Element identification based on phase analysis

Abstract

The invention discloses element identification based on phase analysis. In some embodiments, a support apparatus for a scientific instrument includes 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 corresponds to a respective pixel of the CPM image. The support apparatus further includes one or more electronic processing devices configured to: calculate a phase map of the sample by applying a 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 group of elements based on the EDS spectrum corresponding to the group; and for the selected chemical element, calculating a corresponding element map for the sample based on the identified group of pixels and the determined corresponding group of elements.

Description

Technical Field

[0001] The various examples relate generally but not exclusively to charged particle microscopy components, instruments, systems, and methods. Summary of the Invention

[0002] Energy-dispersive X-ray spectroscopy (EDS, also abbreviated as EDX or XEDS) is an analytical technique 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. The outer-shell electrons then continue to fill the vacancies, releasing the energy difference as X-ray photons. The spectral composition of the emitted X-ray photons is characteristic of the originating atoms. Thus, the X-ray spectrum measured in this way allows compositional analysis of the sample volume excited by the energy source. The position of the peaks in the spectrum identifies the element, while the intensity of the signal represents the concentration of the element 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 over the sample, characteristic X-rays are emitted and measured with a suitable EDS detector. Each recorded EDS spectrum is then mapped to the corresponding pixel position on the sample. The quality of subsequent elemental identification typically depends on the signal intensity and the signal-to-noise ratio (SNR). For example, the SNR can be increased by appropriately binning the recorded EDS spectra corresponding to different pixel positions. However, for at least some samples, some types of binning may result in relatively low elemental identification accuracy. For example, for small particles that occupy a small number of pixels in the field of view (FOV), some of the constituent elements may be unfavorably missed during the automatic identification of the binned spectra because the corresponding peaks are buried in the background of the spectrum.

[0004] Various examples, aspects, features, and embodiments of a scientific instrument are particularly disclosed herein. The scientific instrument includes a charged particle microscope (CPM) and an associated detector for acquiring electron microscope images and pixel-level EDS spectra. In one example, an electronic controller of the scientific instrument runs phase analysis in the background of an ongoing acquisition process and calculates phase spectra of different phases identified via phase analysis by binning the corresponding pixel-level EDS spectra. The electronic controller then calculates a preliminary elemental map of the sample based on the phase map and the phase spectra. The elemental map can be updated in real time when new measurement results are received from the detector.

[0005] One example provides an automated method for providing support to a scientific instrument via a computing device, the method comprising: 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, wherein each of the EDS spectra in 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 group of elements based on the EDS spectrum 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 groups of elements.

[0006] Another example 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 a support device for a scientific instrument, the support 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, wherein each of the EDS spectra in the EDS spectra corresponds 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, determine a respective group of elements based on the EDS spectrum corresponding to the group; and for a selected chemical element, calculate a corresponding elemental map of the sample based on the identified groups of pixels and the determined respective groups of elements. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] The foregoing aspects and many of the attendant advantages of this disclosure will become more readily appreciated when considered in conjunction with the following detailed description, taken in conjunction with the accompanying drawings.

[0009] Figure 1 is a block diagram illustrating an example scientific instrument in accordance with some embodiments.

[0010] Figure 2 is an illustration in accordance with one embodiment of Figure 1 a block diagram of a portion of a scientific instrument.

[0011] Figures 3 to 5 is an illustration in accordance with some examples of different energy dispersive X-ray spectroscopy (EDS) merging configurations used in a Figure 1 scientific instrument.

[0012] Figure 6A flowchart illustrating a method for automatically identifying elements according to some embodiments.

[0013] Figure 7 Shown in graphical form is a scanning electron microscope (SEM) image generated by a scientific instrument according to one example. Figure 1 using

[0014] Figure 8A and Figure 8B Shown in graphical form is a graph of two phases identified in the SEM image of Figure 6 using the method of Figure 7 according to one example.

[0015] Figure 9A and Figure 9B Shown in graphical form is the phase spectrum of two phases illustrated in Figure 6 calculated using the method of Figure 8A and Figure 8B according to one example.

[0016] Figure 10 A flowchart illustrating a method for automatically identifying elements according to one embodiment.

[0017] Figure 11 A block diagram of an example computing device configured to perform at least some scientific instrument support operations according to various embodiments. Detailed Description

[0018] Figure 1 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 Figure 1 ). In an example embodiment, the sample holder 110 is capable of independent movement parallel to the XY coordinate plane and parallel to the Z axis, where the corresponding coordinate system is indicated by the XYZ coordinate triple shown in Figure 1 . In some embodiments, the sample holder 110 can move in six degrees of freedom, including tilting in one or more rotational directions. A sample S to be interrogated using the scientific instrument 100 is mounted in the sample holder 110.

[0019] 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. For purposes of illustration, in Figure 1Only two of them are schematically shown therein, for example, the objective lens 106 and the condenser lens 116. In some examples, a different (than two) number of such lenses may be used in the SEM column 102.

[0020] In 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 are operated to generate electric and magnetic fields that affect the electron trajectories in the electron beam 114. Control signals 152, 156 generated by the electron controller 150 are used to vary the intensity and / or spatial configuration of the fields and impart desired characteristics to the electron beam 114. In general, the CPB lenses 106 and 116, the control signals 152 and 156, and other relevant components of the scientific instrument 100 can be used to perform various operations and support various functions, such as beam focusing, aberration reduction, aperture clipping, filtering, etc. The SEM column 102 also includes a deflection unit 118 that can direct the electron beam 114 in response to a drive signal 154 applied thereto via the electron controller 150. Such beam steering can be used to move the focused portion of the electron beam 114 to a selected point 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 varies over time to move the focused portion of the electron beam 114 across the sample S in a raster or other suitable scan pattern.

