Elemental identification based on phase analysis

Abstract

To provide a support apparatus for a scientific instrument which includes a charged particle microscope (CPM) and associated detectors for acquisition of electron-microscope images and pixelwise energy-dispersive X-ray spectroscopy (EDS) spectra.SOLUTION: The support apparatus includes an interface device configured to receive a dataset including a CPM image of a sample and a plurality of EDS spectra of the sample. Each of the EDS spectra corresponds to a respective pixel of the CPM image. The support apparatus also includes one or more electronic processing devices configured to: compute a phase map of the sample, the phase map identifying groups of pixels representing different respective phases of the sample; determine respective element sets based on the EDS spectra corresponding to the groups; and compute a corresponding elemental map of the sample based on the identified groups of pixels and the determined respective element sets.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] Various embodiments generally relate to, but are not limited to, components, devices, systems, and methods of charged particle microscopes.

Background Art

[0002] Energy-dispersive X-ray spectroscopy (also abbreviated as EDS, EDX, or XEDS) is an analytical technique used for chemical characterization and elemental analysis of materials. A sample excited by an energy source dissipates some of the absorbed energy by emitting core-shell electrons. Outer-shell electrons move to fill the holes and emit the energy difference as X-ray photons. The spectral composition of the emitted X-ray photons is characteristic of the original atoms. The X-ray spectrum measured in this way enables compositional analysis of the sample volume excited by the energy source. The position of the peaks in the spectrum identifies the elements, and the signal intensity represents the concentration of the elements in the corresponding volume.

[0003] The electron beam of an electron microscope provides sufficient energy to emit core-shell electrons and cause X-ray emission. When the electron beam is scanned across the sample, characteristic X-rays are emitted and measured by a suitable EDS detector. Each recorded EDS spectrum is then mapped to its respective pixel position on the sample. The quality of subsequent elemental identification typically depends on the signal intensity and the signal-to-noise ratio (SNR). The SNR can be increased, for example, by appropriately binning the recorded EDS spectra corresponding to different pixel positions. However, for at least some samples, some types of binning can result in relatively low accuracy of elemental identification. For example, for small particles that occupy a small number of pixels within the field of view (FOV), some components may be unfavorably overlooked during the automatic identification of the binned spectra due to the corresponding peaks being buried in the spectrum background.

Summary of the Invention

[0004] In this specification, various examples, aspects, features, and embodiments of scientific instruments, including charged particle microscopes (CPMs) and related detectors for acquiring electron microscope images and EDS spectra in pixel units, are disclosed. In one example, the electronic controller of the scientific instrument performs phase analysis in the background of an ongoing acquisition process and calculates the phase spectra of different phases identified through phase analysis by binning the corresponding EDS spectra in pixel units. Next, the electronic controller calculates a preliminary elemental map of the sample based on the phase map and the phase spectra. The elemental map can be updated live when new measurements are received from the detector.

[0005] One example provides an automated method executed via a computing device to assist a scientific instrument. The method includes calculating a phase map of a sample by applying phase analysis to a data set including a CPM image of the sample and a plurality of EDS spectra of the sample, where each of the EDS spectra corresponds to a respective pixel of the CPM image and the phase map identifies groups of pixels representing different respective phases of the sample; for each group of the identified groups of pixels, determining a respective set of elements based on the EDS spectra corresponding to the group; and for a selected chemical element, calculating a corresponding elemental map of the sample based on the identified groups of pixels and the determined respective sets of elements.

[0006] Another embodiment provides a non-transitory computer-readable medium storing instructions that, when executed by a computing device, cause the computing device to perform operations including the above-described automated method.

[0007] Yet another example provides an assistance device for a scientific instrument, the assistance device comprising an interface device configured to receive a data set including a CPM image of a sample and a plurality of EDS spectra of the sample, each of the EDS spectra corresponding to a respective pixel of the CPM image; and one or more electronic processing devices configured to calculate a phase map of the sample by applying phase analysis to the data set, the phase map identifying groups of pixels representing different respective phases of the sample; for each of the identified groups of pixels, determining a respective set of elements based on the EDS spectrum corresponding to the group; and for a selected chemical element, calculating a corresponding element map of the sample based on the identified groups of pixels and the determined respective sets of elements. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] The foregoing aspects and many of the attendant advantages of the present disclosure will be more readily appreciated as they become better understood by reference to the following detailed description, when taken in conjunction with the accompanying drawings.

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DETAILED DESCRIPTION OF THE INVENTION

[0009] FIG. 1 is a block diagram illustrating a scientific instrument 100 according to some embodiments. The scientific instrument 100 includes a charged particle beam column 102 coupled to a vacuum chamber 108. In some examples, the charged particle beam column 102 is a scanning electron microscope (SEM) column, a scanning transmission electron microscope (STEM) column, or a focused ion beam (FIB) column. The vacuum chamber 108 houses a movable sample holder 110 and can be evacuated using one or more vacuum pumps (not explicitly shown in FIG. 1). In an exemplary embodiment, the sample holder 110 is independently movable parallel to the XY coordinate plane and parallel to the Z coordinate axis, and the corresponding coordinate system is indicated by the XYZ coordinate triad shown in FIG. 1. In some embodiments, the sample holder 110 is movable in six degrees of freedom including tilting in one or more rotational directions. A sample S to be investigated using the scientific instrument 100 is attached to the sample holder 110.

[0010] In one example, the (SEM type) charged particle beam column 102 includes an electron source 112 and two or more charged particle beam (CPB) lenses, only two of which, for example, the objective lens 106 and the condenser lens 116, are schematically shown in FIG. 1 for illustrative purposes. In some embodiments, a (different) number of such lenses may be used within the SEM column 102.

[0011] During operation, the electron source 112 generates an electron beam 114 that propagates generally along the longitudinal axis 115 of the SEM column 102. The CPB lenses 106 and 116 operate to generate electric and magnetic fields that affect the electron trajectories within the electron beam 114. The control signals 152, 156 generated by the electron controller 150 are used to change the intensity and / or spatial configuration of these fields to impart desired characteristics to the electron beam 114. Generally, the CPB lenses 106 and 116, the control signals 152 and 156, and other related components of the scientific instrument 100 are used to perform various operations and assist with various functions such as beam focusing, aberration relaxation, aperture clipping, filtering, and the like. The SEM column 102 further includes a deflection unit 118 that can steer the electron beam 114 in response to a drive signal 154 applied via the electron controller 150. Such beam steering can be used to move the focused portion of the electron beam 114 to a selected spot on the sample S or along a desired path across the sample S. In an exemplary implementation of the imaging mode, the drive signal 154 changes over time to move the focused portion of the electron beam 114 across the sample S in a raster or other suitable scanning pattern.

