Charged particle microscope scan masking for three-dimensional reconstruction

Abstract

To provide charged particle microscope (CPM) support systems, as well as related apparatuses, methods, computing devices, and computer readable media.SOLUTION: In some embodiments, a charged particle microscope computational support apparatus may include: first logic to, for each angle of a plurality of angles, receive an associated image of a specimen at the angle, and generate an associated scan mask based on one or more regions-of-interest in the associated image; second logic to, for each angle of the plurality of angles, generate an associated data set of the specimen by processing data from a scan, in accordance with the associated scan mask, by a charged particle microscope of the specimen at the angle; and third logic to provide, for each angle of the plurality of angles, the associated data set of the specimen to reconstruction logic to generate a three-dimensional reconstruction of the specimen.SELECTED DRAWING: Figure 1

Description

Background Art

[0001] Microscopy is a technical field for better viewing objects that are difficult to see with the naked eye using a microscope. Different fields of microscopy include, for example, optical microscopy, charged particle (electron and / or ion) microscopy, and scanning probe microscopy. Charged particle microscopy involves using a beam of accelerated charged particles as an illumination source. Types of charged particle microscopy include, for example, transmission electron microscopy, scanning electron microscopy, scanning transmission electron microscopy, and focused ion beam microscopy.

Brief Description of the Drawings

[0002] Embodiments will be readily understood by the following detailed description in conjunction with the accompanying drawings. For ease of explanation, the same reference numerals refer to the same structural elements. Embodiments are illustrated by way of example and not limitation in the figures of the accompanying drawings. [Figure 1] Illustrates a charged particle microscope (CPM) system configured for scan masking for three-dimensional reconstruction according to various embodiments. [Figure 2] It is a block diagram of an exemplary CPM reconstruction support module for performing reconstruction support operations according to various embodiments. [Figure 3] It is a flowchart of an exemplary method for performing reconstruction support operations according to various embodiments. [Figure 4A] Illustrates various calculation outputs and displays that can be part of some or all of the performance of the CPM reconstruction support method disclosed herein according to various embodiments. [Figure 4B] Illustrates various calculation outputs and displays that can be part of some or all of the performance of the CPM reconstruction support method disclosed herein according to various embodiments. [Figure 4C] Illustrates various calculation outputs and displays that can be part of some or all of the performance of the CPM reconstruction support method disclosed herein according to various embodiments. [Figure 4D] Various computational outputs and displays that may constitute part of the performance of some or all of the CPM reconstruction support methods disclosed herein, according to various embodiments, are illustrated. [Figure 4E] Various computational outputs and displays that may constitute part of the performance of some or all of the CPM reconstruction support methods disclosed herein, according to various embodiments, are illustrated. [Figure 4F] Various computational outputs and displays that may constitute part of the performance of some or all of the CPM reconstruction support methods disclosed herein, according to various embodiments, are illustrated. [Figure 4G] Various computational outputs and displays that may constitute part of the performance of some or all of the CPM reconstruction support methods disclosed herein, according to various embodiments, are illustrated. [Figure 4H] Various computational outputs and displays that may constitute part of the performance of some or all of the CPM reconstruction support methods disclosed herein, according to various embodiments, are illustrated. [Figure 5] Examples of graphical user interfaces that may be used to perform some or all of the CPM reconfiguration support methods disclosed herein, according to various embodiments. [Figure 6] This is a block diagram of an exemplary computing device capable of performing some or all of the CPM reconfiguration support methods disclosed herein, in various embodiments. [Figure 7] This is a block diagram of an exemplary CPM support system in which some or all of the CPM reconfiguration support methods disclosed herein can be performed according to various embodiments. [Modes for carrying out the invention]

[0003] Disclosed herein are charged particle microscope (CPM) support systems, as well as related apparatus, methods, computing devices, and computer-readable media. For example, in some embodiments, a charged particle microscope computing support system may include: a first logic for receiving associated images of a specimen at each of a plurality of angles and generating associated scan masks based on one or more regions of interest within the associated images; a second logic for generating associated datasets of a specimen by processing data from scans of the specimen at each of a plurality of angles according to the associated scan masks using a charged particle microscope; and a third logic for generating a three-dimensional reconstruction of a specimen by providing the associated datasets of the specimen to reconstruction logic for each of a plurality of angles.

[0004] Conventional three-dimensional analytical tomography typically involves repeated acquisition of full-frame CPM images and full-frame analytical measurement mapping for multiple tilt angles of the specimen. Full-frame analytical data can be quantified to identify individual material components and create tilt series of full-frame images of individual material components. Conventional tomographic reconstruction algorithms can be applied to full-frame CPM images and full-frame images of individual material components to generate CPM and analytical volume data. This conventional approach may have several constraints and limitations. For example, conventional acquisition of analytical signals from a specimen during charged particle microscopy (e.g., using energy-dispersive spectroscopy (EDS) or electron energy loss spectroscopy (EELS)) is a very slow process and typically requires delivering high doses of radiation to the specimen, which can damage it. When these analytical signals are acquired for the purpose of three-dimensional reconstruction (e.g., three-dimensional analytical particle investigation by scanning transmission electron microscopy tomography), the acquisition time and / or sample dose can become enormous unless the size of the area being investigated (e.g., the number of particles being investigated) is kept very small (and therefore the amount of statistical information regarding the composition of the particles and their spatial distribution within the sample is very limited).

[0005] Embodiments of CPM reconstruction assistance disclosed herein can achieve improved performance compared to conventional approaches. For example, in some embodiments, “high-resolution” analysis acquisition at each tilt angle may be performed selectively according to a scan mask created from initial “low-resolution” images. In such embodiments, the radiation dose delivered to the specimen may be less than the dose delivered during full-frame analysis acquisition (for example, “irrelevant” areas of the specimen may not be irradiated, thereby potentially reducing overall damage), and the acquisition time may be significantly shorter than the time required for full-frame analysis acquisition. In some specific embodiments, improvements of more than an order of magnitude in acquisition time and / or radiation dose can be achieved. In some embodiments, after selective analysis acquisition, the results of spectral quantification for a particular tilt angle (e.g., energy-dispersive X-ray spectroscopy (EDX) data) may be inserted into the correct positions in an empty full-frame image, and the images obtained for multiple tilt angles may be provided to a standard reconstruction workflow (e.g., a standard tomography reconstruction workflow) to achieve successful three-dimensional reconstruction of the specimen. This result contradicts the conventional assumption that a full-frame image without zero data regions is necessary for successful three-dimensional reconstruction, and therefore the CPM reconstruction-assisted embodiments disclosed herein represent a significant advance over conventional approaches. Accordingly, the embodiments disclosed herein provide substantial improvements to CPM techniques (e.g., improvements to the computer techniques assisting CPM, among other improvements).

[0006] In the following detailed description, reference is made to the accompanying drawings that form part of this specification, where the same numbers indicate the same parts throughout, and embodiments that may be implemented are shown as illustrative examples. It should be understood that other embodiments may be utilized and structural or logical modifications may be made without departing from the scope of this disclosure. Therefore, the following detailed description should not be construed as restrictive.

[0007] Various operations may be described as a series of separate actions or operations in the manner most helpful in understanding the subject matter disclosed herein. However, the order of description should not be construed as meaning that these operations are necessarily order-dependent. In particular, these operations may not be performed in the order shown. The operations described may be performed in a different order than in the embodiments described. In additional embodiments, various additional operations may be performed, and / or the operations described may be omitted.

[0008] For the purposes of this disclosure, the phrases "A and / or B" and "A or B" mean (A), (B), or (A and B). For the purposes of this disclosure, the phrases "A, B, and / or C" and "A, B, or C" mean (A), (B), (C), (A and B), (A and C), (B and C), or (A, B, and C). Some elements may be referred to in the singular (e.g., "processing device"), but any appropriate element may be represented by multiple examples of that element, and vice versa. For example, a set of operations described as being performed by a processing device may be performed by different operations of operations performed by different processing devices.

[0009] The description uses the phrases “embodiments,” “various embodiments,” and “several embodiments,” each of which may refer to one or more of the same or different embodiments. Furthermore, terms such as “equipment,” “includes,” and “have” as used in reference to embodiments of this disclosure are synonyms. When used to describe a range of dimensions, the phrase “between X and Y” refers to a range that includes X and Y. As used herein, “apparatus” may refer to any individual device, a collection of devices, a part of a device, or a collection of parts of a device. Drawings are not necessarily to scale.

[0010] Figure 1 shows an embodiment of a CPM system 100 including a CPM1 coupled to a display device 120. The CPM1 may include any preferred type of CPM, such as a transmission electron microscope (TEM), scanning electron microscope (SEM), scanning transmission electron microscope (STEM), cryo-electron microscope (cryoEM), or ion beam microscope. The CPM1 may include an enclosure 2 having a charged particle source 4 within it. In some embodiments, the enclosure 2 may be a vacuum enclosure, while in other embodiments, a specific gaseous environment may be maintained within the enclosure 2 (e.g., for “environmental STEM” applications). The charged particle source 4 may be, for example, an electron source (e.g., a Schottky gun), a positive ion source (e.g., a gallium ion source or a helium ion source), a negative ion source, a proton source, or a positron source. The charged particle source 4 may generate a beam of charged particles that crosses an illuminator 6 that directs and / or focuses the particles onto a region of the specimen S. The illuminator 6 may also perform aberration reduction, trimming, and / or filtering of charged particles output by the charged particle source 4. The illuminator 6 may have an axis 8 and may include one or more subcomponents such as an electrostatic lens, a magnetic lens, a scanning deflector, a corrector (e.g., an astigmatism corrector), and / or a capacitor system.

[0011] The specimen S may be held on a specimen holder 10 which can be positioned with multiple degrees of freedom by a positioning device 12. For example, the specimen holder 10 may include fingers that can translate in the xy plane and may be rotated about an axis in the xy plane to achieve different inclination angles of the specimen with respect to the axis 8 of the charged particle beam from the illuminator 6. Such movement may allow different areas of the specimen S to be illuminated, scanned, and / or inspected at different angles by the charged particle beam moving along the axis 8 (and / or may allow scanning motion to be performed as an alternative to beam scanning). A cooling device 14 may be in thermal contact with the specimen holder 10 and, if desired, may maintain the specimen holder 10 at a cryogenic temperature (e.g., using a circulating cryogenic coolant to achieve and maintain a desired low temperature).

[0012] A focused charged particle beam traveling along axis 8 may interact with the sample S in such a manner that it emits various types of radiation from the sample S. Such radiation may include secondary charged particles (e.g., secondary electrons), backscattered charged particles (e.g., backscattered electrons), X-rays, and / or optical radiation (e.g., cathode-ray luminescence). One or more of these radiation types, or other radiation types, may be detected by the detector 22. In some embodiments, the detector 22 may include, for example, a scintillator / photomultiplier tube or EDX detector combination. Alternatively or additionally, charged particles may traverse the sample S, emerge from the sample S, and continue propagating along axis 8 (with substantially, but generally, some deflection / scattering). Such transmitted electrons may enter an imaging system 24, which may act as a combined objective / projection lens and may include various electrostatic and / or magnetic lenses, deflectors, correctors (e.g., astigmatism correctors), etc., as needed. In non-scanning mode, the imaging system 24 can focus transmitted electrons onto the fluorescence screen 26, which can be retracted (as schematically shown by arrow 28) or otherwise pulled out, if desired, so as to move away from axis 8. A partial image of specimen S may be formed on the screen 26 by the imaging system 24, which can be viewed through an observation port 30 located in a preferred part of the enclosure 2 of the CPM 1. Retraction mechanisms for the screen 26 may be, for example, essentially mechanical and / or electrical, and are not shown here.