[0021] In another example, the (FIB-type) charged particle beam column 102 includes an ion source 112 and ion beam optics 106, 116, 118. In some instances, 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 in the gas volumes or mixtures thereof for use in the ion source. Example gases supplied to the ion source 112 in this manner are xenon, argon, oxygen, hydrogen, and nitrogen. In 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 generally propagates along the longitudinal axis 115 of the FIB column 102. The ion beam optics 106, 116, 118 can be used in particular to focus the ion beam 114 at the sample S and move the focused portion of the ion beam 104 along a desired path across the sample S, such as for example to perform a raster or vector scan of the sample S. In some other instances, the ion source 112 can include a liquid metal ion source (LMIS) or any other ion source compatible with the FIB column 102. In various configurations of the FIB column 102, the ion beam 114 can be used to perform imaging or machining operations on the sample S, such as for example cutting, milling, etching, deposition, etc.

[0022] The scientific instrument 100 also includes detectors 160, 170, 180 located in the vacuum chamber 108 relatively close to the sample S. During operation, the detectors 160, 170, and 180 generate measurement result streams 162, 172, and 182 that are received by the electronic controller 150. The specific types of the detectors 160, 170, 180 depend on the implementation of the scientific instrument 100 and can generally be selected from a variety of detector types suitable for detecting different types of emissions and / or radiation from the sample S in response to the electron beam 114. Example types of emissions / radiation that can be generated in this manner include, but are not limited to, X-rays, infrared light, visible light, ultraviolet light, backscattered electrons, secondary electrons, Auger electrons, elastically scattered electrons, unscattered (e.g., zero energy loss) electrons, and inelastically scattered electrons. In various implementations, different (other than three) numbers of such detectors can be used. In some implementations, 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 emissions / radiation among the above types of emissions / radiation can also be used in various additional implementations.

[0023] In the illustrated example, the detectors 160, 170 are positioned above the sample S. The detector 180 is positioned below the sample S. Herein, the terms “above” and “below” are used relative to the propagation direction of the electron beam 114, which generally propagates along the Z coordinate axis from the electron source 112 towards the sample S. In this sense, a position “above” the sample S has a non-zero offset from the sample S along the Z coordinate axis in the upstream direction of the electron beam 114. Similarly, a position “below” the sample S has a non-zero offset from the sample S along the Z coordinate axis in the downstream direction of the electron beam 114. In various additional implementations, different numbers of detectors can be located above and below the sample S. In some implementations, all such detectors can be located below or above the sample S.

[0024] In some examples, the scientific instrument 100 is a dual-beam instrument that includes first and second instances of a charged particle beam column 102 coupled to the vacuum chamber 108. In such examples, the first instance is the SEM-type charged particle beam column 102 described above, and the second instance 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 between approximately 30 degrees and 60 degrees relative to each other.

[0025] For purposes of illustration and without any implied limitation, the following describes exemplary embodiments with reference to a scientific instrument 100 equipped with an SEM column 102 or a STEM column 102. Based on the provided description, a person of ordinary skill in the relevant 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 obtain charged particle microscope images of a sample, including SEM images, FIB images, and / or STEM images.

[0026] Generally, the various embodiments disclosed herein can be used with charged particle microscopy (CPM) systems and instruments (including dual-beam systems and instruments) designed for electron microscopy and / or focused ion beam microscopy. Exemplary ion sources used in the FIB column include, but are not limited to, H, He, Ar, Ne, and Ga ion sources. The corresponding systems can generally provide the ability to pattern below 10 nm and image non-conductive and magnetic samples.

[0027] Figure 2 is a block diagram of a portion 200 of a scientific instrument 100 according to one exemplary embodiment. Portion 200 is an illustrative example of a detector configuration suitable for combining spectroscopy and microscopy in the scientific instrument 100. Both spectroscopy and microscopy are useful tools for sample analysis individually, but when combined, spectroscopy and microscopy can produce even more effective results. In the example shown, portion 200 provides STEM imaging capabilities. Spectroscopy techniques implemented in portion 200 include, but are not limited to, electron energy loss spectroscopy (EELS) and energy dispersive X-ray spectroscopy (EDS).

[0028] Portion 200 includes an objective lens 106, a sample S, and detectors 160, 180, also shown in Figure 1 . Portion 200 also includes a projection lens 210, one or more annular detectors 220, 230, and 240, an aperture 250, and a magnetic field sector 260. Figure 2 Also schematically shown is an electron beam 114 delivered to the sample S as described above with reference to Figure 1 . The STEM imaging modality of the scientific instrument involves sequentially scanning a 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 select electrons having different scattering angles relative to the propagation direction of the electron beam 114. Pixel-by-pixel data acquisition performed in this manner is used, in particular, to construct a STEM image of the scanned area of the sample S.

[0029] In some examples, the annular detector 220 is a high-angle annular dark-field (HAADF) detector configured to detect incoherent inelastic electron scattering from atomic nuclei, which is typically the dominant component at scattering angles above 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 between approximately 30 and 80 mrad, and the annular detector 240 is an annular bright-field (ABF) detector configured to detect electrons at low scattering angles.

