Publications

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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 successfully 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.

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Time-of-flight secondary ion mass spectrometry (TOF-SIMS) instruments are capable of saving an entire mass spectrum at each pixel of an image, allowing for retrospective analysis of masses that were not selected for analysis during data collection. These TOF-SIMS spectral images contain a wealth of information, but few tools are available to assist the analyst in visualizing the entire raw data set and as a result, most of the data are not analyzed. Automated, nonbiased, multivariate statistical analysis (MVSA) techniques are useful for converting the massive amount of data into a smaller number of chemical components (spectra and images) that are needed to fully describe the TOF-SIMS measurement. Many samples require two back-to-back TOF-SIMS measurements in order to fully characterize the sample, one measurement of the fraction of positively charged secondary ions (positive ion fraction) and one measurement of the fraction of negatively charged secondary ions (negative ion fraction). Each measurement then needs to be individually evaluated. In this paper, we report the first MVSA analysis of a concatenated TOF-SIMS date set comprising positive ion and negative ion spectral images collected on the same region of a sample. MVSA of concatenated data sets provides results that are intuitive and fully describe the sample. The analytical insight provided by MVSA of the concatenated data set was not obtained when either polarity data set was analyzed separately. © 2005 American Chemical Society.

Boron sub-arsenide, B{sub 12}As{sub 2}, is based on twelve-atom clusters of boron atoms and two-atom As-As chains. By contrast, SiC is a tetrahedrally bonded covalent semiconductor. Despite these fundamental differences, the basal plane hexagonal lattice constant of boron sub-arsenide is twice that of SiC. This coincidence suggests the possibility of heteroepitaxial growth of boron sub-arsenide films on properly aligned SiC. However, there are a variety of incommensurate alignments by which heteroepitaxial growth of B{sub 12}As{sub 2} on (0001) 6H-SiC can occur. In this study, we first used geometrical crystallographic considerations to describe the possible arrangements of B{sub 12}As{sub 2} on (0001) 6H-SiC. We identified four translational and two rotational variants. We then analyzed electron backscattered diffraction and transmission electron microscopy images for evidence of distinct domains of such structural variants. Micron-scale regions with each of the two possible rotational alignments of B{sub 12}As{sub 2} icosahedra with the SiC surface were seen. On a finer length scale (100-300 nm) within these regions, boron-rich boundaries were found, consistent with those between pairs of the four equivalent translational variants associated with a two-to-one lattice match. Boron-carbide reaction layers were also observed at interfaces between SiC and B{sub 12}As{sub 2}.

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 TEM 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.

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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 counting 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.

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 is 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.

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Sandia and Rontec have developed an annular, 12-element, 60 mm{sup 2}, Peltier-cooled, translatable, silicon drift detector called the SDD-12. The body of the SDD-12 is only 22.8 mm in total thickness and easily fits between the sample and the upstream wall of the Sandia microbeam chamber. At a working distance of 1 mm, the solid angle is 1.09 sr. The energy resolution is 170 eV at count rates <40 kcps and 200 eV for rates of 1 Mcps. X-ray count rates must be maintained below 50 kcps when protons are allowed to strike the full area of the SDD. Another innovation with this new {mu}PIXE system is that the data are analyzed using Sandia's Automated eXpert Spectral Image Analysis (AXSIA).

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