[0013] Alternatively or in addition to displaying an image on the screen 26, a charged particle detector D may be used. In such embodiments, the adjuster lens 24' can shift the focus of charged particles emerging from the imaging system 24 and redirect them towards the charged particle detector D (rather than on the retracted plane of the screen 26 as considered above). In the charged particle detector D, charged particles can form an image (e.g., a diffraction pattern), which can be processed by the controller 50 and displayed on the display device 120. In STEM mode, the output from the detector D can be recorded as a function of the (x, y) scanning beam position and the tilt angle of the sample S, and an image, which is a map of the detector output, can be constructed. Generally, the CPM1 may include one or more detectors arranged as desired. Examples of such detectors include, in particular, photomultiplier tubes (e.g., solid-state photomultiplier tubes), photodiodes, complementary metal-oxide-semiconductor (CMOS) detectors, charge-coupled device (CCD) detectors, and photocells used in combination with scintillator films. This disclosure uses the term “image” to refer to a set of data generated by one or more detectors of a CPM, and such image may include a scalar value at each pixel, a vector value at each pixel, or any other preferred arrangement of information.

[0014] The controller 50 may be connected to various illustrative components via control lines 50' (e.g., buses). The controller 50 may provide various functions such as synchronizing actions, providing setpoints, processing signals, performing calculations, and displaying messages / information on the display device 120. While the controller 50 is shown in Figure 1 as being inside the enclosure 2 of the CPM 1, this is merely illustrative; the controller 50 may be located inside or outside the enclosure 2, or distributed among components both inside and outside the enclosure 2. For example, in some embodiments, some operations of the controller 50 may be performed by hardware located inside the enclosure 2, while other operations of the controller 50 may be performed by hardware located outside the enclosure 2 (e.g., a computing device such as a laptop or desktop computer).

[0015] Figure 2 is a block diagram of a CPM reconfiguration support module 1000 for performing reconfiguration support operations in various embodiments. The CPM reconfiguration support module 1000 may be part of the controller 50 of the CPM system 100 in Figure 1. The CPM reconfiguration support module 1000 may be implemented by circuitry (including, for example, electrical and / or optical components) such as a programmed computing device. The logic of the CPM reconfiguration support module 1000 may be contained in a single computing device or distributed across multiple computing devices communicating with each other as needed. Examples of computing devices that may implement the CPM reconfiguration support module 1000, either individually or in combination, are described with reference to the computing device 4000 in Figure 6, and examples of a system of interconnected computing devices in which the CPM reconfiguration support module 1000 may be implemented across one or more computing devices are considered herein with reference to the CPM support system 5000 in Figure 7.

[0016] The CPM reconstruction support module 1000 may include imaging logic 1002, scan mask logic 1004, processing logic 1006, output logic 1008, and reconstruction logic 1010. As used herein, the term “logic” may include devices for performing a set of operations associated with the logic. For example, any of the logic elements included in the CPM reconstruction support module 1000 may be implemented by one or more computing devices programmed with instructions to perform a set of operations associated with one or more processing devices of a computing device. In certain embodiments, the logic element may include one or more non-temporary computer-readable media having instructions to perform a set of operations associated with one or more computing devices when executed by one or more processing devices of one or more computing devices. As used herein, the term “module” may refer to a set of one or more logic elements that together perform a function associated with the module. Different logic elements within a module may take the same form or different forms. For example, some logic within a module may be implemented by programmed general-purpose processing devices, while other logic within a module may be implemented by application-specific integrated circuits (ASICs). In another example, different logic elements within a module may be associated with different sets of instructions that can be executed by one or more processing devices. A module may not contain all the logic elements shown in the relevant drawings. For example, a module may contain a subset of the logic elements shown in the relevant drawings if it performs a subset of the operations considered herein in relation to that module.

[0017] The imaging logic 1002 can cause the CPM (e.g., CPM1 in Figure 1) to generate an image of a specimen (e.g., specimen S in Figure 1) at a specific angle (e.g., a specific tilt angle), and can do so for multiple different angles. The imaging logic 1002 may be configured to capture different types of images. For example, in some embodiments, the imaging logic 1002 may be configured to capture low-resolution and high-resolution images. Low-resolution images of a specimen may require lower radiation doses and / or acquisition times than high-resolution images of a specimen. In some embodiments, as will be further discussed below, the imaging logic 1002 may cause the CPM to generate a low-resolution image of the specimen at a certain angle, and then the low-resolution image may be used (e.g., by the scan mask logic 1004) to generate a scan mask, which is applied when the imaging logic 1002 causes the CPM to generate a high-resolution image of the specimen at that angle, thereby capturing only a subset of the CPM's field of view in high resolution. In some embodiments, the images generated by the CPM at the direction of the imaging logic 1002 may include brightfield images, annular brightfield (ABF) images, integrated differential phase contrast (iDPC) images, or high-angle annular darkfield (HAADF) images. Figure 4A shows a graphic representation 102 of an image of a sample (e.g., a "low-resolution" image) that may be generated by the imaging logic 1002. The particular graphic representation 102 in Figure 4A is a HAADF image, but the imaging logic 1002 may generate any preferred image.

[0018] The scan mask logic 1004 can receive an image of a specimen (e.g., the specimen S in FIG. 1) at a specific angle (e.g., a specific tilt angle) (e.g., from the imaging logic 1002), and can generate a related scan mask for subsequent imaging by a CPM (e.g., CPM1 in FIG. 1) based on the received image. The scan mask logic 1004 can do so for a plurality of different angles. As used herein, a "scan mask" can be a data set that indicates to a CPM (e.g., CPM1 in FIG. 1) which regions within its full field of view will be imaged in a subsequent imaging operation. For example, the scan mask can indicate which squares within a "full-frame" grid of the field of view will be imaged in a subsequent imaging operation. Thus, the scan mask can correspond to a subset of the full-frame field of view of the CPM. In some embodiments, the scan mask logic 1004 can identify a region of interest within the received low-resolution image and generate a scan mask that identifies the region of interest as the region to be scanned in a subsequent high-resolution imaging operation of the CPM. The scan mask logic 1004 can identify the region of interest of the received image using any of several image processing techniques whose selection can depend on specific features of interest within the specimen. For example, a particle detection setting can include individual particles and adjacent pixels within an image of the specimen in the region of interest. The scan mask logic 1004 can identify the region of interest, among other settings, such as sediment detection, grain boundary detection, or cell membrane detection. Any of several well-known image processing techniques, such as centroid detection techniques (e.g., Crocker-Grieg centroid detection technique), thresholding techniques, attention-based TransNet network techniques, or neural network techniques, can be used to generate a region of interest for an image of a particle or other feature.

[0019] The scan mask generated by the scan mask logic 1004 may indicate to the CPM that the portion of the field of view corresponding to the region of interest will be imaged in a subsequent imaging operation, and the portion of the field of view that does not correspond to the region of interest will not be imaged in a subsequent imaging operation. Reducing the area of the field of view to be imaged can reduce the radiation to which the specimen is exposed and can reduce the acquisition time of a subsequent imaging operation as compared to an imaging operation in which the entire field of view is imaged. In some embodiments, the scan mask generated by the scan mask logic may have a higher resolution than the image used by the scan mask logic 1004 to generate the scan mask.

[0020] FIG. 4B shows a graphic representation 104 of an exemplary set of regions of interest (enclosed by white squares) that the scan mask logic 1004 may identify in the graphic representation 102 of FIG. 4A. In the graphic representation 104, the regions of interest are overlaid on the graphic representation 102 (FIG. 4A). The regions of interest can center around or otherwise include possible particles or other features of interest in the graphic representation 102, and the scan mask logic can identify the regions of interest by applying any of the techniques discussed above (e.g., the centroid detection technique). The regions of interest shown in the graphic representation 104 of FIG. 4B have square boundaries, but this need not be the case, and in other embodiments, the regions of interest can have other rectangular boundaries, rounded rectangular boundaries (e.g., substantially rectangular boundaries with rounded corners and / or edges), non-rectangular boundaries (e.g., circular or hexagonal boundaries), or other suitably shaped boundaries.

[0021] Figure 4C shows an exemplary scan mask graphic representation 106 associated with the region of interest of graphic representation 104 (Figure 4B), which may be generated by the scan mask logic 1004. In graphic representation 106, white areas represent regions within the field of view of the CPM that will be imaged in a later scan, and black areas represent regions within the field of view of the CPM that will not be imaged in a later scan. The white areas in graphic representation 106 correspond to the combination of regions of interest in graphic representation 104 of Figure 4B, although in some embodiments, the scan mask associated with a set of regions of interest may be larger than the combination of regions of interest (e.g., by a fixed number or a percentage of pixels). Note that graphic representations 104 and 106 in Figures 4A and 4B, respectively, are associated with the angles at which the sample was imaged to generate graphic representation 102 in Figure 4A. Graphic representations such as graphic representations 104 and 106 may be generated for each of multiple angles at which the sample is imaged.

[0022] The processing logic 1006 can generate a dataset representing a sample (e.g., sample S in Figure 1) and associated with a specific angle by processing a scan by a CPM (e.g., CPM1 in Figure 1) at a certain angle, according to a scan mask generated by the scan mask logic 1004. The CPM scan on which the dataset generated by the processing logic 1006 is based may be a higher resolution scan than the scan performed to generate the associated image on which the scan mask (generated by the scan mask logic 1004) is based. In particular, the imaging logic 1002 can cause the CPM to generate a low-resolution image of the sample at a certain angle, the scan mask logic 1004 can generate a scan mask using that low-resolution image of the sample at that angle, the imaging logic 1002 can cause the CPM to generate a high-resolution image of the sample at that angle according to the scan mask, and the processing logic 1006 can generate a dataset of the sample at that angle based on the high-resolution image. Therefore, for a particular sample and angle, the imaging logic 1002 may cause the CPM to generate two images: an initial low-resolution image used by the scan mask logic 1004 to generate a scan mask, and a later high-resolution image captured according to the scan mask, used by the processing logic 1006 to generate a dataset representing the sample at that angle. In some embodiments, the imaging logic 1002 may perform the first round of image capture by causing the CPM to generate images of the sample (e.g., low-resolution images) at several different angles, after which the scan mask logic 1004 may generate a scan mask associated with each angle based on the relevant images. Once the scan mask associated with each angle has been generated, the imaging logic 1002 may perform a second round of image capture by causing the CPM to generate images of the sample (e.g., high-resolution images) at several different angles according to the relevant scan mask. Next, the processing logic 1006 may process the images from this second round of imaging to generate a dataset associated with each angle.In some embodiments, the dataset generated by the processing logic 1006 may be considered a “high-resolution” image in itself.

