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Heat-treated FeCo-based magnetic alloys were characterized using a suite of electron microscopy techniques to gain insight into their structural properties. Electron channeling contrast imaging (ECCI) in the scanning electron microscope (SEM) found unique grains towards the outer edge of a FeCo sample with nonuniform background contrast. High-magnification ECCI imaging of these nonuniform grains revealed a weblike network of defects that were not observed in standard uniform background contrast grains. High-resolution electron backscattered diffraction (HR-EBSD) confirmed these defect structures to be dislocation networks and additionally found subgrain boundaries within the nonuniform contrast grains. The defect content within these grains suggests that they are unrecrystallized grains, and ECCI can be used as a rapid method to quantify unrecrystallized grains. To demonstrate the insight that can be garnered via ECCI on these unique grains, the sample was imaged before and after micro indentation. This experiment showed that slip bands propagate throughout the material until interacting with the dislocation networks, suggesting that these specific defects provide a barrier to plastic deformation. Taken together, these results show how ECCI can be used to better understand failure mechanisms in alloys and provides further evidence that dislocation networks play a critical role in the brittle failure of FeCo alloys.

A new aberration-corrected scanning transmission electron microscope equipped with an array of Si-drift energy-dispersive X-ray spectrometers has been utilized to acquire spectral image data at atomic resolution. The resulting noisy data were subjected to multivariate statistical analysis to noise filter, remove an unwanted and partially overlapping non-sample-specific X-ray signal, and extract the relevant correlated X-ray signals (e.g., channels with L and K lines). As an example, the Y2Ti2O7pyrochlore-structured oxide (assumed here to be ideal) was interrogated at the [011] projection. In addition to pure columns of Y and Ti, at this projection, there are also mixed 50-50 at. % Y-Ti columns. An attempt at atomic-resolution quantification is presented. The method proposed here is to subtract the non-column-specific signal from the elemental components and then quantify the data based upon an internally derivedk-factor. However, a theoretical basis to predict this non-column-specific signal is needed to make this generally applicable.

Multivariate statistical analysis (MSA) is applied to the extraction of chemically relevant signals acquired with a micro-X-ray fluorescence (μ-XRF) mapping (full-spectral imaging) system. The separation of components into individual histograms enables separation of overlapping peaks, which is useful in qualitatively determining the presence of chemical species that have overlapping emission lines, and holds potential for quantitative analysis of constituent phases via these same histograms. The usefulness of MSA for μ-XRF analysis is demonstrated by application to a geological rock core obtained from a subsurface compressed air energy storage (CAES) site. Coupling of the μ-XRF results to those of quantitative powder X-ray diffraction analysis enables improved detection of trace phases present in the geological specimen. The MSA indicates that the spatial distribution of pyrite, a potentially reactive phase by oxidation, has low concentration and thus minimal impact on CAES operations.

Obtaining particulate compositional maps from scanned PIXE (proton-induced X-ray emission) measurements is extremely difficult due to the complexity of analyzing spectroscopic data collected with low signal-to-noise at each scan point (pixel). Multivariate spectral analysis has the potential to analyze such data sets by reducing the PIXE data to a limited number of physically realizable and easily interpretable components (that include both spectral and image information). We have adapted the AXSIA (automated expert spectral image analysis) program, originally developed by Sandia National Laboratories to quantify electron-excited X-ray spectroscopy data, for this purpose. Samples consisting of particulates with known compositions and sizes were loaded onto Mylar and paper filter substrates and analyzed by scanned micro-PIXE. The data sets were processed by AXSIA and the associated principal component spectral data were quantified by converting the weighting images into concentration maps. The results indicate automated, nonbiased, multivariate statistical analysis is useful for converting very large amounts of data into a smaller, more manageable number of compositional components needed for locating individual particles-of-interest on large area collection media.

Automated, nonbiased, multivariate statistical analysis techniques are useful for converting very large amounts of data into a smaller, more manageable number of chemical components (spectra and images) that are needed to describe the measurement. We report the first use of the multivariate spectral analysis program AXSIA (Automated eXpert Spectral Image Analysis) developed at Sandia National Laboratories to quantitatively analyze micro-PIXE data maps. AXSIA implements a multivariate curve resolution technique that reduces the spectral image data sets into a limited number of physically realizable and easily interpretable components (including both spectra and images). We show that the principal component spectra can be further analyzed using conventional PIXE programs to convert the weighting images into quantitative concentration maps. A common elemental data set has been analyzed using three different PIXE analysis codes and the results compared to the cases when each of these codes is used to separately analyze the associated AXSIA principal component spectral data. We find that these comparisons are in good quantitative agreement with each other.

