X-Ray Microanalysis

X-Ray Microanalysis Nicholas A. Bulloss nergy-dispersive spectroscopy (EDS) and wavelength-dispersive spectroscopy Thermo Fisher Scientific E (WDS) provide a key set of analytical tools for the characterization of materi- Madison, Wisconsin als in the electron microscope. Modern developments in industrial materials and processing have necessitated observation and analysis of samples at ever increas- The ability to ing magnifications. Advanced electron microscope technologies (e.g. FEG-SEM, rapidly acquire STEM) enable the observation of materials under such high magnification conditions. and accurately Correspondingly, developments in X-ray microanalysis hardware (X-ray detectors, electronics) and software (data acquisition and processing) have significantly improved process sparse analytical performance under low beam energy and low X-ray-count conditions. Ad- or low volume ditionally, this ability to “do more with less” allows increased sample throughput for spectral images the typical microanalysis lab and increased confidence in analytical results. has enabled the This article discusses, with examples, how modern X-ray microanalytical instru- mentation and data processing have improved the characterization of materials in the microscopist to electron microscope. perform microanalysis X-Ray Detector Developments with confidence The electrically cooled silicon drift detector (SDD) has replaced the liquid nitrogen at low cooled Si(Li) detector as the standard for energy-dispersive spectrometry in the SEM. SDDs have a number of advantages over the Si(Li) detectors. accelerating SDDs can collect X-rays at significantly higher count rates (over 300,000cps stored) voltages and high without significant degradation in resolution. Figure 1 shows the contrasting perform- magnifications. ances of the Si(Li) and SDD detectors at increasing count rates. It is clear that resolution is, for all intents and purposes, independent of count rate for the SDD, whereas the resolution performance of the Si(Li) detector 200 degrades significantly with increased X-ray throughput. Similarly, spectra collected via 190 SDD exhibit no peak shift even at high X-ray Si(Li) 180 storage rates. This enables accurate peak identification under all analytical conditions. 170 The excellent low energy performance (beryl- lium detection) makes the SDD ideal for EDS 160 analysis at low accelerating voltages, with concurrent high resolution imaging. 150 Spectral Imaging 140 SDD Historically, raw count single element X- 130 EDS resolution, [email protected] keV EDS resolution, ray maps have been collected to study the spatial distribution of elements across a sample. This involves the collection of the X-ray 100,000 200,000 300,000 X-Ray count rate, cps counts at a given elemental X-ray line (usually a narrow energy band region of interest Fig. 1— EDS resolution around the peak) at each pixel across the rastered area. vs. stored X-ray count rate performance In contrast, with the advent of high-throughput detectors and improved computer differences for Si(Li) and processing power, we are able to rapidly collect and process a full EDS X-ray spectrum SDD detectors. at each pixel across the sample area. This large dataset is referred to as a “spectral Elements detected image,” and contains full spatial and chemical information across the sample. include silicon, tantalum, and tungsten, The operator does not need to have prior knowledge of the sample composition to with full-scale counts perform element mapping or phase analysis, since a full EDS spectrum is collected and equal to 443,913. the peaks are identified from each pixel. One advantage of collecting the entire data cube is that post-acquisition analysis may be done offline, which opens up the SEM for other users, thereby improving SEM laboratory sample throughput and productivity. A modern spectral imaging system has the capability to terminate the spectral image acquisition when a statistically significant number of X-ray counts has been processed. This is preferable over collection for a fixed time, which may not collect a ADVANCED MATERIALS & PROCESSES • JULY 2010 29 the dataset produces the tungsten and tanta- 500 lum net count (quantitative) maps shown in Fig. 5. Now, the spatial distribution of tung- W, Mα1 400 Si, Kα sten and tantalum is clearly different, showing the power of the peak deconvolution Ta, Mα1 routines, and how potentially deceptive raw 300 Si, Kβ count element maps may be. Robust peak deconvolution techniques are 200 critical at the low accelerating voltages in high resolution imaging, as many potential peak Count rate X 1000 100 overlaps are possible (e.g. the transition metal L lines – Fe, Cr, Mn, Co, Ni, etc). Statistical Analysis 1.6 1.7 1.8 1.9 2.0 Voltage, keV To completely characterize a sample, analysts need to know not only the elemental dis- Fig. 2 — EDS spectrum of an alloy containing silicon, tantalum, and tungsten, showing tributions, but also the distribution of the peak overlaps. constituent phases. Simple