Method for performing high dynamic range charged particle analysis

Abstract

To provide a method for HDR charged particle analysis which can easily determine the optimum pattern of the irradiation position.SOLUTION: A method for high dynamic range (HDR) charged particle analysis using clustering processes includes accessing a plurality of instances of image data for a region of a sample, where each instance of image data is captured with different parameters by a charged particle microscope. The difference in parameters may include one or more of a difference in contrast, brightness, beam strength, beam type, gamma, and / or a combination thereof. An HDR charged particle microscopy data structure is then generated using the captured image data, and one or more features of the sample are identified based on the HDR charged particle microscopy data structure.SELECTED DRAWING: Figure 1

Description

[Background technology]

[0001] In charged particle imaging, a region of a sample is irradiated with a beam of charged particles (e.g., an electron beam), and an image of the region is generated based on radiation generated from the sample due to the irradiation. For example, in electron imaging, a region of a sample is irradiated with an electron beam, imaging data is generated based on radiation emitted from the sample in response to the irradiation (e.g., backscattered electrons), and the imaging data is used to generate an image of the region of the sample. Because different elements / molecules produce different characteristic radiation, a charged particle microscope system can generate an image of a region of a sample by scanning a charged particle beam across the surface of the sample and assigning a pixel value to each pixel of the image based on the radiation emitted from the corresponding portion of the sample.

[0002] However, current charged particle imaging struggles to distinguish between elements / molecules that produce very similar radiation despite having very different chemistries. For example, quartz (SiO2) and albite (NaAlSiO3) have very different chemistries but nearly the same average atomic number. Therefore, it is very difficult for electron backscattering imaging to distinguish quartz deposits from albite deposit samples because the amount of backscattered electrons emitted by an element / molecule / material is directly related to the element / molecule's atomic number. This difficulty prevents automated segmentation algorithms and / or non-expert human operators from identifying adjacent deposits of albite and quartz as a single deposit.

[0003] If the composition of the sample is known prior to imaging, charged particle microscope parameters can be preselected to identify transitions between materials known to be present in the sample. For example, the contrast of the microscope detector can be adjusted so that the images produced by the charged particle system display material transitions that would otherwise be difficult to detect. Preselecting charged particle microscope parameters in this manner can display subtle material transitions in the generated images, but it can also wash out other areas of the image to white or black. Therefore, if the composition of the sample is unknown, the user cannot preselect charged particle microscope parameters because they would not know the optimal parameters for the sample's composition (e.g., which may include carbon-based materials, uranium-based materials, both, etc.). Therefore, a charged particle microscope and imaging process that can capture images that distinguish between different materials and / or features, regardless of sample composition, is desirable.

[0004] A particular application where this is desirable is energy dispersive X-ray spectroscopy (EDS or EDX), where the composition of a sample is determined by irradiating portions of the sample and determining the corresponding composition based on the spectroscopic information captured by the system. To avoid the need to irradiate each portion of the sample, current EDS processes require an expert user to select a pattern of irradiation locations that will allow them to obtain compositional information for the material of interest within the sample. Because the composition of the sample in EDS is generally unknown, it is not possible to preselect the parameters of the charged particle microscope that generates the initial image of the sample so that the user / algorithm can easily determine the optimal pattern of irradiation locations. Therefore, a charged particle microscope and imaging process that can easily allow the user / algorithm to determine the optimal pattern of irradiation locations is also desirable. Summary of the Invention

[0005] Disclosed herein are methods and systems for high dynamic range (HDR) charged particle analysis using a clustering process. The method includes accessing first image data of a first image of a region of a sample and second image data of a second image of the region of the sample, each image data being captured with different parameters by a charged particle microscope. In various embodiments, the parameter differences may include one or more differences in contrast, brightness, beam intensity, beam type, gamma, and / or combinations thereof. Furthermore, the present invention is not limited to only two instances of image data, but may include embodiments in which multiple images are captured (in parallel, sequentially, or a combination thereof), each instance of image data being captured with different charged particle microscope parameters. An HDR charged particle microscope data structure is then generated using the captured image data, and one or more features of the sample are identified based on the HDR charged particle microscope data structure. In some methods according to the present invention, the method further includes performing EDS analysis on the sample based on the determined one or more identified features.

[0006] A system for HDR charged particle analysis using a clustering process according to the present disclosure includes a sample holder configured to support a sample, a focused charged beam source configured to apply a focused charged beam to the sample, and a detector system configured to detect radiation from the sample upon irradiation with the charged particle beam source. The system further includes one or more processors and a memory storing non-transitory computer-readable instructions that, when executed by the one or more processors, cause a microscope system to perform artificial intelligence to irradiate the sample with the focused charged beam and capture multiple images of an area of ​​the sample, each image of the multiple images having different parameters. The instructions further cause a HDR charged particle microscope data structure to be generated using data from the multiple images and to identify one or more features of the sample based on the HDR charged particle microscope data structure. In some embodiments, the system herein may further include an EDS system configured to perform EDS analysis on material deposits of the sample. The instructions may further cause the EDS system to analyze the one or more material deposits of the sample based on the identified one or more features.

[0007] The detailed description will be set forth with reference to the accompanying drawings, in which the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The same reference number in different figures indicates similar or identical items. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 1 illustrates an example system for performing HDR charged particle analysis using a clustering process.

[0009] [Figure 2] FIG. 2 illustrates a schematic of a computing architecture for performing HDR charged particle sample analysis using a clustering process.

[0010] [Figure 3]FIG. 3 is a flow diagram of an exemplary process for performing HDR charged particle sample analysis.

[0011] [Figure 4] Figure 4 shows a sample process for automatically generating labeled microscopic images.

[0012] [Figure 5] FIG. 5 shows multiple instances of image data 500 for performing HDR charged particle sample analysis.

[0013] [Figure 6] FIG. 6 shows examples of images generated from instances of image data acquired with various charged particle microscope parameters.

[0014] [Figure 7] FIG. 7 shows a collection of examples of improved performance of automatic segmentation algorithms when used with the methods and systems of the present disclosure compared to current technology. [Figure 8] FIG. 8 shows a collection of examples of improved performance of automatic segmentation algorithms when used with the methods and systems of the present disclosure compared to current technology. [Figure 9] FIG. 9 shows a collection of examples of improved performance of automatic segmentation algorithms when used with the methods and systems of the present disclosure compared to current technology. [Figure 10] FIG. 10 shows a collection of examples of improved performance of automatic segmentation algorithms when used with the methods and systems of the present disclosure compared to current technology.

[0015] [Figure 11] FIG. 11 visually illustrates a sample process for performing EDS analysis of a sample using multiple instances of image data acquired with different charged particle microscope parameters in accordance with the present disclosure.

[0016] Like reference numerals refer to corresponding parts throughout the several views of the drawings. DETAILED DESCRIPTION OF THE INVENTION

[0017] A method and system for high dynamic range (HDR) charged particle analysis using a clustering process is disclosed. More specifically, the present disclosure includes a method and system for using multiple images and / or instances of image data to generate an HDR charged particle microscopy data structure. The method and system can then determine one or more features of a sample based on the HDR charged particle microscopy data structure. The multiple images and / or instances of image data may be acquired by a charged particle microscopy system in parallel, sequentially, or a combination thereof. Each image / instance was acquired by the charged particle microscopy system using a different set of charged particle microscopy parameters. In various embodiments, parameter differences between individual images / instances of image data may include one or more of differences in contrast, brightness, beam intensity, beam type, gamma, and / or combinations thereof. One or both of generating the HDR charged particle microscopy data structure and / or identifying one or more features includes applying a clustering algorithm to one or both of the image data and the multiple images / instances of the HDR charged particle microscopy data structure. By generating and analyzing HDR charged particle microscope data structures in this manner, methods and systems according to the present disclosure can identify even subtle transitions between different elements / molecules in a sample without the need to pre-configure a charged particle microscope system and / or without the need to know the composition of the sample prior to imaging. That is, the methods and systems described herein can generate images that distinguish between different materials and / or features of a sample without requiring knowledge of the sample's composition.

[0018] In some embodiments of the present disclosure, one or more features determined using the HDR charged particle microscope data structure are then used to perform EDS analysis of the sample. For example, one or more identified features may correspond to transitions between material deposits in the region of the sample depicted by the captured image. Such transitions may be used to segment the image of the sample so that individual segments of the image correspond to individual material deposits. The system and method may then identify locations within one or more segmented regions to perform EDS analysis and label the entire segmented region with the chemical composition identified by the EDS analysis. In this way, instead of having to perform EDS on every pixel region of the image, the system and method of the present disclosure enable the determination of an optimal pattern of EDS irradiation locations (i.e., one pixel location within each segmented region), significantly reducing the time to data for EDS analysis of the sample.

[0019] Generally, in the figures, elements that are likely to be included in a given example are shown with solid lines, while elements that are optional in a given example are shown with dashed lines. However, elements shown with solid lines are not required for all examples of the present disclosure, and elements shown with solid lines may be omitted from particular examples without departing from the scope of the present disclosure.

[0020] FIG. 1 illustrates an exemplary system 100 for performing HDR charged particle analysis using a clustering process. FIG. 1 illustrates an example of the system 100 that optionally includes a charged particle microscope system 102 for generating multiple images of a sample 104. In accordance with the present invention, each of the multiple images is acquired with different charged particle microscope parameters. For example, the individual images may be acquired with various contrasts, brightness levels, beam intensities, beam types, gammas, and / or combinations thereof. The exemplary charged particle microscope system 100 may be or include one or more different types of EM and / or charged particle microscopes, such as, but not limited to, a scanning electron microscope (SEM), a scanning transmission electron microscope (STEM), a transmission electron microscope (TEM), a charged particle microscope (CPM), a dual beam microscope system, and the like. FIG. 1 illustrates the charged particle microscope system 100 as an SEM system 106. However, one skilled in the art will understand from FIG. 1 how the methods and systems of the present disclosure may be embodied with other types of EM and / or charged particle microscopes.