[0012] In another example, a charged particle beam column 102 (of the FIB type) comprises an ion source 112 and ion beam optics 106, 116, 118. Optionally, the ion source 112 is a plasma source connected to a plurality of gas volumes (not explicitly shown). The gas volumes can be individually connected to the plasma source via respective valves, to select individual gases stored within the gas volumes or to create mixtures thereof for the ion source. Examples of gases thus supplied to the ion source 112 are xenon, argon, oxygen, hydrogen, and nitrogen gas. During operation, the ion source 112 ionizes the supplied gas, thereby forming a plasma. Ions extracted from the plasma are accelerated through the FIB column 102 to form an ion beam 114 that propagates generally along the longitudinal axis 115 of the FIB column 102. The ion beam optics 106, 116, 118 are used, inter alia, to focus the ion beam 114 onto the sample S and to move the focused portion of the ion beam 104 along a desired path across the sample S, for example, to perform a raster or vector scan of the sample S. In some other examples, the ion source 112 may comprise a liquid metal ion source (LMIS) or any other ion source suitable for the FIB column 102. In various configurations of the FIB column 102, the ion beam 114 can be used to image the sample S or to perform machining operations such as, for example, cutting, milling, etching, deposition, etc.

[0013] The scientific instrument 100 also includes detectors 160, 170, 180 located within the vacuum chamber 108 relatively close to the sample S. During operation, the detectors 160, 170, and 180 generate a stream of measurement values 162, 172, and 182 received by the electronic controller 150. The specific type of detectors 160, 170, 180 depends on the embodiment of the scientific instrument 100 and can typically be selected from various detector types suitable for detecting different types of emissions and / or radiation from the sample S generated in response to the electron beam 114. Exemplary types of emissions / radiation that can be generated in this way include, but are not limited to, X-rays, infrared rays, visible light, ultraviolet rays, backscattered electrons, secondary electrons, Auger electrons, elastically scattered electrons, unscattered (e.g., zero energy loss) electrons, and inelastically scattered electrons. In various embodiments, different numbers of such detectors can be used. In some embodiments, the detectors 160, 170, 180 are selected from the group consisting of a high-angle annular dark-field detector, a medium-angle annular dark-field detector, an annular bright-field detector, a segmented annular detector, a differential phase contrast detector, an electron energy loss spectroscopy (EELS) detector, an EDS detector, and a two-dimensional pixelated diffraction pattern detector. Other detectors capable of detecting various ones of the types of emissions / radiations mentioned above can also be used in various additional embodiments.

[0014] In the example shown, detectors 160, 170 are positioned above the sample S, and detector 180 is positioned below the sample S. As used herein, the terms “above” and “below” are used with respect to the propagation direction of the electron beam 114 that generally propagates along the Z-axis from the electron source 112 towards the sample S. In this sense, a location “above” the sample S has a non-zero offset from the sample S along the Z-axis in the upstream direction of the electron beam 114. Similarly, a location “below” the sample S has a non-zero offset from the sample S along the Z-axis in the downstream direction of the electron beam 114. In various additional embodiments, different numbers of detectors can be positioned above and below the sample S. In some embodiments, all such detectors can be positioned either below or above the sample S.

[0015] In some examples, the scientific instrument 100 is a dual-beam instrument that includes first and second examples of charged particle beam columns 102 coupled to the vacuum chamber 108. In such examples, the first example is the SEM-type charged particle beam column 102 described above, and the second example is the FIB-type charged particle beam column 102 described above. In various geometric arrangements, the longitudinal axes 115 of the SEM-type and FIB-type charged particle beam columns 102 are oriented at an angle of about 30 degrees to 60 degrees with respect to each other.

[0016] For purposes of illustration and without any implied limitation, the exemplary embodiments are described below with reference to the scientific instrument 100 comprising the SEM column 102 or the STEM column 102. Based on the provided description, one of ordinary skill in the art will be able to make and use other embodiments suitable for use with other configurations of the scientific instrument 100 without undue experimentation. In particular, the scientific instrument 100 is configured to acquire charged particle microscope images of a sample including SEM images, FIB images, and / or STEM images.

[0017] Generally, the various embodiments disclosed herein can be used with charged particle microscope (CPM) systems and apparatuses designed for electron microscopes and / or focused ion beam microscopes, including dual beam systems and apparatuses. Exemplary ion sources used in FIB columns include, but are not limited to, sources of H, He, Ar, Ne, and Ga ions. The corresponding systems can typically provide patterning below 10 nm and the ability to image non-conductive and magnetic samples.

[0018] FIG. 2 is a block diagram illustrating a portion 200 of a scientific instrument 100 according to one exemplary embodiment. The portion 200 is an exemplary example of a detector configuration suitable for the combined use of spectroscopy and microscopy in the scientific instrument 100. Both spectroscopy and microscopy are useful tools for analyzing samples individually, but spectroscopy and microscopy can produce even more powerful results when combined. In the example shown, the portion 200 provides STEM imaging capabilities. Spectroscopic techniques implemented in the portion 200 include, but are not limited to, electron energy loss spectroscopy (EELS) and energy dispersive X-ray spectroscopy (EDS).

[0019] The portion 200 includes the objective lens 106 also shown in FIG. 1, the sample S, and the detectors 160, 180. The portion 200 further includes a projection lens 210, one or more annular detectors 220, 230, and 240, an aperture 250, and a magnetic field sector 260. FIG. 2 also schematically shows the electron beam 114 delivered to the sample S as described above with reference to FIG. 1. The STEM imaging modality of the scientific instrument involves the sequential scanning of the focused portion of the electron beam 114 over a selected portion of the sample S using some or all of the annular detectors 220, 230, and 240, which have different scattering angles with respect to the propagation direction of the electron beam 114. Pixel-by-pixel data acquisition performed in this way is used to construct, among other things, a STEM image of the scanned area of the sample S.

[0020] In some examples, the annular detector 220 is a high-angle annular dark field (HAADF) detector configured to detect incoherent inelastic electron scattering by nuclei and is typically a major component for scattering angles higher than 80 mrad. Similarly, in some examples, the annular detector 230 is a medium-angle annular dark field (MAADF) detector configured to detect electrons having scattering angles of about 30 - 80 mrad, and the annular detector 240 is an annular bright field (ABF) detector configured to detect low-scattering-angle electrons.

