Dr. René de Kloe, Applications Specialist EDAX.
EBSD analysis is all about characterizing the crystalline microstructure of materials. When we are analyzing materials using EBSD the goal is to perform a comprehensive analysis on the entire field of interest. We strive to obtain the highest possible indexing rates and when we happen to misindex points we feel compelled to replace or clean these with other “valid” measurements that we simply copy from neighbor points so we do not have to show these failures in a report or paper.
But what should we do when we don’t expect data from specific spots in the first place? For example if a sample is porous or contains non-crystalline patches, or perhaps we have phases that don’t produce patterns? Then we typically simply try to ignore that. We may perhaps state that a certain fraction of our scan field refuses to produce indexing results and show which pixels these are, but that’s about it.
Figure 1: Pearlitic cast iron with graphite nodules – 13.5% graphite.
And that is strange as such areas where we don’t expect patterns are truly an integral part of a material and as such should also be characterized for a complete microstructural description. A traditional example of such a material is a cast iron which, although not porous, contains graphite inclusions which typically do not produce indexable EBSD patterns (Figure 1). Another example is the characterization of material produced by 3D printing of different metals, where small metal particles are sintered together using localized laser heating. This process doesn’t generate a fully dense product (Figure 2) and understanding the pore structure is important in predicting its mechanical response to stressed conditions.
Figure 2: Porous 3D printed steel – Indexing success 97.2%
Analyzing the non-indexed areas creates some challenges for the data treatment, especially any cleanup that you may want to do. You need to be careful to ensure that individual misindexed points, for example along grain boundaries or inside grains are not considered as pores. For a full analysis we need to be able to treat pore spaces as a special type of grain. Not one where pixels are grouped together based on similarity in measured orientation, but just the opposite, where pixels are combined based on misfit. This poses a special challenge on cleaning your data. When a typical clean-up acts like an in-situ grain growth experiment, where grains are expanded to consume bad points, in porous materials data cleanup needs to be done carefully to prevent the real grains from growing into real non-indexed spaces.
In general, EBSD data cleanup should be done in 3 steps:
1. Identify the good points,
2. Preserve the good points
and only then
3. Replace bad points.
For steps 1 and 2 we can use the patented Confidence Index in the OIM™ Analysis software. For step 1 we setup a filter to allow only correctly indexed points (typically with CI>0.1). However, this may remove too many points along grain boundaries, for example, where patterns overlap and indexing is uncertain. In step 2 we apply a confidence index standardization to retrieve all pixels that were indexed correctly, but had a low CI value and were excluded in step 1. This step assumes that if the orientation of a pixel matches that of adjacent pixels that had a high CI value, it was correctly indexed and needs to be included. This step does not change any measured orientations.
In step 3 we must be more careful as it is easy to accidentally replace too many points and shrink the non-indexed space (Figure 3):
Figure 3: Effect of too rigorous cleanup of partially crystalline material – Cu interconnects.
A cleanup method that verifies whether a minimum number of neighboring points belong to a single grain such as the neighbor orientation correlation, is preferred.
Now that we know where the holes in the material are, we can get serious about analyzing them. First we need to define our real grains. Grains in EBSD analysis are defined by groups of points with similar orientations and a minimum number of pixels, for example maximum point to point misorientation less than 5 degrees and minimal 3 pixels in size. When you remove these grains from your partition, the left over pixels that do not fit into the grains can now be recognized. Coherent clusters of these misfit pixels are then grouped together into what might be called antigrains (Figure 4).
Figure 4: Grain and antigrain definition.
But even when the pores are recognized this way, the antigrains are not characterized by their orientation and as such their boundaries will do not show up in a traditional misorientation boundary overlay, which only shows the misorientation between recognized grains (Figure 5a). In order to make the antigrains visible as well, a boundary type that does not use misorientation as a criterion, but rather the position of triple junctions, needs to be selected. Between the triple junction nodes, vectors that follow all grain and antigrain interfaces are then constructed (Figure 5b).
Figure 5a) Standard grain boundary overlay on IQ map based on grain orientation recognition. Non-indexed areas are white. b) IQ map with reconstructed boundaries including the antigrain edges.
Once the antigrains are fully defined, all normal grain characterization tools are also available to describe the pore properties ranging from a basic size distribution (Figure 6) to a full analysis of the pore elongation and alignment (Figure 7).
Figure 6: Pore size distribution with colored highlighting in 3D printed iron sample.
Figure 7: Alignment of pore elongation direction.
With non-indexed points now properly assigned into antigrains, a full microstructural description of not fully dense materials or materials containing areas that cannot be indexed, is possible.
Finally we can do a (w)hole EBSD analysis.