[0021] The STEM system 106 includes an electron source 108 that emits an electron beam 110 along an emission axis 112 toward a focusing column 114. In some embodiments, the focusing column 114 may include one or more of a condenser lens, an aperture, a scanning coil, an upper objective lens, etc. The focusing column 114 focuses electrons from the electron source 108 to a small spot on the sample 104. By adjusting the direction of the electron beam 110 via the scanning coil, different locations on the sample can be scanned. For example, by operating the scanning coil, the electron beam 110 can be shifted to be incident on different locations on the sample 104.

[0022] The sample 104 may be held by a sample holder 116. Radiation 118 emitted by the sample 104 as a result of irradiation of the sample by the charged particle beam 110 is detected by the detector system 120. For example, the radiation 118 may correspond to secondary backscattered electrons emitted by individual elements / molecules in response to absorption of electrons from the charged particle beam 110. The detector system 120 is configured to generate image data based on a portion of the radiation 118 it detects, and the image data may be used to generate an image of the sample 104 illuminated by the charged particle beam 110. As described above, individual instances of image data are acquired with different charged particle microscope parameters. Such instances of image data may be acquired sequentially, in parallel, or a combination thereof. For example, in one embodiment, the charged particle microscope 102 may irradiate a region of the sample 104 to generate a first instance of image data, one or more parameters of the charged particle microscope 102 may be changed, and then a second instance of image data may be generated by irradiating a region of the sample 104. Alternatively, detector system 120 may be configured to simultaneously capture multiple instances of image data having different charged particle microscope parameters based on a single illumination of an area of ​​sample 104. For example, it may include a first detector and a second detector, each of which has a different configuration, position, orientation, filter, detector type, or combination thereof, such that the image data generated by each detector has different parameters. Alternatively or additionally, a single detector of detector system 120 may be comprised of different detector regions, each configured with different parameters. For example, a first surface of a detector may have a filter that generates image data having a first contrast level, while a second surface of the same detector may have a different filter, causing the image data generated by the second surface to have a second contrast level.

[0023] 1 further illustrates the charged particle microscope setup 100 as including a computing device 122 operable to generate an HDR charged particle data structure based on multiple instances of image data generated by the detector system 120 and to determine one or more characteristics of the sample based on the HDR charged particle data structure. The computing device 122 may include a computing device that is a component of the charged particle microscope setup 100, may communicate with the charged particle microscope setup 100 via a network communication interface, or a combination thereof. For example, the charged particle microscope setup 100 may include a computing device that is a component part of the charged particle microscope setup 100 that functions as a controller to drive operation of the charged particle microscope setup 100 (e.g., adjusting a scan position on the sample 104 by operating a scan coil, adjusting a profile of the incident beam 110 by adjusting one or more apertures and / or lenses, adjusting an orientation of the sample relative to the incident beam 110 by adjusting the sample holder 116, etc.). Alternatively or additionally, the computing device 122 may include a computing device that is separate from the charged particle microscope setup 100 and configured to receive the multiple image data generated by the detector system 120 via electronic data transfer (e.g., wireless data transfer, network data transfer, etc.) or hardware data transfer (e.g., CD-ROM, thumb drive, etc.).

[0024] The computing device 122 is executable to generate an HDR charged particle data structure based on multiple instances of image data generated by the detector system 120. That is, the computing device 122 is configured to generate an HDR charged particle data structure based on multiple instances of image data from charged particle imaging of the sample 104, each captured with different charged particle microscope parameters.

[0025] In some embodiments, the HDR charged particle data structure corresponds to an HDR charged particle image or other type of data formation (e.g., a table) that stores single pixel values ​​for individual pixel locations on a sample generated based on multiple instances of image data. In such embodiments, the HDR charged particle image or data formation can be generated by applying an HDR algorithm to multiple instances of image data. In various embodiments, the HDR algorithm applies weights to individual pixel values ​​of the image data instances based on the importance of the pixel information. For example, pixels with faded values ​​(i.e., close to 1 or 0 in a grayscale image) can be assigned a low weight because they do not convey much information about the sample 104. Alternatively or additionally, the weight of a pixel value at a particular pixel location can also be determined based on pixel values ​​associated with pixels located nearby the pixel location. For example, a pixel can be assigned a higher weight for a first set of nearby pixels with very similar values ​​and a second set of nearby pixels with very different values, indicating that the pixel value may be indicative of a material transition characteristic of the sample 104. In another example, a pixel value may be assigned a lower value based on neighboring pixels that consistently have different values, likely indicating noise. An example of such weighted image data is shown in FIG. 5. The HDR algorithm may determine the pixel value of an individual pixel location in an HDR charged particle image based on one or more pixel values ​​associated with some or all individual pixel locations in the image data instance, as well as any assigned weights (e.g., weighted averages). For example, the HDR algorithm may consider only a subset of pixel values ​​with individual pixel locations across multiple instances of the associated image data, such as a set of pixel values ​​with weights above a threshold, a preset number of values ​​with the highest associated weights, or a combination thereof.

[0026] If the multiple image data are converted into multiple images of the sample 104 before converting and / or generating an HDR charged particle image, assigning weights to pixel values ​​may correspond to generating a mask for each image that identifies portions of the corresponding image that contain important information about the sample. The mask may also assign weights to one or more pixel locations within the corresponding image. In such an embodiment, an HDR charged particle image may be generated based on which masks and / or regions of each image indicated correspond to useful sample information. The computing device 122 may then determine one or more characteristics of the sample (e.g., geometric features, material transitions, etc.) based on the HDR charged particle images.

[0027] Alternatively, the HDR charged particle data structure may correspond to a multi-dimensional data structure that stores, in parallel, data values ​​from some or all of the instances of image data for the sample 104. For example, for each pixel location, the HDR charged particle data structure may store a value corresponding to that pixel location from each instance of image data associated with each other. An example of such an HDR charged particle data structure is shown in FIG. 4. Generating the multi-dimensional data structure includes assigning weights to individual pixel values ​​therein based on the importance of the corresponding pixel information.

[0028] The computing device 122 may then determine one or more features (e.g., geometric features, material transitions, etc.) of the sample by applying a segmentation algorithm (e.g., a clustering algorithm, a watershed algorithm, etc.) to the multidimensional data structure. For example, the segmentation algorithm may determine HDR pixel values ​​for the HDR charged particle image based at least in part on corresponding values ​​in the multidimensional data structure associated with the corresponding individual pixel locations, additional values ​​associated with additional pixel locations surrounding the corresponding individual pixels, weights assigned thereto, or a combination thereof. In some embodiments, the segmentation algorithm may consider only a subset of pixel values ​​associated with individual pixel locations across multiple instances of the associated image data, such as a set of pixel values ​​having weights above a threshold, a preset number of values ​​having the highest associated weights, or a combination thereof.

[0029] In some embodiments, the computing device 122 may be further configured to segment the HDR charged particle image based on one or more determined features. For example, based on the plurality of determined material transitions, the computing device 122 may generate a segmented HDR charged particle image, with each segment corresponding to a distinct material deposit within the sample. This may also include one or more smoothing algorithms to remove noise from the HDR charged particle image by removing outliers. In this manner, noise artifacts in the HDR charged particle image may be removed, providing a more accurate representation of the arrangement of material deposits present in the sample.

[0030] 1 and optionally includes an energy dispersive X-ray spectroscopy (EDS or EDX) system 124 configured to perform EDS processing of the sample 104. During EDS processing in response to a point on the sample 104 being irradiated, elements / molecules located at the irradiated point emit spectral radiation 126 (e.g., X-rays) that are detected by an EDS detector 128. Based on the EDS data generated by the EDS detector, the computing device 122 can determine a characteristic radiation pattern of the detected spectral radiation 126. The computing device 122 can then determine the chemical composition of the elements / molecules located at the irradiated point based on the determined characteristic radiation pattern.

[0031] 1 shows a charged particle microscope setup 100 including a single beam (i.e., charged particle beam 110) from a single source (i.e., charged particle source 108) configured to perform irradiation for both the imaging and EDS processes, in other embodiments, the charged particle microscope setup 100 may have two or more beam sources, such that a first beam is used for irradiation in charged particle imaging of the sample 104 and a second beam is used for EDS processing of the sample 104. For example, the charged particle microscope setup 100 may include an electron beam source that generates an electron beam used to perform electron microscope imaging of the sample 104 and an X-ray source that is used to irradiate points on the sample with X-rays during EDS processing of the sample 104.

