Dislocation Analysis Method

Abstract

The objective is to provide a method for analyzing lattice distortion within a sample. The method includes acquiring, for each of a plurality of target locations within the sample, crystal lattice orientation information for the sample at each of a plurality of surrounding locations along a path corresponding to a perimeter of a region of the sample containing the target location, and generating strain information for the target location within the region according to the acquired crystal lattice orientation information, the strain information representing crystal lattice distortions attributable to crystal lattice dislocations within the region. Each region containing one of the plurality of target locations overlaps another region containing a different one of the target locations. The method further includes outputting a set of output data including the generated strain information for the plurality of target locations.

Description

[Technical field]

[0001] The present invention relates to a method for analysing dislocations in a sample, in particular, which can provide improved spatial resolution and precision of information when analysing lattice distortions in a sample. [Background technology]

[0002] Introduction to EBSD Electron backscatter diffraction (EBSD) is a well-established technique for the analysis of crystalline materials. It allows the characterization of materials over a range of scales from nanometers (nm) to centimeters (cm), providing information such as phase identification, distribution of phases, crystal lattice orientation within individual grains, grain size and shape, texture (i.e., the nature and strength of any favorable alignment of crystal lattices within the sample), inter- and intra-grain properties, and the nature of the material's response to any deformation. The technique uses a detector to capture an electron diffraction pattern ("Kikuchi pattern") generated by focusing an electron beam onto the sample surface in a scanning electron microscope (SEM). The technique can operate in a conventional backscatter geometry (EBSD) for analyzing polished surfaces of bulk samples, or a transmission geometry can be used on samples polished to electron transparency to form an image of the electron diffraction pattern using transmitted electrons (sometimes called transmission EBSD or transmission Kikuchi diffraction (TKD)).

[0003] Data obtained using EBSD is typically an array of measurements collected from a regular square or hexagonal grid of analysis points on the face of the sample. Each measurement contains confirmation of the phase at that point and details of the 3D orientation of the crystal lattice with respect to the sample face. Additional information on the chemical composition at each point, as well as various parameters related to the quality of the electron diffraction pattern, can optionally be stored (using simultaneous X-ray measurements). It is standard procedure to use the grid of phases and orientation measurements to reconstruct the properties of the sample face in the form of a map. The information shown in the map can be tailored to emphasize the desired properties, for example using coloring techniques that convert the 3D crystal lattice orientation into colors that represent the relationship of this orientation to the sample coordinate system. In such an orientation map, multiple regions with similar colors are easily visualized as "grains", and dominant colors can indicate stronger crystallographic textures. An exemplary orientation map is shown in FIG. 1.

[0004] Distortion and dislocations EBSD is a technique often used to probe the characteristics of deformation within a sample. For example, when a sample is stressed due to the application of a tensile or compressive force, the material may change size or shape. This change can be measured as strain, and if the change in size or shape is retained when the force is removed, it is known as plastic strain or plastic deformation. Plastic strain is possible due to the formation and movement of dislocations in the crystal lattice. Dislocations can be described as line imperfections where some atoms are displaced within the crystal lattice. There are two types of dislocations in nature: edge dislocations and screw dislocations, but most dislocations are likely a combination of the two. These dislocations are depicted in Figure 2.

[0005] This figure illustrates some important properties of dislocations, including that they cause small local orientation changes in the crystal lattice and that they can be characterized (in part) by their Burgers vector. The Burgers vector (b, marked with red arrows above each dislocation in the image) can be defined as the final offset after tracing a symmetric loop (in terms of atomic steps) about the dislocation line t, i.e., the so-called "Burgers circuit" (green arrow in the image). In effect, the Burgers vector is the direction of slip on the slip plane and will provide important information about how the crystal is deforming in response to stress.

[0006] Dislocations can be assigned a direction or sign, and if the dislocations in a material have a random sign, they will not result in any overall change in crystallographic orientation, i.e., the small orientation changes for each dislocation will cancel out. All materials contain such dislocations with mixed signs, and these dislocations are potentially called statistically accumulated dislocations (SS dislocations). However, if the majority of the dislocations are of the same sign, they will result in a measurable change in orientation. Usually, these dislocations are called geometrically necessary dislocations (GN dislocations), and they are shown in Figure 3.

[0007] EBSD is a well-suited technique to measure the accumulated lattice bending induced by GN dislocations, since the orientation changes are usually larger than the detection limit of the EBSD technique (typically around 0.1°). Many researchers characterize the nature of plastic deformation in samples by measuring the orientation changes within individual grains using EBSD. This characterization can then provide information on the localization of deformation (e.g., for the propagation of a crack through the material), the activation of specific slip systems, and the density of GN dislocations.

[0008] Dislocation density measurement Measurement of GN dislocation density using EBSD is becoming increasingly common and is based on JFNye's research paper "Some geometrical relations in dislocated crystals", Acta Mater. 1 (1953) pp. 153-162, which derived a formula linking the curvature of the crystal lattice to the dislocation density. Given that conventional EBSD measures crystallographic orientations on a 2D plane within the sample, this technique cannot measure the maximum z-component of the required rotation tensor, and therefore measurements of GN dislocation density are usually at the lower limit of the true density. However, even this value has become invaluable to many researchers, providing information about the physical properties of the material.

[0009] Most existing techniques for deriving GN dislocation density from EBSD data involve measuring the local orientation change in the immediate vicinity of each analysis point. This technique, known as "kernel average misorientation" (KAM), calculates the average difference in orientation between each measurement and the surrounding measurements in a user-defined pixel array centered on that point.

[0010] FIG. 4 illustrates a schematic diagram of the determination of KAM values ​​on a pixel array excluding analysis points spanning high angle grain boundaries (marked with red crosses) and an exemplary KAM map of a cracked duplex stainless steel sample exhibiting increased plastic strain at the crack tip.

[0011] KAM measurements quantify the local orientation gradient, which can then be linked to the GN dislocation density. The technique presented in W. Pantleon, "Resolving the geometrically necessary dislocation content by conventional electron backscattering diffraction," Scr. Mater. 58 (2008) pp. 994-997, is commonly used as a basis for calculating GN dislocation density from local lattice curvature. For example, PJ Konijnenberg, S. Zaefferer, and D. Raabe, "Assessment of geometrically necessary dislocation levels derived by 3D EBSD," Acta Materialia 99 (2015) pp. 402-414, treats the orientation change between the central pixel and each surrounding pixel as a single low-angle boundary (with misorientation θ) and then calculates the average misorientation (θ) between the central pixel and the neighboring pixels. av ), the measurement step size a, and the magnitude (|b|) of the Burgers vector are used to determine the total dislocation length as follows: TIFF2024543909000002.tif833

[0012] Then, by applying the relevant geometric calculations, the above equation can be converted to either 2D or 3D GN dislocation density.