[0030] Various electron microscope-based spectroscopic techniques generally involve obtaining quantitative and / or qualitative information from various signals generated during scanning of the electron beam 114 over the sample S. After appropriate collection, the spectroscopic signals can be used to construct maps of material properties for presentation with or overlaid on corresponding electron microscope images, such as, for example, images generated using one or more of the annular detectors 220, 230, and 240 and / or other suitable detectors as indicated above. In various examples of the present disclosure, the phase maps or elemental maps described herein can include a graphical representation (i.e., an image) of phase or elemental information or a data array associating phase or elemental information with individual image pixels or with groupings of image pixels.

[0031] In some examples, the detector 160 operates as an EDS detector configured to detect the emission of characteristic X-rays from the sample S excited by the electron beam 114. In the ground state (non-excited state), the atoms within the sample S have their electrons in discrete energy levels of the inner electron shells of the atoms. Interaction of the electron beam 114 with the atoms can cause electrons to be ejected from the inner shells of the atoms, creating electron holes (vacancies). The recombination of such electron holes with electrons from the outer shells of the atoms causes the emission of X-ray photons. The flux and their energies of such X-ray photons are measured by the EDS detector 160, which thereby measures the corresponding X-ray emission spectrum. Since each chemical element has a unique set of peaks in the X-ray emission spectrum, analysis of the X-ray spectrum measured by the EDS detector 160 can be used to reveal the elemental composition of the sample S. The elemental composition can also be referred to as the elemental signature. The elemental composition or elemental signature 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.

[0032] In some examples, detector 180 operates as an EELS detector and is 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 hitting EELS detector 180. Different pixels of EELS detector 180 thus receive electrons of different respective energies, and the pixel readings provide corresponding energy loss spectra. The amount of energy loss measured in this way can be interpreted based on the cause of the energy loss. For example, inelastic interactions that may 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 as detected by EELS detector 180 can be particularly useful for detecting the elemental composition of sample S.

[0033] Figures 3 to 5 FIG. is an illustration of different EDS merging configurations that can be used in scientific instrument 100 according to some examples. In the illustrated example, each of the illustrated merging configurations is applied to the same SEM image 300 of sample S. SEM image 300 has dimensions of 720×1080 pixels 2 . Each pixel of SEM image 300 has a corresponding EDS spectrum associated therewith, and the corresponding data structure is referred to as a spectral image data hypercube. In other examples, the SEM image can have other numbers of pixels.

[0034] Figure 3 The illustrated merging configuration has a single bin 302 of the same size as the entire SEM image 300. The EDS spectrum corresponding to bin 302 is calculated by summing the EDS spectra of all pixels of SEM image 300 (a total of 777,600 EDS spectra).

[0035] Figure 4 The merging configuration illustrated in has 24 bins 402 arranged in four rows and six columns ij , where i = 1, 2, 3, 4 and j = 1, 2, ……, 6. Each bin 402 ij has a square shape with dimensions of 180×180 pixels 2 . The EDS spectrum corresponding to bin 402 is calculated by summing the EDS spectra of its pixels ij (i.e., a total of 32,400 corresponding EDS spectra per bin). The spectral output corresponding to this merging configuration includes 24 different EDS spectra calculated in this way.

[0036] Figure 5The merging configuration illustrated in [has] five bins 502 n , where n = 1, 2, ……, 5. Each bin 502 n has a corresponding irregular shape that covers the region of the corresponding phase 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 sometimes 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 strength.

[0037] In some examples, a computing device associated with the scientific instrument 100 (such as, for example, the electronic controller 150( Figure 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 the different individual phases identified in this way are grouped together to form the different bins 502 n in []. 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.

[0038] Software that can provide phase identification by analyzing the input spectral image data hypercube without human intervention is commercially available. Some instances of such software incorporate certain features disclosed in U.S. Patent Application Nos. 6584413 and 6675106, the entire texts of which are incorporated herein by reference. For example, various X-ray-based phase identification and mapping methods are reviewed in "X-ray Mapping in Electron-Beam Instruments" by John J. Friel and Charles E. Lyman (Microscopy and Microanalysis, Vol. 12, 2006, pp. 2-25), the entire text of which is incorporated herein by reference. 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 Figure 5 the type of phase mapping illustrated in [].

[0039] In the example shown, the phase identification and mapping software running on the electronic controller 150 has identified five different phases (labeled 1, 2, …, 5) in the sample S. The corresponding bins are bins 5021 to 5025. The EDS spectrum corresponding to a separate bin 502 n is calculated by summing the EDS spectra of its pixels. The output corresponding to this merged configuration includes five different EDS spectra, each corresponding to a bin calculated in this way.

[0040] In the example shown, Figure 5 the merged configuration illustrated in cannot be obtained via segmentation of the SEM image 300 based solely on image contrast. For example, phases 1 and 3 have similar contrast values but different chemical compositions. Unlike the contrast values, the different EDS spectra clearly point to different corresponding phases, which enables the SEM image 300 to be segmented into Figure 5 the phases indicated in.