elemental distribu- statistically valid dataset (too few counts) or tions require further interpretation when the may unnecessarily oversample data, wasting elements appear in more than one phase. valuable electron microscope resources. Applying multivariate statistical analysis (MSA) techniques to the whole spectral image Quantitative Element Mapping dataset can rapidly process large amounts of Interpreting raw count elemental X-ray spectral data to identify regions that exhibit maps can be fraught with complications. It may similar spectral signatures. In this technique, be problematic to determine if the counts in the every energy channel in a spectrum is com- 5 μm X-ray map came from the element X-ray line in pared against every other channel in the spec- question, from “bremsstrahlung” (continuum trum and against all of the spectra in the Fig. 3 — Backscatter electron radiation), or from overlapped elements.(Fig. spectral image. This method is completely in- image of a tungsten, tantalum, 2). Additionally, in low count X-ray maps, we dependent of operator input, so there is no bias titanium, and cobalt carbide tool (magnification 4400X). need to determine if the counts are really ele- to the results. One of the key advantages of this ment X-ray counts, or simply system noise. method is that it works with low volume (few One way to remove the effects of X-ray counts in a spectrum) and sparse (many bremsstrahlung and peak overlaps is zero energy channels in a spectrum) spectral to take each spectrum in the spectral datasets. Accurate phase identifications is pos- imaging data cube, remove the back- sible with as few as 50 X-ray counts per pixel. ground, and carry out a peak decon- Additionally, phases represented by only a few volution. The resultant maps are pixels in the sample will be identified. designated quantitative maps, and can Thermo Scientific’s Direct-to-Phase (DTP) be expressed in weight percent, atomic program is based on software developed at San- percent, or net counts. dia National Laboratories. It takes the MSA one 5 μm Figure 3 is a backscattered elec- step further by analyzing during data acquisi- tron image (BEI) of a tungsten, tan- tion (rather than as a post-processing step), and Fig. 4 — Raw count element maps for talum, titanium, and cobalt carbide matching the resultant phases against a data- tungsten (green) and tantalum (blue). tool. This dataset was collected at base to identify them by name. This greatly re- 10kV. In this lower energy range, duces both the total analysis time and the only the M lines for tungsten and interpretation required by the analyst. Figure tantalum are excited by the beam. As 6 is a DTP display of the carbide tool showing seen in Figure 2, the tungsten and the tungsten carbide phase in red, the tanta- tantalum Mα lines cannot be distin- lum-titanium carbide phase in yellow, and a guished, even at the best EDS spec- cobalt-rich phase in blue. This phase map was tral resolutions. collected in less than five minutes. The raw count element maps (Fig. Often, during a preliminary examination of 4) clearly look similar, which may a sample, analysts begin by collecting single 5 μm lead to the assumption that tungsten spectra from selected points on the sample. and tantalum appear in the same These points are usually chosen by observing Fig. 5 — Net count element maps for phase. Completing background re- differences in contrast on the backscattered tungsten (green) and tantalum (blue). moval and peak deconvolution on electron image (BEI), which are a function of 30 ADVANCED MATERIALS & PROCESSES • JULY 2010 mean atomic weight. This method does not Figure 7 shows a boron WDS map of a Fe-Cr-B fully characterize the sample and can be mis- alloy analyzed at 5kV. The image is approximately leading when the BEI contrast is similar be- 10 microns across. The highest boron concentra- tween two or more phases. With the advent of tion region (blue) contains about 31wt% B, the faster detectors and processing capabilities, yellow areas - 14wt% B, and the red areas – complete spectral images can be collected from 6.5wt% B. across the entire field of view with sufficient The Thermo Scientific MagnaRay WDS statistics to identify all phases in only a few spectrometer has attained significant increases minutes. This reduces the risk of missing im- in diffracting crystal and counter movement 5 μm portant phases, particularly when BEI contrast speed over older hardware designs. This allows is similar across the sample. rapid WDS confirmation of EDS peak identifi- Fig. 6 — Direct-to-Phase map of a tungsten, tantalum, titanium, cations during EDS spectral acquisitions, giv- and cobalt carbide tool. Wavelength-Dispersive Spectrometry ing the analyst further confidence in the results. A modern fully focusing wavelength- Peak confirmation is particularly useful in cases dispersive spectrometer (WDS) uses both graz- of EDS peak overlaps or when the EDS peak to ing incidence and polycapillary optics.

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