[0021] Various electron microscope-based spectroscopy techniques typically aim to obtain quantitative and / or qualitative information from various signals generated during scanning of the electron beam 114 over the sample S. After being appropriately collected, the spectroscopic signals can be presented together with or overlaid on the corresponding electron microscope images generated as shown above using one or more of, for example, the annular detectors 220, 230, and 240 and / or other suitable detectors for construction of maps of material properties. In various examples of the present disclosure, the phase maps or elemental maps described herein can include a graphical representation of phase or elemental information (i.e., an image), or a data array associating phase or elemental information with individual image pixels or groups of image pixels.

[0022] In some examples, detector 160 operates as an EDS detector configured to detect the emission of characteristic X-rays from sample S stimulated by electron beam 114. In the ground (unstimulated) state, the atoms within sample S have electrons at discrete energy levels in the inner electron shells of the atoms. When electron beam 114 interacts with an atom, an electron can be emitted from the inner shell of the atom, creating an electron hole (vacancy). The recombination of this electron hole with an electron from the outer shell of the atom causes the emission of an X-ray photon. The flux of such X-ray photons and their energies are measured by EDS detector 160, thereby measuring the corresponding X-ray emission spectrum. Since each chemical element has a unique set of peaks in the X-ray emission spectrum, the elemental composition of sample S can be elucidated using the analysis of the X-ray spectrum measured by EDS detector 160. The elemental composition can also be referred to as a set of elements. The elemental composition or set of elements includes the identification of one or more elements present in the sample and, in some cases, the relative compositional ratios between the elements present in the sample.

[0023] In some examples, detector 180 operates as an EELS detector configured to detect inelastically scattered electrons of electron beam 114 propagating within a narrow angular range selected by aperture 250. The electrons passing through aperture 250 further pass through magnetic field sector 260, where the electrons are angularly dispersed by the magnetic field before colliding with EELS detector 180. Thus, different pixels of EELS detector 180 receive electrons of different respective energies, and the pixel readout provides the corresponding energy loss spectrum. The amount of energy loss measured in this way can elucidate what caused the energy loss. For example, inelastic interactions that can cause energy losses detectable by EELS detector 180 include phonon excitation, interband and intraband transitions, plasmon excitation, inner shell ionization, and Cherenkov radiation. Inner shell ionization detected by EELS detector 180 can be particularly useful for detecting the elemental composition of sample S.

[0024] Figures 3 to 5 are diagrams showing different EDS binning configurations that can be used in the scientific instrument 100 according to several examples. In the illustrated examples, each of the illustrated binning configurations is applied to the same SEM image 300 of the sample S. The size of the SEM image 300 is 720×1080 pixels 2 and is. Each pixel of the SEM image 300 has a corresponding EDS spectrum associated therewith, and the corresponding data structure is called a spectral image data hypercube. In other examples, the SEM image may have other numbers of pixels.

[0025] The binning configuration shown in FIG. 3 has a single bin 302 that is the same size as the entire SEM image 300. The EDS spectrum corresponding to the bin 302 is calculated by summing the EDS spectra of all the pixels of the SEM image 300 (a total of 777,600 EDS spectra).

[0026] The binning configuration shown in FIG. 4 has 24 bins 402 arranged in 4 rows and 6 columns ij where i = 1, 2, 3, 4 and j = 1, 2,..., 6. Each of the bins 402 ij has a square shape with a size of 180×180 pixels 2 . The EDS spectrum corresponding to the bin 402 ij is calculated by summing the EDS spectra of its pixels (i.e., 32,400 respective EDS spectra for each bin). The spectral output corresponding to this binning configuration includes 24 different EDS spectra calculated in this way.

[0027] The binning configuration shown in FIG. 5 has five bins 502 n where n = 1, 2,..., 5. The bins 502 nEach of them has a respective irregular shape that overlays the corresponding phase region of the sample S in the SEM image 300. As used herein, the term "phase" refers to a region of the sample S that is substantially chemically homogeneous, physically distinct, and in some cases mechanically separable. The term "phase" is not synonymous with the term "state of matter". For example, a sample of solid iron can contain multiple solid phases such as ferrite, martensite, austenite, etc. In a representative example, a phase is a spatial region of the sample S where a selected set of material properties is substantially uniform. In some examples, such a set of material properties includes one or more of chemical composition, density, refractive index, or magnetization.

[0028] In some examples, a computing device associated with the scientific instrument 100, such as, for example, the electronic controller 150 (FIG. 1), is configured to apply a multivariate statistical analysis (MSA) method to extract statistical groups from the spectral image data hypercube that can be interpreted as phases. The pixels of the SEM image 300 corresponding to each of the different phases thus identified are grouped together to form different bins of the bin 502 n . In some examples, the regions of the SEM image 300 corresponding to the different bins 502 n are pseudo-colored for more convenient visualization on a display device.

[0029] Software is commercially available that can provide phase identification by analyzing an input spectral image data hypercube without human intervention. Some examples of such software incorporate certain features disclosed in U.S. Patent Applications Nos. 6584413 and 6675106, both of which are incorporated herein by reference in their entirety. Various X-ray based phase identification and mapping methods are reviewed, for example, in "X-ray Mapping in Electron-Beam Instruments" by John J. Friel and Charles E. Lyman, Microscopy and Microanalysis, 2006, Vol. 12, pp. 2-25, which is incorporated herein by reference in its entirety. In some examples, at least some of such methods are implemented in software and executed by a computing device associated with the scientific instrument 100 to provide phase mapping of the type shown in FIG. 5.

[0030] In the example shown, the phase identification and mapping software executed on the electronic controller 150 has identified five different phases (labeled 1, 2, ..., 5) within the sample S. The corresponding bins are bins 5021 - 5025. The EDS spectrum corresponding to each individual bin 502 n is calculated by summing the EDS spectra of the pixels in that bin. The output corresponding to this binning configuration includes five different EDS spectra, each corresponding to the bin calculated in this way.

[0031] In the example shown, the binning configuration shown in FIG. 5 is not obtainable via segmentation of the SEM image 300 based only on image contrast. For example, phase 1 and phase 3 have similar contrast values but different chemical compositions. Unlike contrast values, different EDS spectra clearly indicate different respective phases, which enables segmentation of the SEM image 300 into the phases shown in FIG. 5.

[0032] FIG. 6 is a flowchart showing a method 600 for element identification. Method 600 is implemented in the scientific instrument 100 according to some embodiments. In other embodiments, method 600 (or a part thereof) may be implemented via one or more computing devices remote from the instrument 100. In different examples, method 600 is executed offline or online. In an offline configuration, the input 601 to method 600 includes a previously acquired spectral image data hypercube corresponding to the sample S, and the acquisition process has already been completed. In an online configuration, the input 601 includes a partially acquired or non-final spectral image data hypercube corresponding to the sample S currently being investigated in the scientific instrument 100.