[0041] Figure 6 is a flowchart illustrating a method 600 for element identification. According to some embodiments, the method 600 is implemented in the scientific instrument 100. In other embodiments, the method 600 (or a portion thereof) may be implemented via one or more computing devices remote from the instrument 100. In different examples, the method 600 is performed offline or online. In an offline configuration, the input 601 to the method 600 includes a previously acquired spectral image data hypercube corresponding to the sample S, where the acquisition process has been completed. In an online configuration, the input 601 includes a partially acquired or non-final spectral image hypercube corresponding to the sample S currently being interrogated in the scientific instrument 100.

[0042] The method 600 includes applying phase analysis to the input 601 (at block 602). In some examples, the phase analysis applied at block 602 uses an MSA-based method for phase identification and phase mapping implemented in software. In Figure 5 an example of the phase map generated at block 602 is illustrated.

[0043] The method 600 further includes calculating the corresponding EDS spectra for the individual phases (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-level EDS spectra of the corresponding bins of the SEM image (e.g., Figure 5 one of the bins 5021 to 5025 of the SEM image 300). In Figure 5 the example illustrated, five phase spectra corresponding to phases 1 to 5 are calculated at block 604.

[0044] Method 600 also includes performing elemental identification (at block 606) for each of the individual phases. At block 606, elemental identification of the individual phases is performed based on the respective phase spectra calculated at block 604. In some examples, elemental identification includes the following operations: (i) finding one or more peaks in the phase spectrum; (ii) determining the spectral positions of the found peaks; (iii) measuring the intensity of each of the found peaks; and (iv) identifying one or more chemical elements based on the spectral position and intensity of the peak.

[0045] In some examples of block 606, peaks in the phase spectrum are located by passing a "top-hat digital filter" through the phase spectrum channel by channel. One effect of this filter is to reduce the background part of the spectrum to a level close to 0 while substantially preserving the peaks. As the program filters each point in the spectrum, a check is made to determine whether the filtered point is positive and is significantly higher than the background points scattered near the 0 level. When these conditions are met, a peak is found, and the estimation of the peak position is performed 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.

[0046] In some examples of block 606, the program calculates the peak intensity based on an estimate of the net count in the located peak from the total count in the positive lobe of the peak from 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 may be used.

[0047] In some examples of block 606, the program examines the list of peaks in the phase spectrum, for example, in decreasing order of intensity, to find which X-ray energies match the peak energy within a specified tolerance (which is a parameter of the algorithm). During this matching process, certain rules regarding the peaks expected to be present under the applicable EDS acquisition conditions are followed. For example, X-ray energies above the accelerating energy of the electron beam are excluded, and a K-β peak can only be present if there is also a corresponding K-α peak, 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 found, 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 corresponding peak groups.

[0048] Method 600 also includes calculating one or more elemental maps (at block 608). In some examples, the operations of block 608 include: (i) combining element identification results for different individual phases to cover regions corresponding to two or more phases of a sample; (ii) for each identified element, determining in the combined result the corresponding pixel groups in block 606 in which the element is considered to be present; and (iii) generating an elemental map of the identified elements by color-coding the corresponding pixel groups and overlaying the color-coded pixel groups on the corresponding SEM image. In response to appropriate input from a user, the individual elemental maps generated in this manner can be reproduced and displayed for viewing via a user interface.

[0049] Figure 7 , Figure 8A , Figure 8B , Figure 9A and Figure 9B are diagrams illustrating method 600 according to one example. More specifically, Figure 7 illustrates an example SEM image 700 of a sample S generated by a scientific instrument 100. Figure 8A and Figure 8B respectively illustrate maps 802 and 804 of two phases identified in SEM image 700 at block 602 of method 600. Figure 9A and Figure 9B respectively illustrate phase spectra 902 and 904 calculated at block 604 of method 600. Phase spectra 902 and 904 respectively correspond to Figure 8A and Figure 8B the two phases illustrated in

[0050] The sample S shown in SEM image 700 includes small particles 704 on a substantially uniform substrate 702. The phase maps 802 and 804 identified for SEM image 700 at block 602 of method 600 respectively correspond to substrate 702 and particles 704. Based on the element identification performed at block 606 of method 600, the phase spectra 902, 904 calculated at block 604 of method 600 also have element labels inserted therein. Based on this element identification, the main element of substrate 702 is aluminum (Al). The main elements of particles 704 are aluminum and tin (Sn). Note that the signal intensity (net count) in phase spectrum 904 is typically many orders of magnitude lower than the signal intensity (net count) in phase spectrum 906. For comparison, when using Figure 3 and Figure 4 any of the combination schemes illustrated in Figure 3 and Figure 4The merging scheme in [it] mixes information from pixels corresponding to different phases. Thus, spectral information from the primary phase (with significantly larger pixel counts) substantially overshadows spectral information from the smaller phase (with smaller pixel counts), and the spectral information from the smaller phase may be lost, for example, in background noise. Thus, this comparison clearly illustrates the utility and exemplary benefits of method 600 for at least some samples.

[0051] For some samples, Figure 4 the merging scheme of [it] can also lead to misidentification due to the overlap of the 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 results in partial overlap of these peaks in the EDS spectrum of a bin containing both elements. Since the Ka peak is typically about 5 times stronger, the information represented by the consistent Kb peak may be unfavorably lost Figure 4 with the merging scheme of [it]. In contrast, with Figure 5 the merging scheme and method 600 of [it], such an undesired result is significantly less likely.