[0013] However, the same work by Konijnenberg et al. shows that the Burgers circuit can be applied not only in crystallographic coordinates but also in sample coordinates. This is shown in Figure 5, which depicts the Burgers circuit in crystal (left) and sample (right) coordinate systems. It can be seen that both Burgers circuits provide the same Burgers vector (center arrow b), and in the case of the sample coordinate system the loop is closed, so b is the net sum of the individual vectors at each step (small red arrows). For the loop in crystal coordinates, the Burgers vector is an incomplete circuit section.

[0014] The above mentioned approach is adopted by J. Wheeler, E. Mariani, S. Piazolo, DJ Prior, P. Trimby, and MR Drury in “The weighted Burgers vector: a new quantity for constraining dislocation densities and types using electron backscatter diffraction on 2D sections through crystalline materials”, in which the “weighted Burgers vector” (WBV) is defined as: W = [(density of intersections between dislocation lines and the map) × (Burgers vector)] sum over all types of dislocations.

[0015] The standard method of calculating the WBV is by a "differential" approach in the same manner as described above, which is used to calculate the KAM map, i.e., to calculate the local orientation gradient at each point, and to determine the dislocation components necessary to take into account that gradient.

[0016] However, Wheeler et al. introduced the concept of an "integral loop" approach to measuring WBV. Differential approaches rely on accurate measurement of small orientation changes, and any error in this measurement results in significant error in the final GN dislocation density value. By summing the orientation changes around a loop on the sample face, errors related to poor angular precision are minimized, and thus the integral loop approach can provide a more reliable GN dislocation density value. In Wheeler's paper, the integral loop is presented as an iterative approach that can be considered to be employed to probe a feature or area of ​​interest.

[0017] Figures 6 and 7 illustrate the use of the integral loop approach to determine WBV values. Figure 6 is an orientation map of a Mg alloy showing the locations of six user-specified WBV loops. The table shown in Figure 7 shows the equivalent WBV for each of these loops (for sample Mg4.3a).

[0018] Although the WBV calculated using the integral loop approach allows for scrutiny of specific features within a data set (such as dislocation arrays or "low angle boundaries"), if the loop encircles multiple dislocations with different characteristics, the integral WBV will provide a sum of these dislocations, which may provide false information. Furthermore, the integral loop approach does not allow for an automated overview of the dislocation characteristics of the entire data set.

[0019] In response to this, Wheeler implemented a "tiling" approach, where the EBSD data set is subdivided into a series of equally sized tiles (e.g., 5x5 pixels) and an integration loop is performed around the perimeter of each tile, which, although it produces a more reliable view of the entire data set (in that each tile is less susceptible to WBV errors caused by noise in the orientation measurements), has several disadvantages: 1. Spatial resolution - The dataset resolution is reduced by a factor of n, where n is the side length of a tile. This makes the technique unsuitable for analysis of smaller microstructural features. 2. Tiling Shape - Due to tiling, the loop shape (when using data collected on an orthogonal grid) must be rectangular or square. This introduces a measurement bias towards structures parallel to X or Y.

[0020] In addition, when the loop crosses an analysis point that has pixels indexed as having a significantly different orientation (e.g., >5° difference) or a different phase, the integration loop will not return a valid WBV result and the tile will contain no information.

[0021] Figure 8 illustrates in schematic form the basic WBV integration loop tiling technique using tiles of size 5 x 5. The final data on the right only has a significantly lower resolution than the initial data.

[0022] This WBV tiling technique has been used for one published paper to date (N. Timms, M. Pearce et al., New shock microstructures in titanite (CaTiSiO5) from the peak ring of the Chicxulub impact structure, Mexico, Contributions to Mineralogy and Petrology, 2019, 174:38). [Prior art documents] [Patent documents]

[0023] [Patent Document 1] US 2015 / 0369760 A1 [Non-patent literature]

[0024] [Non-Patent Document 1] J. F. Nye, "Some geometrical relations in dislocated crystals", Acta Mater. 1 (1953), pp. 153-162 [Non-Patent Document 2] W. Pantleon, "Resolving the geometrically necessary dislocation content by conventional electron backscattering diffraction", Scr. Mater. 58 (2008) pp. 994-997 [Non-Patent Document 3] P.J. Konijnenberg, S. Zaefferer, and D. Raabe, "Assessment of geometrically necessary dislocation levels derived by 3D EBSD", Acta Materialia 99 (2015), pp. 402-414 [Non-Patent Document 4] "The weighted Burgers vector: a new quantity for constraining dislocation densities and types using electron backscatter diffraction on 2D sections through crystalline materials" by J. Wheeler, E. Mariani, S. Piazolo, D.J. Prior, P. Trimby, and MR Drury [Non-Patent Document 5] N. Timms, M. Pearce et al., "New shock microstructures in titanite (CaTiSiO5) from the peak ring of the Chicxulub impact structure, Mexico, Contributions to Mineralogy and Petrology," (2019), 174:38 Summary of the Invention [Problem to be solved by the invention]

[0025] A need exists for techniques for analyzing lattice dislocations that provide a high level of accuracy while retaining high spatial resolution. [Means for solving the problem]

[0026] According to a first aspect of the present invention, there is provided a method of analysing lattice distortion in a sample, the method comprising the steps of: acquiring crystal lattice orientation information for the sample at each of a plurality of surrounding locations along a path corresponding to a periphery of a planar region containing the target location for each of a plurality of target locations within the sample; generating strain information for the target locations within a region in accordance with the acquired crystal lattice orientation information, the strain information representing crystal lattice distortion attributable to crystal lattice dislocations within the region, wherein each region containing one of the plurality of target locations overlaps another region containing a different one of the target locations; and outputting a set of output data including the generated strain information for the plurality of target locations.

[0027] The present method provides a technique for analyzing lattice dislocations to obtain strain information that overcomes the existing problems described above. That is, the inventors have recognized that by generating strain information for each of a plurality of target locations using crystal lattice orientation information from surrounding locations around the periphery of each region containing the target locations, and doing so such that the regions overlap each other, the accuracy of the strain information across the target locations is improved without compromising the spatial resolution of the obtained data. In this manner, the present method can provide output data including strain information that can indicate the predominant dislocation type for each of the target locations or the corresponding regions containing them, without the information loss caused by conventional tile-based techniques.

