Sample support grid identification

Abstract

Embodiments herein relate to a method for sample support identification. A system may include a memory storing computer executable components and a processor executing the computer executable components. These computer executable components may include: an imaging component that captures an image of an unknown sample support comprising a layer of material; and a matching component that matches the unknown sample support to a known sample support based on an unknown heterogeneity profile in the image of the unknown sample support that includes one or more heterogeneity of the material layer.

Description

Background Art

[0001] Scientific instruments for materials analysis can help determine the composition and properties of unknown components. In one or more examples, scientific instruments can provide high-resolution positioning, manipulation, and / or analysis relative to a sample within a range of hundreds of nanometers or less in one dimension. BRIEF DESCRIPTION OF THE DRAWINGS

[0002] Each embodiment will be readily understood by the following detailed description in conjunction with the accompanying drawings. For ease of description, identical reference numerals denote identical structural elements. Each embodiment is illustrated in the various figures of the accompanying drawings by way of example and not limitation.

[0003] Figure 1 A block diagram illustrating an example scientific instrument for performing operations according to one or more embodiments described herein is illustrated.

[0004] Figure 2 Illustrate the use of one or more embodiments described herein Figure 1 A flowchart of an example method for performing operations on a scientific instrument.

[0005] Figure 3 A graphical user interface (GUI) that can be used to perform one or more of the methods described herein is illustrated, according to one or more embodiments described herein.

[0006] Figure 4 A block diagram illustrating an example computing device that can perform one or more of the methods disclosed herein, according to one or more embodiments described herein.

[0007] Figure 5 Illustrated is a block diagram of an example non-limiting system that can facilitate a process for identifying a sample support grid according to one or more embodiments described herein.

[0008] Figure 6 Illustrated is a block diagram of another example non-limiting system that can facilitate a process for identifying a sample support grid according to one or more embodiments described herein.

[0009] Figure 7 An illustration of a pair of sample support containers according to one or more embodiments described herein is provided.

[0010] Figure 8 Provided are diagrams of a set of sample support grid fabrication steps according to one or more embodiments described herein.

[0011] Figure 9 Illustrated according to one or more embodiments described herein Figure 6 Schematic diagram of the imaging setup of a non-limiting system.

[0012] Figure 10 A diagram illustrating an exemplary sample support grid image according to one or more embodiments described herein.

[0013] Figure 11 Provided are examples of methods that can be implemented according to one or more embodiments described herein. Figure 6 A non-limiting system performs a matching step for a set of sample support grid images.

[0014] Figure 12 Further examples of methods that can be used according to one or more embodiments described herein are provided. Figure 6 Schematic diagram of the matching steps performed by a non-limiting system.

[0015] Figure 13 Illustrate that according to one or more embodiments described herein, Figure 6 A flowchart of one or more processes performed by an automatic positioning system.

[0016] Figure 14 Illustrate that according to one or more embodiments described herein, Figure 6 A flowchart of one or more processes performed by an automatic positioning system.

[0017] Figure 15 Illustrate that according to one or more embodiments described herein, Figure 6 One or more processes performed by a non-limiting system Figure 14 Continuation of the flowchart.

[0018] Figure 16 Illustrate that according to one or more embodiments described herein, Figure 6 One or more processes performed by a non-limiting system Figure 14 Continuation of the flowchart.

[0019] Figure 17 Illustrate that according to one or more embodiments described herein, Figure 6 Another flow chart of one or more processes performed by an automatic positioning system.

[0020] Figure 18 Illustrate that according to one or more embodiments described herein, Figure 6 One or more processes performed by a non-limiting system Figure 17 Continuation of the flowchart.

[0021] Figure 19 Illustrated is a block diagram of an example scientific instrument system in which one or more of the methods described herein may be performed, according to one or more embodiments described herein.

[0022] Figure 20 A block diagram illustrates an example operating environment in which implementations of the subject matter described herein may be incorporated.