[0052] Although the above illustrative example has a relatively small number (<6) of different phases in sample S, method 600 is not limited thereto. For example, method 600 can also be used for samples with a larger (more than five) number of phases. In some examples, the corresponding sample S can have more than ten or more than one hundred phases. In some examples, method 600 can also be adapted for particle analysis of sample S, where the number of small particles in the region of interest is in the range between 1 and 10 5 inclusive. In such examples, the number of phases can be less than the number of particles.

[0053] Figure 10 is a flowchart of method 1000 for illustrative element identification. According to one embodiment, method 1000 is implemented in scientific instrument 100. However, in other embodiments, method 1000 (or parts thereof) can 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 ongoing. Method 1000 incorporates an embodiment of method 600 as part of it, as explained in more detail below.

[0054] One parameter of the spectral image data hypercube acquisition process is the dwell time, and the value of the dwell time determines how long the electron beam 114 remains at each scanned pixel position while the X-ray detector 160 accumulates the EDS spectrum corresponding to that position. Depending on the selected dwell time value, the final spectral image data hypercube can be acquired in many different ways. For example, when a relatively long dwell time is specified, a single raster scan of the FOV can be used to acquire the final spectral image data hypercube. That is, the EDS spectra acquired from the scanned pixel positions within a single dwell time are incorporated into the acquired final spectral image data hypercube, and as the raster scan progresses towards its end, the final spectral image data hypercube is gradually built up position by position. Conversely, when a relatively short dwell time is specified, multiple raster scans of the FOV are typically required to build the final spectral image data hypercube because the pixel-level EDS spectra corresponding to a single raster scan may not have a high enough SNR. In this case, the SNR is improved by repeating the raster scan multiple times and cumulatively adding the EDS spectra acquired in 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 built. Other ways of updating the spectral image data hypercube with newly measured EDS data blocks can also be implemented in additional examples.

[0055] Method 1000 beneficially provides the user with the ability to have a real-time preliminary elemental map while the acquisition process is in progress. The "appearance" of such a real-time preliminary elemental map will depend on the details of the acquisition process. For example, in the above "single raster scan" example, the preliminary elemental map will be smaller than the corresponding entire SEM image because the corresponding spectral image data hypercube will only have the EDS data for the pixels that have dwelled, while the remaining pixels do not have associated EDS data. The latter pixels can be blacked out in the corresponding preliminary elemental map. In the above "repeated raster scan" example, after the first raster scan, the preliminary elemental map will have the same dimensions as the corresponding entire SEM image. However, due to the relatively low SNR of the accumulated EDS data, the accuracy of such a preliminary elemental map may be relatively low. As more and more new EDS data sets are cumulatively added to the spectral image data hypercube with each new scan, the accuracy of subsequent preliminary elemental maps will generally increase.

[0056] Method 1000 includes acquiring 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 within the FOV; and (iv) one or more full scans of the FOV. Other configurations of the new EDS data set can also be used in additional examples.

[0057] Method 1000 also includes updating the spectral image data hypercube (at block 1004). This update is performed using the new EDS data set acquired at block 1002 at block 1004. In some examples, such an update can include filling vacant (empty) positions in the spectral image data hypercube with newly acquired EDS spectra, or adding the newly acquired EDS spectra to the cumulative EDS spectra previously stored in the corresponding positions in the spectral image data hypercube.

[0058] The updated spectral image data hypercube produced at block 1004 is then provided as input 601 to method 600, which can operate in the background of the acquisition process (see also Figure 6 ). Based on the received input 601, method 600 generates one or more preliminary elemental maps of sample S as described above.

[0059] As Figure 10 shown, decision block 1006 is used to control the termination of method 1000. When the electronic controller 150 determines that the data acquisition process is complete (a "yes" at decision block 1006), method 1000 terminates. When the electronic controller 150 determines that the data acquisition process is not complete (a "no" at decision block 1006), the operation of method 1000 loops back to block 1002.

[0060] Figure 11 is a block diagram of an example 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 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 multiple computing devices 1100.

[0061] Figure 11 Computing device 1100 of Figure 11One or more of the illustrated components, but may include interface circuitry for coupling to the one or more components using any suitable interface (e.g., Universal Serial Bus (USB) interface, High-Definition Multimedia Interface (HDMI) interface, Controller Area Network (CAN) interface, Serial Peripheral Interface (SPI) interface, Ethernet interface, wireless interface, or any other suitable interface). For example, computing device 1100 may not include display device 1110, but may include display device interface circuitry (e.g., connectors and drive circuitry) capable of coupling to an external display device 1110.

[0062] Computing device 1100 includes processing device 1102 (e.g., one or more processing devices). As used herein, the terms “electronic processing device” and “processing device” may interchangeably refer to any device or portion of a device that processes electronic data from registers and / or memory to transform the electronic data into other electronic data that may be stored in registers and / or memory. In various embodiments, 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.

[0063] Computing device 1100 also includes storage device 1104 (e.g., one or more storage devices). In various embodiments, storage device 1104 may include one or more memory devices, such as random access memory (RAM) devices (e.g., static RAM (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, storage device 1104 may include memory that shares a die with processing device 1102. In such embodiments, the memory may function as a cache memory and include, for example, embedded dynamic random access memory (eDRAM) or spin-transfer torque magnetic random access memory (STT-MRAM). In some embodiments, storage device 1104 may include a non-transitory computer-readable medium having instructions thereon that, when executed by one or more processing devices (e.g., processing device 1102), cause computing device 1100 to perform any suitable method or portions of such methods disclosed herein below.