[0023] In some embodiments, the processing performed by the processing logic 1006 on the output of a CPM scan of a sample according to a scan mask may include storing the scan output. In some embodiments, the processing performed by the processing logic 1006 on the output of a CPM scan according to a scan mask may include filtering the scan output. For example, the processing logic 1006 may apply a Gaussian space filter to the scan output to reduce noise. In some embodiments, the processing performed by the processing logic 1006 on the output of a CPM scan according to a scan mask may include aligning outputs associated with different angles. For example, the processing logic 1006 may use any preferred technique known in the art to compare the scan outputs associated with different angles and shift the data of each scan to better align the scan features and compensate for sample drift. The processing logic 1006 may perform any combination of these operations (e.g., storage, filtering, and aligning) in any desired order as part of processing data from a “masked” scan, and thus the processing logic 1006 may generate a dataset of samples associated with each angle.

[0024] As described above, the “initial” image of the sample used to generate a specific angle, scan mask, may be a lower-resolution dataset than the “later” dataset generated by the processing logic 1006. That is, the scanning technique used to generate the “initial” image of the sample may involve lower radiation doses and / or require shorter acquisition times than the scanning technique performed to feed data into the “later” dataset. In some embodiments, the “initial” image of the sample may be a HAADF image or other CPM image, while the scanning technique performed to feed data into the “later” dataset may include analytical acquisition techniques such as electron diffraction techniques, EDS, and / or EELS. Such analytical data may include, for example, a two-dimensional array of data, or a higher-dimensional array of data, associated with each position of the sample at a given angle. Thus, the dataset generated by the processing logic 1006 may, in some embodiments, include electron diffraction data (e.g., position-mean-converged beam electron diffraction (PACBED) data), EDS data, and / or EELS data.

[0025] In some embodiments in which electron diffraction data is included in the dataset generated by processing logic 1006, the summed diffraction patterns may be recorded at each angle as part of the acquisition of analytical data, and these summed diffraction patterns may be added for each region across all angles, resulting in an electron diffraction pattern that can be provided to reconstruction logic 1010 (considered below) to determine the crystal structure of the sample. In some embodiments in which PACBED patterns are recorded, the diffraction patterns may not be averaged but may be stored individually, and for each angle, the electric and / or magnetic fields may be determined by measuring the shift of the diffraction pattern or by identifying the shift of the centroid of the central disk, and combined with the angles, this information may be used by reconstruction logic 1010 (considered below) to determine the three-dimensional distribution of electric and / or magnetic fields in the sample. In embodiments in which PACBED patterns are recorded, the process of three-dimensional structural analysis of multiple particles using discrete or continuous gradient diffraction tomography may be accelerated compared to conventional approaches. In some embodiments, the dataset generated by processing logic 1006 may include spectral information (e.g., emission spectra generated by EDS that enable elemental analysis or other chemical characterization). In some embodiments, the dataset generated by the processing logic 1006 may include cathode ray emission (CL) data, Raman imaging data, Auger electron spectroscopy (ASE) data, backscattered electron (BSE) data, secondary electron (SE) data, color SEM data, or data acquired by any multimodal acquisition scheme that can be used to acquire information about the three-dimensional structure of a specimen (e.g., information about three-dimensional field mapping, three-dimensional electronic structure, three-dimensional surface roughness, etc.).

[0026] Figure 4D shows a graphic representation 108 of the dataset generated by the processing logic 1006 by processing data from a CPM scan of the sample at the same angle used to generate the graphic representation 102 (Figure 4A). The graphic representation 108 may be a higher-resolution representation than the graphic representation 102 and may indicate the location of a particular element or other type of particle in the sample (as a result of the analytical data generated during the analytical acquisition of the sample according to the scan mask generated by the scan mask logic 1004). In some embodiments, the graphic representation 108 may indicate the location of a single element (e.g., gold or palladium), while in other embodiments, the graphic representation 108 may use multiple colors to indicate the locations of multiple elements (e.g., different elements are shown in different colors, not shown).

[0027] As discussed above, the dataset generated by processing logic 1006 may be based on scans performed according to the scan mask generated by scan mask logic 1004. These scans may generate data for some areas (e.g., the white areas in the graphic representation of Figure 4C) and not for other areas (e.g., the black areas in the graphic representation of Figure 4C). Therefore, the dataset generated by processing logic 1006 may contain non-zero data areas (corresponding to imaged areas) and zero data areas (corresponding to unimaged areas). For example, the dataset generated by processing logic 1006 for a particular angle may contain the results of spectral quantification of a sample for that particular angle inserted into the correct position in an empty full-frame image. Some of the processing operations performed by the processing logic 1006 on the data generated from the scan may result in blurring or shifting of non-zero data areas / zero data areas relative to imaged / unimaged areas (e.g., filtering data from a scan may result in non-zero data values ​​existing in areas that were not imaged), but the boundary of the relative zero data area may generally correspond to the boundary of the area of ​​interest / imaged area, as discussed above (e.g., the boundary of the relative zero data area may be a square, another rectangle, rounded, etc.). For example, Figure 4E shows a graphic representation 110 of the dataset illustrated in the graphic representation 108 of Figure 4D, where the boundary of the non-zero data area (e.g., a rounded and / or noisy substantially rectangular boundary) is drawn as a dotted rectangle, and the area outside the non-zero data area may be the zero data area.

[0028] The output logic 1008 may provide a dataset generated by the processing logic 1006 for further processing by the reconfiguration logic 1010. In some embodiments, the output logic 1008 may provide the dataset directly to the reconfiguration logic 1010 (for example, when the output logic 1008 and the reconfiguration logic 1010 are implemented as part of a common software package and / or run on a common computing device), while in other embodiments, the output logic 1008 may provide the dataset in an intermediate form that can later be provided to the reconfiguration logic 1010. An example of this latter embodiment may include the output logic 1008 exporting the dataset to a storage device (for example, a networked storage device or a physical storage device such as a Universal Serial Bus (USB) stick) that can later be accessed by the reconfiguration logic 1010. In some embodiments, the processing logic 1006 may be contained in a separate software package from the software package containing the reconfiguration logic 1010.

[0029] The reconstruction logic 1010 may generate a three-dimensional reconstruction of the specimen using the dataset generated by the processing logic 1006. The reconstruction logic 1010 may use any suitable known technique for this reconstruction. For example, in various embodiments, the reconstruction logic 1010 may use the dataset to perform tomography reconstruction, weighted back projection (WBP), simultaneous iterative reconstruction technique (SIRT), HAADF energy-dispersive spectroscopy (EDS) bimodal tomography (HEBT) technique, conjugate gradient least squares (CGLS) technique, expectation maximization (EM) technique, contemporary numerical reconstruction technique (SART), diffraction tomography technique, or a combination thereof. In some embodiments, the reconstruction logic 1010 may generate the crystal structure of the specimen as part of the three-dimensional reconstruction. In some embodiments, the reconstruction logic 1010 may work in conjunction with other logics of the CPM reconstruction support module 1000 to fully automatically find a specific number of particles in the specimen and determine their three-dimensional structure, composition, and morphology.

[0030] Figure 3 is a flowchart of Method 2000 for performing a reconfiguration support operation in various embodiments. While the operations of Method 2000 may be illustrated with reference to specific embodiments disclosed herein (e.g., the CPM reconfiguration support module 1000 discussed herein with reference to Figure 2), Method 2000 can be used in any preferred setting and to perform any preferred reconfiguration support operation. Although the operations are illustrated once each in a specific order in Figure 3, the operations may be appropriately rearranged and / or repeated as desired (e.g., different operations to be performed may be performed in parallel as needed).

[0031] In 2002, for each of several angles, an image of the sample at that angle can be generated by CPM. For example, the imaging logic 1002 of the CPM reconstruction support module 1000 can perform the operations in 2002. The operations in 2002 may include any preferred operations among those considered above with reference to the imaging logic 1002, and / or any other preferred operations.

[0032] In 2004, for each angle, a scan mask may be generated based on the image of the sample at that angle (generated in 2002). For example, the scan mask logic 1004 of the CPM reconstruction support module 1000 may perform operation 2004. Operation 2004 may include any preferred operation among those considered above with reference to the scan mask logic 1004, and / or any other preferred operation.

[0033] In 2006, for each angle, a dataset can be generated by processing data from a scan of the sample according to the associated scan mask (e.g., analytical acquisition to generate electrical, magnetic, elemental, or chemical property information). For example, the processing logic 1006 of the CPM reconstruction support module 1000 may perform the operations in 2006. The operations in 2006 may include any preferred operations among those considered above with reference to the processing logic 1006, and / or any other preferred operations.

[0034] In 2008, for each angle, a dataset (generated in 2006) may be provided to the reconstruction logic. For example, the output logic 1008 of the CPM reconstruction support module 1000 may perform the operation in 2008. The operation in 2008 may include any preferred operation among those considered above with reference to the output logic 1008, and / or any other preferred operation.

[0035] In 2010, a three-dimensional reconstruction of the sample can be generated using the dataset (provided in 2008). For example, the reconstruction logic 1010 of the CPM reconstruction support module 1000 can perform the operation in 2010. The operation in 2010 may include any preferred operation among those considered above with reference to the reconstruction logic 1010, and / or any other preferred operation.

[0036] The CPM reconstruction assistance methods disclosed herein may include interaction with a human user (e.g., via a user-local computing device 5020, for example, as considered herein with reference to Figure 7). These interactions may include providing information to the user (e.g., information about the operation of a CPM, such as CPM 5010 in Figure 7, information about a specimen being analyzed, or information about other tests or measurements performed by the CPM, information retrieved from a local or remote database, or other information), or providing the user with options to input commands (e.g., to control the operation of a CPM, such as CPM 5010 in Figure 7, or to control the analysis of data generated by the CPM), queries (e.g., to a local or remote database), or other information. In some embodiments, these interactions may be performed via a graphical user interface (GUI) including a visual display on a display device (e.g., display device 4010, for example, as considered herein with reference to Figure 6) that provides output to the user and / or prompts the user to provide input (e.g., via one or more input devices such as a keyboard, mouse, trackpad, or touchscreen, as included in other I / O devices 4012, for example, as considered herein with reference to Figure 6). The CPM support systems disclosed herein may include any preferred GUI for user interaction.

[0037] Figure 5 shows an exemplary GUI 3000 that may be used to perform some or all of the reconfiguration assistance methods disclosed herein, according to various embodiments. As described above, the GUI 3000 may be provided on a display device (e.g., display device 4010, as discussed herein with reference to Figure 6) of a computing device (e.g., computing device 4000, as discussed herein with reference to Figure 6) of a CPM assistance system (e.g., CPM assistance system 5000, as discussed herein with reference to Figure 7), and a user may interact with the GUI 3000 using any preferred input device (e.g., any input device included in other I / O device 4012, as discussed herein with reference to Figure 6), and input techniques (e.g., cursor movement, motion capture, face recognition, gesture detection, voice recognition, button activation, etc.). Any preferred logic discussed above with reference to the CPM reconfiguration assistance module 1000 may provide instructions for causing the GUI 3000 to display various kinds of information.