[0023] Figure 21 An example schematic block diagram illustrating a computing environment with which the subject matter described herein may interact and / or be at least partially implemented is illustrated. Summary of the Invention

[0024] An overview is presented below to provide a basic understanding of one or more embodiments described herein. This overview is not intended to identify key or important elements and / or to delineate the scope of a particular embodiment or the scope of the claims. Its sole purpose is to present the concepts in a simplified form as a prelude to a more detailed description presented later. In one or more embodiments, the systems, computer-implemented methods, devices, and / or computer program products described herein can provide a process for identifying a sample support grid for supporting a sample using an observation system. For example, such an observation system can include an electron microscope (EM), such as a scanning electron microscope (SEM) or a transmission electron microscope (TEM), and / or a focused ion beam (FIB) device.

[0025] According to one embodiment, a system may include a memory storing computer-executable components and a processor executing the computer-executable components. The computer-executable components may include an imaging component that captures an image of an unknown sample support including a material layer, and a matching component that matches the unknown sample support to a known sample support based on an unknown inhomogeneity profile in the image of the unknown sample support including one or more inhomogeneities of the material layer.

[0026] According to another embodiment, a computer-implemented method may include capturing, by a system operatively coupled to a processor, an image of an unknown sample support including a material layer; and matching, by the system, the unknown sample support to a known sample support based on a matching of one or more unknown non-uniformities of the material layer in the image of the unknown sample support with one or more known non-uniformities in an image of the known sample support.

[0027] According to yet another embodiment, a computer program product that facilitates a process for sample support identification may include a computer-readable storage medium having program instructions embodied therein, the program instructions executable by a processor to: compare, by the processor, an unknown inhomogeneity profile at an image of an unknown sample support that includes one or more inhomogeneities of the unknown sample support with a known inhomogeneity profile of an image of a known sample support; and based on a result of the comparison, identify, by the processor, the unknown sample support as the known sample support.

[0028] For example, one or more embodiments disclosed herein can achieve improved sample support grid identification compared to conventional techniques that employ only labeling and guesswork. Conversely, one or more embodiments described herein can provide improved performance of imaging systems by providing an approved and recognized baseline for imaging using a validated sample support grid. That is, such a system can employ known dimensions and / or other measurements of a sample support grid to which a sample of interest can be attached. Verifying that the sample support grid being used is indeed the sample support grid corresponding to such dimensions and / or measurements can reduce imaging and / or measurement errors during subsequent analysis of the corresponding sample of interest.

[0029] In conjunction with the foregoing, one or more embodiments described herein can provide verifiable tracking of one or more (e.g., multiple) sample support grids at different locations and / or by different entities across various stages of their manufacture, processing, and / or preparation. That is, physical properties of the sample support grids themselves can be employed to identify the sample support grids, thereby providing for identifying one or more sample support grids of interest as the correct sample support grids (e.g., those designated for use) with respect to one or more imaging systems.

[0030] For example, based on a designated application of a light source to a sample support grid, one or more non-uniformities of one or more materials of the sample support grid can be used to track (e.g., as a fingerprint or other profile) the identification of the sample support grid, both initially and during one or more subsequent identification periods. This can allow for more efficient and accurate identification of sample supports and / or verified confirmation that a designated sample support grid was indeed employed, compared to prior art techniques. Furthermore, this can allow for more accurate placement of samples, such as thin layers, on and / or at the sample support, compared to prior art techniques.

[0031] By using a matching process that utilizes one or more inhomogeneities of the material of the selected unknown sample support grid compared to one or more inhomogeneities of an image of a known sample support grid, identification of the selected unknown sample support grid can be made increasingly more efficient. In one or more embodiments, a comparison score generated for this comparison can be aggregated with one or more parameter scores corresponding to a comparison of one or more secondary parameters of the selected unknown sample support grid with one or more corresponding secondary parameters of the known sample support grid. DETAILED DESCRIPTION

[0032] The following detailed description is merely illustrative and is not intended to limit the application or utilization of the embodiments and / or embodiments. In addition, there is no intention to be constrained by any express or implied information presented in the preceding Summary of the Invention section or the Detailed Description of the Invention section. One or more embodiments will now be described with reference to the accompanying drawings, wherein the same reference numerals are used throughout to refer to the same elements. In the following description, for the purpose of explanation, many specific details are set forth to provide a more thorough understanding of one or more embodiments. However, it is apparent that in various cases, one or more embodiments may be practiced without these specific details.