[0064] The computing device 1100 also 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 for managing wireless communication for transmitting data to and from computing device 1100. The term "wireless" and its derivatives may be used to describe circuits, devices, systems, methods, techniques, communication channels, etc., that can transmit data via modulated electromagnetic radiation through a non-solid medium. The term does not imply that the associated devices do not contain any wires, although in some embodiments there may be no wires. The circuitry included in interface device 1106 for managing wireless communication may implement any one of a plurality of wireless standards or protocols, including but not limited to Institute of Electrical and Electronics Engineers (IEEE) standards, including Wi-Fi (IEEE 802.11 series), IEEE 802.16 standard, Long Term Evolution (LTE) project, and any amendments, updates, and / or revisions (e.g., Advanced LTE project, Ultra Mobile Broadband (UMB) project (also known as "3GPP2"), etc.). In some embodiments, the circuitry included in interface device 1106 for managing wireless communication may operate in accordance with Global System for Mobile Communications (GSM), General Packet Radio Service (GPRS), Universal Mobile Telecommunications System (UMTS), High Speed Packet Access (HSPA), Evolved HSPA (E-HSPA), or LTE networks. In some embodiments, the circuitry included in interface device 1106 for managing wireless communication may operate in accordance with Enhanced Data 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 communication may operate in accordance with Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Digital Enhanced Cordless Telecommunications (DECT), Evolution-Data Optimized (EV-DO), and their derivatives, and any other wireless protocols designated as 3G, 4G, 5G, and later. In some embodiments, interface device 1106 may include one or more antennas (e.g., one or more antenna arrays) configured to receive and / or transmit wireless signals.

[0065] In some embodiments, interface device 1106 may include circuitry for managing wired communications, such as electrical communication protocols, optical communication protocols, or any other suitable communication protocol. For example, interface device 1106 may include circuitry that supports communication according to Ethernet technology. In some embodiments, interface device 1106 may support both wireless and wired communications, and / or may support multiple wired communication protocols and / or multiple wireless communication protocols. For example, a first set of circuitry of interface device 1106 may be dedicated to short-range wireless communication such as Wi-Fi or Bluetooth, while a second set of circuitry of interface device 1106 may 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 circuitry of interface device 1106 may be dedicated to wireless communication, and a second set of circuitry of interface device 1106 may be dedicated to wired communication.

[0066] Computing device 1100 also includes battery / power circuitry 1108. In various embodiments, battery / power circuitry 1108 may include one or more energy storage devices (e.g., batteries or capacitors) and / or circuitry for coupling components of computing device 1100 to an energy source separate from computing device 1100 (e.g., coupled to an AC line power).

[0067] Computing device 1100 also includes display device 1110 (e.g., one or more separate 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.

[0068] 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 transmission interfaces, audio I / O devices (e.g., a microphone or microphone array, a speaker, headphones, earbuds, 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.), an image capture device (e.g., one or more cameras), a human-machine interface device (e.g., a keyboard, a cursor control device such as a mouse, a stylus, a trackball, or a touchpad), etc.

[0069] Depending on the specific implementation 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 be configured to output appropriate control signals (e.g., 152, 154, 156) for various components of the scientific instrument 100, receive appropriate control / telemetry signals from various components of the scientific instrument 100, and receive measurement streams (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 includes one or more analog-to-digital converters (ADCs) for converting the received analog signals into a digital form suitable for operations to be 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 includes one or more digital-to-analog converters (DACs) for converting digital signals provided by the processing device 1102 and / or the storage device 1104 into an analog form suitable for being communicated to corresponding components of the scientific instrument 100.

[0070] According to one example disclosed above, for instance, in the Summary of the Invention section and / or with reference to Figures 1 to 11 any one or any combination of some or all of those in, there is provided an automated method for providing support to a scientific instrument via a computing device, the method comprising: calculating a phase map of a sample by applying phase analysis to a data set including a charged particle microscopy (CPM) image of the sample and a plurality of energy-dispersive X-ray spectroscopy (EDS) spectra of the sample, wherein each EDS spectrum in 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 group of elements based on the EDS spectrum corresponding to that 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 groups of elements.

[0071] In some examples of the above method, the determining includes: calculating a respective phase spectrum by summing the EDS spectra corresponding to that group; and determining the respective group of elements based on the respective phase spectrum.

[0072] In some examples of any of the above methods, determining the respective group of elements includes matching a set of peaks in the respective phase spectrum with reference spectra corresponding to different elements of the periodic table.

[0073] In some examples of any of the above methods, the applying includes applying multivariate statistical analysis to the data set to extract statistical groups representing different respective phases of the sample.

[0074] In some examples of any of the above methods, the method further includes displaying a corresponding elemental map on a display device.

[0075] In some examples of any of the above methods, the data set is a final spectral image data hypercube previously acquired with a scientific instrument.

[0076] In some examples of any of the above methods, the corresponding elemental map is a preliminary elemental map calculated during EDS spectroscopy acquisition with a scientific instrument.

[0077] In some examples of any of the above methods, the preliminary elemental map represents an area smaller than the entire area of the CPM image.

[0078] In some examples of any of the above methods, the method further includes: acquiring a new set of EDS spectra of a sample with a scientific instrument; updating the data set with the new set of EDS spectra; and recalculating a phase map by applying phase analysis to the updated data set.