[0028] That is, the method can advantageously provide a map or image of high resolution dislocation information that can, for example, show the response of a sample material to stress across multiple target locations. This important benefit can be understood in light of the prior art involving non-overlapping "tiles," where it is believed that different or opposing types of dislocations occurring within a given tile area can result in these different types of information or implications being lost or excluded from the data acquired for the entire tile.

[0029] The method may be used to generate a set of output data for a target portion of the sample, which may be referred to as a mapping portion with respect to the possibility of using the output data to generate a map or image representative of the strain within the portion. Thus, the multiple target locations are preferably within the target portion of the sample, and may themselves define the target portion. Typically, any of the position, orientation, and extent of the target portion, particularly relative to the sample itself, may also be referred to as an orientation data set, is limited by or at least established by the particular portion of the sample scanned to obtain a data set from which crystal lattice orientation information may be obtained. However, in some embodiments, for example, where the initial data set is more extensive, the target portion may be selected as a larger portion of the sample. The multiple target locations may be understood as being within, corresponding to, or defining the portion or section of the sample to be analyzed.

[0030] The target portion, or in other words the portion of the sample on which the locations used in the method are located, may correspond to, be considered as, or be represented as a surface or a portion thereof. Although some embodiments may involve a target portion being positioned on or conforming to a surface that is specifically external or an outer surface of the sample, it will be understood that the term "surface" as used herein means more generally any two-dimensional topological space and is therefore not limited to an outer surface. This term will be understood to mean that a moving point on such a surface, of which the target portion may be considered as a part, can move in two directions (have two degrees of freedom), or in other words there is a coordinate patch around most points on which a two-dimensional coordinate system is defined.

[0031] It will also be understood that in general, a surface is a continuous boundary that divides a three-dimensional space into two subspaces. For example, if the surface on which the target location lies is an outer surface of the sample, then the surface is a continuous boundary between a three-dimensional cross-section of the sample and a three-dimensional region outside of it. As another example, if the surface is within the sample, e.g., a plane that bisects or otherwise passes through the sample, then the surface is a continuous boundary between two three-dimensional regions of the sample. In light of the above, it will be understood that a surface being partially or entirely "inside" the sample does not require or otherwise indicate that any internal surface exists within the sample, or that a physical boundary exists, or that a continuous boundary demarcates a number of particular media or regions, either containing different types of materials. Conversely, the conceptual surface may be arbitrary and / or independent of any topographical or structural features of the sample, and / or may be defined by the manner in which data was obtained from the sample, rather than by any particular characteristic of the sample.

[0032] The target portion of the sample as described above can itself be considered as a surface as defined above. The target locations as described above can therefore be considered as being in a surface coincident with the sample. Similarly, the target portion or mapping portion can be partially or entirely continuous over the non-zero areas of these portions in a surface coincident with the sample. The surface may be partially or entirely internal to the sample. The surface can be partially or entirely coincident with the outer surface of the sample, i.e. the interface between the sample and an external medium or environment.

[0033] Whether the surface on which the target locations lie is a plane, e.g. a two-dimensional cross-section of the sample, a curved surface, or a more complex or irregular topology, e.g. having one or more points that lie outside the average plane defined by the sampling locations or outside the plane on which the majority or a predetermined amount of the target locations lie, the surface or target portion can preferably be mapped or projected onto a two-dimensional point array. In other words, a correspondence can be established between each of a plurality of target locations in the target portion of the sample and each of a plurality of locations in a two-dimensional pixel array or a two-dimensional image, such as a strain information map. In this sense, the surface or mapping portion generally has an extent, and in each of two orthogonal dimensions, can be considered as two-dimensional in terms of which a coordinate axis or coordinate system can be defined (generally or at least locally), regardless of whether the distribution of target locations in the sample has any extent in the third (generally perpendicular) dimension.

[0034] The configuration of the target portion that can be considered as being a surface, or at least a portion thereof, matching with, corresponding to, or continuously matching over the portion, is typically determined by the nature of the instrument or scanning procedure used to obtain the data employed in the method. In a preferred embodiment, the analysis is performed on a two-dimensional cross section through the sample, and thus the scanning data is preferably representative of the target portion in two dimensions. However, in some embodiments, this analysis is less practical or possible. For example, an irregular outer surface of a fracture can be measured using EBSD, and / or a non-planar surface such as a curved outer surface of a sample can be measured. For example, local deformations on a curved outer surface of an object can be analyzed. Typically, the data set provided for use in the method corresponds to a set of scan locations that lie within a given surface, some or all of which may be internal, external, irregular, curved, or planar, and in some cases may lie within a plane that is planar. Preferably, the mapping portion is planar and may constitute all of the provided data set or a selection or extraction therefrom. Thus, in some preferred embodiments, the multiple target locations lie in a plane that is coincident with the sample.

[0035] The regions of the sample can be understood as sub-portions of the target portion, i.e., sub-portions of the mapping portion. Thus, the regions can be understood as regions of a surface that coincides with the sample. The regions typically lie within the surface and the mapping portion, and can typically be considered two-dimensional in the same sense. That is, for the purposes of the method, the regions can be treated as two-dimensional shapes bounded by a perimeter loop (e.g., on which the Burgers vector calculation may be based), regardless of whether the surface, mapping portion, one or more of the regions, or the perimeter thereof, has a third spatial dimension of extent due to deviation from a plane by one or more of the target locations or surrounding locations. Thus, the method can be understood as comprising the step of obtaining crystal lattice orientation information for the sample at each of a plurality of surrounding locations along a path that corresponds, for each target location, to the perimeter of the surface containing the target location or to the region of the mapping portion of the sample containing the target location. Thus, in embodiments in which the face of the sample, and therefore the mapping portion, is planar, the method may include obtaining, for each of a plurality of target locations in a plane coincident with the sample, crystal lattice orientation information for the sample at a plurality of surrounding locations along a path corresponding to a perimeter of an area of ​​the planar containing the target location.

[0036] It will be understood that a surface, and in preferred embodiments a plane, coinciding with a sample means that the surface or plane resides wholly or partially on or within the sample, i.e., on the surface of the sample and / or extends through its internal volume. In other words, the surface or plane may be configured as intersecting the sample, or in some embodiments may be defined as the intersection of the surface or plane with the sample. However, in typical embodiments, the surface or plane coincides with a portion of the outer surface of the sample. In such cases, the multiple target locations and surrounding locations are on the surface of the sample.