[0032] In some embodiments, the charged particle microscope setup 100 is further configured to perform and / or cause the EDS system to perform an EDS analysis of the sample 104. Specifically, the charged particle microscope setup 100 can be configured to use one or more features determined by the computing device 122 to determine an optimized pattern of irradiation locations for EDS analysis of the sample 104. For example, based on the segmented HDR charged particle image, the computing device 122 can identify locations within each segmented region to be irradiated during EDS processing of the sample. Thus, instead of having to perform an EDS analysis of each location on the sample, the computing device 122 can use one or more features to identify regions of the sample 104 corresponding to individual material deposits and cause the EDS system 124 to perform only a single irradiation / EDS measurement of each region. For example, the computing device 122 can use the segmented HDR charged particle image to identify regions of the sample corresponding to individual material deposits. Then, automatically or in response to user input, the computing device 122 can cause the EDS system to analyze single points within one or more portions of the sample corresponding to the individual segmented regions. The computing device 122 can use the results from the EDS analysis to label entire portions of the sample 104 and / or HDR charged particle image corresponding to particular segmented regions with the chemical composition identified by the analysis. For example, the computing device 122 can present a graphical user interface (GUI) that allows a user to select one or more segmented regions of the sample on which the EDS system will perform EDS analysis and respond to the selection of each segmented region, and the computing device 122 can determine locations within each segmented region to be irradiated during the EDS analysis.Alternatively or additionally, the computing device 122 may select a subset of the sample segmented regions based on a preset number of segmented regions and / or characteristics of the individual segmented regions (e.g., the size of the individual segmented regions, the shape of the individual segmented regions, etc.).

[0033] According to the present invention, the locations within each segmented region to be irradiated can be determined based on an algorithm that finds the location within the segmented region that is farthest from the boundary of the segmented region. For example, such an algorithm can be applied to a smoothed version of the segmented HDR charged particle image to identify the point within one or more segmented regions that is farthest from their respective boundaries. Alternatively or additionally, some or all of the noise removed from the smoothed version of the segmented HDR charged particle image can be reintroduced before the algorithm is applied so that the locations determined by the algorithm do not overlap and / or are not close to noise artifacts. In this way, the computing device 122 can prevent EDS irradiation from occurring at locations on the sample that contain small defects, impurities, or geographic anomalies (e.g., cracks, micro-deposits, etc.). Thus, systems and methods according to the present disclosure can dramatically reduce the time to data for EDS analysis of samples with unknown material compositions. Furthermore, systems and methods according to the present disclosure further enable automation of some or all of the processes for performing EDS analysis, particularly for samples with unknown material compositions.

[0034] 2 shows a schematic diagram illustrating a computing architecture 300 of an exemplary computing device 122 operable to at least partially perform HDR charged particle sample analysis using a clustering process. For example, FIG. 2 shows additional details of hardware and software components that may be used to implement the techniques described in this disclosure. In the exemplary computing architecture 200, the computing device 122 includes one or more processors 202 and a memory 204 communicatively coupled to the one or more processors 202.

[0035] The exemplary computing architecture 200 may include a control module 206, an HDR charged particle module 208, a characterization module 210, and an EDS module 212 stored in memory 204. As used herein, the term “module” is intended to represent an exemplary portion of executable instructions for purposes of illustration and is not intended to represent any type of requirement or necessary method, manner, or organization. Thus, while various “modules” are described, their functions and / or similar functions may be arranged differently (e.g., combined into fewer modules, divided among more modules, etc.). Furthermore, while certain functions and modules are described herein as being implemented by software and / or firmware executable on a processor, in other examples, any or all modules may be implemented in whole or in part by hardware (e.g., specialized processing units, etc.) to perform the described functions. In various implementations, the modules described herein in connection with the exemplary computing architecture 200 may be executed across multiple devices.

[0036] The control module 206 may be executable by the processor 202 to function as a controller that drives the operation of an exemplary charged particle microscope setup, such as the exemplary charged particle microscope setup 100. For example, the control module 206 may operate a scan coil to adjust the scan position of the charged particle beam on the sample, adjust one or more apertures and / or lenses to adjust the profile of the incident beam, and / or adjust a sample holder to adjust the orientation of the sample relative to the incident beam. For example, the control module 206 may be executable to cause the charged particle microscope setup to acquire multiple instances of image data of a region of the sample, each instance of image data being acquired using different charged particle microscope parameters. As described above, the control module 206 may cause such instances of image data to be acquired sequentially, in parallel, or a combination thereof. For example, a first instance of image data may be generated by a first illumination of a region of the sample, then a second instance of image data may be generated by a second illumination of the region of the sample, with one or more parameters of the charged particle microscope being changed between the first and second illuminations of the sample. Alternatively, if the charged particle microscope system includes a detector system configured to simultaneously capture multiple instances of image data with different charged particle microscope parameters (e.g., different detectors with different parameters, different regions of the detector with different parameters, etc.), the control module 206 may cause one or more instances of image data with different parameters to be captured based on a single illumination of a region of the sample.

[0037] The HDR charged particle module 208 is executable by the processor 202 to generate an HDR charged particle data structure based on multiple instances of image data having different charged particle microscope parameters. In some embodiments, the HDR charged particle module 208 is executable to generate an HDR charged particle image, or other type of data structure that stores single pixel values ​​for individual pixel locations of a sample, based on multiple instances of image data. In such embodiments, the HDR charged particle module 208 may generate an HDR charged particle image by applying an HDR algorithm to multiple instances of image data configured to divide associated pixel values ​​corresponding to specific regions of a sample across the multiple instances of image data. In some embodiments, the HDR algorithm further applies weights to individual pixel values ​​of the instances of image data based on the importance of the pixel information. For example, pixels with faded values ​​(i.e., close to 1 or 0 in a grayscale image) may be assigned a low weight because they do not convey much information about the sample. Alternatively or additionally, the weight of a pixel value for a particular pixel location may be determined based on pixel values ​​associated with pixels located proximate to the pixel location. For example, a pixel may be assigned a higher weight due to a first set of neighboring pixels having very similar values ​​and a second set of neighboring pixels having very different values, indicating that the pixel value may be indicative of a material transition characteristic of the sample. In another example, a pixel value may be assigned a lower value based on neighboring pixels having consistently different values, as this is likely indicative of noise.

[0038] The HDR charged particle module 208 may be operable to determine pixel values ​​for individual pixel locations of an HDR charged particle image based on one or more pixel values ​​associated with some or all of the individual pixel locations of the image data instances and any assigned weights (e.g., weighted averages). For example, the HDR algorithm may consider only a subset of pixel values ​​with individual pixel locations across multiple instances of associated image data, such as a set of pixel values ​​with weights above a threshold, a preset number of values ​​with the highest associated weights, or a combination thereof. If multiple image data are converted into multiple images of the sample before converting and / or generating an HDR charged particle image, assigning weights to pixel values ​​may correspond to generating a mask for each image that identifies portions of the corresponding image that contain important information about the sample. The mask may also assign weights to one or more pixel locations within the corresponding image. In such an embodiment, an HDR charged particle image may be generated based on which masks and / or regions of each image shown correspond to useful sample information.

[0039] In an alternative embodiment, the HDR charged particle module 208 can be executed to generate a multi-dimensional data structure that stores, in parallel, data values ​​from some or all of the instances of image data for the sample 104. For example, for each pixel location, the HDR charged particle data structure may store a value corresponding to the pixel location from each associated instance of image data. The HDR charged particle module 208 can also be configured to assign weights to individual pixel values ​​within the multi-dimensional data structure, including assignments based on the importance of the corresponding pixel information.

[0040] The feature determination module 210 may be executable by the processor 202 to determine one or more features of the sample based on the HDR charged particle data structure. The one or more features may correspond to geometric features of the sample (e.g., deposits, texture, crystal shape, cracks, voids, etc.) or material features of the sample (e.g., transitions between material deposits, etc.).

[0041] In some embodiments, the feature determination module 210 may determine one or more features of the sample by applying a segmentation algorithm (e.g., a clustering algorithm, a watershed algorithm, etc.) to the HDR charged particle data structure (e.g., an HDR charged particle image, a multidimensional data structure, etc.). For example, the feature determination module 210 may apply a segmentation algorithm to segment pixel values ​​associated with individual regions of the sample and then identify patterns among the segmented or otherwise interrelated pixel values ​​that indicate material transitions within the sample. In some embodiments, determining the one or more features may include the feature determination module 210 determining HDR pixel values ​​of the HDR charged particle image based at least in part on corresponding values ​​in the multidimensional data structure associated with the corresponding individual pixel locations, additional values ​​associated with additional pixel locations surrounding the corresponding individual pixels, weights assigned thereto, or a combination thereof. In some embodiments, the segmentation algorithm may consider only a subset of pixel values ​​associated with individual pixel locations across multiple instances of the associated image data, such as a set of pixel values ​​having a weight above a threshold, a preset number of values ​​having the highest associated weights, or a combination thereof.

[0042] In some embodiments, the feature determination module 210 may be further configured to segment the HDR charged particle image based on one or more determined features. For example, based on a plurality of determined material transitions, the feature determination module 210 may generate a segmented HDR charged particle image, with each segment corresponding to a distinct material deposit within the sample. This may also include one or more smoothing algorithms to remove noise from the HDR charged particle image by removing outliers. In this way, noise artifacts in the HDR charged particle image may be removed, providing a more accurate representation of the arrangement of material deposits present in the sample.

[0043] The EDS module 212 is executable by the processor 302 to cause the EDS system to perform EDS analysis of the sample based on one or more features determined by the feature determination module 210. Specifically, the EDS module 212 can be configured to use the one or more features to determine an optimized pattern of irradiation locations for EDS analysis of the sample. For example, based on the segmented HDR charged particle images, the EDS module 212 can identify locations within one or more segmented regions to be irradiated during EDS processing of the sample. Thus, rather than needing to perform EDS analysis of each location on the sample, the EDS system (such as the EDS system in the charged particle microscope system shown in FIG. 1) can use the identified locations to perform only a single irradiation / EDS measurement of each region.