[0079] In some examples of any of the above methods, the new set of EDS spectra corresponds to one of the following: a partial scan line of a raster scan of the field of view (FOV) of the sample with a scientific instrument; one or more full scan lines of the raster scan; an arbitrarily shaped partial scan of the FOV with the scientific instrument; and one or more full scans of the FOV with the scientific instrument.

[0080] According to another example disclosed above, for example, in the Summary of the Invention section and / or with reference to Figures 1 to 11 any one of the figures or any combination of some or all of the figures, 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 one of the above methods.

[0081] According to yet another example disclosed above, for example, in the Summary of the Invention section and / or with reference to Figures 1 to 11Any one or any combination of some or all of the following provides a support device for a scientific instrument, the support device comprising: an interface device configured to receive a data set including a charged particle microscopy (CPM) image of a sample and a plurality of energy-dispersive X-ray spectroscopy (EDS) spectra of the sample, wherein each EDS spectrum in the EDS spectra corresponds 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, determine a respective group of elements based on the EDS spectrum corresponding to the group; and for a selected chemical element, calculate a corresponding element map of the sample based on the identified groups of pixels and the determined respective groups of elements.

[0082] In some examples of the above device, the one or more electronic processing devices are further configured to: calculate a respective phase spectrum by summing the EDS spectra corresponding to the group; and determine the respective group of elements based on the respective phase spectrum.

[0083] In some examples of any of the above devices, the one or more electronic processing devices are further configured to: match a set of peaks in the respective phase spectrum with reference spectra corresponding to different elements of the periodic table; and determine the respective group of elements based on the matches found.

[0084] In some examples of any of the above devices, the one or more electronic processing devices are further configured to apply multivariate statistical analysis to the data set to extract statistical groups representing different respective phases of the sample.

[0085] In some examples of any of the above devices, the device further includes a display device configured to display the corresponding element map.

[0086] In some examples of any of the above devices, the data set is a final spectral image data hypercube previously acquired with a scientific instrument.

[0087] In some examples of any of the above devices, the one or more electronic processing devices are further configured to calculate a preliminary element map during acquisition of the EDS spectra with the scientific instrument.

[0088] In some examples of any of the above devices, the interface device is configured to receive a new set of EDS spectra of the sample; and wherein the one or more electronic processing devices are further configured to: update the data set with the new set of EDS spectra; and recalculate the phase map by applying phase analysis to the updated data set.

[0089] In some examples of any of the above devices, the new EDS spectral set corresponds to one of the following: a partial scan line of a raster scan of the field of view (FOV) of a sample with a scientific instrument; one or more full scan lines of the raster scan; an arbitrarily shaped partial scan of the FOV with the scientific instrument; and one or more full scans of the FOV with the scientific instrument.

[0090] It should be understood that the above description is intended to be illustrative and not restrictive. Many specific implementations and applications other than the provided examples will be apparent by reading the above description. The scope should not be determined with reference to the above description, but rather should be determined with reference to the appended claims and the full scope of equivalents to which those claims are entitled. It is anticipated and intended that future developments will occur in the technology discussed herein, and the disclosed systems and methods will be incorporated into these future examples. In summary, it should be understood that this application is capable of modification and variation.

[0091] All terms used in the claims are intended to be given their broadest reasonable interpretation and their ordinary meaning as understood by those skilled in the art of the technology described herein, unless an explicit contrary indication is made herein. Specifically, the use of singular articles such as "a", "the", "said", etc. should be understood to enumerate one or more of the indicated elements, unless the claim enumerates an explicit contrary limitation.

[0092] A summary is provided to allow the reader to quickly ascertain the nature of the technical disclosure. It should be understood that it will not be used to interpret or limit the scope or meaning of the claims. Additionally, in the foregoing detailed description, it can be seen that for the purpose of streamlining the disclosure, various features are grouped together in various examples. The method of the disclosure should not be construed as reflecting an intention that the claimed subject matter includes more features than are expressly recited in each claim. Rather, as reflected in the following claims, the subject matter of the invention lies in less than all of the features of a single disclosed example. Accordingly, the following claims are hereby incorporated into the detailed description, where each claim stands on its own as a separately claimed subject matter.

[0093] Unless otherwise explicitly stated, each numerical value and range should be interpreted as approximate, as if the word "about" or "approximately" preceded the value or range.

[0094] Although the elements (if any) in the following method claims are recited in a specific order with corresponding labels, these elements are not necessarily intended to be limited to being implemented in the recited specific order, unless the claim recites otherwise to imply a specific order for implementing some or all of these elements.

[0095] Unless otherwise specified herein, the use of ordinal adjectives “first,” “second,” “third,” etc. to refer to one of a plurality of like objects merely indicates a different instance of such like objects being referred to and is not intended to imply that the like objects so referred to must be in a corresponding order or sequence in time, space, rank, or in any other manner.

[0096] Unless otherwise indicated herein, in addition to its ordinary meaning, the conjunction “if” may also or alternatively be construed to mean “when” or “while” or “in response to determining” or “in response to detecting,” the interpretation of which may depend on the corresponding specific context. For example, the phrase “if determined” or “if [the condition] is detected” may be construed to mean “when determined” or “in response to determining” or “when [the condition or event] is detected” or “in response to detecting [the condition or event].”