[0037] The crystal lattice orientation information obtained for the sample can be considered as part of or provided as an orientation data set. This data set can include data for a number of locations in a plane coincident with the sample or other non-planar surface. Typically, these locations include target and surrounding locations of the sample area under analysis, and can additionally include data for locations in one or more further areas. It will be appreciated that any one or more of the locations represented in the orientation data set, preferably the multiple locations, can be either or both of target and surrounding locations. That is, a given location in the orientation data set can act as a surrounding location for generating distortion information for one area and as a target location corresponding to another area. Indeed, a given location can be used as a surrounding location in the generation of distortion information for any number of areas having surroundings coincident with it. The sample area under analysis can be overlapping or non-overlapping areas of the same surface or plane, and / or one or more of the areas can be located in different surfaces or planes. The regions of the sample for which the orientation datasets contain available data may be superimposed or coincident with one another, as may the planes in which they lie. Preferably, the process of acquiring the orientation datasets results in orientation information that can be used to generate strain information for multiple sample regions.

[0038] A path corresponding to the perimeter of an area for a given target location can also be understood as a path that defines a perimeter or a path that is defined by a perimeter. The outer boundaries of the area from which data or signals are collected from these surrounding locations may not necessarily be equal to a path that passes through the surrounding locations. Typically, the surrounding locations can be considered to be defined as points located at the centers of respective sub-areas, which can be considered as pixels, for example, and the perimeter can be considered to be defined by the outer (generally relative to the overall area) edges of these sub-areas. Alternatively, in some embodiments, the perimeter or surrounding sub-areas are considered to be each centered on the perimeter line of the area.

[0039] The area of ​​a surface or plane for a given target location may be defined as an area within the surface or plane, in other words, the area is typically defined as a two-dimensional area, i.e., an area that is a portion of the surface or plane.

[0040] Typically, the distortion information represents the magnitude and / or direction of crystal lattice distortions that can be attributed to crystal lattice dislocations within the region. A crystal lattice may also be referred to as an atomic lattice. The crystal lattice distortions may be attributed to one or more crystal lattice dislocations within the region. Such dislocations may be understood to be intraregional in the sense that they intersect or at least are near a portion of a face or plane that corresponds to a given region for a given target location.

[0041] As mentioned above, each target location and surrounding location can be understood as coinciding with the sample, i.e., each of these locations typically lies either within the sample or on the surface of the sample.

[0042] Generating distortion information for each target location includes combining crystal lattice orientation information obtained for at least a subset of a plurality of surrounding locations along a path corresponding to the perimeter of each region, in other words, crystal lattice orientation information at a plurality of locations around the perimeter of each region can be advantageously combined and employed to generate distortion information for a corresponding target location for or within that region.

[0043] Preferably, this combining step includes calculating the integral of the crystal orientation gradient values ​​around the perimeter of the region, or in other words integrating these values ​​or over the values. Techniques for generating strain information in this manner are provided in more detail later in this disclosure.

[0044] As mentioned above, the multiple target locations are preferably on the surface of the specimen, and therefore, in such an embodiment, each region is typically also on the surface of the specimen.

[0045] Preferably, the set of output data includes or represents a lattice strain image for the sample. Typically, the lattice strain image includes a plurality of pixels corresponding to a plurality of target locations. Typically, a pixel has a value corresponding to the generated strain information for each target location. In this manner, a map or image of dislocation information as described above can be generated by the method. Due to the advantageous area overlay technique described above, such a lattice strain image can show a higher degree of detail and value variation across a plurality of pixels than the prior art, and can represent the strain present in the sample more accurately than the prior art.

[0046] In some embodiments, the method may further include obtaining dislocation classification data for each of the plurality of target locations based on the distortion information. In some applications, it may be advantageous to categorize or classify lattice dislocations shown or represented in the obtained data, preferably automatically or without requiring user involvement or input.

[0047] In such an embodiment, preferably, for each target location, classified strain data is obtained according to one or more of: dislocation density information inferred from the strain information; and one or more values ​​or information indicative of lattice strain orientation information inferred from the strain information.

[0048] The method can be applied with a wide range of crystallographic orientation data sets, which generally include data collected from a number of regularly arranged or spaced locations on or within the sample. Typically, the collection of such data may involve an electron beam and backscattered electrons, i.e., electron backscatter diffraction (EBSD). However, it is also contemplated that data may be obtained using, for example, transmission Kikuchi diffraction (TKD) in a scanning electron microscope or transmitted electrons in one of several transmission electron microscope (TEM) techniques. The data may be generated from data collected using optical techniques such as X-ray techniques or Raman analysis. Typically, any of these data collection methods can generate data representing crystal lattice orientations at a number of regularly arranged locations in an array that spans a face or plane coincident with the sample. Typically, the method can utilize any such grid of orientation data.

[0049] Thus, in some embodiments, the plurality of locations, including the plurality of surrounding locations or the plurality of target locations, are arranged in a periodic grid in the sample. Typically, the grid is an arrangement of locations regularly spaced or distributed over a mapping portion of the sample, and can be considered to be arranged in a plane coincident with the sample. When the orientation data set relates to a two-dimensional planar cross section or outer surface of the sample, the surface can be considered to be a plane coincident with the sample, and the grid can be a periodic two-dimensional grid in the plane, in particular in the plane intersection between the plane and the sample. Typically, the grid is on the surface of the sample, and is preferably an orthogonal grid or a hexagonal grid. The plurality of locations typically includes the plurality of target locations and the plurality of surrounding locations therewith. In other words, a regular location array can be defined in the plane, and the surrounding locations for obtaining crystal lattice orientation information can be included in this location array. The grid or array is typically two-dimensional, corresponds to a two-dimensional portion of the sample, and can define orientation data for an array of points lying in the plane of this portion. Typically, data for a grid of points is provided and then used by defining a number of overlapping regions or tiles that make up overlapping sub-grids of the grid. The method may include obtaining data for all or a subset of the locations to which the acquired sample data or grid corresponds. Typically, such subset corresponds to a target portion of the sample for which distortion data is desired.

[0050] Typically, for one or more, and preferably all, of the plurality of target locations, the perimeter of each region, and thus the region itself, typically defines a circular or substantially circular shape. It will be appreciated that due to the presence and shape of smaller regions or pixels around the perimeter of each region for which the acquired orientation data contains relevant information, it is believed that the outer boundary defined by these smaller regions is not a smooth circle, but may consist of discontinuous portions that on average fit a circular "footprint" for each region.

[0051] Alternatively or additionally, for one or more of the plurality of target locations, and in some embodiments for all of the target locations, the perimeter of the respective region defines a regular hexagonal shape. The method can be applied using regions of various shapes, including ellipses and geometrically irregular shapes, but orthogonal and hexagonal geometric shapes have been found to produce improved results. Typically, for each of the plurality of target locations, the respective region has the same size and / or shape.

[0052] Typically, for one or more, and preferably all, of the plurality of target locations, the target location is at the centroid or geometric center of its respective region.