[0038] GUI3000 may include a data display area 3002, a data analysis area 3004, a CPM control area 3006, and a settings area 3008. The specific number and arrangement of areas shown in Figure 5 are merely illustrative, and any number and arrangement of areas containing any desired features may be included in GUI3000.

[0039] The data display area 3002 may display data generated by a CPM (e.g., a CPM 5010 discussed herein with reference to Figure 7). For example, the data display area 3002 may display an image received by the scan mask logic 1004 (e.g., graphic representation 102 (Figure 4A)), a dataset output by the processing logic 1006 (e.g., graphic representation 108 (Figure 4D)), or a three-dimensional reconstruction output by the reconstruction logic 1010 (which may be output in video or other formats that are conductive to communication of three-dimensional structures, not shown).

[0040] The data analysis area 3004 may display the results of data analysis (e.g., the results of analyzing the data illustrated in the data display area 3002 and / or other data). For example, the data analysis area 3004 may display a graphical representation of a region of interest identified by the scan mask logic 1004 (e.g., graphic representation 104 (Figure 4B)) or a graphical representation of a scan mask generated by the scan mask logic 1004 (e.g., graphic representation 106 (Figure 4C)). In some embodiments, the data display area 3002 and the data analysis area 3004 may be combined within the GUI 3000 (e.g., to include data output from the CPM and some analysis of the data in a common graph or area).

[0041] In some embodiments, the data display area 3002 / data analysis area 3004 may display a graphic representation of the associated scan mask (generated by the scan mask logic 1004) and simultaneously display an image of the sample at a specific angle (received by the scan mask logic 1004). In some such embodiments, the graphic representation of the scan mask may be overlaid on the image of the sample. For example, the display may take the form of the graphic representation 104 in Figure 4B (where the region of interest corresponds to the region imaged in the scan mask) or the graphic representation 114 in Figure 4G (where the boundary of the scan mask region is indicated by the outline of the white area in the graphic representation 106 in Figure 4C).

[0042] In some embodiments, the data display area 3002 / data analysis area 3004 may display a graphic representation of the associated scan mask (generated by the scan mask logic 1004) and simultaneously display analysis data of the sample at a specific angle (e.g., basic characteristic data) (generated by the processing logic 1006). In some such embodiments, the graphic representation of the scan mask may be overlaid on the analysis data of the sample. For example, the display may take the form of the graphic representation 112 in Figure 4F (the boundary of the scan mask area is indicated by the outline of the white area in the graphic representation 106 in Figure 4C).

[0043] In some embodiments, the data display area 3002 / data analysis area 3004 may display the image of the sample at a specific angle (received by the scan mask logic 1004) at the same time as displaying the analysis data of the sample at a specific angle (e.g., elemental property data) (generated by the processing logic 1006). For example, the graphic representation 114 in Figure 4G may be displayed simultaneously with the graphic representation 116 in Figure 4H (where the boundary of the scan mask area is indicated by the outline of the white area in the graphic representation 108 in Figure 4D). The CPM control area 3006 may be used simultaneously to indicate to the user when a proper alignment is achieved between the image of the sample at an angle and the display of the analysis data of the sample at an angle (and therefore, three-dimensional reconstruction should be performed), or when an improper alignment is achieved between the image of the sample at an angle and the display of the analysis data of the sample at an angle (and therefore, three-dimensional reconstruction should not be performed).

[0044] In some embodiments, the data display area 3002 / data analysis area 3004 may display a graphic representation of a dataset (associated with a certain angle) generated by the processing logic 1006 simultaneously with a graphic representation of an image (associated with the same angle) received by the scan mask logic 1004. For example, the graphic representation 102 in Figure 4A may be displayed simultaneously with the graphic representation 108 in Figure 4D. In some embodiments, a graphic representation of a dataset generated by the processing logic 1006 (e.g., a graphic representation of analytical data such as elemental composition) may be overlaid on the image received by the scan mask logic 1004. For example, the graphic representation 108 in Figure 4D may be overlaid (not shown) on the graphic representation 102 in Figure 4A using a color used to distinguish graphic representation 108 from graphic representation 102. Such a display may use different colors to indicate the positions of different elements (determined by the processing logic 1006) in the image of graphic representation 102 in Figure 4A.

[0045] The CPM control region 3006 may include options that allow the user to control the CPM (e.g., CPM 5010, as discussed herein with reference to Figure 7). For example, the CPM control region 3006 may include user interface features for adjusting the scan mask generated by the scan mask logic 1004, controlling the type of scan performed by the CPM, scheduling scans, and / or other control features.

[0046] The configuration area 3008 may include options that allow the user to control the features and functions of GUI 3000 (and / or other GUIs) and / or perform common computing operations relating to the data display area 3002 and the data analysis area (3004) (e.g., saving data on a storage device such as storage device 4004 considered herein with respect to Figure 6, sending data to another user, labeling data, any of the operations considered herein with respect to output logic 1008, etc.).

[0047] As described above, the CPM reconfiguration support module 1000 can be implemented by one or more computing devices. Figure 6 is a block diagram of a computing device 4000 that can perform some or all of the CPM reconfiguration support methods disclosed herein in various embodiments. In some embodiments, the CPM reconfiguration support module 1000 can be implemented by a single computing device 4000 or multiple computing devices 4000. Furthermore, as will be discussed below, the computing device 4000 (or multiple computing devices 4000) implementing the CPM reconfiguration support module 1000 may be part of one or more of the CPM 5010, the user-local computing device 5020, the service-local computing device 5030, or the remote computing device 5040 in Figure 7.

[0048] The computing device 4000 in Figure 6 is illustrated as having several components, but one or more of these components may be omitted or duplicated as is suitable for the application and configuration. In some embodiments, some or all of the components included in the computing device 4000 may be mounted on one or more motherboards and enclosed in a housing (e.g., including plastic, metal, and / or other materials). In some embodiments, some of these components may be manufactured on a single system-on-a-chip (SoC) (e.g., the SoC may include one or more processing devices 4002 and one or more storage devices 4004). Additionally, in various embodiments, the computing device 4000 may not include one or more components illustrated in Figure 6, but may include interface circuits (not shown) for coupling one or more components using any suitable interface (e.g., a Universal Serial Bus (USB) interface, a High Definition Multimedia Interface (HDMI®) interface, a Controller Area Network (CAN) interface, a Serial Peripheral Interface (SPI) interface, an Ethernet interface, a wireless interface, or other suitable interface). For example, computing device 4000 does not have to include display device 4010, but may include display device interface circuitry (e.g., connector and driver circuitry) to which display device 4010 can be coupled.

[0049] The computing device 4000 may include processing devices 4002 (e.g., one or more processing devices). As used herein, the term “processing device” may mean any device or part of a device that processes electronic data from registers and / or memory and converts that electronic data into other electronic data that can be stored in registers and / or memory. The processing devices 4002 may include one or more digital signal processors (DSPs), application-specific integrated circuits (ASICs), central processing units (CPUs), graphics processing units (GPUs), cryptographic processors (specialized processors that execute cryptographic algorithms in hardware), server processors, or other suitable processing devices.

[0050] The computing device 4000 may include a storage device 4004 (e.g., one or more storage devices). The storage device 4004 may include one or more memory devices such as random access memory (RAM) (e.g., static RAM (SRAM) devices, magnetoresistive RAM (MRAM) devices, dynamic RAM (DRAM) devices, resistive random access 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 4004 may include memory that shares a die with the processing device 4002. 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 magneto-random access memory (STT-MRAM). In some embodiments, the storage device 4004 may include a non-temporary computer-readable medium having instructions that, when executed by one or more processing devices (e.g., processing device 4002), cause the computing device 4000 to execute any suitable one or part of the methods disclosed herein.

[0051] The computing device 4000 may include an interface device 4006 (for example, one or more interface devices 4006). The interface device 4006 may include one or more communication chips, connectors, and / or other hardware and software to manage communication between the computing device 4000 and other computing devices. For example, the interface device 4006 may include circuitry for managing wireless communication for transferring data to and from the computing device 4000. The term “wireless” and its derivatives may be used to describe circuits, devices, systems, methods, techniques, communication channels, etc., that can communicate data through the use of modulated electromagnetic radiation over a non-solid medium. This term does not mean that the devices in question do not include any wires, although this may not be the case in some embodiments. The circuitry included in interface device 4006 for managing wireless communication may implement any of several wireless standards or protocols, including, but not limited to, Institute of Electrical and Electronics Engineers (IEEE) standards, including Wi-Fi (IEEE 802.11 family), IEEE 802.16 standards (e.g., IEEE 802.16-2005 amendment), and Long-Term Evolution (LTE) projects with any modifications, updates, and / or revisions (e.g., Advanced LTE project, Ultra-Mobile Broadband (UMB) project (also known as "3GPP®2")). In some embodiments, the circuitry included in interface device 4006 for managing wireless communication may operate according to 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 the interface device 4006 for managing wireless communication may operate according to GSM Evolutionary High-Speed ​​Data (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 the interface device 4006 for managing wireless communication may operate according to Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Digital Extended Cordless Communications (DECT), Evolutionary Data Optimization (EV-DO), and their derivatives, and any other wireless protocols designated as 3G, 4G, 5G, and later. In some embodiments, the interface device 4006 may include one or more antennas (e.g., one or more antenna arrays) for receiving and / or transmitting wireless communication.

[0052] In some embodiments, the interface device 4006 may include circuitry for managing wired communications, such as electrical, optical, or any other preferred communication protocol. For example, the interface device 4006 may include circuitry for supporting communications using Ethernet technology. In some embodiments, the interface device 4006 may support both wireless and wired communications and / or support multiple wired communication protocols and / or multiple wireless communication protocols. For example, a first set of circuitry in the interface device 4006 may be dedicated to shorter-range wireless communications such as Wi-Fi or Bluetooth®, and a second set of circuitry in the interface device 4006 may be dedicated to longer-range wireless communications such as Global Positioning System (GPS), EDGE, GPRS, CDMA, WiMAX, LTE, EV-DO. In some embodiments, a first set of circuitry in the interface device 4006 may be dedicated to wireless communications, and a second set of circuitry in the interface device 4006 may be dedicated to wired communications.

[0053] The computing device 4000 may include a battery / power supply circuit 4008. The battery / power supply circuit 4008 may include a circuit for coupling one or more energy storage devices (e.g., batteries or capacitors) and / or components of the computing device 4000 to an energy source separate from the computing device 4000 (e.g., an AC line power supply).

[0054] The computing device 4000 may include a display device 4010 (e.g., multiple display devices). The display device 4010 may include any visual indicator such as a heads-up display, computer monitor, projector, touchscreen display, liquid crystal display (LCD), light-emitting diode display, or flat panel display.