Spectral imaging in the scanning electron microscope (SEM) equipped with an energy-dispersive X-ray (EDX) analyzer has the potential to be a powerful tool for chemical phase identification, but the large data sets have, in the past, proved too large to efficiently analyze. In the present work, we describe the application of a new automated, unbiased, multivariate statistical analysis technique to very large X-ray spectral image data sets. The method, based in part on principal components analysis, returns physically accurate (all positive) component spectra and images in a few minutes on a standard personal computer. The efficacy of the technique for microanalysis is illustrated by the analysis of complex multi-phase materials, particulates, a diffusion couple, and a single-pixel-detection problem.

Multivariate statistical analysis methods have been applied to scanning transmission electron microscopy (STEM) energy-dispersive X-ray spectral images. The particular application of the multivariate curve resolution (MCR) technique provides a high spectral contrast view of the raw spectral image. The power of this approach is demonstrated with a microelectronics failure analysis. Specifically, an unexpected component describing a chemical contaminant was found, as well as a component consistent with a foil thickness change associated with the focused ion beam specimen preparation process. The MCR solution is compared with a conventional analysis of the same spectral image data set.

Spectral imaging, where complete x-ray spectra are acquired from 2D or 3D arrays of points, is a powerful microanalytical technique, especially when combined with multivariate statistical analysis [1]. Chemical imaging, typically spectroscopic imaging or mapping with energy-loss electrons or x rays has been performed on flat [2-3] or pillar-shaped specimens [4] in the TEM to achieve 3D chemical images. In this paper we describe results from acquisition and analysis, via multivariate statistical analysis (MSA), of x-ray spectral images acquired from a needle-shaped FIB specimen, tilted from -90 to +90 in a newly available TEM specimen holder from Fischione Instruments [5], with full EDS coverage and constant thickness throughout the tomographic series.

This paper assesses the potential of multivariate statistical analysis (MSA) applied to electron backscattered diffraction (EBSD) data. Instead of directly indexing EBSD patterns on an individual basis, this multivariate approach reduces a large (thousands) set of individual EBSD patterns into a core set of statistically derived component EBSD patterns which can be subsequently indexed. The following hypotheses are considered in this paper: (1) experimental EBSD patterns from a microstructure can be analytically treated as linear combinations of spatially simple components, (2) MSA has an angular resolution on par with standard EBSD, (3) MSA can discriminate between similar and dissimilar phases, and (4) the MSA approach can improve the effective spatial resolution of automated EBSD.

Electron backscattered diffraction (EBSD) is a widely used technique for both identifying the crystallographic phase and for mapping the orientation of crystalline materials on the micron length scale. Often the operating conditions necessary for phase identification are not suitable for orientation mapping and vice versa. In an effort to optimize the speed involved in the mapping technique, pattern quality is sacrificed and the wealth of information present in an EBSD pattern is compressed to basically 4 values: a matched phase and three Euler angles. However, ab initio identification of phases from EBSD patterns requires high quality patterns and fairly intense computation. Spectrum imaging is an analytical approach that may offer some solutions to the aforementioned problems. Spectrum imaging consists of collecting a whole spectrum at each pixel in a mapping style measurement. This large set of data is then analyzed using multivariate statistical analysis (MSA) techniques such as principle components analysis, multivariate curve resolution, or other least squares based techniques. The result of these calculations is a set of component spectral shapes with corresponding abundances that allow the analyst to extract the greatest amount of physically relevant information from an otherwise enormous data set. Spectrum imaging has been used successfullymore » in EDX microanalysis (both in the SEM and TEM), TOF-SIMS, WDS, and EELS. To examine the potential benefits of the spectrum imaging approach for EBSD data, a series of basic experiments and calculations were run. Test data sets (20 x 20 patterns in .jpeg format) on polycrystalline Al and on the directionally solidified eutectic oxide, CoO/ZrO{sub 2}(CaO), were collected using the HKL Channel 5 system with a Nordlys detector under normal mapping conditions. The data was collected on a FEI dual beam FIB (model DB235) and a Zeiss (Supra 55 VP) SEM at 20keV for Al and CoO/ZrO{sub 2}(CaO), respectively. The data sets were analyzed according to the schematic shown in Figure 1. Each EBSD pattern was hough transformed, unzipped into a 1-D vector of channels with intensities ranging from 0-255, and then added to an overall data matrix. A range of treatments (edge/no edge detection, spatial simplicity/spectral simplicity, etc.) were examined to determine the optimal way of treating the data. The multivariate analyses were performed using the AXSIA code developed at Sandia National Laboratories. The MSA techniques were able to correctly identify individual grains in the Al sample and individual phases in the CoO/ZrO{sub 2}(CaO) sample. For each component EBSD pattern identified from the Al data, a corresponding color map of abundance can be seen which clearly corresponds to a single grain (Figure 2). The success in the CoO/ZrO{sub 2}(CaO) sample is particularly notable due to both phases sharing the Fm-3m space group which would confuse most autoindexing routines. The range of analytical treatments identified two extremes in results: a minimal number of components (patterns) with only kikuchi line positions present or a larger number of components with full intensity information present. The further application of these results to phase mapping will be discussed.« less