[0053] In some embodiments, the method may further comprise, for each of the plurality of target locations, defining the respective region as a pixel array, preferably a regular array. Typically, the array corresponds to a sub-grid of the periodic grid described above. Each of the plurality of surrounding locations for the plurality of target locations typically corresponds to a surrounding subset of the pixel array. Typically, the pixels occupy portions of the plane (or, typically, the sample surface) that are of equal extent, i.e., equal area and / or size. The pixels are typically rectangular, preferably square, but may be hexagonal, for example, if the provided orientation data set comprises pixels of a continuous hexagonal grid. The orientation data for a given pixel may have been obtained from a sample of equal, larger, or smaller area, volume, or quantity than the pixel. For example, a signal, such as a backscattered or transmitted electron signal, from which the orientation data for a location or pixel is derived may be emitted from a position interaction volume having an extent or intersection in the plane that is smaller than the pixel. For example, a pixel may be one micrometer wide and may be associated with orientation data collected from a volume or area that is only a few nanometers wide. In either case, orientation data is typically associated with a corresponding pixel.

[0054] In some such embodiments, the surrounding pixel subset substantially surrounds the region or surrounds or encloses an inner portion of the region. Typically, each of the surrounding pixel subsets is located at the outer boundary of the region. Depending on the spread of the pixels, the surrounding pixel subset may partially or at least substantially entirely surround the region. That is, in some embodiments, adjacent pixels comprising the surrounding pixels may be spaced apart or overlapping, but are preferably contiguous. Preferably, the surrounding pixels for generating distortion information for a target location using the orientation data form a closed perimeter or closed loop around the outer boundary of the region or array. Typically, a closed perimeter is defined that is made up of the outer pixels, and preferably all of the pixels in this perimeter correspond to the corresponding region and are used to generate distortion information for the target location therein.

[0055] In such an embodiment, preferably each region containing one of the multiple target locations overlaps the other region such that only pixels contained in its surrounding pixel subset do not overlap any pixels of the other region. Preferably, in the two overlapping arrays, a single pixel column in each array is excluded from the intersection, i.e., the overlap area. In this way, the degree of overlap of adjacent regions is maximized while simultaneously maximizing the spatial resolution of the output data.

[0056] Typically, multiple target locations are arranged in a regular array in a surface or plane, typically on the surface of the sample. As described above, various types of orientation data sets can be used. The periodicity or spatial period between locations or their centroids in the data set can vary considerably for different embodiments. For example, when using TEM acquired data, the adjacent location spacing can be on the order of 1 nanometer or less. In other embodiments, the spatial period can be on the order of 1 micrometer.

[0057] In some embodiments, the method may include collecting orientation data from the sample. Thus, in some embodiments, the acquisition of crystal lattice orientation information includes a step of incidenting a particle beam on the sample such that the particles obtained are emitted from a plurality of locations in the sample, in particular in a mapping portion thereof, or on a surface or plane coinciding with the sample, the plurality of locations including a plurality of surrounding locations of each region including one of the plurality of target locations, and a step of monitoring the particles obtained using a detection device to obtain crystal lattice orientation information for the sample at each of the plurality of locations. Thus, typically, the acquisition step includes a step of incidenting a particle beam on the sample surface at a plurality of locations, i.e., at the target locations and the surrounding locations on the surface. Typically, these plurality of locations can be understood as including the same as the plurality of locations mentioned above, i.e., the plurality of surrounding locations, and preferably also including the plurality of target locations.

[0058] Typically, the particle beam is an electron beam and the resulting particles include electrons. The method may further include monitoring the emitted x-rays from the multiple locations to obtain chemical composition information about the sample at the multiple surrounding locations. In any of these embodiments, the resulting electrons may include electrons backscattered by the sample. Alternatively or additionally, the resulting electrons may include electrons transmitted through the sample.

[0059] According to a second aspect of the present invention there is provided a computer readable storage medium having stored thereon program code configured to perform the method according to the first aspect.

[0060] Examples of the invention will now be described with reference to the accompanying drawings, in which: [Brief description of the drawings]

[0061] [Figure 1] FIG. 1 shows an EBSD orientation map of a cracked duplex stainless steel sample. [Diagram 2] FIG. 1 is a schematic diagram of an edge dislocation and a screw dislocation. [Diagram 3] Two schematic diagrams showing how multiple dislocations with the same sign will contribute to significant crystal lattice bending (i.e., plastic deformation) (left), whereas multiple dislocations with mixed signs cancel each other out and do not result in any significant lattice bending (right). [Figure 4] FIG. 1 shows a schematic diagram of the determination of KAM values ​​on a 3×3 pixel array on the left and an exemplary KAM map of a cracked duplex stainless steel sample on the right. [Diagram 5] FIG. 1 shows two Burgers circuits in the coordinate systems of the crystal (left) and the sample (right). [Figure 6] 1 is an orientation map of a Mg alloy showing weighted Burgers vector loops. [Figure 7] 7 is a table showing weighted Burgers vectors for the loops in the example of FIG. 6; [Figure 8] FIG. 1 is a schematic diagram illustrating a conventional weighted Burgers vector integration loop tiling technique. [Figure 9] FIG. 2 is a schematic diagram of an exemplary sliding weighted Burgers vector integration loop technique in accordance with the present invention. [Figure 10] FIG. 1 illustrates a comparison between a conventional loop approach and an exemplary sliding loop approach on the same exemplary geological dataset. [Figure 11] FIG. 13 illustrates a comparison between data obtained by different exemplary weighted Burgers vector calculation techniques for an exemplary GaN thin film. [Figure 12] FIG. 10 shows a comparison of using a square pixel loop with using a circular loop in an exemplary method according to the present invention on the same exemplary GaN data set. [Figure 13] 3 is a flow chart illustrating steps involved by an exemplary method according to the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0062] An exemplary method for analyzing lattice strain in a sample according to the present invention will now be described with reference to the accompanying drawings.

[0063] The exemplary method may allow for the modification of the WBV integral loop tiling approach proposed and published by Wheeler et al. (2009) and Timms et al. (2019). The main drawbacks of the WBV techniques used so far are: 1. The differential technique is susceptible to errors due to relatively small measured misorientations, and is therefore susceptible to large effects of any lack of precision in the orientation measurement. 2. The integral loop tiling approach overcomes this first drawback, but suffers from a significant loss of spatial resolution and therefore of fine structural detail, and is limited to rectangular or square loop shapes (unless a hexagonal grid is used for data collection).

[0064] Instead, the inventors propose to use an integral loop, but used as a "sliding" loop around each pixel as shown below. The basic principle of this approach is as follows.