[0055] The computing device 4000 may include other input / output (I / O) devices 4012. Other I / O devices 4012 may include, for example, one or more audio output devices (e.g., speakers, headsets, earphones, alarms, etc.), one or more audio input devices (e.g., microphones or microphone arrays as known in the art), location devices (e.g., GPS devices that communicate with satellite-based systems to receive the location of the computing device 4000), audio codecs, video codecs, printers, sensors (e.g., thermocouples or other temperature sensors, humidity sensors, pressure sensors), vibration sensors, accelerometers, gyroscopes, etc.), image capture devices such as cameras and keyboards, cursor control devices such as mice, styluses, trackballs, and touchpads, barcode readers, quick response (QR) code readers, or radio frequency identification (RFID) readers.

[0056] The computing device 4000 may have any form factor suitable for its application and configuration, such as a handheld or mobile computing device (e.g., a mobile phone, smartphone, mobile internet device, tablet computer, laptop computer, netbook computer, ultrabook computer, personal digital assistant (PDA), ultramobile personal computer, etc.), a desktop computing device, or a server computing device or other networked computing component.

[0057] One or more computing devices implementing any of the CPM reconfiguration support modules or methods disclosed herein may be part of a CPM support system. Figure 7 is a block diagram of an exemplary CPM support system 5000 in which some or all of the CPM reconfiguration support methods disclosed herein may be performed according to various embodiments. The CPM reconfiguration support modules and methods disclosed herein (e.g., CPM reconfiguration support module 1000 in Figure 2 and method 2000 in Figure 3) may be implemented by one or more CPMs 5010, user-local computing devices 5020, service-local computing devices 5030, or remote computing devices 5040 of the CPM support system 5000.

[0058] Any of the CPM5010, user-local computing device 5020, service-local computing device 5030, or remote computing device 5040 may include any embodiment of the computing device 4000 considered herein with reference to Figure 6, and any of the CPM5010, user-local computing device 5020, service-local computing device 5030, or remote computing device 5040 may take any suitable form of any embodiment of the computing device 4000 considered herein with reference to Figure 6.

[0059] The CPM5010, user-local computing device 5020, service-local computing device 5030, or remote computing device 5040 may each include a processing device 5002, a storage device 5004, and an interface device 5006. The processing device 5002 may take any preferred form, including any form of the processing device 4002 considered herein with reference to Figure 4, and the processing device 5002 included in different versions of the CPM5010, user-local computing device 5020, service-local computing device 5030, or remote computing device 5040 may take the same or different forms. The storage device 5004 may take any preferred form, including any form of the storage device 5004 considered herein with reference to Figure 4, and the storage device 5004 included in different versions of the CPM5010, user-local computing device 5020, service-local computing device 5030, or remote computing device 5040 may take the same or different forms. Interface device 5006 may take any preferred form, including any form of interface device 4006 considered herein with reference to Figure 4, and interface device 5006 included in different CPM 5010, user local computing device 5020, service local computing device 5030, or remote computing device 5040 may take the same or different forms.

[0060] CPM 5010 may include any of the CPMs considered herein (e.g., CPM 1 in Figure 1). CPM 5010, user local computing device 5020, service local computing device 5030, and remote computing device 5040 may communicate with other elements of the CPM support system 5000 via a communication path 5008. The communication path 5008 may communicatively couple interface devices 5006 of different elements of the CPM support system 5000, as shown, and may be a wired or wireless communication path (e.g., according to any of the communication techniques considered herein with reference to interface device 4006 of computing device 4000 in Figure 6). The particular CPM support system 5000 shown in Figure 7 includes communication paths between each pair of CPM 5010, user local computing device 5020, service local computing device 5030, and remote computing device 5040, but this “fully connected” implementation is merely illustrative, and various versions of the communication path 5008 may not exist in various embodiments. For example, in some embodiments, the service local computing device 5030 may not have a direct communication path 5008 between its interface device 5006 and the interface device 5006 of the CPM 5010. Instead, it may communicate with the CPM 5010 via the communication path 5008 between the service local computing device 5030 and the user local computing device 5020, and the communication path 5008 between the user local computing device 5020 and the CPM 5010.

[0061] The user-local computing device 5020 may be a computing device that is local to the user of the CPM 5010 (for example, according to any embodiment of the computing device 4000 considered herein). In some embodiments, the user-local computing device 5020 may also be, but is not required to be, local to the CPM 5010. For example, the user-local computing device 5020 located at the user's home or office is remote from the CPM 5010 but can communicate with the CPM 5010, and as a result, the user can use the user-local computing device 5020 to control and / or access data from the CPM 5010. In some embodiments, the user-local computing device 5020 may be a laptop, smartphone, or tablet device. In some embodiments, the user-local computing device 5020 may be a portable computing device.

[0062] The service local computing device 5030 may be a computing device that is local to an entity servicing the CPM 5010 (for example, according to any embodiment of the computing device 4000 considered herein). For example, the service local computing device 5030 may be local to the manufacturer of the CPM 5010 or a third-party service company. In some embodiments, the service local computing device 5030 may communicate with the CPM 5010, the user local computing device 5020, and / or the remote computing device 5040 (for example, via a direct communication path 5008 or via multiple “indirect” communication paths 5008, as considered above) and receive data regarding the operation of the CPM 5010, the user local computing device 5020, and / or the remote computing device 5040 (for example, the results of a self-test of the CPM 5010, the calibration coefficients used by the CPM 5010, the measurements of sensors associated with the CPM 5010, etc.). In some embodiments, the service local computing device 5030 may communicate with the CPM 5010, the user local computing device 5020, and / or the remote computing device 5040 (for example, via a direct communication path 5008 or via multiple “indirect” communication paths 5008, as considered above), and transmit data to the CPM 5010, the user local computing device 5020, and / or the remote computing device 5040 (for example, updating programmed instructions such as firmware in the CPM 5010, initiating the execution of a test or calibration sequence in the CPM 5010, updating programmed instructions such as software in the user's local computing device 5020 or the remote computing device 5040, etc.).Users of the CPM5010 may utilize the CPM5010 or the user local computing device 5020 for purposes such as communicating with the service local computing device 5030, reporting problems with the CPM5010 or user local computing device 5020, requesting a technician visit to improve the operation of the CPM5010, ordering consumables or replacement parts associated with the CPM5010, or other purposes.

[0063] The remote computing device 5040 may be a computing device located away from the CPM 5010 and / or the user local computing device 5020 (for example, according to any embodiment of the computing device 4000 considered herein). In some embodiments, the remote computing device 5040 may be located in a data center or other large-scale server environment. In some embodiments, the remote computing device 5040 may include network-attached storage (for example, as part of storage device 5004). The remote computing device 5040 may store data generated by the CPM 5010, perform analysis of the data generated by the CPM 5010 (for example, according to programmed instructions), facilitate communication between the user local computing device 5020 and the CPM 5010, and / or facilitate communication between the service local computing device 5030 and the CPM 5010.

[0064] In some embodiments, one or more elements of the CPM support system 5000 illustrated in Figure 7 may be absent. Furthermore, in some embodiments, multiple of the various elements of the CPM support system 5000 in Figure 7 may be present. For example, the CPM support system 5000 may include multiple user local computing devices 5020 (e.g., different user local computing devices 5020 associated with different users or in different locations). In another example, the CPM support system 5000 may include multiple CPMs 5010 all communicating with a service local computing device 5030 and / or a remote computing device 5040. In such embodiments, the service local computing device 5030 may monitor these multiple CPMs 5010, and the service local computing device 5030 may trigger updates or "broadcast" other information to the multiple CPMs 5010 simultaneously. Different CPMs 5010 within the CPM support system 5000 may be located close to each other (e.g., in the same room) or far apart from each other (e.g., on different floors of a building, in different buildings, in different cities, etc.). In some embodiments, the CPM 5010 may be connected to an Internet-of-Things (IoT) stack that enables command and control of the CPM 5010 via web-based applications, virtual or augmented reality applications, mobile applications, and mobile / or desktop applications. Any of these applications may be accessed by a user who communicates with the CPM 5010 and operates a user-local computing device 5020 via an intervening remote computing device 5040. In some embodiments, the CPM 5010 may be sold by the manufacturer as part of a local CPM computing unit 5012, together with one or more associated user-local computing devices 5020.

[0065] The following paragraphs provide various examples of embodiments disclosed herein.

[0066] Example 1 is a charged particle microscope computation support device that includes: a first logic for receiving associated images of a specimen at each of a plurality of angles and generating associated scan masks based on one or more regions of interest in the associated images; a second logic for generating associated datasets of a specimen by processing data from scans of the specimen at each of a plurality of angles according to the associated scan masks using a charged particle microscope; and a third logic for generating a three-dimensional reconstruction of a specimen by providing the associated datasets of the specimen to a reconstruction logic for each of a plurality of angles.

[0067] Example 2 includes the subject matter of Example 1 and further specifies that the multiple angles are multiple inclination angles.

[0068] Example 3 includes the subject matter of any of Examples 1 or 2, and further includes reconstruction logic.

[0069] Example 4 includes the subject of any of Examples 1 to 3, and further specifies that for at least one of the multiple angles, the relevant dataset of the sample includes at least one zero-data region.

[0070] Example 5 includes the subject of any of Examples 1 to 4, and further specifies that for at least one of the multiple angles, the relevant dataset of the sample includes at least one non-zero data region.

[0071] Example 6 includes the subject matter of any of Examples 1 to 5, and further specifies that at least one non-zero data region has a rounded rectangular border.

[0072] Example 7 includes the subject matter of any of Examples 1 to 6, and further specifies that at least one non-zero data region has a rectangular boundary.

[0073] Example 8 includes the subject matter of any of Examples 1 to 7, and further specifies that at least one non-zero data region has a square boundary.

[0074] Example 9 includes the subject of any of Examples 1 to 8 and further specifies that, for at least one of a plurality of angles, the relevant dataset of the sample includes a first non-zero data region corresponding to a first part of the sample, a second non-zero data region corresponding to a second part of the sample, and a zero data region between the first non-zero data region and the second non-zero data region.

[0075] Example 10 includes the subject matter of any of Examples 1 to 9 and further specifies that the reconstruction logic is to perform tomography reconstruction, weighted back projection (WBP), simultaneous iterative reconstruction technique (SIRT), high-angle annular dark-field imaging (HAADF)-energy-dispersive spectroscopy (EDS) bimodal tomography (HEBT) technique, conjugate gradient least squares (CGLS) technique, expectation maximization (EM) technique, contemporary numerical reconstruction technique (SART), or diffraction tomography technique using a sample dataset.

[0076] Example 11 includes a subject from any of Examples 1 to 10, and further specifies that the sample dataset includes energy-dispersive spectroscopy (EDS) data.

[0077] Example 12 includes the subject matter of any of Examples 1 to 11, and further specifies that the sample dataset includes electron energy loss spectroscopy (EELS) data.

[0078] Example 13 includes the subject matter of any of Examples 1 to 12, and further specifies that the sample dataset includes diffraction pattern data.