[0044] In some embodiments, the EDS module 212 can use the segmented HDR charged particle image to identify regions of the sample corresponding to individual material deposits. Then, automatically or in response to user input, the control module 206 can cause the EDS system to analyze single points within one or more portions of the sample corresponding to the individual segmented regions. The EDS module 212 can then use the results from the EDS analysis to label entire portions of the sample and / or HDR charged particle image corresponding to particular segmented regions with the chemical composition identified by the analysis. The EDS module 212 can present a graphical user interface (GUI) that allows a user to select one or more segmented regions of the sample on which the EDS system will perform EDS analysis and respond to the selection of the individual segmented regions, and the EDS module 212 can determine locations within the individual segmented regions to be irradiated during the EDS analysis. Alternatively or additionally, the EDS module 212 can select a subset of the segmented regions of the sample based on a preset number of segmented regions and / or characteristics of the individual segmented regions (e.g., the size of the individual segmented regions, the shape of the individual segmented regions, etc.).

[0045] In an exemplary embodiment, the EDS module 212 may identify locations within each segmented region to be irradiated based on an algorithm that finds the location within the segmented region that is farthest from the boundary of the segmented region. For example, such an algorithm may be applied to a smoothed version of the segmented HDR charged particle image to identify the point within one or more segmented regions that is farthest from their respective boundaries. Alternatively or additionally, some or all of the noise removed from the smoothed version of the segmented HDR charged particle image may be reintroduced before the algorithm is applied so that the locations determined by the algorithm do not overlap and / or are not close to noise artifacts. In this way, the EDS module 212 may prevent EDS irradiation from occurring at locations on the sample where small defects, impurities, or geographic anomalies (e.g., cracks, micro-deposits, etc.) are present.

[0046] Those skilled in the art will appreciate that computing architecture 200 is merely exemplary and is not intended to limit the scope of the present disclosure. In particular, computing systems and devices may include any combination of hardware or software capable of performing the depicted functions, including computers, network devices, Internet appliances, PDAs, wireless telephones, controllers, oscilloscopes, amplifiers, etc. Computing architecture 200 may also be connected to other devices not shown, or may instead operate as a stand-alone system. Furthermore, functionality provided by the depicted components may, in some implementations, be combined into fewer components or distributed among additional components. Similarly, in some implementations, functionality of some of the depicted components may not be provided, and / or other additional functionality may be available.

[0047] The one or more processors 202 may be configured to execute instructions, applications, or programs stored in memory 204. In some examples, the one or more processors 202 may include a hardware processor, including, but not limited to, a hardware central processing unit (CPU), a graphics processing unit (GPU), etc. While in many cases the techniques are described herein as being performed by one or more processors 202, in some cases the techniques may be implemented by one or more hardware logic components, such as a field programmable gate array (FPGA), a complex programmable logic device (CPLD), an application specific integrated circuit (ASIC), a system on a chip (SoC), or a combination thereof.

[0048] Memory 204 is an example of a computer-readable medium. Computer-readable media can include two types of computer-readable media: computer storage media and communication media. Computer storage media can include volatile and nonvolatile media, removable and non-removable media, implemented in any method or technology for storage of information, such as computer-readable instructions, data structures, program modules, or other data. Computer storage media include, but are not limited to, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage devices, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store desired information and that can be accessed by a computing device. Generally, computer storage media contain computer-executable instructions that, when executed by one or more processing units, cause various functions and / or operations described herein to be performed. In contrast, communication media embodies computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transmission mechanism. As defined herein, computer storage media does not include communication media.

[0049] Those skilled in the art will also understand that while various items are shown as being stored in memory or storage devices during use, these items, or portions thereof, may be transferred between memory and other storage devices for purposes of memory management and data integrity. Alternatively, in other implementations, some or all of the software components may execute in the memory of another device and communicate with the illustrated computing architecture 200. Some or all of the system components or data structures may also be stored on a non-transitory computer-accessible medium or in a portable product readable by a suitable drive (e.g., as instructions or structured data); various examples of suitable drives are described above. In some implementations, instructions stored on a computer-accessible medium separate from the computing architecture 200 may be transmitted to the computing architecture 200 via a signal, such as an electrical, electromagnetic, or digital signal conveyed over a transmission medium or a communications medium, such as a wireless link. Various embodiments may further include receiving, sending, or storing instructions and / or data implemented in accordance with the preceding description of a computer-accessible medium.

[0050] The architectures, systems, and individual elements described herein may include many other logical, programmatic, and physical components, of which those shown in the accompanying figures are merely examples relevant to the description herein.

[0051] 3 is a flow diagram of an exemplary process depicted as a collection of blocks of a logical flow graph, which represent a sequence of operations that can be implemented in hardware, software, or a combination thereof. In a software context, the blocks represent computer-executable instructions stored on one or more computer-readable storage media that perform the recited operations when the computer-executable instructions are executed by one or more processors. Generally, computer-executable instructions include routines, programs, objects, components, data structures, etc. that perform particular functions or implement particular abstract data types. The order in which the operations are described should not be construed as a limitation, and any number of the described blocks can be combined in any order and / or in parallel to implement a process.

[0052] 3 is a flow diagram of an exemplary process 300 for performing HDR charged particle sample analysis. Process 300 may be implemented in the exemplary charged particle microscope setup 100 and / or by the computing architecture 200 described above, or in other environments and architectures.

[0053] At 302, the sample is optionally imaged using a charged particle microscope system. For example, if the charged particle microscope system is a scanning electron microscope, imaging the sample includes scanning an electron beam over a region of the sample's surface and detecting radiation emitted by the sample in response to the scanning. One or more detectors in the charged particle microscope system detect the radiation and generate an instance of image data of the region of the sample, which can be used by a computing system to generate an image of the region of the sample. Characteristics of the instance of image data are determined by one or more associated parameters, such as contrast, brightness, beam intensity, beam type, gamma, and / or combinations thereof. For example, based on a contrast setting associated with the instance of image data, a first material transition in the sample may be highly defined, while a second material transition in the sample may be indistinguishable from the instance of image data. In some embodiments, two or more instances of image data with different parameters may be acquired simultaneously during imaging of the sample. For example, a charged particle microscope system may have two or more detectors and / or regions within individual detectors that acquire separate instances of image data with different parameters based on their respective detected radiation. For example, the first detector and the second detector may have different filters applied thereto, such that the contrast of the image data from the first detector visualizes different material transitions and / or other sample features than can be determined from the second image data.

[0054] At 304, one or more parameters of the charged particle microscope system are optionally changed. This may include adjusting contrast, brightness, beam intensity, beam type, gamma, and / or combinations thereof. The process then continues to step 302, where at least a new instance of the image data is acquired, optionally using the new charged particle microscope system parameters.

[0055] At 306, multiple instances of image data are acquired, each instance of image data having different associated charged particle microscope parameters. In various embodiments according to the present disclosure, acquiring the instances of image data is performed via a hardwired or wireless connection to one or more detectors in the charged particle microscope system. Alternatively, acquiring the instances of image data may correspond to accessing the instances of image data via data transfer over a network (e.g., LAN, WAN, Internet, wireless, Bluetooth, etc.) or hardware media (e.g., CD-ROM, USB drive, etc.).

[0056] At 308, an HDR data charged particle microscope data structure is generated based on multiple instances of image data having different charged particle microscope parameters. In some embodiments, generating the HDR charged particle microscope data structure optionally includes combining the instances of image data into a multidimensional data structure at step 310. Such a multidimensional data structure may store data values ​​from some or all of the instances of image data of the sample in parallel. For example, for each pixel location, the HDR charged particle data structure may store a value corresponding to the pixel location from each instance of image data relative to one another. In some embodiments, generating the multidimensional data structure further includes assigning weights to individual pixel values ​​in the multidimensional data structure based on the importance of the corresponding pixel information.

[0057] Generating an HDR charged particle microscope data structure may also optionally include generating an HDR charged particle microscope image in step 312. Generating such an HDR charged particle microscope image may be based on multiple instances of image data, a multidimensional data structure, or a combination thereof. For example, the HDR charged particle image may be generated by applying an HDR algorithm to multiple instances of image data, where the HDR algorithm is configured to split related pixel values ​​corresponding to particular regions of a sample across the multiple instances of image data. Once the interrelated pixel values ​​are identified, they can be used to identify corresponding HDR pixel values ​​in the HDR charged particle microscope image.

[0058] In some embodiments, the HDR algorithm further applies and / or considers weights for individual pixel values ​​of an instance of image data. The weights assigned to pixel values ​​correspond to the importance of the pixel information. For example, pixels with faded values ​​(i.e., close to 1 or 0 in a grayscale image) may be assigned a low weight because they do not convey much information about the sample. In some embodiments, the weight of a pixel value at a particular pixel location may be determined based on pixel values ​​associated with pixels located nearby the pixel location. For example, a pixel may be assigned a higher weight because of a first set of neighboring pixels with very similar values ​​and a second set of neighboring pixels with very different values, indicating that the pixel value may be indicative of a material transition characteristic of the sample. In another example, a pixel value may be assigned a low value based on neighboring pixels with consistently different values ​​because the pixel value is likely indicative of noise.

[0059] In some embodiments, pixel values ​​for individual pixel locations of an HDR charged particle image may be determined based on one or more pixel values ​​associated with the individual pixel locations of some or all of the instances of image data, as well as any assigned weights (e.g., weighted averages). For example, the HDR algorithm may consider only a subset of pixel values ​​with the individual pixel locations across multiple instances of associated image data, such as a set of pixel values ​​with weights above a threshold, a preset number of values ​​with the highest associated weights, or a combination thereof.