[0065] Weighted Burgers Vector Calculation To mathematically define the Burgers vector for an EBSD measurement, we calculate how a closed loop in an undistorted reference system (the "sample system") looks in a locally deformed (rotated and sometimes distorted) crystal structure coordinate system. sample The corresponding path C in the crystal coordinate system crystal exists, and this route C crystal is not necessarily closed due to bending of the material. The excess distance from the end point to the start point of a path in a crystalline system is the closed loop L, which is the “weighted Burgers vector WBV” introduced in Wheeler et al. (2009). sample is related to the net Burgers vector of all dislocation line components perpendicular to

[0066] Neglecting elastic deformations, a step along either curve is given by the rotation matrix g iα The Burgers vector is calculated as the integral along a path in the crystal system according to equations (5) and (15) of Wheeler et al. (2009). TIFF2024543909000003.tif2281

[0067] Since 2D EBSD measurements are constrained to the xy plane of the sample coordinate system, only the Burgers vector (WBV) of the dislocation line, which has a z component in the sample system, can be sensed as a result.

[0068] The "sliding loop" approach involves the creation of a loop L of a given shape. sample Equations (5) and (15) from Wheeler et al. (2009) are used such that the center points of are obtained over every given 2D map point, i.e., there are as many loops as there are map points. In this way, a local average WBV is calculated for each individual map point, increasing spatial resolution compared to the "tiling" approach.

[0069] Exemplary Methods 9 shows a sliding WBV integration loop approach using tiles of size 5×5 that can be used for the exemplary method. The WBV calculated at each point is based on the Burgers vector summed around the loop, while maintaining the spatial resolution integrity of the data. In this figure, the loops are shown as having a square shape, but they can also be approximately circular.

[0070] The effect of this sliding loop approach is significant: the technique preserves the resolution of the initial data (although the dislocation structure will be smoothed for the selected loop size), but benefits from the superior accuracy and lower noise level of the integral loop approach compared to the differential approach. The improvement in resolution is illustrated in the example shown in FIG. 10.

[0071] This figure illustrates a comparison between a traditional loop method (left) and an exemplary sliding loop method (right) on the same geological data set. Although these analyses were not performed using the same loop size, note the superior resolution of small dislocation structures in the sliding loop data.

[0072] The superior accuracy and lower noise level of the sliding loop technique compared to the differential technique is demonstrated in the following example. These images compare the differential WBV technique with the sliding integral loop technique on a GaN film with individual threading dislocations (isolated dislocations that penetrate the sample due to mismatch between the GaN film and the substrate material and are visible in the corresponding electron image). The noise reduction using the sliding integral loop technique is very clear and the advantage of using a circular loop geometry is illustrated in Figure 11.

[0073] This figure shows a comparison between different WBV calculation techniques for a GaN thin film: A-differential WBV method, B-sliding integral loop technique, and C-channeling contrast electron image showing individual dislocations. Note the improved signal-to-noise of the sliding integral loop WBV method. Both techniques use a 3x3 square pixel array.

[0074] Figure 12 illustrates a comparison between using a square 9x9 pixel loop (left) and a "circular" 9x9 loop (located in an orthogonal measurement grid) (right) on the same GaN data set. Note the horizontal and vertical anamorphism when using the square loop.

[0075] The implications of the sliding loop approach compared to previously published techniques are clear from these examples, allowing for a significantly more powerful analysis of dislocation structures from orientation map data, such as those typically produced using EBSD techniques.

[0076] FIG. 13 illustrates an exemplary method 1300 for analyzing lattice strain in a sample that may employ the "sliding loop" principle described above, for example, as shown in FIGS.

[0077] The exemplary method includes performing both step 1301 of acquiring crystal lattice orientation information for each of a plurality of target locations within a portion of the sample, and step 1302 of generating strain information according to the crystal lattice orientation information. Each of the target locations, which may be referred to as map points, typically reside on or at a surface of the sample, but may alternatively reside within the sample, i.e., below the surface of the sample.

[0078] In this example, the orientation dataset comprises a dataset obtained by electron backscatter diffraction (EBSD) using a scanning electron microscope (SEM). As described above in this disclosure, the dataset comprises a plurality of EBSD measurements for a corresponding plurality of locations within a scanned portion of the sample. In this example, the orientation dataset is obtained prior to commencing the method. However, in other implementations, the method may be performed partially or wholly concurrently with the acquisition of the dataset from which crystal lattice orientation information is obtained.

[0079] In this example, the orientation data set was generated by performing an EBSD analysis on a two-dimensional cross section through the sample. Thus, in a preferred embodiment, the data set represents a number of points that also lie in a single plane, and thus the region including the target region is a planar region and can be considered a region in the plane or in one or more of each of the planes. However, it is contemplated that in other examples, it may not be possible or practical to obtain data on a planar cross section of the sample. In some cases, one or more of the data sets and regions, or each, may represent or include data on locations within the sample that do not necessarily lie in a plane. For example, the irregular outer surface of a fracture surface can be measured using EBSD, and in some examples, the curved outer surface of the sample can be measured to obtain the orientation data set.

[0080] Typically, and in this example, the entire scanned sample portion represented by the orientation data set is analyzed, i.e., the output data maps the entire area or cross-section scanned by the SEM, however, it is contemplated that in some cases a particular sub-area of ​​the scanned portion corresponding to a subset of the orientation data set may be selected to probe, for example, deformation at a crack tip, deformation at the sample face, or deformation within a particular grain or phase.

[0081] In this example, the orientation data set from which crystal lattice orientation information for the sample is derived is composed of 60,705 orientation measurements collected using an orthogonal grid with 25 nm spacing between each measurement. These data are typically stored in a hierarchical data format (HDF), with the orientation data for each measurement saved as three Euler angles. In this example, these data are stored with additional information about the diffraction pattern quality, measurement parameters, and indexing settings.

[0082] The exemplary techniques of this description can be used to probe data ranging from 10,000 analyses to 50 million with measurement intervals ranging from within a few nanometers to a few micrometers. However, data sets outside these ranges are contemplated for various embodiments, and the methods provided in this disclosure are applicable to any size of suitable orientation data set. Preferably, the measurement interval is significantly smaller (e.g., an order of magnitude smaller) than the average grain size of the material under analysis in order to resolve the structure with respect to dislocations. For most deformed materials, the absolute resolution of the EBSD technique (typically on the order of 10-100 nm) limits how small the measurement interval can be effectively set so that the integral loop method will encompass multiple dislocations almost all the time. Thus, the net weighted Burgers vector components are measured. In certain cases, such as the case of the GaN thin film shown in Figures 11 and 12, individual dislocations are sufficiently spatially distinct to be individually measurable using the present method.