[0079] Example 14 includes the subject matter of Example 13 and further specifies that the three-dimensional reconstruction of the specimen includes the crystal structure of the specimen.

[0080] Example 15 includes a subject from any of Examples 1 to 14, and further specifies that the sample dataset includes high-resolution images.

[0081] Example 16 includes a subject from any of Examples 1 to 15, and further specifies that the sample dataset includes spectral information.

[0082] Example 17 includes the subject matter of any of Examples 1 to 16 and further includes a fourth logic that causes a charged particle microscope to generate associated images of the specimen at each of multiple angles.

[0083] Example 18 includes the subject matter of Example 17 and further specifies that, for each of the multiple angles, the acquisition time for generating the associated image of the sample at each angle is less than the acquisition time for generating the associated dataset of the sample at each angle, according to the associated scan mask.

[0084] Example 19 includes the subject matter of any of Examples 17-18 and further specifies that, for each of the multiple angles, the radiation dose for generating the associated dataset of the sample at each angle according to the associated scan mask is less than the radiation dose for generating the pale frame dataset of the sample at each angle, or, for each of the multiple angles, the resolution of the associated dataset of the sample at each angle according to the associated scan mask is less than the resolution of the pale frame dataset of the sample at each angle.

[0085] Example 20 includes a subject from any of Examples 17-19 and further specifies that the images of the specimen include bright-field images, annular bright-field (ABF) images, integrated differential phase-contrast (iDPC) images, or high-angle annular dark-field (HAADF) images.

[0086] Example 21 includes the subject matter of any of Examples 1 to 20, and further specifies that for each of the multiple angles, the associated scan mask indicates that it scans one or more regions of interest and not scans at least one other region.

[0087] Example 22 includes the subject matter of Example 21 and further specifies that the first logic includes identifying one or more regions of interest in an associated image for each of a plurality of angles, and identifying one or more regions of interest in an associated image includes identifying at least one particle in an associated image.

[0088] Example 23 includes the subject matter of any of Examples 21 to 22 and further specifies that the first logic identifies one or more regions of interest in the associated images for each of multiple angles, and that identifying one or more regions of interest in the associated images includes applying centroid discovery techniques, thresholding techniques, attention-based transformer network techniques, or neural network techniques to the associated images.

[0089] Example 24 includes the subject of any of Examples 1 to 23, and further specifies that the associated image of the specimen is a low-resolution image.

[0090] Example 25 includes the subject matter of Example 24, and further specifies that the dataset associated with the sample consists of high-resolution images.

[0091] Example 26 includes the subject matter of any of Examples 1 to 25, and further specifies that the first logic is for displaying a graphic representation of the scan mask associated with the display device for each of the multiple angles.

[0092] Example 27 includes the subject matter of Example 26 and further specifies that the first logic is for providing a command to a display device to display an image of the sample at each angle, simultaneously with displaying a graphic representation of the associated scan mask, for each individual angle of a plurality of angles.

[0093] Example 28 includes the subject matter of Example 27, and further specifies that the first logic is to cause the display device to display an image of the specimen at each angle, overlaid with a graphic representation of the associated scan mask, for each individual angle of a plurality of angles.

[0094] Example 29 includes the subject matter of any of Examples 26-28, and further specifies that the associated scan mask graphic representation includes one or more shapes indicating the boundaries of the area being scanned.

[0095] Example 30 includes the subject matter of any of Examples 26-29, and further specifies that the first logic causes the display device to display the analysis data of the sample at each angle, simultaneously with the display of the associated graphic representation of the scan mask, for each of the multiple angles.

[0096] Example 31 includes the subject matter of Example 30 and further specifies that the first logic causes the display device to display, for each of the multiple angles, (1) an image of the specimen at each angle overlaid with a first graphic representation of the associated scan mask, and (2) analysis data of the specimen at each angle overlaid with a second graphic representation of the associated scan mask.

[0097] Example 32 includes the subject matter of any of Examples 1 to 31, and further specifies that for each of the multiple angles, the associated dataset includes the identification of one or more chemical elements present in the sample at the associated angle.

[0098] Example 33 includes the subject matter of Example 32 and further specifies that the second logic is to cause the display device to display a graphic representation of the identification of one or more chemical elements in the sample at each angle for each of the multiple angles.

[0099] Example 34 includes the subject matter of Example 33, and further specifies that the second logic causes the display device to display a graphic representation of the identification of one or more chemical elements in the specimen at each angle, overlaid on the image of the specimen at each angle, for each of the multiple angles.

[0100] Example 35 includes the subject matter of Example 34, and further specifies that the sample image is a low-resolution image.

[0101] Example 36 includes a subject from any of Examples 34-35 and further specifies that the image of the specimen is a bright-field image, annular bright-field (ABF) image, integrated differential phase-contrast (iDPC) image, or high-angle annular dark-field (HAADF) image.

[0102] Example 37 includes the subject of any of Examples 1 to 36, and further specifies that for each of the multiple angles, the associated dataset includes a two-dimensional array associated with each position of the sample at that angle.

[0103] Example 38 includes the subject matter of any of Examples 1 to 37 and further specifies that the charged particle microscope includes a scanning electron microscope (SEM), a transmission electron microscope (TEM), a scanning transmission electron microscope (STEM), or a cryo-electron microscope (cryoEM).

[0104] Example 39 includes the subject matter of any of Examples 1 to 38, and further specifies that the second logic is for generating a related dataset of a sample by processing the electron diffraction data of the sample at the angle generated by a charged particle microscope according to an associated scan mask for each of the multiple angles.

[0105] Example 40 includes the subject matter of Example 39, and further specifies that the electron diffraction data includes position-averaged focused beam electron diffraction (PACBED) data.

[0106] Example 41 includes the subject matter of any of Examples 1 to 40 and further specifies that for each of the multiple angles, the associated dataset includes cathode ray emission (CL) data, Raman imaging data, Auger electron spectroscopy (ASE) data, backscattered electron (BSE) data, or secondary electron (SE) data.

[0107] Example 42 includes the subject matter of any of Examples 1 to 41 and further includes a charged particle microscope.

[0108] Example 43 includes the subject matter of any of Examples 1 to 42 and further specifies that processing the data from the scan according to the associated scan mask by an angle-based charged particle microscope of the specimen includes storing the data from the scan according to the associated scan mask by an angle-based charged particle microscope of the specimen.

[0109] Example 44 includes the subject matter of any of Examples 1 to 43 and further specifies that processing the data from the scan according to the associated scan mask by an angle-based charged particle microscope of the specimen includes filtering the data from the scan according to the associated scan mask by an angle-based charged particle microscope of the specimen.

[0110] Example 45 includes the subject matter of any of Examples 1 to 44 and further specifies that processing the data from the scan by a charged particle microscope of the specimen at an angle, according to the associated scan mask, includes aligning the data associated with a first angle to the data associated with a second angle.

[0111] Example 46 includes the subject matter of any of Examples 1 to 45 and further specifies that it includes processing the data from the scan according to the associated scan mask by a charged particle microscope of the specimen at an angle, and performing elemental analysis on the data from the scan.

[0112] Example 47 is a charged particle microscope computation support device that includes: first logic for generating associated scan masks for scanning a specimen for each angle of a plurality of angles, wherein the scan mask associated with the first angle of the plurality of angles is different from the scan mask associated with the second angle of a plurality of angles that is different from the first angle; and second logic for generating associated datasets of a specimen for each angle of the plurality of angles, based on a charged particle microscope scan of the specimen at that angle according to the associated scan mask.

[0113] Example 48 includes the subject matter of Example 47 and further includes:

[0114] A third logic for generating a three-dimensional reconstruction of a sample, which provides the associated dataset of the sample to the reconstruction logic for each of the multiple angles.

[0115] Example 49 includes the subject matter of Example 48 and further includes reconstruction logic.

[0116] Example 50 includes the subject of any of Examples 48-49 and further specifies that for at least one of the multiple angles, the relevant dataset of the sample includes at least one zero-data region.

[0117] Example 51 includes a subject from any of Examples 48-50 and further specifies that for at least one of the multiple angles, the relevant dataset of the sample includes at least one non-zero data region.

[0118] Example 52 includes the subject matter of any of Examples 48 to 51, and further specifies that at least one non-zero data region has a rounded rectangular border.

[0119] Example 53 includes the subject matter of any of Examples 48 to 52, and further specifies that at least one non-zero data region has a rectangular boundary.

[0120] Example 54 includes the subject matter of any of Examples 48 to 53, and further specifies that at least one non-zero data region has a square boundary.

[0121] Example 55 includes the subject of any of Examples 48 to 54 and further specifies that, for at least one of a plurality of angles, the relevant dataset of the sample includes a first non-zero data region corresponding to a first part of the sample, a second non-zero data region corresponding to a second part of the sample, and a zero data region between the first non-zero data region and the second non-zero data region.

[0122] Example 56 includes the subject matter of any of Examples 48 to 55, and further specifies that the sample dataset includes electron diffraction data and the three-dimensional reconstruction of the sample includes the crystal structure of the sample.

[0123] Example 57 includes the subject matter of any of Examples 48-56 and further specifies that the reconstruction logic is to perform tomography reconstruction, weighted back projection (WBP), simultaneous iterative reconstruction technique (SIRT), high-angle annular dark-field imaging (HAADF)-energy-dispersive spectroscopy (EDS) bimodal tomography (HEBT) technique, conjugate gradient least squares (CGLS) technique, expectation maximization (EM) technique, contemporary numerical reconstruction technique (SART), or diffraction tomography technique using a sample dataset.

[0124] Example 58 includes the subject matter of any of Examples 47-57, and further specifies that the sample dataset includes energy-dispersive spectroscopy (EDS) data.

[0125] Example 59 includes the subject matter of any of Examples 47-58, and further specifies that the sample dataset includes electron energy loss spectroscopy (EELS) data.

[0126] Example 60 includes a subject from any of Examples 47-59, and further specifies that the sample dataset includes diffraction pattern data.

[0127] Example 61 includes a subject from any of Examples 47-60, and further specifies that the sample dataset includes high-resolution images.

[0128] Example 62 includes a subject from any of Examples 47-61, and further specifies that the sample dataset includes spectral information.

[0129] Example 63 includes the subject matter of any of Examples 47-62, and further specifies that the first logic is to receive associated images of the sample at each of the multiple angles and generate associated scan masks based on the associated images.

[0130] Example 64 includes the subject matter of Example 63 and further includes a fourth logic that causes a charged particle microscope to generate associated images of the specimen at each of a plurality of angles.

[0131] Example 65 includes the subject matter of Example 64 and further specifies that, for each of the multiple angles, the acquisition time for generating associated images of the sample at each angle is less than the acquisition time for generating associated datasets of the sample at each angle, according to the associated scan mask, or, for each of the multiple angles, the resolution of the associated datasets of the sample at each angle, according to the associated scan mask, is less than the resolution of the full-frame dataset of the sample at each angle.

[0132] Example 66 includes the subject matter of any of Examples 64-65 and further specifies that, for each of the multiple angles, the radiation dose for generating the associated dataset of the sample at each angle, according to the associated scan mask, is less than the radiation dose for generating the full-frame dataset of the sample at each angle.