[0033] Method 600 includes applying a phase analysis to the input 601 (at block 602). In some examples, the phase analysis applied at block 602 uses an MSA-based method of phase identification and phase mapping implemented in software. An example of the phase map generated at block 602 is shown in FIG. 5.

[0034] Method 600 also includes calculating the EDS spectrum of each individual phase (at block 604). In some examples, the phase spectra of the individual phases identified at block 602 are calculated at block 604 by summing the pixel-wise EDS spectra of the corresponding bins of the SEM image (e.g., one of the bins 5021-5025 of the SEM image 300 in FIG. 5). In the example shown in FIG. 5, five phase spectra corresponding to phases 1-5 respectively are calculated at block 604.

[0035] Method 600 also includes performing elemental identification for each of the individual phases (at block 606). At block 606, based on each of the phase spectra calculated at block 604, elemental identification for the individual phases is performed. In some examples, elemental identification includes the following operations, namely, (i) detecting one or more peaks within the phase spectrum, (ii) determining the spectral positions of the detected peaks, (iii) measuring the intensity of each of the detected peaks, and (iv) identifying one or more chemical elements based on the spectral positions and intensities of the peaks.

[0036] In some examples of block 606, peaks in the phase spectrum are located by passing a "top-hat digital filter" through the phase spectrum for each channel. One effect of this filter is to reduce the background portion of the spectrum to a level close to zero while substantially preserving the peaks. When the program filters each point in the spectrum, a check is made to determine whether the filtered point is positive and significantly above the background points scattered around the zero level. When these conditions are met, a peak is detected, and an estimate of the peak's position is made by calculating the centroid of the points in the positive lobe of the filtered peak. After the peak is located, the energy of the peak is determined.

[0037] In some examples of block 606, the program calculates the peak intensity based on an estimate of the net counts within the located peak from the total counts within the positive lobe of the peak in the filtered spectrum. In some other examples of block 606, the program calculates the peak intensity by least-squares fitting the filtered peak to a suitable peak function and then calculating the area under the resulting fitted peak function. In other examples, other suitable methods for calculating peak intensity can be used.

[0038] In some examples of block 606, the program examines a peak list of the phase spectrum, for example in order of decreasing intensity, to detect which X-ray energies match the peak energy within a specified tolerance (which is a parameter of the algorithm). During this matching process, specific rules are followed regarding peaks expected to be present under the applicable EDS acquisition conditions. For example, X-ray energies above the acceleration energy of the electron beam are excluded, and the K-beta peak can only be present if the corresponding K-alpha peak is also present, and so on. The matching process can use a library of reference spectra corresponding to different elements of the periodic table. When a match with a reference spectrum is detected, the corresponding element is considered to be present in the phase being analyzed. The relative concentrations of different elements in the phase can be estimated based on the relative intensities of the respective peak sets.

[0039] Method 600 also includes calculating (at block 608) one or more element maps. In some examples, the operation of block 608 includes (i) merging the elemental identification results of different individual phases to cover regions corresponding to two or more phases of the sample, (ii) for each identified element, determining the set of corresponding pixels in the merged result where that element is considered to be present within block 606, and (iii) generating an element map for the identified element by color-coding the set of corresponding pixels and overlaying the set of color-coded pixels on the corresponding SEM image. In response to suitable input from the user, the individual element maps thus generated can be rendered and displayed for viewing via the user interface.

[0040] Figures 7, 8A, 8B, 9A, and 9B are graphs showing an example method 600. More specifically, FIG. 7 shows an exemplary SEM image 700 of a sample S generated using the scientific instrument 100. FIGS. 8A and 8B show maps 802 and 804 of two phases identified in the SEM image 700 in block 602 of method 600, respectively. FIGS. 9A and 9B show phase spectra 902 and 904 calculated in block 604 of method 600, respectively. Phase spectra 902 and 904 correspond to the two phases shown in FIGS. 8A and 8B, respectively.

[0041] The sample S shown in the SEM image 700 includes small particles 704 on a substantially uniform substrate 702. The phase maps 802 and 804 identified for the SEM image 700 in block 602 of method 600 correspond to the substrate 702 and the particles 704, respectively. The phase spectra 902, 904 calculated in block 604 of method 600 also have element labels inserted therein based on the elemental identification performed in block 606 of method 600. Based on that elemental identification, the main element of the substrate 702 is aluminum (Al). The main elements of the particles 704 are aluminum and tin (Sn). Note that the signal intensity (raw count) in the phase spectrum 904 is generally several orders of magnitude lower than the signal intensity (raw count) in the phase spectrum 906. For comparison, if either of the binning methods shown in FIGS. 3 and 4 is used for the spectral image hypercube corresponding to the SEM image 700, tin is not identified as one of the elements present in the corresponding sample S by the automatic elemental identification algorithm. The binning methods of FIGS. 3 and 4 mix information from pixels corresponding to different phases, resulting in misidentification. As a result, the spectral information from the dominant phase (having a significantly larger number of pixels) substantially overwhelms the spectral information from the smaller phase (having a smaller number of pixels), and the spectral information from the smaller phase may be lost, for example, in the background noise. Thus, this comparison clearly demonstrates the usefulness and exemplary benefits of method 600 for at least some samples.

[0042] For some samples, the binning method of FIG. 4 can also cause misidentifications due to the overlap of Ka and Kb spectral peaks of different elements. For example, the Kb peak of titanium (Ti) and the Ka peak of vanadium (V) have similar energies, which causes these peaks to partially overlap in the EDS spectrum of a bin containing both elements. Since the Ka peak is typically about five times stronger, the information represented by the corresponding Kb peak may be disadvantageously lost in the binning method of FIG. 4. In contrast, this undesirable result is significantly less likely with the binning method of FIG. 5 and method 600.

[0043] The exemplary embodiments described above have a relatively small number (<6) of distinct phases within sample S, but method 600 is not so limited. For example, method 600 can also be used with samples having a larger (more than five) number of phases. In some examples, the corresponding sample S can have more than 10, or more than 100, phases. In some examples, method 600 can also be adapted for particle analysis of sample S where the number of small particles within the region of interest is in the range of 1 - 10 5 . In such examples, the number of phases can be less than the number of particles.

[0044] FIG. 10 is a flowchart showing a method 1000 for element identification. Method 1000 is implemented in a scientific instrument 100 according to one embodiment. However, in other embodiments, method 1000 (or a portion thereof) may be implemented via one or more computing devices remote from scientific instrument 100. In various examples, method 1000 is executed online while the data acquisition process is in progress. Method 1000 incorporates an embodiment of method 600 as part thereof, as will be described in more detail below.