[0083] Analysis of lattice distortion benefits from highly accurate orientation measurements. Current methods can use standard EBSD measurements (e.g., with angular accuracy in the range of 0.1-0.5°), but effective characterization of very small orientation changes, such as on the order of individual threading dislocations in this GaN thin film, is desirable. In this example, diffraction pattern indexing was performed using an iterative improvement method (e.g., "precision-improved" indexing as described in US 2015 / 0369760 A1), which provides an angular accuracy of <0.05°. Further improvement is possible using recently developed pattern matching techniques that allow for angular accuracy of ~0.01°.

[0084] The portion of the sample for which a two-dimensional map representing the crystal lattice distortion within the sample is to be generated is defined by a set of map points. The target locations or map points can define or be located within the map portion of the sample, typically a substantially two-dimensional portion. In this example, this set is a subset of points for which the orientation data set will provide orientation measurements. Similarly, some implementations contemplate that the entire scanned area can be mapped, and this mapping may necessarily relate the entire set of measurement locations represented by the orientation data set within the sample. In this case, the map points are selected based on predetermined or previously identified areas or features of interest on or within the sample. The map points correspond to locations that are arranged as a regular two-dimensional rectangular array for reasons of SEM scanning and sampling parameters within the sample. However, other array types and distributions of sampling points can be used.

[0085] For each map point or target location in the array, a region is defined that includes the map point. These regions can be considered as a small portion of the two-dimensional sub-sample represented in the EBSD orientation data set, each delineating an orientation data subset to be used to generate strain information for a given location in the sample or a given pixel of the strain map. Each of the established regions includes and defines a subset of the orientation information that will be used to calculate strain for different ones of the map points. The strain information represents crystal lattice distortions that can be attributed to crystal lattice dislocations in the aforementioned region. The strain information is calculated using an integration loop technique, and thus the method benefits from the higher quality GN dislocation data that this technique enables with reduced noise effects in the EBSD data.

[0086] However, in contrast to the known contiguous tiling approach illustrated by FIG. 8, this exemplary method includes a step of defining these regions to overlap each other. The introduction of overlap between adjacent ones of the regions or "tiles" that define how the strain information is generated allows for a significant improvement in the resolution of the spatial information representative of the strain within the sub-sample. These overlapping tiles may also be referred to as loops, with the series of data points corresponding to a closed loop around the perimeter of each region. Thus, in the context of the method described in this disclosure, the terms "tile" and "loop" can be understood to equally refer to the overlapping region based on which the strain information is generated for the target location that is inside the tile or loop and corresponds thereto.

[0087] In this exemplary method, the regions overlap one another in the manner shown in Figure 9, i.e., multiple tiles have the same positioning relative to another tile, as do multiple target locations or map points. This arrangement, which configures the difference in position between the regions to be the same as the corresponding map point spacing, results in a set of high spatial resolution output data, since the output spatial resolution is the same as the map point array.

[0088] In this example, this output spatial resolution is the same as the spatial resolution provided by the orientation dataset from which the crystal lattice orientation information was obtained in step 1301. However, it is contemplated that one or more or all of the target locations or map points to be represented in the output image may not correspond exactly to the locations of the sample represented by the given orientation dataset. That is, it is contemplated that the correspondence between the target locations and the locations in the orientation dataset may be one-to-two or one-to-many, rather than one-to-one as is the case in this example. In such variations, the output strain map may be of lower resolution, i.e., it may include fewer pixels in representing a given area or two-dimensional portion of the sample, than the orientation dataset obtained by EBSD analysis.

[0089] In this example, the tiles are defined as square regions. However, regions having any suitable shape may be defined, for example, following a particular arrangement of a grid or array represented in the orientation data set. In some implementations, the regions, or at least some of them, are defined as regular polygons of the same type, for example, squares, triangles, and hexagons. Alternatively, the multiple regions may include one or more regions of a second, third, or even different type. The size and shape of the regions may be configured depending on predetermined characteristics of the sample, for example, to exclude or include certain features in the integral calculation.

[0090] In this example, all tile regions defined for the purposes of the method are of the same size, in particular having the same area and the same shape. To facilitate direct comparison of pixel-by-pixel outputs, it is advantageous for the tiles to be of the same size and shape for each pixel. Different applications, samples, and scanning conditions may require the use of different tile shapes and tile sizes. However, for a given analysis of a data set, these tile characteristics preferably remain constant. It is believed that mixing shapes or sizes for a single analysis of a data set will give results that make comparison of one tile with another impractical or impossible, and furthermore, since size and shape will affect the degree of spatial smoothing and therefore the absolute magnitude, correlation of the magnitude of the weighted Burgers vector from one part of the map to another part will be precluded.

[0091] It is contemplated that some implementations could include sampling only every nth pixel as a way to speed up the process. However, even for large data sets, the time required for the described calculations is typically on the order of 10 seconds, and therefore it is preferable to use the exact correspondence shown in FIG. 9. Furthermore, in this example, the tiles have the exact same relative placement as the pixels they relate to; that is, they are all shifted by one pixel width / height relative to one another. This shift provides maximum resolution for a given SEM data set.

[0092] The spatial extent of the tiles can be selected or configured depending on the data set. For example, the extent can be defined in terms of finding an optimal balance between data quality and the degree of "smoothing" achieved in the image. For data collected with very high angular accuracy (e.g., using modern pattern matching techniques), a small tile size can be defined as only the nearest pixels, e.g., 3x3 square or sometimes "diamond" shaped. For data with only poor angular accuracy, enlarging the tile size improves the reduction of orientation noise and gives a better measurement of the dislocation components, but at the expense of spatial resolution (the data will be spatially smoothed).

[0093] Each tile overlaps at least one tile that includes and corresponds to a different target location. Preferably, each tile overlaps multiple other tiles at least because the minimum inter-tile centroid distance, or minimum distance between tile positions defined in any other manner, is less than the extent of the tiles in the direction of the vector defined by the separation width. Preferably, the linear tile size is a multiple of the minimum inter-tile position difference, as in this example.

[0094] The method continues by obtaining 1301 crystal lattice orientation information for all of the multiple target locations and generating 1302 distortion information based on this information. In this example, steps 1301 and 1302 are performed for all of the rectangular array of target locations contained within the orientation data set. The method 1300 illustrates these steps as being performed as an iterative sequence of step 1301 followed by step 1302 for each target location in turn. In fact, the tile region process could itself be performed in turn for each tile region containing each target location. It is contemplated that this may allow the size, shape, or arrangement of the tiles to be changed as the method progresses.