Metallic Phases in extraterrestrial materials are composed of Fe-Ni with minor amounts of Co, P, Si, Cr, etc. Electron microscopy techniques (SEM, TEM, EPMA, AEM) have been used for almost 50 years to study micron and submicron microscopic features in the metal phases (Fig. 1) such as clear taenite, cloudy zone, plessite, etc [1,2]. However lack of instrumentation to prepare TEM thin foils in specific sample locations and to obtain micro-scale crystallographic data have limited these investigations. New techniques such as the focused ion beam (FIB) and the electron backscatter electron diffraction (EBSD) techniques have overcome these limitations. The application of the FIB instrument has allowed us to prepare {approx}10 um long by {approx} 5um deep TEM thin sections of metal phases from specific regions of metal particles, in chondrites, irons and stony iron meteorites, identified by optical and SEM observation. Using a FEI dual beam FIB we were able to study very small metal particles in samples of CH chondrites [3] and zoneless plessite (ZP) in ordinary chondrites. Fig. 2 shows a SEM photomicrograph of a {approx}40 um ZP particle in Kernouve, a H6 chondrite. Fig. 3a,b shows a TEM photograph of a section of the FIB prepared TEMmore » foil of the ZP particle and a Ni trace through a tetrataenite/kamacite region of the particle. It has been proposed that the Widmanstatten pattern in low P iron meteorites forms by martensite decomposition, via the reaction {gamma} {yields} {alpha}{sub 2} + {gamma} {yields} {alpha} + {gamma} in which {alpha}{sub 2}, martensite, decomposes to the equilibrium {alpha} and {gamma} phases during the cooling process [4]. In order to show if this mechanism for Widmanstatten pattern formation is correct, crystallographic information is needed from the {gamma} or taenite phases throughout a given meteorite. The EBSD technique was employed in this study to obtain the orientation of the taenite surrounding the initial martensite phase and the kamacite which forms as {alpha}{sub 2} or as Widmanstatten plates in a series of IVB irons. Fig. 4a,b shows EBSD orientation maps of taenite and kamacite from the Tawallah Valley IVB iron. We observe that the orientation of the taenite in the IVB meteorites is the same throughout the sample consistent with the orientation of the high temperature single phase taenite before formation of the Widmanstatten pattern.« less

Spectral imaging where a complete spectrum is collected from each of a series of spatial locations (1D lines, 2D images or 3D volumes) is now available on a wide range of analytical tools - from electron and x-ray to ion beam instruments. With this capability to collect extremely large spectral images comes the need for automated data analysis tools that can rapidly and without bias reduce a large number of raw spectra to a compact, chemically relevant, and easily interpreted representation. It is clear that manual interrogation of individual spectra is impractical even for very small spectral images (< 5000 spectra). More typical spectral images can contain tens of thousands to millions of spectra, which given the constraint of acquisition time may contain between 5 and 300 counts per 1000-channel spectrum. Conventional manual approaches to spectral image analysis such as summing spectra from regions or constructing x-ray maps are prone to bias and possibly error. One way to comprehensively analyze spectral image data, which has been automated, is to utilize an unsupervised self-modeling multivariate statistical analysis method such as multivariate curve resolution (MCR). This approach has proven capable of solving a wide range of analytical problems based upon the countingmore » of x-rays (SEM/STEM-EDX, XRF, PIXE), electrons (EELS, XPS) and ions (TOF-SIMS). As an example of the MCR approach, a STEM x-ray spectral image from a ZrB2-SiC composite was acquired and analyzed. The data were generated in a FEI Tecnai F30-ST TEM/STEM operated at 300kV, equipped with an EDAX SUTW x-ray detector. The spectral image was acquired with the TIA software on the STEM at 128 by 128 pixels (12nm/pixel) for 100msec dwell per pixel (total acquisition time was 30 minutes) with a probe of approximately the same size as each pixel. Each spectrum in the image had, on average, 500 counts. The calculation took 5 seconds on a PC workstation with dual 2.4GHz PentiumIV Xeon processors and 2Gbytes of RAM and resulted in four chemically relevant components, which are shown in Figure 1. The analysis region was at a triple junction of three ZrB2 grains that contained zirconium oxide, aluminum oxide and a glass phase. The power of unbiased statistical methods, such as MCR as applied here, is that no a priori knowledge of the material's chemistry is required. The algorithms, in this case, effectively reduced over 16,000 2000-channel spectra (64Mbytes) to four images and four spectral shapes (72kbytes), which in this case represent chemical phases. This three order of magnitude compression is achieved rapidly with no loss of chemical information. There is also the potential to correlate multiple analytical techniques like, for example, EELS and EDS in the STEM adding sensitivity to light elements as well as bonding information for EELS to the more comprehensive spectral coverage of EDS.« less