[0045] One parameter of the spectral image data hypercube acquisition process is the dwell time, and its value determines how long the electron beam 114 stays at each scanned pixel position while the X-ray detector 160 accumulates the EDS spectrum corresponding to that position. Depending on the selected value of the dwell time, the final spectral image data hypercube can be acquired in many different ways. For example, if a relatively long dwell time is specified, the final spectral image data hypercube can be acquired using a single raster scan of the FOV. That is, the EDS spectra obtained from the scanned pixel positions over a single dwell time are included in the acquired final spectral image data hypercube, and the final spectral image data hypercube is gradually constructed for each position as the raster scan progresses towards its end. In contrast, if a relatively short dwell time is specified, multiple raster scans of the FOV are typically required to construct the final spectral image data hypercube because the pixel-wise EDS spectra corresponding to a single raster scan may not have a sufficiently high SNR. In such cases, the SNR is improved by repeating the raster scan multiple times and cumulatively adding the EDS spectra obtained from different raster scans for each pixel. In the latter example, multiple updates of the spectral image data hypercube (e.g., after each new raster scan) are performed before the final spectral image data hypercube is constructed. Other ways of updating the spectral image data hypercube with newly measured chunks of EDS data can also be implemented in additional examples.

[0046] Method 1000 advantageously provides the user with the ability to have a live preliminary element map while the acquisition process is in progress. The “appearance” of such a live preliminary element map depends on the details of the acquisition process. For example, in the “single raster scan” example described above, the preliminary element map is smaller than the corresponding SEM image as a whole because the corresponding spectral image data hypercube has only the EDS data of the pixels that have stayed, and the remaining pixels do not have the associated EDS data. The latter pixels may be painted black in the corresponding preliminary element map. In the “repeated raster scan” example described above, after the first raster scan, the preliminary element map is the same size as the corresponding SEM image as a whole. However, the accuracy of such a preliminary element map may be relatively low due to the relatively low SNR of the accumulated EDS data. The accuracy of subsequent preliminary element maps typically improves as more new EDS data sets are cumulatively added to the spectral image data hypercube in each new scan.

[0047] Method 1000 includes obtaining a new set of EDS spectra (at block 1002). In some examples, the new set of EDS spectra can correspond to (i) a partial scan line of a raster scan, (ii) one or more full scan lines of a raster scan, (iii) a partial scan of any shape of the FOV, and (iv) one or more full scans of the FOV. In additional examples, other configurations of the new set of EDS data can also be used.

[0048] Method 1000 also includes updating the spectral image data hypercube (at block 1004). This update is performed at block 1004 using the new set of EDS data obtained at block 1002. In some examples, such an update may include filling empty (blank) positions within the spectral image data hypercube with the newly obtained EDS spectra, or adding the newly obtained EDS spectra to the previously stored cumulative EDS spectra at the corresponding positions of the spectral image data hypercube.

[0049] Next, the updated spectral image data hypercube generated in block 1004 is provided as input 601 to method 600, which can be executed in the background of the acquisition process (see also FIG. 6). Based on the received input 601, method 600 generates one or more preliminary element maps of sample S as described above.

[0050] As shown in FIG. 10, decision block 1006 is used to control the end of method 1000. When the electronic controller 150 determines that the data acquisition process is complete (yes in decision block 1006), method 1000 ends. If the electronic controller 150 determines that the data acquisition process is not yet complete (no in decision block 1006), the operation of method 1000 loops back to block 1002.

[0051] FIG. 11 is a block diagram of an exemplary computing device 1100 configured to perform at least some scientific instrument support operations according to various embodiments. For example, in some embodiments, computing device 1100 is an electronic controller 150 or performs at least some of the operations of electronic controller 150. In various embodiments, the support module of scientific instrument 100 can be implemented by a single computing device 1100 or by a plurality of computing devices 1100.

[0052] The computing device 1100 of FIG. 11 is illustrated as having several components, but any one or more of these components may be omitted or duplicated as suitable for the application and settings. In some embodiments, some or all of the components included in the computing device 1100 may be attached to one or more motherboards and enclosed in a housing. In some embodiments, some of these components may be fabricated on a single system-on-a-chip (SoC) (e.g., the SoC may include one or more electronic processing devices 1102 and one or more storage devices 1104). Additionally, in various embodiments, the computing device 1100 may not include one or more of the components illustrated in FIG. 11, but may include an interface circuit for coupling to one or more components using any suitable interface (e.g., Universal Serial Bus (USB) interface, High-Definition Multimedia Interface (HDMI (registered trademark)) interface, Controller Area Network (CAN) interface, Serial Peripheral Interface (SPI) interface, Ethernet interface, wireless interface, or any other appropriate interface). For example, the computing device 1100 may not include a display device 1110, but may include a display device interface circuit (e.g., a connector and driver circuit) to which an external display device 1110 may be coupled.

[0053] The computing device 1100 includes a processing device 1102 (e.g., one or more processing devices). As used herein, the terms "electronic processor device" and "processing device" may interchangeably refer to any device or portion of a device that processes electronic data from registers and / or memory and converts that electronic data into other electronic data that may be stored in registers and / or memory. In various embodiments, the processing device 1102 may include one or more digital signal processors (DSPs), application-specific integrated circuits (ASICs), central processing units (CPUs), graphics processing units (GPUs), server processors, or any other suitable processing device.

[0054] The computing device 1100 also includes a storage device 1104 (e.g., one or more storage devices). In various embodiments, the storage device 1104 may be a random-access memory (RAM) device (e.g., static One or more memory devices, such as random access memory (RAM), static random access memory (SRAM) devices, magnetic RAM (MRAM) devices, dynamic RAM (DRAM) devices, resistive RAM (RRAM) devices, or conductive-bridging RAM (CBRAM) devices, hard drive-based memory devices, solid state memory devices, network drives, cloud drives, or any combination of memory devices. In some embodiments, the storage device 1104 may include memory that shares a die with the processing device 1102. In such embodiments, the memory may be used as cache memory and may include, for example, embedded dynamic random-access memory (eDRAM) or spin transfer torque magnetic random-access memory (STT-MRAM). In some embodiments, the storage device 1104 may include a non-transitory computer-readable medium having instructions that, when executed by one or more processing devices (e.g., the processing device 1102), cause the computing device 1100 to perform any suitable method disclosed herein below, or a portion of such a method.

