and green pixels. Grouping similar crystal structures and implementing the band width in the indexing cycle enables differentiation of these similar crystal structures. The materials presented here are summary in nature, subject to change, and intended for general information only. Performances are configuration dependent, and are based on AZtec Release 1.1. Additional details are available. Oxford Instruments NanoAnalysis Quality Management System is certified to meet ISO 9001: AZtec is a Registered Trademark of Oxford Instruments plc, all other trademarks acknowledged. Oxford Instruments plc, All rights reserved. Document reference: OINA//AN112/0812