[0060] If the multiple image data are converted into multiple images of the sample before converting and / or generating an HDR charged particle image, assigning weights to pixel values ​​may correspond to generating a mask for each image that identifies portions of the corresponding image that contain important information about the sample. The mask may also assign weights to one or more pixel locations within the corresponding image. In such embodiments, the HDR charged particle image may be generated based on which masks and / or regions of each image indicated correspond to useful sample information.

[0061] At 314, one or more characteristics of the sample are determined. Specifically, the one or more characteristics of the sample are determined based on the HDR charged particle microscope image. The one or more characteristics may correspond to geometric features of the sample (e.g., deposits, texture, crystal shape, cracks, voids, etc.) or material features of the sample (e.g., transitions between material deposits, etc.). In some embodiments, the one or more characteristics of the sample are determined at least in part by applying a segmentation algorithm (e.g., a clustering algorithm, a watershed algorithm, etc.) to the HDR charged particle data structure (e.g., the HDR charged particle image, the multidimensional data structure, etc.). For example, a segmentation algorithm may be applied that segments pixel values ​​of the HDR charged particle microscope image and / or the multidimensional data structure associated with individual regions of the sample and then identifies patterns among the segmented or otherwise interrelated pixel values ​​that indicate material transitions within the sample.

[0062] At 316, a segmented HDR charged particle microscope image is optionally created based on one or more features of the sample determined in step 314. For example, a segmented HDR charged particle image may be generated based on a plurality of determined material transitions, with individual segments of the image corresponding to distinct material deposits within the sample. In some embodiments, generation of the segmented image may also include one or more smoothing algorithms that remove noise from the HDR charged particle image by removing outliers. In this manner, noise artifacts in the HDR charged particle image may be removed, providing a more accurate representation of the arrangement of material deposits present in the sample.

[0063] At 318, EDS processing of the sample is optionally performed. Specifically, the one or more features determined in step 314 may be used to cause the EDS system to perform an optimized EDS analysis of the sample. The one or more features may be used to determine an optimized pattern of irradiation locations for EDS analysis of the sample. For example, based on the segmented HDR charged particle image optionally generated in step 316, locations within one or more segmented regions to be irradiated during EDS processing of the sample may be identified. Thus, the EDS system need not perform EDS analysis of each location on the sample, but can use the identified locations to perform only a single irradiation / EDS measurement for each region.

[0064] FIG. 4 illustrates a multidimensional data structure 400 for performing HDR charged particle sample analysis. FIG. 4 illustrates the multidimensional data structure 400, in which a plurality of pixel values ​​402 for each of a plurality of instances of image data 404 of a sample are stored in association with their corresponding pixel locations 406. Each instance of image data is generated by a charged particle microscope system based on illumination of the sample, and each instance of image data has a different set of associated microscope parameters. In methods and systems according to the present disclosure, an HDR algorithm may be applied to the multidimensional data structure 400 configured to determine HDR pixel values ​​of the HDR charged particle image of the sample. Alternatively or additionally, a segmentation algorithm (e.g., a clustering algorithm, a watershed algorithm, etc.) and / or the HDR charged particle image of the sample may be used to determine characteristics of the sample. FIG. 4 further illustrates the multidimensional data structure 400, which includes optional weights 408 assigned to the individual pixel values ​​402.

[0065] FIG. 5 illustrates multiple instances of image data 500 for performing HDR charged particle sample analysis. Specifically, FIG. 5 illustrates a first instance of image data 502, a second instance of image data 502, and a third instance of image data 506, each instance of image data generated by a charged particle microscope system based on illumination of a sample. Additionally, each instance of image data 502-506 has a different set of associated microscope parameters. FIG. 5 further illustrates each instance of image data 502-506, including optional weights 510 assigned to individual pixel values ​​508.

[0066] FIG. 6 is an illustration of an exemplary image 600 generated from instances of image data acquired with different charged particle microscope parameters. Specifically, FIG. 6 shows a collection of eight images 602-618 of a sample acquired with different contrast settings. As can be seen, the various contrast settings display different levels of visibility for various materials and material transitions within the sample. For example, in the first image 602, the first material deposit 618 is clearly visible, while the second material deposit 620 is barely visible and the third material deposit 622 is completely invisible. That is, in images 602-606, the contrast settings do not reveal any information about the third material because the associated pixel values ​​(and those associated with the surrounding area of ​​the sample) are blacked out. It is not until image 608 that the third material deposit 622 first becomes discernible from the surrounding area of ​​the sample. As noted above, if the composition of the sample is known prior to imaging, the parameters of the charged particle microscope can be adjusted to ensure that the specific material deposits / features of interest are displayed in the resulting image data. Conversely, if the composition of the sample is unknown prior to imaging, an operator may expect that at least some of the features and / or deposited materials will not be visible from image data acquired with a single set of parameters. However, using methods and systems according to the present disclosure, an operator can use multiple instances of image data acquired with different charged particle microscope parameters to determine the features and / or deposited materials of a sample without requiring prior information about the sample composition.

[0067] 7-10 are a collection of examples of improved performance of automated segmentation algorithms when used with the methods and systems of the present disclosure, compared to current technology. For example, FIG. 7 shows a charged particle microscope image 702 and an HDR charged particle microscope image 704 generated based on multiple instances of image data acquired with different charged particle microscope parameters. FIG. 7 also shows a first automated segmented image 706 of the sample acquired using image 702 and a second automated segmented image 708 of the sample acquired using image 704.

[0068] Similarly, Figure 8 shows a charged particle microscope image 802 and an HDR charged particle microscope image 804 generated based on multiple instances of image data acquired with different charged particle microscope parameters. Figure 8 also shows a first auto-segmented image 806 of the sample acquired using image 802 and a second auto-segmented image 808 of the sample acquired using image 804. Figure 9 shows a charged particle microscope image 902 and an HDR charged particle microscope image 904 generated based on multiple instances of image data acquired with different charged particle microscope parameters. Figure 9 also shows a first auto-segmented image 906 of the sample acquired using image 902 and a second auto-segmented image 908 of the sample acquired using image 904.

[0069] Figure 10 shows a charged particle microscope image 1002 and an HDR charged particle microscope image 1004 generated based on multiple instances of image data acquired with different charged particle microscope parameters. Figure 10 also shows a first auto-segmented image 1006 of the sample acquired using image 1002 and a second auto-segmented image 1008 of the sample acquired using image 1004. As can be seen in each of Figures 7-10, the methods and systems according to the present invention enable the creation of HDR charged particle data structures with dramatically improved levels of information over current state-of-the-art techniques, allowing algorithms (such as k-means clustering algorithms) to be applied thereto to more accurately determine features of the imaged sample.

[0070] 11 is a visual flow diagram illustrating an exemplary process 1100 for performing EDS analysis of a sample using multiple instances of image data acquired with different charged particle microscope parameters in accordance with the present disclosure. Specifically, FIG. 11 shows a visual output of an exemplary method for performing EDS on a sample using multiple instances of image data acquired with different charged particle microscope parameters.

[0071] Image 1102 shows multiple instances of image data acquired with different charged particle microscope parameters. Image 1104 shows an HDR data structure generated based on the multiple instances of acquired image data. In some embodiments, the HDR data structure is generated based at least in part on applying an HDR algorithm to the multiple instances of image data, a multidimensional data structure generated using the multiple instances of image data, one or more images generated from the multiple instances of image data, or a combination thereof. Image 1104 shows the HDR data structure as an HDR charged particle image.

[0072] Image 1006 shows a segmented HDR charged particle image. In some embodiments, the segmented HDR charged particle image can be generated by applying an algorithm to the HDR charged particle image that determines features of the sample (e.g., material transitions). Image 1008 shows a smoothed segmented HDR charged particle image. The smoothed segmented HDR charged particle image can be generated by applying a smoothing algorithm to the segmented HDR charged particle image that removes noise artifacts.

[0073] Image 1110 shows a segmented region of the sample selected for EDS processing. In various embodiments of the present disclosure, the segmented region may be selected based on user input (e.g., user selection via a GUI), automatically by a computing system (e.g., based on size or shape), or a combination thereof (e.g., a region having properties matching a user-selected material profile).

[0074] Image 1112 shows a segmented region of the sample where at least a portion of the noise artifact has been reintroduced. Image 1114 shows an algorithm for selecting a location within the segmented region of the sample where EDS analysis will be performed. In some embodiments, the algorithm finds a location within the segmented region that is farthest from the boundary of the segmented region and / or the noise artifact. In this way, the locations determined by the algorithm do not overlap and / or are not close to the noise artifact, ensuring that the EDS analysis will not occur at a location on the sample where there is a small defect, impurity, or geographic anomaly (e.g., crack, micro-deposit, etc.). Once a location is selected, instructions may be set to cause the EDS to perform EDS analysis of the sample by irradiating a location on the sample corresponding to the selected location.

[0075] Examples of inventive subject matter according to the present disclosure are described in the following listed paragraphs.

[0076] A1. A method for performing HDR charged particle analysis using a clustering process, the method including: accessing first image data of a first image of a region of a sample, the first image data being acquired by a charged particle microscope, the first image data being acquired with a first parameter configuration; accessing second image data of a second image of the region of the sample, the second image data being acquired by a charged particle microscope system, the second data being acquired with a second parameter configuration different from the first parameter configuration; generating an HDR charged particle microscope data structure using the first image data and the second image data; and identifying one or more features of the sample based on the HDR charged particle microscope data structure.

[0077] A1.1. The method of paragraph 1, wherein the HDR charged particle microscope data structure is generated, at least in part, by applying an HDR algorithm to at least the first data and the second data.