[0133] Example 67 includes a subject from any of Examples 64 to 66, and further specifies that the images of the specimen include bright-field images, annular bright-field (ABF) images, integrated differential phase-contrast (iDPC) images, or high-angle annular dark-field (HAADF) images.

[0134] Example 68 includes the subject matter of any of Examples 63 to 67, and further specifies that the first logic is for identifying at least one region of interest and at least one other region in the associated image for each angle of a plurality of angles, and the associated scan mask indicates that the identified at least one region of interest is scanned and at least one other region is not scanned.

[0135] Example 69 includes the subject matter of Example 68 and further specifies that the identification of at least one region of interest in the associated image includes the identification of at least one particle in the associated image.

[0136] Example 70 includes the subject matter of any of Examples 68-69 and further specifies that the identification of at least one region of interest in the associated images includes the application of a centroid-finding technique, thresholding technique, attention-based transformer network technique, or neural network technique to the associated images.

[0137] Example 71 includes a subject from any of Examples 63-70, and further specifies that the associated image of the specimen is a low-resolution image.

[0138] Example 72 includes the subject matter of Example 71, and further specifies that the dataset associated with the specimen is a high-resolution image.

[0139] Example 73 includes the subject matter of any of Examples 47-72 and further specifies that the first logic is for displaying a graphic representation of the scan mask associated with the display device for each of the multiple angles.

[0140] Example 74 includes the subject matter of Example 73 and further specifies that the first logic causes the display device to display an image of the sample at each angle, simultaneously with displaying a graphic representation of the associated scan mask, for each of the multiple angles.

[0141] Example 75 includes the subject matter of Example 74, and further specifies that the first logic is to cause the display device to display an image of the specimen at each angle, overlaid with a graphic representation of the associated scan mask, for each individual angle of a plurality of angles.

[0142] Example 76 includes the subject matter of any of Examples 73-75, and further specifies that the associated scan mask graphic representation includes one or more shapes indicating the boundaries of the area being scanned.

[0143] Example 77 includes the subject matter of any of Examples 73 to 76, and further specifies that the first logic causes the display device to display, for each of the multiple angles, the analytical data of the sample at each angle, simultaneously with the display of the associated graphic representation of the scan mask.

[0144] Example 78 includes any subject matter from Example 77 and further specifies that the first logic causes the display device to display, for each of the multiple angles, (1) an image of the specimen at each angle overlaid with a first graphic representation of the associated scan mask, and (2) analysis data of the specimen at each angle overlaid with a second graphic representation of the associated scan mask.

[0145] Example 79 includes the subject matter of any of Examples 47-78 and further specifies that for each of the multiple angles, the associated dataset includes the identification of one or more chemical elements present in the sample at the associated angle.

[0146] Example 80 includes the subject matter of Example 79, and further specifies that the second logic causes the display device to display a graphic representation of the identification of one or more chemical elements at the associated angle for each of the multiple angles.

[0147] Example 81 includes the subject matter of Example 80, and further specifies that the second logic causes the display device to display, for each of the multiple angles, a graphic representation of the identification of one or more chemical elements at the associated angle, which is overlaid on an image of the specimen at the associated angle.

[0148] Example 82 includes the subject matter of Example 81, and further specifies that the sample image is a low-resolution image.

[0149] Example 83 includes the subject of any of Examples 81-82 and further specifies that the image of the specimen is a bright-field image, annular bright-field (ABF) image, integrated differential phase-contrast (iDPC) image, or high-angle annular dark-field (HAADF) image.

[0150] Example 84 includes the subject of any of Examples 47-83 and further specifies that for each of the multiple angles, the associated dataset includes a two-dimensional array associated with each position of the sample at that angle.

[0151] Example 85 includes the subject matter of any of Examples 47 to 84 and further specifies that the charged particle microscope includes a scanning electron microscope (SEM), a transmission electron microscope (TEM), a scanning transmission electron microscope (STEM), or a cryo-electron microscope (cryoEM).

[0152] Example 86 includes the subject matter of any of Examples 47-85 and further specifies that the second logic is for generating a related dataset of the sample based on electron diffraction data of the sample at the angle generated by a charged particle microscope according to an associated scan mask, for each of the multiple angles.

[0153] Example 87 includes the subject matter of Example 86, and further specifies that the electron diffraction data includes position-averaged focused beam electron diffraction (PACBED) data.

[0154] Example 88 includes the subject matter of any of Examples 47 to 87 and further specifies that for each of the multiple angles, the associated dataset includes cathode ray emission (CL) data, Raman imaging data, Auger electron spectroscopy (ASE) data, backscattered electron (BSE) data, or secondary electron (SE) data.

[0155] Example 89 includes the subject matter of any of Examples 47 to 88 and further includes a charged particle microscope.

[0156] Example 90 includes the subject matter of any of Examples 47 to 89 and further specifies that processing a charged particle microscope scan of a specimen at a certain angle according to an associated scan mask includes storing the scan data generated by the charged particle microscope of the specimen at that angle according to the associated scan mask.

[0157] Example 91 includes the subject matter of any of Examples 47 to 90 and further specifies that processing a charged particle microscope scan of a specimen at a certain angle according to an associated scan mask includes filtering the scan data generated by the charged particle microscope of the specimen at that angle according to the associated scan mask.

[0158] Example 92 includes the subject matter of any of Examples 47 to 91 and further specifies that processing a charged particle microscope scan of a specimen at an angle according to an associated scan mask includes aligning the scan data associated with a first angle with the scan data associated with a second angle.

[0159] Example 93 includes the subject matter of any of Examples 47-92 and further specifies that processing a charged particle microscope scan of the specimen at that angle according to an associated scan mask includes performing elemental analysis on the scan data.

[0160] Example 94 includes the subject matter of any of Examples 47 to 93, and further specifies that the multiple angles are multiple inclination angles.

[0161] Example 95 is a charged particle microscope computation support device that includes: a first logic for generating associated scanning masks for scanning a specimen for each of a plurality of angles; a second logic for causing a charged particle microscope to scan the specimen at each of a plurality of angles according to the associated scanning mask and generate associated datasets of the specimen based on the scans of the specimen at each of a plurality of angles; and a third logic for providing the associated datasets of the specimen to a reconstruction logic for each of a plurality of angles to generate a three-dimensional reconstruction of the specimen.

[0162] Example 96 includes the subject matter of Example 95 and further specifies that the multiple angles are multiple inclination angles.

[0163] Example 97 includes the subject matter of any of Examples 95-96 and further includes reconstruction logic.

[0164] Example 98 includes the subject of any of Examples 95-97 and further specifies that for at least one of the multiple angles, the relevant dataset of the sample includes at least one zero data region.

[0165] Example 99 includes a subject from any of Examples 95-98 and further specifies that for at least one of the multiple angles, the relevant dataset of the sample includes at least one non-zero data region.

[0166] Example 100 includes the subject matter of any of Examples 95 to 99, and further specifies that at least one non-zero data region has a rounded rectangular border.

[0167] Example 101 includes the subject matter of any of Examples 95 to 100, and further specifies that at least one non-zero data region has a rectangular boundary.

[0168] Example 102 includes the subject matter of any of Examples 95 to 101, and further specifies that at least one non-zero data region has a square boundary.

[0169] Example 103 includes the subject of any of Examples 95 to 102 and further specifies that for at least one of a plurality of angles, the relevant dataset of the sample includes a first non-zero data region corresponding to a first part of the sample, a second non-zero data region corresponding to a second part of the sample, and a zero data region between the first non-zero data region and the second non-zero data region.

[0170] Example 104 includes the subject matter of any of Examples 95-103 and further specifies that the reconstruction logic is to perform tomography reconstruction, weighted back projection (WBP), simultaneous iterative reconstruction technique (SIRT), high-angle annular dark-field imaging (HAADF)-energy-dispersive spectroscopy (EDS) bimodal tomography (HEBT) technique, conjugate gradient least squares (CGLS) technique, expectation maximization (EM) technique, contemporary numerical reconstruction technique (SART), or diffraction tomography technique using a sample dataset.

[0171] Example 105 includes a subject from any of Examples 95-104 and further specifies that the sample dataset includes energy-dispersive spectroscopy (EDS) data.

[0172] Example 106 includes the subject matter of any of Examples 95-105, and further specifies that the sample dataset includes electron energy loss spectroscopy (EELS) data.

[0173] Example 107 includes the subject matter of any of Examples 95-106, and further specifies that the sample dataset includes diffraction pattern data.

[0174] Example 108 includes the subject matter of Example 107 and further specifies that the three-dimensional reconstruction of the specimen includes the crystal structure of the specimen.

[0175] Example 109 includes a subject from any of Examples 95-108, and further specifies that the sample dataset includes high-resolution images.

[0176] Example 110 includes a subject from any of Examples 95-109, and further specifies that the sample dataset includes spectral information.

[0177] Example 111 includes the subject matter of any of Examples 95 to 110, and further specifies that the first logic is to receive associated images of the sample at each of a plurality of angles and generate associated scan masks based on the associated images.

[0178] Example 112 includes the subject matter of Example 111 and further includes a fourth logic that causes a charged particle microscope to generate associated images of the specimen at each of a plurality of angles.

[0179] Example 113 includes the subject matter of Example 112, and further, for each of the multiple angles, the acquisition time for generating associated images of the sample at each angle is greater than the acquisition time for generating associated datasets of the sample at each angle, according to the associated scan mask. Specify that for short or multiple angles, the resolution of the associated dataset of the sample at each angle, according to the associated scan mask, is lower than the resolution of the full-frame dataset of the sample at each angle.

[0180] Example 114 includes the subject matter of any of Examples 112-113 and further specifies that, for each of the multiple angles, the radiation dose for generating the associated dataset of the sample at each angle, according to the associated scan mask, is less than the radiation dose for generating the full-frame dataset of the sample at each angle.

[0181] Example 115 includes a subject from any of Examples 112 to 114, and further specifies that the images of the specimen include bright-field images, annular bright-field (ABF) images, integrated differential phase-contrast (iDPC) images, or high-angle annular dark-field (HAADF) images.

[0182] Example 116 includes the subject matter of any of Examples 111 to 115, and further specifies that the first logic is for identifying at least one region of interest and at least one other region in the associated image for each angle of a plurality of angles, and the associated scan mask indicates that the identified at least one region of interest is scanned and at least one other region is not scanned.

[0183] Example 117 includes the subject matter of Example 116 and further specifies that the identification of at least one region of interest in the associated image includes the identification of at least one particle in the associated image.

[0184] Example 118 includes the subject matter of any of Examples 116-117 and further specifies that the identification of at least one region of interest in the associated image includes the application of a centroid-finding technique, thresholding technique, attention-based transformer network technique, or neural network technique to the associated image.

[0185] Example 119 includes the subject of any of Examples 111 to 118, and further specifies that the associated image of the specimen is a low-resolution image.

[0186] Example 120 includes the subject matter of Example 119, and further specifies that the dataset associated with the sample is a high-resolution image.