[0033] The various operations may be described as a plurality of discrete actions or operations in a manner that is most helpful in understanding the subject matter disclosed herein. However, the order described should not be interpreted as implying that these operations must rely on the order. Specifically, these operations may be performed in an order different from the order presented. The described operations may be performed in an order different from the described embodiment. Various additional operations may be performed, and / or the described operations may be omitted in additional embodiments.

[0034] Turning now to the subject of materials analysis and one or more embodiments described herein, one method of obtaining compositional imaging can be electron microscopy, wherein a sample is targeted by a source such as an ion source or an electron source, ultimately resulting in the emission (and / or generation) of secondary charged particles, such as secondary electrons and / or secondary ions that can be detected and aligned to then generate an image of the sample.

[0035] Using existing technology, setups for this type of material analysis currently rely on manually performed marking systems to track the identification of different sample support grids relative to each other during one or more pre-processing steps for the sample support grids (also referred to herein as sample supports, sample grids, and / or support grids). During such material analysis, a sample of interest may be attached to the sample support. The attachment, analysis, and / or imaging of the sample of interest may depend, at least in part, on one or more specified parameters of the sample support, such as material composition, size, surface angle, mass, and / or the like. Thus, where a sample support is specified relative to one or more other sample supports, it may be desirable to employ the specified sample support rather than accidentally employing a different sample support.

[0036] These one or more specified parameters can also be used to identify an attachment area of ​​the sample support where it is desirable to attach and / or otherwise position a sample of interest to be analyzed by the materials analysis system. It should be noted that this arrangement can be employed with respect to a plurality of imaging systems, such as, but not limited to, a scanning electron microscope or a transmission electron microscope (S / TEM), a focused ion beam (FIB) instrument, and / or a dual beam system including both an S / TEM and a FIB instrument.

[0037] Existing techniques for identifying a sample support as being the same as a designated sample support are, but are not limited to, manual, slow, subjective, error-prone, and / or unverifiable. Consequently, one or more errors during material analysis stages (e.g., setup, sample attachment, imaging, and / or other analysis) may result from misidentification of the sample support and / or misidentification of the selected sample support as the designated sample support.

[0038] Furthermore, as described above, such one or more deficiencies may be compounded by different stages of pre-processing of a set of two or more sample supports. That is, each pre-processing step may provide an opportunity to undesirably mix up the order or labeling of sample supports, such as with respect to placement within one or more storage containers. See, for example, the diagram illustrating an open environment sample support container 702 and a sealed vacuum container 704. Figure 7 Each of these containers can hold multiple sample supports therein. That is, while a sample support may initially be placed into a labeled aperture 708 of one or more sample support containers 702, 704, a sample support may be mistakenly rearranged and / or placed back into the wrong aperture 708, a sample support container 702, 704 may be dropped or bumped, causing one or more sample supports to be dislodged from one or more apertures 708, and / or a sample support may be placed into an aperture that is not labeled on the corresponding sample support. See, for example, the labeling of sample support containers 702 and 704 using a combination of letters and numbers. It should be noted that any suitable labeling may be used.

[0039] To address one or more deficiencies or shortcomings of existing frameworks (e.g., existing sample support identification and / or recognition frameworks), one or more embodiments are described herein that can employ a sample support identification system to automatically identify one or more specified sample supports as corresponding one or more known sample supports, such as relative to one or more data records defining the one or more known sample supports. Thus, based on an automated approach, one or more embodiments described herein can achieve high information acquisition relative to a sample support, thereby resulting in more accurate identification of the sample support, regardless of the stage of use of the sample support (e.g., pre-processing, post-processing, material analysis setup, material analysis, and / or material post-analysis). As used herein, the terms "identification" and "recognition" are used interchangeably. The term "recognition" will be used throughout the remainder of this specification.

[0040] One or more automated sample support recognition frameworks described herein may include capturing an image of an unknown sample support, comparing the image of the unknown sample support with one or more images of one or more known sample supports, and matching the unknown sample support with one of the known sample supports. This may be accomplished by employing an automated system for defining a non-uniformity profile of one or more non-uniformities of the unknown sample support, employing the non-uniformity profile during comparison with the non-uniformity profile of a known sample support, generating a comparison score defining a level of similarity between the unknown sample support and the known sample support, generating a match between the unknown sample support and the known sample support based on the comparison score, and generating a notification providing notification of the match.