[0078] A1.1.1. The method of paragraph A1.1, wherein the HDR algorithm applies weights to individual pixel values ​​of the first image data and the second image data.

[0079] A1.1.1.1. The method of paragraph A1.1.1, wherein each weight corresponds to the importance of a corresponding individual pixel value, and then determines the individual pixel values ​​of the HDR image based on the weights.

[0080] A1.1.1.2. The method of any one of paragraphs A1.1.1 to A1.1.1.1, wherein the individual weights of individual pixel values ​​are based on neighboring pixel values ​​of pixels proximate to the corresponding individual pixel.

[0081] A1.1.2. The method of any one of paragraphs A1 to A1.1.1.2, wherein constructing the HDR charged particle microscope data structure includes converting first image data into a first charged particle image, converting second data into a second charged particle image, and applying an HDR algorithm to the first image and the second image to generate the HDR charged particle image.

[0082] A1.1.2.1. The method of paragraph A1.1.2, wherein generating an HDR image includes generating a first mask that identifies important regions of a first image, generating a second mask that identifies important regions of a second image, and generating the HDR based on the first mask and the second mask.

[0083] A1.1.2.1.1. The method of paragraph A1.1.2.1, wherein the first mask applies weights to at least pixels of the first image.

[0084] A1.1.2.2. The method of any one of paragraphs A1.1.2 to A1.1.2.1.1, wherein the HDR algorithm generates HDR pixel values ​​for each pixel location based on pixel values ​​of the first image data and the second image data corresponding to the each pixel location.

[0085] A1.1.2.2.1. The method of paragraph A1.1.2.2, wherein the HDR algorithm determines HDR pixel values ​​at least in part by averaging corresponding pixel values ​​of the first image data and the second image data that correspond to individual pixel locations.

[0086] A1.1.2.2.2. The method of any one of paragraphs A1.1.2.2 through A1.1.2.2.1, wherein the HDR algorithm further generates an HDR pixel value for each pixel location based on additional pixel values ​​corresponding to additional pixel locations surrounding the each pixel location.

[0087] A1.1.2.2.3. The method of any one of paragraphs A1.1.2.2 to A1.1.2.2.2, wherein the HDR algorithm determines HDR pixel values ​​based at least in part on weights assigned to individual pixel values ​​of the first image data and the second image data.

[0088] A1.1.2.2.4. The method of any one of paragraphs A1.1.2.2 to A1.1.2.2.3, wherein the HDR algorithm determines the HDR pixel value based at least in part on a weighted average that takes into account one or more of weights assigned to individual pixel values ​​of the first image data and the second image data and additional pixel values ​​corresponding to additional pixel locations surrounding the individual pixel location.

[0089] A1.1.2.2.5. The method of any one of paragraphs A1.1.2.2 to A1.1.2.2.4, wherein the HDR algorithm generates HDR pixel values ​​based on pixel values ​​having associated weights greater than or equal to a threshold.

[0090] A1.2. The method of any one of paragraphs A1 to 1.1.2.1.1, wherein one or more features of the sample are identified at least in part by applying a segmentation algorithm to the HDR charged particle microscope data structure.

[0091] A1.3. A method according to any one of paragraphs A1 to A1.2, wherein constructing the HDR charged particle microscope data structure includes generating a multidimensional data structure, and individual values ​​from each image are stored in the multidimensional data structure in relation to corresponding pixel locations.

[0092] A1.3.1. The method of paragraph A1.3, wherein a first value associated with a first pixel location in a first image is stored in association with a second pixel value associated with the first pixel location in a second image in a multidimensional data structure.

[0093] A1.3.2. The method of any one of paragraphs A1.3 to A1.3.1, wherein identifying the one or more features of the sample includes applying a segmentation algorithm to the multidimensional data structure.

[0094] A1.3.3. The method of any one of paragraphs A1.3 to A1.3.2, wherein the segmentation algorithm determines HDR pixel values ​​of the HDR charged particle image based at least in part on averaging corresponding values ​​in a multidimensional data structure associated with corresponding individual pixel locations.

[0095] A1.3.4. The method of any one of paragraphs A1.3 to A1.3.3, wherein the segmentation algorithm further generates HDR pixel values ​​for individual pixel locations of the HDR charged particle image based on additional values ​​associated with additional pixel locations surrounding the corresponding individual pixel location.

[0096] A1.3.5. The method of any one of paragraphs A1.3 to A1.3.4, wherein the segmentation algorithm determines HDR pixel values ​​based at least in part on weights assigned to individual pixel values ​​in the multi-dimensional data structure.

[0097] A1.3.6. The method of any one of paragraphs A1.3 to A1.3.5, wherein the segmentation algorithm determines the HDR pixel value based at least in part on a weighted average that takes into account one or more of weights assigned to individual pixel values ​​of the multidimensional data structure and additional pixel values ​​in the multidimensional data structure that correspond to additional pixel locations surrounding the individual pixel location.

[0098] A1.3.7. The method of any one of paragraphs A1.3 to A1.3.6, wherein the segmentation algorithm generates HDR pixel values ​​based on pixel values ​​having associated weights greater than or equal to a threshold.

[0099] A1.3.8. The method of any one of paragraphs A1.3 to A1.3.6, wherein the segmentation algorithm generates HDR pixel values ​​based on a subset of pixel values ​​associated with corresponding individual pixel locations.

[0100] A1.3.8.1. The method of paragraph A1.3.8.1, wherein the subset of pixel values ​​corresponds to a preset number of pixel values ​​associated with corresponding individual pixel locations having the highest values ​​and / or pixel values ​​associated with corresponding individual pixel locations that are equal to or greater than a threshold value.

[0101] A2. The method of any one of paragraphs A1-A1.2, wherein identifying one or more features of the sample based on the HDR charged particle microscope data structure corresponds to determining a material transition within the sample.

[0102] A2.1. The method of paragraph 2, wherein identifying one or more features of the sample based on the HDR charged particle microscopy data structure corresponds to determining a plurality of material transitions within the sample.

[0103] A2.1.1. The method of paragraph 2.1, further comprising generating a segmented HDR image of the sample based on a plurality of material transitions.

[0104] A2.1.1.1. The method of paragraph 2.1.1, wherein the segmented HDR image segments regions of the HDR image that correspond to distinct material deposits within the sample.

[0105] A3. The method of any one of paragraphs A1 to A2.1.1.1, wherein identifying one or more features includes identifying geometric features of the sample.

[0106] A4. The method of any one of paragraphs A1 to A3, wherein identifying one or more features includes determining an area of ​​the sample that corresponds to a single material deposit.

[0107] A5. The method of any one of paragraphs A1 to A3, wherein the charged particle microscope is an electron microscope.

[0108] A5.1. The method of paragraph A5, wherein the first image data and the second image data are each generated based on electron backscatter detected by a sensor of the electron microscope.

[0109] A6. The method of any one of paragraphs A1 to A5.1, wherein the difference between the first parameter and the second parameter includes a difference in contrast, brightness, beam intensity, beam type, gamma, and / or combinations thereof.

[0110] A7. The method of any one of paragraphs A1 to A6, wherein the first image data and the second image data are acquired sequentially by a charged particle microscope.

[0111] A7.1. The method of paragraph A7, wherein the configuration of the charged particle microscope is changed from first parameters to second parameters between the acquisition of the first image data and the acquisition of the second image data.

[0112] A8. The method of any one of paragraphs A1 to A6, wherein the first image data and the second image data are acquired in parallel.

[0113] A8.1. The method of paragraph A8, wherein the first image data is detected by a first detector and the second image data is acquired using a second detector separate from the first detector.

[0114] A8.1.1. The method of paragraph A8.1, wherein the first detector and the second detector are different types of detectors.

[0115] A8.1.2. The method of any one of paragraphs A8.1 to A8.1.1, wherein the first detector and the second detector have different configurations, positions, orientations, filters, or combinations thereof.

[0116] A8.2. The method of paragraph A8, wherein the first image data and the second image data are identically detected using the same detector.

[0117] A8.2.1. The method of paragraph A8.2, wherein the first image data is detected using a first portion of the detector and the second image data is detected using a second portion of the detector.

[0118] A8.2.1.1. The method of paragraph A8.2.1, wherein the first portion of the detector includes a filter that causes parameters of the first portion to differ from parameters of the second portion of the detector.

[0119] A9. The method of any one of paragraphs A1 to A8.2.1.1, further comprising causing a charged particle system to acquire at least first image data and second image data.

[0120] A9.1. The method of paragraph A9, wherein the first image data and the second image data are acquired using a charged particle imaging process.

[0121] A10. The method of any one of paragraphs A1 to A9.1, further comprising accessing third image data of a third image of the region of the sample, the third image data being acquired by the charged particle microscope system, the third data being acquired at a third parameter configuration different from the first parameter configuration and the second parameter configuration.

[0122] A10.1. The method of paragraph A10, wherein generating the HDR charged particle microscope data structure is further performed using third image data.

[0123] A11. The method of any one of paragraphs A1 to A10.1, further comprising accessing a plurality of additional image data for a plurality of additional images of the region of the sample, each additional image data being acquired by a charged particle microscope system, and each additional image data being acquired with a different parameter configuration.

[0124] A11.1. The method of paragraph A11, wherein the HDR charged particle microscope data structure is further implemented using a plurality of additional image data.

[0125] A12. The method of any one of paragraphs A1 to A11.1, further comprising performing EDS analysis on the sample based on the identified one or more features.