[0187] Example 121 includes the subject matter of any of Examples 95-120 and further specifies that the first logic is for displaying a graphic representation of the scan mask associated with the display device for each of the multiple angles.

[0188] Example 122 includes the subject matter of Example 121 and further specifies that the first logic causes the display device to display an image of the sample at each angle, simultaneously with displaying a graphic representation of the associated scan mask, for each individual angle of a plurality of angles.

[0189] Example 123 includes the subject matter of Example 122, and further specifies that the first logic is to cause the display device to display an image of the specimen at each angle, overlaid with a graphic representation of the associated scan mask, for each individual angle of a plurality of angles.

[0190] Example 124 includes the subject matter of any of Examples 121 to 123, and further specifies that the associated scan mask graphic representation includes one or more shapes indicating the boundaries of the area being scanned.

[0191] Example 125 includes the subject matter of any of Examples 121 to 124, and further specifies that the first logic causes the display device to display the analysis data of the sample at each angle, simultaneously with the display of the associated graphic representation of the scan mask, for each of the multiple angles.

[0192] Example 126 includes the subject matter of Example 125 and further specifies that the first logic causes the display device to display, for each of the multiple angles, (1) an image of the specimen at each angle overlaid with a first graphic representation of the associated scan mask, and (2) analysis data of the specimen at each angle overlaid with a second graphic representation of the associated scan mask.

[0193] Example 127 includes the subject matter of any of Examples 95-126 and further specifies that for each of the multiple angles, the associated dataset includes the identification of one or more chemical elements present in the sample at the associated angle.

[0194] Example 128 includes the subject matter of Example 127, and further specifies that the second logic causes the display device to display a graphic representation of the identification of one or more chemical elements at the associated angle for each of the multiple angles.

[0195] Example 129 includes the subject matter of Example 128, and further specifies that for each of the multiple angles, the second logic causes the display device to display a graphic representation of the identification of one or more chemical elements present in the specimen at the associated angle, which is overlaid on the image of the specimen at the associated angle.

[0196] Example 130 includes the subject matter of Example 129, and further specifies that the sample images are low-resolution images.

[0197] Example 131 includes a subject from any of Examples 129-130 and further specifies that the image of the specimen is a bright-field image, annular bright-field (ABF) image, integrated differential phase-contrast (iDPC) image, or high-angle annular dark-field (HAADF) image.

[0198] Example 132 includes the subject of any of Examples 95 to 131, and further specifies that for each of the multiple angles, the associated dataset includes a two-dimensional array associated with each position of the sample at that angle.

[0199] Example 133 includes the subject matter of any of Examples 95 to 132 and further specifies that the charged particle microscope includes a scanning electron microscope (SEM), a transmission electron microscope (TEM), a scanning transmission electron microscope (STEM), or a cryo-electron microscope (cryoEM).

[0200] Example 134 includes the subject matter of any of Examples 95-133 and further specifies that the second logic is for generating a related dataset of a sample by processing the electron diffraction data of the sample at the angle generated by a charged particle microscope according to an associated scan mask for each of the multiple angles.

[0201] Example 135 includes the subject matter of Example 134, and further specifies that the electron diffraction data includes position-averaged focused beam electron diffraction (PACBED) data.

[0202] Example 136 includes the subject matter of any of Examples 95 to 135 and further specifies that for each of the multiple angles, the associated dataset includes cathode ray emission (CL) data, Raman imaging data, Auger electron spectroscopy (ASE) data, backscattered electron (BSE) data, or secondary electron (SE) data.

[0203] Example 137 includes the subject matter of any of Examples 95 to 136 and further includes a charged particle microscope.

[0204] Example 138 includes the subject matter of any of Examples 95 to 137 and further specifies that processing the data from the scan according to the associated scan mask by an angle-based charged particle microscope of the specimen includes storing the data from the scan according to the associated scan mask by an angle-based charged particle microscope of the specimen.

[0205] Example 139 includes the subject matter of any of Examples 95 to 138 and further specifies that processing the data from the scan according to the associated scan mask by an angle-based charged particle microscope of the specimen includes filtering the data from the scan according to the associated scan mask by an angle-based charged particle microscope of the specimen.

[0206] Example 140 includes the subject matter of any of Examples 95 to 139 and further specifies that processing the data from the scan by a charged particle microscope of the specimen at an angle, according to the associated scan mask, includes aligning the data associated with a first angle to the data associated with a second angle.

[0207] Example 141 includes the subject matter of any of Examples 95-140 and further specifies that it includes processing the data from the scan according to the associated scan mask by charged particle microscopy of the specimen at an angle, and performing elemental analysis on the data from the scan.

[0208] Example 142 includes the subject matter of any of Examples 95 to 141, and further specifies that the scan mask associated with a first angle of multiple angles is different from the scan mask associated with a second angle of multiple angles that are different from the first angle.

[0209] Example 143 is a method that includes any of the operations performed by any of the charged particle microscope computation support systems of Examples 1 to 142.

[0210] Example 144 is one or more non-temporary computer-readable media having instructions that, when executed by one or more processing devices of the charged particle microscope calculation support system, cause the charged particle microscope calculation support system to perform any of the operations of the charged particle microscope calculation support system of Examples 1 to 142.

[0211] Example 145 is a charged particle microscope computation support system that includes means for performing any of the operations performed by any of the charged particle microscope computation support systems of Examples 1 to 142.

[0212] Example 146 includes a CPM reconfiguration support module disclosed herein.

[0213] Example 147 includes any of the methods disclosed herein.

[0214] Example 148 includes one of the graphical user interfaces disclosed herein.

[0215] Example 149 includes any of the CPM-assisted computing devices and systems disclosed herein.

Claims

1. A charged particle microscope calculation support device, for each of a plurality of angles, to receive an associated image of a specimen at the angle and to generate an associated scan mask based on one or more regions of interest in the associated image, a first logic; for each of the plurality of angles, to generate an associated data set of the specimen by processing data from a scan according to the associated scan mask by a charged particle microscope of the specimen at the angle, a second logic; for each of the plurality of angles, to provide the associated data set of the specimen to a reconstruction logic to generate a three-dimensional reconstruction of the specimen, a third logic; A charged particle microscope calculation support device comprising the above.

2. For at least one of the plurality of angles, the associated data set of the specimen includes at least one zero data region, The charged particle microscope calculation support device according to Claim 1.

3. The data set of the specimen includes energy dispersive spectroscopy (EDS) data, diffraction pattern data, or electron energy loss spectroscopy (EELS) data, The charged particle microscope calculation support device according to Claim 1.

4. For each of the plurality of angles, the charged particle microscope further comprises a fourth logic for generating an associated image of the specimen at the angle, The charged particle microscope calculation support device according to Claim 1.

5. For each individual angle of the plurality of angles, is the acquisition time for the generation of the associated image of the specimen at the individual angle less than the acquisition time for the generation of the associated data set of the specimen at the individual angle according to the associated scan mask, For each individual angle of the plurality of angles, is the radiation dose for the generation of the associated data set of the specimen at the individual angle according to the associated scan mask less than the radiation dose for the generation of a full-frame data set of the specimen at the individual angle, or, For each of the individual angles of the plurality of angles, the resolution of the associated data set of the specimen at the individual angle, in accordance with the associated scan mask, is less than the resolution of the full-frame data set of the specimen at the individual angle. The charged particle microscope calculation support device according to claim 4.

6. For each angle of the plurality of angles, the first logic identifies the one or more regions of interest in the associated image, and identifying the one or more regions of interest in the associated image includes identifying at least one particle in the associated image, or the first logic is for causing a display device to display a graphic representation of the associated scan mask for each individual angle of the plurality of angles. The charged particle microscope calculation support device according to any one of claims 1 to 5.

7. When the first logic is for causing a display device to display a graphic representation of the associated scan mask for each individual angle of the plurality of angles, the first logic is for providing an instruction for causing a display device to display an image of the specimen at the individual angle simultaneously with the display of the graphic representation of the associated scan mask for each individual angle of the plurality of angles. The charged particle microscope calculation support device according to any one of claims 1 to 5.

8. A charged particle microscope calculation support device, a first logic for generating an associated scan mask for scanning a specimen for each angle of a plurality of angles, wherein the scan mask associated with a first angle of the plurality of angles is different from the scan mask associated with a second angle of the plurality of angles different from the first angle, the first logic; a second logic for generating an associated data set of the specimen based on a scan of the specimen by a charged particle microscope according to the associated scan mask for each angle of the plurality of angles; A charged particle microscope calculation support device comprising:

9. For at least one angle of the plurality of angles, the associated data set of the specimen includes at least one zero data region. For at least one of the plurality of angles, the associated data set of the specimen includes at least one non-zero data region, wherein the at least one non-zero data region has a substantially rectangular boundary, The charged particle microscope calculation support device according to claim 8.

10. The first logic is for causing a display device to display a graphic representation of the associated scan mask for each of the individual angles of the plurality of angles. The charged particle microscope calculation support device according to claim 8 or 9.

11. The first logic is for causing a display device to display analysis data of the specimen at the individual angles simultaneously with the display of the graphic representation of the associated scan mask for each of the individual angles of the plurality of angles. The charged particle microscope calculation support device according to claim 10.

12. The first logic is for causing a display device to display, for each of the individual angles of the plurality of angles, (1) an image of the specimen at the individual angle overlaid with a first graphic representation of the associated scan mask, and (2) analysis data of the specimen at the individual angle overlaid with a second graphic representation of the associated scan mask. The charged particle microscope calculation support device according to claim 11.

13. A charged particle microscope calculation support device, a first logic for generating an associated scan mask for scanning a specimen for each of a plurality of angles; a second logic for causing a charged particle microscope to scan the specimen at each of the plurality of angles according to the associated scan mask and generating an associated data set of the specimen based on the scan of the specimen at the angle; a third logic for providing the associated data set of the specimen to a reconstruction logic for each of the plurality of angles to generate a three-dimensional reconstruction of the specimen; A charged particle microscope calculation support device comprising:

14. The second logic is for causing the display device to display, for each of the individual angles of the plurality of angles, a graphic representation of the identification of one or more chemical elements present in the specimen at the associated angle. The charged particle microscope calculation support device according to claim 13.

15. The second logic is for causing the display device to display, for each of the individual angles of the plurality of angles, a graphic representation of the identification of one or more chemical elements present in the specimen at the associated angle overlaid on an image of the specimen at the associated angle. The charged particle microscope calculation support device according to claim 14.

16. The charged particle microscope includes a scanning electron microscope (SEM), a transmission electron microscope (TEM), a scanning transmission electron microscope (STEM), or a cryogenic electron microscope (cryoEM). The charged particle microscope calculation support device according to any one of claims 13 to 15.

17. Processing data from a scan according to the associated scan mask by the charged particle microscope of the specimen at the angle includes (1) filtering data from the scan by the charged particle microscope of the specimen at the angle according to the associated scan mask, or (2) aligning data associated with a first angle with data associated with a second angle. The charged particle microscope calculation support device according to any one of claims 13 to 15.

Images