[0041] To achieve this matching, the automated system can identify one or more non-uniformities (e.g., scratches, blemishes, color variations, indentations, channels, markings, and / or the like) in one or more materials and / or at one or more different surfaces and / or layers of the unknown sample support. The automated system can access a data repository comprising one or more data records defining non-uniformity profiles for one or more known sample supports. The automated system can also identify one or more additional parameters, such as the material, color, mass, and / or size of the unknown sample support, and compare the one or more additional parameters to one or more corresponding additional parameters of one or more known sample supports.

[0042] The match can be based on a threshold likelihood that the match is met, generation of one or more scores, comparison of one or more scores to a threshold, comparison of one or more different comparison scores to determine the highest and / or best score, and / or evaluation of one or more other matches and / or scores relative to one or more other unknown sample supports in a set of unknown sample supports.

[0043] Any one or more of the foregoing steps may be based on a comparison of one or more images of an unknown sample support with one or more images of one or more known sample supports.

[0044] In one or more embodiments, the automated system can assist (such as suggest and / or control) one or more steps for facilitating the capture of one or more images. In one or more embodiments, the automated system can output a suggestion for capturing one or more additional images of the unknown sample support using one or more modified conditions.

[0045] An automated system may include one or more scientific instrument systems and related methods, computing devices, and computer-readable media described herein. For example, in one or more embodiments, the system may include a memory that can store computer-executable components and a processor that executes the computer-executable components stored in the memory. The computer-executable components may include: an imaging component that can capture an image of an unknown sample support; a comparison component that compares the non-uniformity profile of the unknown sample support to the non-uniformity profile of at least one known sample support; and / or a matching component that can match the unknown sample support to a sample support based on the captured image and based on the results of the comparison.

[0046] As noted above, one or more embodiments disclosed herein can achieve improved performance relative to existing methods. For example, based on an analysis of one or more unknown non-uniformities of an unknown sample support and one or more known non-uniformities of a known sample support, a more reliable, more accurate, and less subjective match of an image of an unknown sample with an image of a known sample, and therefore a more reliable, more accurate, and less subjective match of an unknown sample with a known sample, can be generated. This can allow for more efficient and / or more accurate use of the unknown sample support during any subsequent material analysis procedures. For example, this can allow for more accurate placement of samples, such as thin layers, on and / or at the sample support as compared to the prior art. In one or more embodiments, in conjunction with sample placement, a reduced amount and / or smaller area of ​​sample substrate (relative to the preparation of the sample support for sample placement) can be removed from the identified area of ​​the sample support as compared to the prior art due to the more efficient and / or more accurate identification of the unknown sample support.

[0047] Thus, the embodiments disclosed herein provide improvements to scientific instrumentation technology (e.g., improvements to computer technology that supports such scientific instrumentation, among other improvements) that can be used in a variety of fields, including, but not limited to, microscopy, optics, signal processing, spectroscopy, and nuclear magnetic resonance (NMR). For example, in one or more embodiments, the diameter or longest length across the surface of an exemplary sample support can be in the range of 1 mm to about 5 mm, such as about 3 mm.

[0048] Various embodiments disclosed herein can improve upon existing methods to achieve the technical advantages of increased contrast imaging of sample supports, identification of narrower attachment areas, and / or less subjective sample support preparation. That is, use of one or more sample support identification frameworks discussed herein can allow for the automatic identification of a plurality of one or more unknown sample supports as a plurality of one or more known sample supports. One or more frameworks employed herein can employ a sample support identification system as described herein.

[0049] The above-described technical advantages cannot be achieved through routine and existing methods, and all user entities of a system including such an embodiment can benefit from these advantages (e.g., by helping the user entity perform technical tasks, such as identifying an unknown sample support as a previously recorded known sample support).

[0050] Therefore, the technical features of the embodiments disclosed herein (e.g., comparison of inhomogeneity profiles) are undoubtedly unconventional in the field of microscopic imaging, in addition to the fields of optics, signal processing, spectroscopy and / or NMR, but are not limited to these, and the same is true for the combination of features of the embodiments disclosed herein.