[0126] A12.1. The method of paragraph A12, wherein the EDS analysis includes determining an area of ​​the sample corresponding to a single material deposit based on one or more features.

[0127] A12.2. The method of any one of paragraphs A12 to A12.1, wherein the EDS analysis includes determining a point within the single material deposit and performing EDS analysis on the single material deposit by irradiating the determined point.

[0128] A12.2.1. The method of paragraph A12.2, wherein determining a point within the single-material deposit corresponds to finding the point farthest from the boundary of the single-material deposit.

[0129] A12.2.1.1. The method of paragraph A12.2.1, wherein determining the area of ​​the sample corresponding to the single material deposit includes applying a smoothing algorithm to the HDR charged particle data structure to remove noise.

[0130] A12.2.1.1.1. The method of paragraph A12.2.1.1, wherein finding a point further away from the boundary of the single-material deposit includes finding a point farthest from either the boundary of the single-material deposit or an instance of noise removed by the smoothing algorithm.

[0131] A12.2.2. The method of any one of paragraphs A12.2 to A12.2.1.1.1, wherein performing EDS analysis on the single-material deposit by irradiating the determined point includes detecting spectral radiation emitted by the single irradiation of the point within the single-material deposit, and determining the chemical composition of the single-material deposit based on the spectral radiation.

[0132] A12.3. The method of any one of paragraphs A12 to A12.2.2, wherein the EDS analysis includes determining, based on one or more features, additional areas of the sample corresponding to additional single material deposits.

[0133] A12.3.1. The method of paragraph A12.3, wherein determining additional areas of the sample corresponding to additional single-material deposits includes determining additional points within the additional single-material deposits and performing EDS analysis on the additional single-material deposits by irradiating the additional determined points.

[0134] A12.4. The method of any one of paragraphs A12 to A12.3.1, wherein performing an EDS analysis includes performing an EDS analysis on all material deposits identified based on the one or more characteristics.

[0135] A12.5. The method of any one of paragraphs A12 to A12.3.1, wherein performing the EDS analysis includes performing the EDS analysis on a plurality of material deposits based on the size of the individual material deposits, the shape of the individual material deposits, and / or a predetermined number of individual material deposits.

[0136] A12.6. The method of any one of paragraphs A12 to A12.3.1, wherein performing the EDS analysis includes receiving a user-selected deposit and causing a charged particle microscope system to automatically perform an EDS analysis on the selected deposit to determine its chemical composition.

[0137] A12.7. The method of any one of paragraphs A12 to A12.5, wherein performing EDS analysis on the sample is an automated process performed without user input.

[0138] B1. A method for performing EDS charged particle analysis using HDR charged particle analysis, the method including: accessing first image data of a first image of a region of a sample, the first image data being acquired by a charged particle microscope, the first image data being acquired with a first parameter configuration; accessing second image data of a second image of a region of the sample, the second image data being acquired by a charged particle microscope system, the second data being acquired with a second parameter configuration different from the first parameter configuration; generating an HDR charged particle microscope data structure using the first image data and the second image data; identifying one or more features of the sample based on the HDR charged particle microscope data structure; and performing EDS analysis on the sample based on the identified one or more features.

[0139] B1.1. The method of paragraph B1, wherein the EDS analysis includes determining an area of ​​the sample corresponding to a single material deposit based on one or more features.

[0140] B1.2. A method according to any one of paragraphs B1 to B1.1, wherein the EDS analysis includes determining a point within the single material deposit and performing EDS analysis on the single material deposit by irradiating the determined point.

[0141] B1.2.1. The method of paragraph B1.2, wherein determining the point within the single-material deposit corresponds to finding the point farthest from the boundary of the single-material deposit.

[0142] B1.2.1.1. The method of paragraph B1.2.1, wherein determining the area of ​​the sample corresponding to the single material deposit includes applying a smoothing algorithm to the HDR charged particle data structure to remove noise.

[0143] B1.2.1.1.1. The method of paragraph B1.2.1.1, wherein finding points further away from the boundary of the single-material deposit includes finding points farthest from either the boundary of the single-material deposit or instances of noise removed by the smoothing algorithm.

[0144] B1.2.2. A method according to any one of B1.2 to B1.2.1.1.1, wherein performing EDS analysis on the single-material deposit by irradiating the determined point includes detecting spectral radiation emitted by the single irradiation of the point within the single-material deposit, and determining the chemical composition of the single-material deposit based on the spectral radiation.

[0145] B1.3. The method of any one of paragraphs B1 to B1.2.2, wherein the EDS analysis includes determining, based on one or more features, additional areas of the sample corresponding to additional single material deposits.

[0146] B1.3.1. The method of paragraph B1.3, wherein determining additional areas of the sample corresponding to additional single-material deposits includes determining additional points within the additional single-material deposits and performing EDS analysis on the additional single-material deposits by irradiating the additional determined points.

[0147] B1.4. The method of any one of paragraphs B1 to B1.3.1, wherein performing EDS analysis includes performing EDS analysis on all material deposits identified based on one or more characteristics.

[0148] B1.5. The method of any one of paragraphs B1 to B1.3.1, wherein performing the EDS analysis includes performing EDS analysis on multiple material deposits based on the size of the individual material deposits, the shape of the individual material deposits, and / or a predetermined number of individual material deposits.

[0149] B1.16. The method of any one of paragraphs B1 to B1.3.1, wherein performing the EDS analysis includes receiving a user-selected deposit and causing a charged particle microscope system to automatically perform an EDS analysis on the selected deposit to determine its chemical composition.

[0150] B1.7. The method of any one of paragraphs B1 to B1.5, wherein performing EDS analysis on the sample is an automated process performed without user input.

[0151] B1.8. The method of paragraph B1, wherein the HDR charged particle microscope data structure is generated, at least in part, by applying an HDR algorithm to at least the first data and the second data.

[0152] B1.8.1. The method of paragraph B1.8, wherein the HDR algorithm applies weights to individual pixel values ​​of the first image data and the second image data.

[0153] B1.8.1.1. The method of paragraph B1.8.1, wherein each weight corresponds to the importance of a corresponding individual pixel value, and the method then determines the individual pixel values ​​of the HDR image based on the weights.

[0154] B1.8.1.2. The method of any one of paragraphs B1.8.1 to B1.8.1.1, wherein the individual weight of each pixel value is based on neighboring pixel values ​​of pixels proximate to the corresponding each pixel.

[0155] B1.8.2. The method of any one of paragraphs B1.1 to B1.8.1.2, wherein constructing the HDR charged particle microscope data structure includes converting the first image data into a first charged particle image, converting the second data into a second charged particle image, and applying an HDR algorithm to the first image and the second image to generate the HDR charged particle image.

[0156] B1.8.2.1. The method of paragraph B1.8.2, wherein generating an HDR image includes generating a first mask that identifies important regions of a first image, generating a second mask that identifies important regions of a second image, and generating the HDR based on the first mask and the second mask.

[0157] B1.8.2.1.1. The method of paragraph B1.8.2.1, wherein the first mask applies weights to at least pixels of the first image. B1.8.2.2. The method of any one of paragraphs B1.8.2 through B1.8.2.1.1, wherein the HDR algorithm generates HDR pixel values ​​for each pixel location based on pixel values ​​of the first image data and the second image data corresponding to the each pixel location.

[0158] B1.8.2.2.1. The method of paragraph B1.8.2.2, wherein the HDR algorithm determines HDR pixel values ​​at least in part by averaging corresponding pixel values ​​of the first image data and the second image data that correspond to individual pixel locations.

[0159] B1.8.2.2.2. The method of any one of paragraphs B1.8.2.2 through B1.8.2.2.1, wherein the HDR algorithm further generates an HDR pixel value for each pixel location based on additional pixel values ​​corresponding to additional pixel locations surrounding the each pixel location.

[0160] B1.8.2.2.3. The method of any one of paragraphs B1.8.2.2 to B1.8.2.2.2, wherein the HDR algorithm determines HDR pixel values ​​based at least in part on weights assigned to individual pixel values ​​of the first image data and the second image data.

[0161] B1.8.2.2.4. The method of any one of paragraphs B1.8.2.2 to B1.8.2.2.3, wherein the HDR algorithm determines the HDR pixel value based at least in part on a weighted average that takes into account one or more of weights assigned to individual pixel values ​​of the first image data and the second image data and additional pixel values ​​corresponding to additional pixel locations surrounding the individual pixel location.

[0162] B1.8.2.2.5. The method of any one of paragraphs B1.8.2.2 through B1.8.2.2.4, wherein the HDR algorithm generates HDR pixel values ​​based on pixel values ​​having associated weights greater than or equal to a threshold.

[0163] B1.9. The method of any one of paragraphs B1 through B1.8.2.1.1, wherein one or more features of the sample are identified at least in part by applying a segmentation algorithm to the HDR charged particle microscope data structure.

[0164] B.1.10. A method according to any one of paragraphs B1 to B1.9, wherein constructing the HDR charged particle microscope data structure includes generating a multidimensional data structure, and individual values ​​from each image are stored in the multidimensional data structure in relation to corresponding pixel locations.

[0165] B.1.10.1. The method of paragraph B.1.10, wherein a first value associated with a first pixel location in a first image is stored in a multidimensional data structure in association with a second pixel value associated with the first pixel location in a second image.

[0166] B.1.10.2. The method of any one of paragraphs B.1.10 to B.1.10.1, wherein identifying the one or more features of the sample includes applying a segmentation algorithm to the multidimensional data structure.