[0055] Computing device 1100 further includes interface device 1106 (e.g., one or more interface devices 1106). In various embodiments, interface device 1106 may include one or more communication chips, connectors, and / or other hardware and software to manage communication between computing device 1100 and other computing devices. For example, interface device 1106 may include circuitry to manage wireless communication for transferring data between computing device 1100. The term "wireless" and its derivatives may be used to describe circuits, devices, systems, methods, techniques, communication channels, etc., that can communicate data via electromagnetic radiation modulated through a non-solid medium. This term does not mean that the associated devices do not include any wiring, but in some embodiments, they may not. The circuitry included in interface device 1106 to manage wireless communication may implement any of a plurality of wireless standards or protocols, including, but not limited to, Wi-Fi (IEEE 802.11 family), Institute of Electrical and Electronic Engineers (IEEE) standards including the IEEE 802.16 standard, Long-Term Evolution (LTE) projects with any modifications, updates, and / or revisions (e.g., Advanced LTE projects, Ultra Mobile Broadband (UMB) projects (also known as "3GPP (registered trademark) 2"), etc.).In some embodiments, the circuitry included in interface device 1106 for managing wireless communications may operate in accordance with a Global System for Mobile Communication (GSM), General Packet Radio Service (GPRS), Universal Mobile Telecommunications System (UMTS), High Speed Packet Access (HSPA), Evolved HSPA (E-HSPA), or Long Term Evolution (LTE) network. In some embodiments, the circuitry included in interface device 1106 for managing wireless communications may operate in accordance with Enhanced Data for GSM Evolution (EDGE), GSM EDGE Radio Access Network (GERAN), Universal Terrestrial Radio Access Network (UTRAN), or Evolved UTRAN (E-UTRAN). In some embodiments, the circuitry included in interface device 1106 for managing wireless communications may operate in accordance with Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Digital Enhanced Cordless Telecommunications (DECT), or other known radio communication standards. It can operate according to Enhanced Cordless Telecommunication (DECT), Evolution-Data Optimized (EV-DO), and their derivatives, as well as any other wireless protocol designated as 3G, 4G, 5G, and later. In some embodiments, the interface device 1106 can include one or more antennas (e.g., one or more antenna arrays) configured to receive and / or transmit wireless signals.

[0056] In some embodiments, the interface device 1106 can include circuitry for managing wired communication such as electrical, optical, or any other suitable communication protocol. For example, the interface device 1106 can include circuitry for assisting communication according to Ethernet technology. In some embodiments, the interface device 1106 can support both wireless communication and wired communication, and / or support multiple wired communication protocols and / or multiple wireless communication protocols. For example, a first set of circuits of the interface device 1106 can be dedicated to short-range wireless communication such as Wi-Fi or Bluetooth, and a second set of circuits of the interface device 1106 can be dedicated to long-range wireless communication such as global positioning system (GPS), EDGE, GPRS, CDMA, WiMAX, LTE, EV-DO, etc. In some other embodiments, a first set of circuits of the interface device 1106 can be dedicated to wireless communication, and a second set of circuits of the interface device 1106 can be dedicated to wired communication.

[0057] The computing device 1100 also includes a battery / power circuit 1108. In various embodiments, the battery / power circuit 1108 can include one or more energy storage devices (e.g., a battery or a capacitor), and / or circuitry for coupling components of the computing device 1100 to an energy source separate from the computing device 1100 (e.g., AC line power).

[0058] Computing device 1100 also includes a display device 1110 (e.g., one or more individual display devices). In various embodiments, display device 1110 may include any visual indicator, such as a heads-up display, a computer monitor, a projector, a touchscreen display, a liquid crystal display (LCD), a light-emitting diode display, or a flat panel display.

[0059] Computing device 1100 also includes additional input / output (I / O) devices 1112. In various embodiments, I / O devices 1112 may include one or more data / signal transfer interfaces, audio I / O devices (e.g., a microphone or microphone array, a speaker, a headset, an earphone, an alarm, etc.), an audio codec, a video codec, a printer, sensors (e.g., a thermocouple or other temperature sensor, a humidity sensor, a pressure sensor, a vibration sensor, etc.), image capture devices (e.g., one or more cameras), human interface devices (e.g., a cursor control device such as a keyboard, a mouse, a stylus, a trackball, or a touchpad), etc.

[0060] Depending on the specific embodiments of the scientific instrument 100 and / or the instrument part 200, various components of the interface device 1106 and / or the I / O device 1112 can output suitable control signals (e.g., 152, 154, 156) for various components of the scientific instrument 100, receive suitable control signals / remote measurement signals from various components of the scientific instrument 100, and be configured to receive a stream of measurement values (e.g., 162, 172, 182) from various detectors of the scientific instrument 100. In some examples, the interface device 1106 and / or the I / O device 1112 include one or more analog-to-digital converters (ADCs) for converting the received analog signals into a digital format suitable for operations performed by the processing device 1102 and / or the storage device 1104. In some additional examples, the interface device 1106 and / or the I / O device 1112 include one or more digital-to-analog converters (DACs) for converting the digital signals provided by the processing device 1102 and / or the storage device 1104 into an analog format suitable for communication to the corresponding components of the scientific instrument 100.

[0061] According to an example disclosed above, for example, in the "Summary of the Invention" and / or with reference to any one or any combination of some or all of FIGS. 1 to 11, an automated method is provided that is executed via a computing device to provide assistance to a scientific instrument. The method includes calculating a phase map of a sample by applying phase analysis to a data set including a charged particle microscope (CPM) image of the sample and a plurality of energy-dispersive X-ray spectroscopy (EDS) spectra of the sample, wherein each of the EDS spectra corresponds to a respective pixel of the CPM image, and the phase map identifies groups of pixels representing different respective phases of the sample; for each of the identified groups of pixels, determining a respective set of elements based on the EDS spectrum corresponding to the group; and for a selected chemical element, calculating a corresponding element map of the sample based on the identified groups of pixels and the determined respective sets of elements.

[0062] In some examples of the above methods, the determining includes calculating a respective phase spectrum by summing EDS spectra corresponding to the groups, and determining a respective element set based on the respective phase spectrum.

[0063] In some examples of any of the above methods, determining the respective sets of elements includes matching sets of peaks in the respective phase spectra to reference spectra corresponding to different elements in the periodic table of the elements.

[0064] In some examples of any of the above methods, the applying includes applying multivariate statistical analysis to the dataset to extract statistical groups from the dataset that represent different respective phases of the sample.

[0065] In some examples of any of the above methods, the method further includes displaying the corresponding elemental map on a display device.

[0066] In some examples of any of the above methods, the dataset is a hypercube of final spectral image data previously acquired using a scientific instrument.