[0097] Likewise, for the purposes of this specification, the terms “coupled,” “connected” refer to any manner known in the art or developed later, in which energy is permitted to transfer between two or more elements, and the insertion of one or more additional elements is contemplated, although not required. Conversely, the terms “directly coupled,” “directly connected,” etc. imply the absence of such additional elements.

[0098] The functions of the various elements shown in the drawings, including any functional blocks labeled “processor” and / or “controller,” may be provided by using dedicated hardware as well as hardware capable of executing software in association with appropriate software. When provided by a processor, the functions may be provided by a single dedicated processor, by a single shared processor, or by multiple individual processors (some of which may be shared). Additionally, the explicit use of the term “processor” or “controller” should not be construed as referring exclusively to hardware capable of executing software, but may implicitly include, but not be 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. Other conventional and / or custom hardware may also be included. Similarly, any switches shown in the figures are merely conceptual. Their functions may be performed by the operation of program logic, by dedicated logic, by the interaction of program control and dedicated logic, or even manually, as more specifically understood from the context, and the particular technique may be selected by the implementer.

[0099] As used in this application, the term "circuit" can refer to one or more or all of the following: (a) only hardware circuit implementations (such as only in analog and / or digital circuits); (b) a combination of hardware circuits and software, such as (if applicable): (i) a combination of analog and / or digital hardware circuits and software / firmware, and (ii) a hardware processor and software (including a digital signal processor), any part of the software and memory, which work together to cause a device such as a mobile phone or a server to perform various functions; and (c) a hardware circuit and / or a processor, such as a microprocessor or a part of a microprocessor, which requires software (e.g., firmware) to operate, but when software is not required to operate, the software may not be present. This definition of a circuit applies to all uses of the term in this application, including in any claims. As another example, as used in this application, the term "circuit" also encompasses only hardware circuits or processors (or multiple processors) or parts of hardware circuits or processors and their (or their) attendant software and / or firmware implementations. The term "circuit" also encompasses (e.g., and if applicable to a particular claim element) a baseband integrated circuit or a processor integrated circuit for a mobile device or a similar integrated circuit in a server, a cellular network device, or other computing or network devices.

[0100] One of ordinary skill in the art will appreciate that any block diagrams herein represent conceptual diagrams of illustrative circuits embodying the principles of the present disclosure. Similarly, it will be recognized that any flowcharts, flow diagrams, state transition diagrams, pseudocode, etc. represent various processes that may be substantially represented in a computer-readable medium and thus executed by a computer or a processor, regardless of whether such computer or processor is explicitly shown.

Claims

1. An automated method for providing support to a scientific instrument, performed via a computing device, the method comprising: Calculating a phase map of a sample by applying phase analysis to a data set comprising 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 in 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 group 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 groups of elements.

2. The automated method according to claim 1, wherein the determining comprises: Calculating a respective phase spectrum by summing the EDS spectra corresponding to the group; And Determining the respective group of elements based on the respective phase spectrum.

3. The automated method according to claim 2, wherein determining the respective group of elements comprises matching a set of peaks in the respective phase spectrum with reference spectra corresponding to different elements of the periodic table.

4. The automated method according to claim 1, wherein the applying comprises applying multivariate statistical analysis to the data set to extract statistical groups representing the different respective phases of the sample.

5. The automated method according to claim 1, further comprising displaying the corresponding elemental map on a display device.

6. The automated method according to claim 1, wherein the data set is a final spectral image data hypercube previously acquired with the scientific instrument.

7. The automated method according to claim 1, wherein the corresponding elemental map is a preliminary elemental map calculated during acquisition of the EDS spectra with the scientific instrument.

8. The automated method according to claim 7, wherein the preliminary elemental map represents a region smaller than the entire region of the CPM image.

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

10. The automated method according to claim 9, wherein the new set of EDS spectra corresponds to one of the following: Partial scan lines of a raster scan of the field of view (FOV) of the sample with the scientific instrument; One or more full scan lines of the raster scan; An arbitrarily shaped partial scan of the FOV with the scientific instrument; and One or more full scans of the FOV with the scientific instrument.

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

12. A support device for a scientific instrument, the support 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, wherein each EDS spectrum in the EDS spectra corresponds 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 pixel groups representing different respective phases of the sample; For each of the identified pixel groups, determine a respective group of elements based on the EDS spectra corresponding to the group; And For a selected chemical element, calculate a corresponding element map of the sample based on the identified pixel groups and the determined respective groups of elements.

13. The support device according to claim 12, wherein the one or more electronic processing devices are further configured to: Calculate a respective phase spectrum by summing the EDS spectra corresponding to the group; and Determine the respective group of elements based on the respective phase spectrum.

14. The support device according to claim 13, wherein the one or more electronic processing devices are further configured to: Match a set of peaks in the respective phase spectrum with reference spectra corresponding to different elements of the periodic table; and Determine the respective group of elements based on the found matches.

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

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

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

18. The support device according to claim 12, wherein the one or more electronic processing devices are further configured to calculate a preliminary element map during acquisition of the EDS spectra with the scientific instrument.

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

20. The support device according to claim 18, wherein the new set of EDS spectra corresponds to one of the following: Partial scan lines of a raster scan of the field of view (FOV) of the sample with the scientific instrument; One or more full scan lines of the raster scan; An arbitrarily shaped partial scan of the FOV with the scientific instrument; and One or more full scans of the FOV with the scientific instrument.

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