[0051] As further discussed herein, various aspects of the embodiments disclosed herein can improve the functionality of the computer itself. That is, the computational and user interface features disclosed herein not only relate to the collection and comparison of information, but also apply new analytical and technical means to change the operation of computer analysis of material compounds. For example, based on the application of various lighting and / or sample support orientations, a more efficient and / or accurate match of an unknown sample support to a known sample support can be generated based on a computer-assisted determination of the acceptability of the sample support image. These processes can all be performed automatically because the matching of sample supports does not rely on manual input as in existing frameworks. Therefore, compared to one or more conventional frameworks, the corresponding computer-guided process of sample support imaging itself can be made easier and more efficient by reducing misidentifications, lacking clear non-uniformity and / or lacking subjective input. Therefore, the non-limiting system described herein, including the sample support identification system 602, can be self-improving.

[0052] Thus, the present disclosure introduces functionality that existing computing devices and humans are unable to perform. Instead, such existing computing devices would instead require subjective manual input to less accurately match unknown sample supports to known sample support records. In addition to the lack of accurate sample support identification, operating within the confines of existing methods is impractical given the time, energy, human error, and lack of automation involved.

[0053] Thus, embodiments of the present disclosure can serve any of a number of technical purposes, such as controlling a particular technical system or process; determining how to control a machine based on measurement results; digital audio, image, or video enhancement or analysis; separation of material sources in a mixed signal; generating data for reliable and / or efficient transmission or storage; providing estimates and confidence intervals for material samples; or providing faster processing of sensor data. In particular, the present disclosure provides technical solutions to technical problems, including but not limited to accurate and repeatable sample support identification, accurate and repeatable non-uniformity profile generation, and / or accurate and repeatable analysis of multiple non-uniformity profiles at least partially in parallel with each other.

[0054] Thus, embodiments disclosed herein provide improvements to materials analysis technology (eg, improvements in computer technology to support materials analysis, among other improvements).

[0055] As used herein, the phrase "based on" should be understood to mean "based, at least in part, on" unless otherwise specified.

[0056] As used herein, the term "component" may refer to an atomic element, a molecular element, a phase of atomic or molecular elements, or a combination thereof.

[0057] As used herein, the terms "compound" and "precursor" are used interchangeably.

[0058] As used herein, the term "data" may include metadata.

[0059] As used herein, the terms "entity," "requesting entity," and "user entity" may refer to a machine, device, component, hardware, software, intelligent device, party, organization, individual, and / or person.

[0060] One or more embodiments will now be described with reference to the accompanying drawings, wherein like reference numerals are used throughout to refer to like elements. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a more thorough understanding of one or more embodiments. However, it will be apparent that in various circumstances, one or more embodiments may be practiced without these specific details.

[0061] Furthermore, it should be understood that the embodiments depicted in one or more of the figures described herein are for illustrative purposes only, and therefore the architecture of the embodiments is not limited to the systems, devices and / or components depicted therein, nor to any specific order, connection and / or coupling of the systems, devices and / or components depicted therein.

[0062] Turning now particularly to one or more of the accompanying drawings, Figure 1 , shows a block diagram of a scientific instrument module 100 for use in preparation and setup associated with performing material analysis operations using microscopy imaging techniques, according to various embodiments described herein. The scientific instrument module 100 may be implemented by circuitry (e.g., including electrical and / or optical components) such as a programmed computing device. The logic of the scientific instrument module 100 may be included in a single computing device or may be distributed across multiple computing devices that communicate with each other, as appropriate. Figure 4 Examples of computing devices that may implement the scientific instrument module 100, either alone or in combination, are discussed in conjunction with the computing device 400 of FIG. Figure 19 The scientific instrument system 1900 discusses an example of a system of interconnected computing devices, where the scientific instrument module 100 can be implemented across one or more of the computing devices.