[0067] In some examples of any of the above methods, the corresponding elemental map is a preliminary elemental map calculated while the acquisition of the EDS spectrum using the scientific instrument is in progress.

[0068] In some examples of any of the above methods, the preliminary element map represents an area that is smaller than the entire area of the CPM image.

[0069] In some examples of any of the above methods, the method further includes acquiring a new set of EDS spectra of the sample using the scientific instrument, updating the dataset with the new set of EDS spectra, and recalculating the phase map by applying phase analysis to the updated dataset.

[0070] In some examples of any of the above methods, the new set of EDS spectra corresponds to one of: a partial scan line of a raster scan of a field of view (FOV) of the sample using the scientific instrument; one or more complete scan lines of the raster scan; an arbitrarily shaped partial scan of the FOV using the scientific instrument; or one or more complete scans of the FOV using the scientific instrument.

[0071] According to another embodiment disclosed above, for example, in the "Summary of the Invention" and / or with reference to any one or any combination of some or all of Figures 1-11, there is provided a non-transitory computer-readable medium storing instructions that, when executed by a computing device, cause the computing device to perform operations including any of the methods described above.

[0072] According to yet another example disclosed above, for example, in the "Summary of the Invention" and / or with reference to any one or any combination of some or all of Figures 1-11, there is provided a support apparatus for a scientific instrument, the support apparatus comprising: an interface device configured to receive a dataset including a charged particle microscope (CPM) image of a sample and a plurality of energy dispersive X-ray spectroscopy (EDS) spectra of the sample, each of the EDS spectra corresponding to a respective pixel of the CPM image; and one or more electronic processing devices configured to: calculate a phase map of the sample by applying a phase analysis to the dataset, wherein the phase map identifies groups of pixels representing different respective phases of the sample; determine, for each identified group of pixels, a respective element set based on the EDS spectrum corresponding to the group; and calculate, for a selected chemical element, a corresponding element map of the sample based on the identified groups of pixels and the determined respective element sets.

[0073] In some examples of the above-described apparatus, one or more electronic processing devices are further configured to calculate respective phase spectra by summing EDS spectra corresponding to groups and to determine respective element sets based on the respective phase spectra.

[0074] In some examples of any of the above-described apparatus, one or more electronic processing devices are further configured to match a set of peaks within each respective phase spectrum to reference spectra corresponding to different elements of the periodic table of elements and to determine respective element sets based on the detected matches.

[0075] In some examples of any of the above-described apparatus, one or more electronic processing devices are further configured to apply multivariate statistical analysis to a data set to extract statistical groups representing respective different phases of a sample from the data set.

[0076] In some examples of any of the above-described apparatus, the apparatus further comprises a display device configured to display a corresponding element map.

[0077] In some examples of any of the above-described apparatus, the data set is a final spectral image data hypercube previously acquired using a scientific instrument.

[0078] In some examples of any of the above-described apparatus, one or more electronic processing devices are further configured to calculate a preliminary element map while acquisition of an EDS spectrum using a scientific instrument is in progress.

[0079] In some examples of any of the above-described apparatus, an interface device is configured to receive a new set of EDS spectra of a sample, and one or more electronic processing devices are further configured to update the data set with the new set of EDS spectra and to recalculate a phase map by applying phase analysis to the updated data set.

[0080] In some examples of any of the above devices, the new set of EDS spectra corresponds to one of a partial line of a raster scan of a field of view (FOV) of the sample using the scientific instrument, one or more complete lines of the raster scan, an arbitrarily shaped partial scan of the FOV using the scientific instrument, or one or more complete scans of the FOV using the scientific instrument.

[0081] It should be understood that the above description is intended to be illustrative, and not limiting. Many implementations and applications other than the examples provided will become apparent upon reading the above description. The scope should not be determined with reference to the above description, but instead with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled. It is anticipated and intended that future developments will occur in the art discussed herein, and that the disclosed systems and methods will be incorporated into such future examples. In short, it should be understood that this application is capable of modification and variation.

[0082] All terms used in the claims are intended to be given their broadest reasonable interpretation and their ordinary meaning as understood by one skilled in the art described herein, unless expressly indicated to the contrary herein. In particular, the use of singular articles such as "a," "the," "said," etc., should be read to recite one or more of the indicated elements, unless the claim describes an express limitation to the contrary.

[0083] The Abstract is provided to allow the reader to quickly ascertain the nature of the technical disclosure. The Abstract is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims. Additionally, in the foregoing Detailed Description, it may be seen that various features are grouped together in various examples for the purpose of streamlining the disclosure. This method of disclosure should not be interpreted as reflecting an intention that the claimed subject matter incorporates more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive subject matter lies in less than all features of a single disclosed example. Accordingly, the following claims are hereby incorporated into the Detailed Description, with each claim standing on its own as independently claimed subject matter.

[0084] Unless expressly stated otherwise, each numerical value and range should be construed as approximation as if the word "about" or "approximately" were before the value or range.

[0085] In the method claims that follow, elements, if any, are listed in a particular order with corresponding labeling, but unless the claim description otherwise suggests a particular order for implementing some or all of the elements, the elements are not necessarily intended to be limited to being implemented in that particular order.

[0086] Unless otherwise specified herein, the use of ordinal adjectives such as "first," "second," "third," etc. to refer to one object among a plurality of similar objects merely indicates that different instances of such similar objects are being referred to and is not intended to imply that the similar objects so referred to must be in a corresponding order or sequence, either temporally, spatially, in ranking, or in any other manner.

[0087] Unless otherwise specified in this specification, the conjunction "if" can be further or alternatively construed to mean "when" or "upon" or "in response to determining" or "in response to detecting", in addition to its plain meaning, and this interpretation may depend on the corresponding specific context. For example, the phrases "when determined" or "when [stated condition] is detected" can be construed to mean "when determining" or "in response to determining" or "when detecting [stated condition or event]" or "in response to detecting [stated condition or event]". Also, for the purposes of this description, the terms "coupling", "couple", "coupled", "connecting", "connected", or "connection" refer to any manner known in the art or later developed that enables energy to be transferred between two or more elements and contemplates the intervention of one or more additional elements, but is not required. Conversely, terms such as "directly coupled" and "directly connected" imply the absence of such additional elements.