[0095] However, it will be understood that the order shown in the flow chart need not necessarily be followed, for example, crystal lattice information may be obtained for more than one, or sometimes all, target locations within a target map portion of the sample prior to generation of strain information for a given target location.

[0096] In step 1303, the generated distortion information is output in the form of a distortion map. The distortion map is an image including a number of pixels having values ​​that represent the distortion information. In this example, the map is a color image in which each pixel has a number of values ​​that in combination define the pixel color. The correspondence between pixel values ​​and distortion information can be selected or configured to optimally visually distinguish the presence of lattice distortion by techniques known in the art.

[0097] The representation of distortion data in digital image and color mapping conventions is known in the art and will not be described in detail here. Methods for plotting weighted Burgers vectors as arrows are described and shown, for example, in the 2009 paper by Wheeler et al.

[0098] Outputting the distortion map at 1303 may occur after all of the distortion information has been generated for all of the plurality of target map points as shown in Figure 13. Alternatively, as noted above, the order may deviate from this order and the map may be generated simultaneously with the ongoing generation of distortion data at 1302. For example, the map may be updated with distortion information as it is obtained.

[0099] For example, the methods and processes described herein executed in a computer or other device based on acquired, received and locally or remotely stored orientation information can be implemented as code (e.g., software code) and / or data. The device implementing the methods described herein can be implemented in hardware or software as known in the art. For example, hardware acceleration using a specially programmed GPU or a specially designed FPGA can provide a predetermined efficiency. For completeness, such code and data can be stored on one or more computer readable media, which can include any device or medium capable of storing code and / or data for use by a computer system. When a computer system reads and executes the code and / or data stored on a computer readable medium, the computer system executes the methods and processes implemented as data structures and code stored in the computer readable storage medium. In certain embodiments, one or more of the steps of the methods and processes described herein can be executed by a processor (e.g., a processor of a computer system or a data storage system).

[0100] Generally, any of the functions described in the present disclosure or illustrated in the figures may be implemented using software, firmware (e.g., fixed logic circuitry), programmable or non-programmable hardware, or a combination of these implementations. As used herein, the term "component" or "function" generally refers to software, firmware, hardware, or a combination thereof. For example, in the case of a software implementation, the term "component" or "function" may refer to program code that performs a specified task when executed on one or more processing devices. The separation of components and functions into separate units as shown may reflect any actual or conceptual physical grouping and allocation of such software and / or hardware, and tasks. Any block, step, module, or otherwise described herein may represent one or more instructions that may be stored as software on a non-transitory computer-readable medium and / or executed by hardware. Any such blocks, modules, steps, or the like, may be implemented in an automatable manner, including the use of dedicated hardware designed to accomplish such purposes through a combination of various software and / or hardware. As noted above, any number of blocks, steps, or modules may be performed in any order, including steps performed substantially simultaneously, i.e., within the tolerances of the systems for performing those blocks, steps, or modules, or may not be performed at all.

Claims

1. 1. A method for analyzing lattice strain in a sample, comprising: For each of a plurality of target locations within the sample: acquiring crystal lattice orientation information for the sample at each of a plurality of circumferential locations along a path corresponding to a perimeter of an area of ​​the sample containing the target location; generating distortion information for the target locations within the region according to the acquired crystal lattice orientation information, the distortion information representing crystal lattice distortion attributable to crystal lattice dislocations within the region, and each region containing one of the plurality of target locations overlapping another region containing a different one of the target locations; outputting a set of output data including the generated distortion information for the plurality of target locations; A method comprising:

2. 2. The method of claim 1, wherein for each target location, generating distortion information comprises combining the crystal lattice orientation information obtained for the plurality of surrounding locations along the path corresponding to the periphery of each of the regions.

3. 3. The method of claim 2, wherein the combining step includes calculating an integral of crystal orientation gradient values ​​around the perimeter of the region.

4. 10. The method of claim 1, wherein the plurality of target locations are on a surface of the specimen.

5. 2. The method of claim 1, wherein the set of output data comprises a lattice strain image for the sample, the lattice strain image comprising a plurality of pixels corresponding to the plurality of target locations, the plurality of pixels having values ​​corresponding to the generated strain information for each of the target locations.

6. The method of claim 1 , further comprising obtaining dislocation classification data for each of the plurality of target locations based on the distortion information.

7. 7. The method of claim 6, wherein for each target location, strain classification data is obtained according to one or more of dislocation density information inferred from the strain information and lattice strain orientation information inferred from the strain information.

8. 2. The method of claim 1, wherein a plurality of locations, including the plurality of surrounding locations relative to the plurality of target locations, are arranged on the sample in a periodic grid, preferably a Cartesian or hexagonal grid.

9. The method of claim 1 , wherein for one or more of the plurality of target locations, the perimeter of each of the regions defines a circular shape.

10. The method of claim 1 , wherein for one or more of the plurality of target locations, the perimeter of each of the regions defines a regular hexagonal shape.

11. The method of claim 1 , wherein for each of a plurality of target locations, the respective regions have the same size and shape.

12. The method of claim 1 , wherein for one or more of the plurality of target locations, the target location is at the centroid of its respective region.

13. For each of the plurality of target locations: defining each said region as an array of pixels, each of said plurality of perimeter locations corresponding to a perimeter subset of said array of pixels; 10. The method of claim 1, comprising:

14. The method of claim 13 , wherein the surrounding subsets of pixels substantially surround the region, each of the surrounding subsets of pixels being located on an outer boundary of the region.

15. 15. The method of claim 14, wherein each region containing one of the plurality of target locations overlaps another region such that only pixels contained in the surrounding subset of pixels of the region overlap no pixels of the other region.

16. The method of claim 1 , wherein the plurality of target locations are arranged in a regular array within the sample.

17. The step of acquiring the crystal lattice orientation information includes: incident a particle beam onto the sample to cause resulting particles to be emitted from a plurality of locations within the sample, the plurality of locations including a plurality of surrounding locations for each region containing one of the plurality of target locations; monitoring the resulting particles using a detection device to obtain the crystal lattice orientation information for the sample at each of the plurality of locations; Including, The method of claim 1.

18. the particle beam is an electron beam; the resulting particles include electrons; 18. The method of claim 17.

19. 20. The method of claim 18, further comprising monitoring x-rays emitted from the plurality of locations to obtain chemical composition information about the sample at the plurality of ambient locations.

20. 20. The method of claim 18, wherein the resulting electrons include electrons backscattered by the sample.

21. 20. The method of claim 18, wherein the resulting electrons include electrons transmitted through the sample.

22. A computer readable storage medium having stored thereon program code configured to perform the method of any one of claims 1 to 21.

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