Scientists, technicians and other users often perform spectral microanalysis of specimens to determine the spatial composition of the specimen—in other words, what makes up the specimen (elements, compounds, alloys, phases, etc.) at various points about an area of interest on the specimen. In essence, spectral microanalysis yields a map of the specimen's composition, and can do so on a microscopic level, yielding an extremely useful tool for science and industry.
Spectral microanalysis is usually performed by directing an excitation beam—such as an electron beam or X-ray beam—at a specimen, and then capturing and analyzing the radiation and/or particles emitted by the specimen in response to the excitation beam. Often, data related to the emitted radiation and/or particles are presented to a user as a spectral histogram (or simply a “spectrum”), wherein the counts (numbers) of emissions from the specimen are plotted versus their energy. Most distinct substances generate a unique spectrum, and thus the substance's spectrum serves as a “fingerprint” for the substance. By capturing spectra from a specimen and then cross-referencing these spectra versus catalogues/databases of “reference spectra”—spectra captured from known substances—one can determine the substances present in the specimen. Usefully, the aforementioned spectra can often be collected along with an image of the specimen, thereby allowing an analyst a useful visual reference against which the spectral (substance identification) data may be reviewed. Thus, one can both view the analyzed region of interest on the specimen, and also determine the composition of the specimen at the viewed region.
Several factors tend to complicate microanalysis. Initially, since specimens rarely consist of pure substances, one usually cannot simply identify the “closest” reference spectrum and assume that it identifies the substance. Rather, a spectrum captured from a specimen often reflects the presence of several substances, each being present in different amounts within the specimen. Thus, the specimen spectrum equates to a combination of the spectra of the component substances, with each component spectrum having a strength within the combination which corresponds to the relative amount of that component. As a result, analyzing a spectrum captured from a specimen to determine its component spectra (and this its component substances) often requires complicated and time-consuming statistical methods.
Further, the method described above only provides information about (i.e., a spectrum for) the area of the specimen which is subjected to the excitation beam. Thus, if one wishes to obtain information about a larger region of the specimen, the excitation beam must be scanned across the specimen (e.g., in an X-Y “rastering” pattern), and spectra must be collected at multiple points during the scan. This leads to large amounts of data, and consequently large data analysis times.
To better illustrate these difficulties, it is useful to consider common spectral microanalysis methods in greater detail. As an excitation beam is scanned between areas or “pixels” distributed about a specimen, a spectrum is collected at each pixel. Each spectrum contains information regarding the elements present in the specimen at the pixel from which the spectrum was captured. In addition, data resulting from the excitation beam is also captured at each pixel and can be used to generate an image of the pixel. For example, where the excitation beam is an electron beam, the image may depict the pixel's backscattered electrons (electrons from the excitation beam which were “reflected” from the specimen), or the pixel's secondary electrons (electrons knocked out of the specimen by the excitation beam). In either case, the image provides a visual representation of the pixel, though the visual representation may not correspond to the pixel's appearance if viewed by the eye under standard light; for example, a backscattered electron image effectively provides a view of the pixel's density, and a secondary electron image effectively provides a view of the pixel's surface roughness. The combination of the spectrum and image data at a particular pixel is often referred to as a “hyperspectral image,” or as a “hyperspectral data cube” when referring to the spectra and image data over the entire scanned region (i.e., over all pixels).
The spectra from the various pixels are then statistically analyzed via Principal Component Analysis (PCA) to determine which components (i.e., compounds or other combinations of elements) seem most likely to be present in the scanned region of the specimen, and at each pixel in the scanned region. In PCA methods, the correlations between the pixel spectra are determined to extract the spectra of the underlying components that seem to be present in the various pixels across the scanned region. This results in a set of statistically-derived spectra, each representing a component, wherein these derived spectra of all of these (derived) components combine in varying proportions at each of the pixels to result in the measured spectra at the pixels.
The information regarding the derived components and their proportions can then be extended to the measured hyperspectral images to determine their likely components. For example, one could look to each pixel and determine the dominant component at that pixel (i.e., the derived component which seems present in greatest amounts at that pixel, and/or which seems to be most likely to be present at that pixel), and then combine/average the measured spectra of all pixels having that same dominant component. The combined spectrum—which at least theoretically represents the component(s) present at its respective pixels—can then be assigned to each of the pixels having that same dominant component. This step, which replaces the measured spectrum for each pixel with its corresponding combined spectrum, can result in a smaller and more manageable set of spectra for the pixels. These spectra can then each be processed versus databases of reference spectra to determine the probable real-world components at each pixel. If desired, the image(s) of the specimen can then have the name(s) of the calculated components overlaid over the areas to which they correspond, or the image can be color-coded or otherwise encoded/labeled to allow a user to visualize the composition of the specimen.
The aforementioned statistical analysis of the measured pixel spectra is often performed by a commercially available software package known as COMPASS, details of which are described in greater detail in U.S. Pat. Nos. 6,584,413 and 6,675,106. While COMPASS is a valuable microanalysis tool, it suffers from the drawback that it can be time-consuming to use: since there are many pixels (e.g., over a million pixels across a 1024×1024 pixel area), and the correlations between their unique measured spectra must be calculated to determine their probable components (and these spectra may themselves consist of thousands of data points, e.g., counts measured across 4096 energy intervals or “bins”), it can require many minutes to provide the desired output, even where exceptionally fast computer processing speeds are used. Other analysis packages, which may employ different statistical processing methods than those of the aforementioned patents, tend to incur similar processing times. This is disadvantageous because an analyst naturally wants microanalysis results as soon as possible.