[0088]

[0089] ​The functionality of the various elements shown in the figures, including any functional blocks labeled "processor" and / or "controller", can be provided through dedicated hardware and the use of hardware capable of executing software in association with appropriate software. When provided by a processor, the functionality can be provided by a single dedicated processor, by a single shared processor, or by a plurality of individual processors some of which may be shared. Further, the explicit use of the terms "processor" or "controller" should not be construed to refer exclusively to hardware capable of executing software and may implicitly include, but is not limited to, digital signal processor (DSP) hardware, network processors, application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), read only memory (ROM) for storing software, random access memory (RAM), and non-volatile storage devices. Conventional and / or other custom hardware may also be included. Similarly, any switches shown in the figures are only conceptual. Their functionality can be implemented through the operation of program logic, through dedicated logic, through the interaction of program control and dedicated logic, or even manually, and the particular technique can be selected by the implementer as more specifically understood from the context.

[0090] As used in this application, the terms "circuit" and "circuitry" can refer to one or more or all of the following: (a) a hardware-only circuit implementation (such as an implementation using only analog and / or digital circuits), (b) (where applicable), (i) a combination of analog and / or digital hardware circuits and software / firmware, and (ii) a combination of any part of a hardware processor and software (including a digital signal processor), software, and memory that cooperate to perform various functions in a device such as a mobile phone or a server, etc., a combination of a hardware circuit and software, and (c) software (e.g., firmware) is required for operation, but there may be no software when it is not required for operation, a hardware circuit and / or a processor such as a microprocessor or a part of a microprocessor. This definition of a circuit applies to all uses of this term in this application, including any claims. As a further example, as used in this application, the term "circuit" also covers simply a hardware circuit or a processor (or a plurality of processors), or a part of a hardware circuit or a processor, and an implementation form of its (or their) accompanying software and / or firmware. The term "circuit" also covers, for example, a baseband integrated circuit or a processor integrated circuit for a mobile device, or a similar integrated circuit within a server, a cellular network device, or other computing device or network device, where applicable to a particular claim element.

[0091] Those skilled in the art should understand that any block diagram in this specification represents a conceptual diagram of an exemplary circuit embodying the principles of the present disclosure. Similarly, any flowchart, flow diagram, state transition diagram, pseudocode, etc. is substantially represented in a computer-readable medium and represents various processes that can be executed by such a computer or processor, whether or not the computer or processor is explicitly shown.

Claims

**Claim 1** An automated method implemented via a computing device to provide assistance to a scientific instrument, the method comprising: Calculating a phase map of the sample by applying phase analysis to a dataset including a charged particle microscope (CPM) image of the sample and a plurality of energy-dispersive X-ray spectroscopy (EDS) spectra of the sample, wherein each of the EDS spectra corresponds to a respective pixel of the CPM image, and the phase map identifies groups of pixels representing different respective phases of the sample; For each group of the identified groups of pixels, determining a respective set of elements based on the EDS spectra corresponding to the group; Calculating a corresponding element map of the sample based on the identified groups of pixels and the determined respective sets of elements for a selected chemical element. An automated method. **Claim 2** The determining comprises: Calculating respective phase spectra by summing the EDS spectra corresponding to the group; Determining the respective sets of elements based on the respective phase spectra. The automated method according to claim 1. **Claim 3** The determining the respective sets of elements comprises matching a set of peaks in the respective phase spectra to reference spectra corresponding to different elements of the periodic table. The automated method according to claim 2. **Claim 4** The applying comprises applying multivariate statistical analysis to the dataset to extract statistical groups representing the different respective phases of the sample from the dataset. The automated method according to claim 1. **Claim 5** The automated method according to claim 1, further comprising displaying the corresponding element map on a display device. **Claim 6** The automated method according to claim 1, wherein the dataset is a final spectral image data hypercube previously acquired using the scientific instrument. **Claim 7** The automated method according to claim 1, wherein the corresponding element map is a preliminary element map calculated while acquisition of the EDS spectra using the scientific instrument is in progress. **Claim 8** The automated method according to claim 7, wherein the preliminary element map represents an area smaller than the entire area of the CPM image.

9. Obtaining a new set of EDS spectra of the sample using the scientific instrument; Updating the data set with the new set of EDS spectra; Recalculating the phase map by applying the phase analysis to the updated data set, the automated method according to claim 7, further comprising.

10. The new set of EDS spectra is A partial scan line of a raster scan of the field of view (FOV) of the sample using the scientific instrument; One or more full scan lines of the raster scan; A partial scan of any shape of the FOV using the scientific instrument; The automated method according to claim 9, corresponding to one of one or more full scans of the FOV using the scientific instrument.

11. A non-transitory computer-readable medium storing instructions that, when executed by a computing device, cause the computing device to perform operations including the automated method according to claim 1.

12. An assistance device for a scientific instrument, the assistance device comprising An interface device configured to receive a data set including a charged particle microscope (CPM) image of a sample and a plurality of energy dispersive X-ray spectroscopy (EDS) spectra of the sample, each of the EDS spectra corresponding to a respective pixel of the CPM image; an interface device; One or more electronic processing devices, the one or more electronic processing devices Calculating a phase map of the sample by applying phase analysis to the data set, the phase map identifying groups of pixels representing different respective phases of the sample; For each group of the identified groups of pixels, determining a respective set of elements based on the EDS spectrum corresponding to the group; Calculating a corresponding element map of the sample based on the identified groups of pixels and the determined respective sets of elements for a selected chemical element. An assistance device configured to perform.

13. The one or more electronic processing devices By summing the EDS spectra corresponding to the group, calculating each phase spectrum, Determining each element set based on each of the phase spectra, and further configured to perform, the support device according to claim 12.

14. The one or more electronic processing devices, Matching a set of peaks in each of the phase spectra to a reference spectrum corresponding to a different element in the periodic table of elements, Determining each element set based on the detected match, and further configured to perform, the support device according to claim 13.

15. The one or more electronic processing devices are further configured to apply multivariate statistical analysis to the data set to extract from the data set statistical groups representing the different phases of the sample, the support device according to claim 12.

16. The support device according to claim 12, further comprising a display device configured to display the corresponding element map.

17. The data set is a final spectral image data hypercube previously acquired using the scientific instrument, the support device according to claim 12.

18. The one or more electronic processing devices are further configured to calculate a preliminary element map while the acquisition of the EDS spectrum using the scientific instrument is in progress, the support device according to claim 12.

19. The interface device is configured to receive a new set of EDS spectra of the sample, The one or more electronic processing devices, Updating the data set with the new set of EDS spectra, Recalculating the phase map by applying the phase analysis to the updated data set, and further configured to perform, the support device according to claim 18.

20. The new set of EDS spectra, A partial scan line of a raster scan of the field of view (FOV) of the sample using the scientific instrument, One or more complete scan lines of the raster scan, An arbitrary-shaped partial scan of the FOV using the scientific instrument, One of one or more complete scans of the FOV using the scientific instrument, the support device according to claim 18.

Images