[0167] B.1.10.3. The method of any one of paragraphs B.1.10 to B.1.10.2, wherein the segmentation algorithm determines HDR pixel values ​​of the HDR charged particle image based at least in part on averaging corresponding values ​​in a multidimensional data structure associated with corresponding individual pixel locations.

[0168] B.1.10.4. A method according to any one of paragraphs B.1.10 to B.1.10.3, wherein the segmentation algorithm further generates HDR pixel values ​​for individual pixel positions of the HDR charged particle image based on additional values ​​associated with additional pixel positions surrounding the corresponding individual pixel positions.

[0169] B.1.10.5. The method of any one of paragraphs B.1.10 to B.1.10.4, wherein the segmentation algorithm determines HDR pixel values ​​based at least in part on weights assigned to individual pixel values ​​in the multi-dimensional data structure.

[0170] B.1.10.6. The method of any one of paragraphs B.1.10 to B.1.10.5, wherein the segmentation algorithm determines the HDR pixel value based at least in part on a weighted average that takes into account one or more of weights assigned to individual pixel values ​​of the multidimensional data structure and additional pixel values ​​in the multidimensional data structure that correspond to additional pixel locations surrounding the individual pixel location.

[0171] B.1.10.7. The method of any one of paragraphs B.1.10 to B.1.10.6, wherein the segmentation algorithm generates HDR pixel values ​​based on pixel values ​​having associated weights greater than or equal to a threshold.

[0172] B.1.10.8. The method of any one of paragraphs B.1.10 to B.1.10.6, wherein the segmentation algorithm generates HDR pixel values ​​based on a subset of pixel values ​​associated with corresponding individual pixel locations.

[0173] B.1.10.8.1. The method of paragraph B.1.10.8.1, wherein the subset of pixel values ​​corresponds to a preset number of pixel values ​​associated with corresponding individual pixel locations having the highest values ​​and / or pixel values ​​associated with corresponding individual pixel locations that are equal to or greater than a threshold value.

[0174] B2. The method of any one of paragraphs B1 to B.1.10.8.1, wherein identifying one or more features of the sample based on the HDR charged particle microscope data structure corresponds to determining a material transition within the sample.

[0175] B2.1. The method of paragraph B2, wherein identifying one or more features of the sample based on the HDR charged particle microscope data structure corresponds to determining a plurality of material transitions within the sample.

[0176] B2.1.1. The method of paragraph B2.1, further comprising generating a segmented HDR image of the sample based on a plurality of material transitions.

[0177] B2.1.1.1. The method of paragraph B2.1.1, wherein the segmented HDR image segments regions of the HDR image that correspond to distinct material deposits within the sample.

[0178] B3. The method of any one of paragraphs B1 to B2.1.1.1, wherein identifying one or more features includes identifying geometric features of the sample.

[0179] B4. The method of any one of paragraphs B1 to B3, wherein identifying one or more features includes determining an area of ​​the sample that corresponds to a single material deposit.

[0180] B5. The method of any one of paragraphs B1 to B3, wherein the charged particle microscope is an electron microscope.

[0181] B5.1. The method of paragraph B5, wherein the first image data and the second image data are each generated based on electron backscatter detected by a sensor of the electron microscope.

[0182] B6. The method of any one of paragraphs B1 to B5.1, wherein the difference between the first parameter and the second parameter includes a difference in contrast, brightness, beam intensity, beam type, gamma, and / or combinations thereof.

[0183] B7. The method of any one of paragraphs B1 to B6, wherein the first image data and the second image data are acquired sequentially by a charged particle microscope.

[0184] B7.1. The method of paragraph B7, wherein the configuration of the charged particle microscope is changed from first parameters to second parameters between the acquisition of the first image data and the acquisition of the second image data.

[0185] B8. The method of any one of paragraphs B1 to B6, wherein the first image data and the second image data are acquired in parallel.

[0186] B8.1. The method of paragraph B8, wherein the first image data is detected by a first detector and the second image data is acquired using a second detector separate from the first detector.

[0187] B8.1.1. The method of paragraph B8.1, wherein the first detector and the second detector are different types of detectors.

[0188] B8.1.2. The method of any one of paragraphs B8.1 to B8.1.1, wherein the first detector and the second detector have different configurations, positions, orientations, filters, or combinations thereof.

[0189] B8.2. The method of paragraph B8, wherein the first image data and the second image data are detected in the same way using the same detector.

[0190] B8.2.1. The method of paragraph B8.2, wherein the first image data is detected using a first portion of the detector and the second image data is detected using a second portion of the detector.

[0191] B8.2.1.1. The method of paragraph B8.2.1, wherein the first portion of the detector includes a filter that causes parameters of the first portion to differ from parameters of the second portion of the detector.

[0192] B9. The method of any one of paragraphs B1 to B8.2.1.1, further comprising causing a charged particle system to acquire at least first image data and second image data.

[0193] B9.1. The method of paragraph B9, wherein the first image data and the second image data are acquired using a charged particle imaging process.

[0194] B10. The method of any one of paragraphs B1 to B9.1, further comprising accessing third image data of a third image of the region of the sample, the third image data being acquired by the charged particle microscope system, the third data being acquired at a third parameter configuration different from the first parameter configuration and the second parameter configuration.

[0195] B10.1. The method of paragraph B10, wherein generating the HDR charged particle microscope data structure is further performed using third image data.

[0196] B11. The method of any one of paragraphs B1 to B10.1, further comprising accessing a plurality of additional image data for a plurality of additional images of the region of the sample, each additional image data being acquired by a charged particle microscope system, and each additional image data being acquired with a different parameter configuration.

[0197] B11.1. The method of paragraph B11, wherein the HDR charged particle microscope data structure is further implemented using a plurality of additional image data.

[0198] C1. A system for performing HDR charged particle analysis using a clustering process, the system including: a sample holder configured to support a sample; a focused charged beam source configured to apply a focused charged beam to the sample; a detector system configured to detect radiation from the sample due to irradiation by the charged particle beam source; one or more processors; and memory storing non-transitory computer-readable instructions that, when executed by the one or more processors, cause the one or more processors to perform a method described in any one of paragraphs A1 through A12.7 and / or B1 through B11.1.

[0199] C2. The system of paragraph C1, further comprising an EDS system.

[0200] D1. Use of a system according to any one of paragraphs C1 to C2 to carry out a method according to any one of paragraphs A1 to A12.7 and B1 to B11.1.

[0201] E1. A non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to begin performing a method described in any one of paragraphs A1 through A12.7 and / or B1 through B11.1.

[0202] F1. Use of the non-transitory computer-readable medium of paragraph F1 to perform the method of any one of paragraphs A1 through A12.7 and / or B1 through B11.1.

Claims

1. 1. A method for performing HDR charged particle analysis using a clustering process, the method comprising: accessing first image data of a first image of an area of ​​a sample, the first image data being acquired by a charged particle microscope, the first image data being acquired with a first parameter configuration; accessing second image data of a second image of the region of the sample, the second image data being acquired by the charged particle microscope, the second image data being acquired with a second parameter configuration different from the first parameter configuration; generating an HDR charged particle microscope data structure using the first image data and the second image data; identifying one or more features of the sample based on the HDR charged particle microscope data structure; wherein generating the HDR charged particle microscope data structure includes generating a multi-dimensional data structure, wherein individual values ​​from each image are stored in the multi-dimensional data structure in association with corresponding individual pixel locations.

2. generating the HDR charged particle microscope data structure; converting the first image data into a first charged particle image; converting the second image data into a second charged particle image; and applying an HDR algorithm to the first image and the second image to generate an HDR charged particle image.

3. generating the HDR charged particle image generating a first mask identifying regions of interest in the first image; generating a second mask identifying regions of interest in the second image; generating the HDR charged particle image based on the first mask and the second mask.

4. The method of claim 2 , wherein identifying the one or more features of the sample comprises applying a segmentation algorithm to the multi-dimensional data structure.

5. 5. The method of claim 4, wherein the segmentation algorithm further generates HDR pixel values ​​for individual pixel locations of the HDR charged particle image based on additional values ​​associated with additional pixel locations surrounding the corresponding individual pixel location.

6. The method of claim 4 , wherein the segmentation algorithm generates HDR pixel values ​​based on pixel values ​​that have an associated weight greater than or equal to a threshold.

7. 7. The method of claim 1, wherein identifying the one or more features of the sample based on the HDR charged particle microscope data structure corresponds to determining a material transition within the sample.

8. The method of claim 7 , further comprising generating a segmented HDR charged particle image of the sample based on the determined material transitions within the sample.

9. 9. The method of claim 1, wherein the difference between the first parameter configuration and the second parameter configuration comprises a difference in contrast, brightness, beam intensity, beam type, gamma, and / or combinations thereof.

10. 10. The method according to claim 1, wherein the first image data and the second image data are acquired sequentially by the charged particle microscope, and between the acquisition of the first image data and the acquisition of the second image data, a parameter configuration of the charged particle microscope is changed from the first parameter configuration to the second parameter configuration.

11. The method of claim 1 , wherein the first image data and the second image data are acquired in parallel.

12. 12. The method of claim 1, further comprising accessing third image data of a third image of the region of the sample, the third image data being acquired by the charged particle microscope, the third image data being acquired with a third parameter configuration different from the first parameter configuration and the second parameter configuration, and generating the HDR charged particle microscope data structure is further performed using the third image data.

13. 13. The method of claim 1, further comprising performing EDS analysis on the sample based on the identified one or more features.

14. The EDS analysis is Determining a point within the single material deposit; and performing an EDS analysis of the single material deposit by irradiating the determined point.

Images