Microanalysis is typically performed to analyze the near surface of materials. There are many instances where chemical information about the third spatial dimension is essential to the solution of materials analyses. The majority of 3D analyses however focus on limited spectral acquisition and/or analysis. For truly comprehensive 3D chemical characterization, 4D spectral images (a complete spectrum from each volume element of a region of a specimen) are needed. Furthermore, a robust statistical method is needed to extract the maximum amount of chemical information from that extremely large amount of data. In this paper, an example of the acquisition and multivariate statistical analysis of 4D (3-spatial and 1-spectral dimension) x-ray spectral images is described. The method of utilizing a single- or dual-beam FIB (w/o or w/SEM) to get at 3D chemistry has been described by others with respect to secondary-ion mass spectrometry. The basic methodology described in those works has been modified for comprehensive x-ray microanalysis in a dual-beam FIB/SEM (FEI Co. DB-235). In brief, the FIB is used to serially section a site-specific region of a sample and then the electron beam is rastered over the exposed surfaces with x-ray spectral images being acquired at each section. All this ismore » performed without rotating or tilting the specimen between FIB cutting and SEM imaging/x-ray spectral image acquisition. The resultant 4D spectral image is then unfolded (number of volume elements by number of channels) and subjected to the same multivariate curve resolution (MCR) approach that has proven successful for the analysis of lower-dimension x-ray spectral images. The TSI data sets can be in excess of 4Gbytes. This problem has been overcome (for now) and images up to 6Gbytes have been analyzed in this work. The method for analyzing such large spectral images will be described in this presentation. A comprehensive 3D chemical analysis was performed on several corrosion specimens of Cu electroplated with various metals. Figure 1A shows the top view of the localized corrosion region prepared for FIB sectioning. The TSI region has been coated with Pt and a trench has been milled along the bottom edge of the region, exposing it to the electron beam as seen in Figure 1B. The TSI consisted of 25 sections and was approximately 6Gbytes. Figure 1C shows several of the components rendered in 3D: Green is Cu; blue is Pb; cyan represents one of the corrosion products that contains Cu, Zn, O, S, and C; and orange represents the other corrosion product with Zn, O, S and C. Figure 1 D shows all of the component spectral shapes from the analysis. There is severe pathological overlap of the spectra from Ni, Cu and Zn as well as Pb and S. in spite of this clean spectral shapes have been extracted from the TSI. This powerful TSI technique could be applied to other sectioning methods well.« less

A comprehensive three-dimensional ~3D! microanalysis procedure using a combined scanning electron microscope ~SEM!/focused ion beam ~FIB! system equipped with an energy-dispersive X-ray spectrom- eter ~EDS! has been developed. The FIB system was used first to prepare a site-specific region for X-ray microanalysis followed by the acquisition of an electron-beam generated X-ray spectral image. A small section of material was then removed by the FIB, followed by the acquisition of another X-ray spectral image. This serial sectioning procedure was repeated 10-12 times to sample a volume of material. The series of two-spatial- dimension spectral images were then concatenated into a single data set consisting of a series of volume elements or voxels each with an entire X-ray spectrum. This four-dimensional ~three real space and one spectral dimension! spectral image was then comprehensively analyzed with Sandia's automated X-ray spectral image analysis software. This technique was applied to a simple Cu-Ag eutectic and a more complicated localized corrosion study where the powerful site-specific comprehensive analysis capability of tomographic spectral imaging ~TSI! combined with multivariate statistical analysis is demonstrated.

Extended abstract of a paper presented at Microscopy and Microanalysis 2013 in Indianapolis, Indiana, USA, August 4 – August 8, 2013.

Extended abstract of a paper presented at Microscopy and Microanalysis 2013 in Indianapolis, Indiana, USA, August 4 – August 8, 2013.

Extended abstract of a paper presented at Microscopy and Microanalysis 2013 in Indianapolis, Indiana, USA, August 4 – August 8, 2013.

Extended abstract of a paper presented at Microscopy and Microanalysis 2012 in Phoenix, Arizona, USA, July 29 – August 2, 2012.

Extended abstract of a paper presented at Microscopy and Microanalysis 2012 in Phoenix, Arizona, USA, July 29 – August 2, 2012.

Extended abstract of a paper presented at Microscopy and Microanalysis 2012 in Phoenix, Arizona, USA, July 29 – August 2, 2012.

Extended abstract of a paper presented at Microscopy and Microanalysis 2012 in Phoenix, Arizona, USA, July 29 – August 2, 2012.

Extended abstract of a paper presented at Microscopy and Microanalysis 2012 in Phoenix, Arizona, USA, July 29 – August 2, 2012.