[0063] The scientific instrument module 100 can function in correspondence with the imaging system 630. The scientific instrument module 100 can include a first logic component 102, a second logic component 104, a third logic component 106, a fourth logic component 108, and a fifth logic component 110. As used herein, the term "logic component" can include a device that performs a set of operations associated with the logic. For example, any of the logic elements included in the module 100 can be implemented by one or more computing devices that are programmed with instructions to cause one or more processing devices of the computing devices to perform an associated set of operations. In certain embodiments, the logic elements can include one or more non-transitory computer-readable media having instructions thereon that, when executed by one or more processing devices of the one or more computing devices, cause the one or more computing devices to perform an associated set of operations. As used herein, the term "module" can refer to a collection of one or more logic elements that, together, perform the functionality associated with the module. Different logic elements in the module can take the same form or can take different forms. For example, some of the logic in a module may be implemented by a programmed general-purpose processing device, while other logic in the module may be implemented by an application-specific integrated circuit (ASIC). In another example, different ones of the logic elements in a module may be associated with different instruction sets executed by one or more processing devices. A module may omit one or more logic elements depicted in the associated figures; for example, when the module is to perform a subset of the operations discussed herein with reference to the module, the module may include a subset of the logic elements depicted in the associated figures.

[0064] The first logic component 102 can cause and / or direct the capture of an image of an unknown sample support. As used herein, the term "unknown sample support" refers to a sample support whose identification has not yet been verified. That is, the correspondence of the unknown sample support to a record of known sample supports has not yet been verified. As used herein, the term "known sample support" therefore refers to a sample support for which a data record has been generated, the data record including a profile analysis and / or one or more other parameters defining one or more non-uniformities of the known sample support.

[0065] The second logic component 104 can cause and / or direct the comparison of the non-uniformity profile of the unknown sample support with the non-uniformities of known sample supports. In other words, the second logic component 104 can direct the comparison of one or more non-uniformities identified from one or more sample support images output from the first logic component 102.

[0066] The third logic component 106 may cause and / or direct the assignment of scores based on the output of the second logic component 104. That is, the third logic component 106 may, based on the operation of the first logic component 102 and the second logic component 104, generate a score assignment that defines a level of similarity between the unknown sample support and the known sample support based on images of the unknown sample support and the known sample support.

[0067] The fourth logic component 108 may cause and / or direct an evaluation of the score assignment output by the third logic component 106. The evaluation may include comparing a score resulting from the score assignment to one or more other scores and / or score thresholds.

[0068] The fifth logic component 110 may cause and / or direct the generation of a match based on the output of the fourth logic component 108 .

[0069] Figure 2 A flow chart illustrating a method 200 of performing operations by the scientific instrument module 100 according to various embodiments is shown. Although reference may be made to specific embodiments disclosed herein (e.g., Figure 1 The scientific instrument module 100 discussed herein is Figure 3 GUI 300 discussed herein, reference Figure 4 The computing device 400 discussed herein and / or referenced herein Figure 19 The operation of method 200 is illustrated using the scientific instrument system 1900 discussed above, but method 200 can be used in any suitable setting to perform any suitable operation. Figure 2 The operations are each illustrated once in a particular order, but the operations may be reordered and / or repeated as needed and appropriate (eg, different operations performed may be performed in parallel where appropriate).

[0070] At 202, a first operation can be performed. For example, the first logic component 102 of the module 100 can perform the first operation 202. The first operation 202 can include directing and / or causing an image capture of the unknown sample support grid. In one or more embodiments, the image capture can include setting and / or modifying one or more image capture angles and / or lighting conditions.

[0071] At 204, a second operation can be performed. For example, the second logic component 104 of the module 100 can perform the second operation 204. The second operation 204 can include directing and / or causing a comparison of the non-uniformity profile of the unknown sample support with the non-uniformity profile of at least one known sample support. That is, the comparison can include evaluating the location, size, proximity, color, and / or any other defined aspect of one or more non-uniformities of the compared non-uniformity profiles.

[0072] At 206, a third operation can be performed. For example, the third logic component 106 of the module 100 can perform the third operation 206. The third operation 206 can include directing and / or causing the assignment of a score, such as a comparative score, a parametric score, and / or an aggregate score, relative to the known sample support and / or relative to a combination of the unknown sample support and the known sample support. The comparative score can define a level of similarity between the image of the unknown sample support and the image of the known sample support, and thus, can define a level of similarity between the unknown sample support and the known sample support.

[0073] At 208, a fourth operation may be performed. For example, the fourth logic component 108 of the module 100 may perform the fourth operation 208. The fourth operation 208 may include directing and / or causing an evaluation of the score output from the third operation 206. The evaluation may include comparing the score resulting from the score assignment to one or more other scores and / or score thresholds.

[0074] At 210, a fifth operation may be performed. For example, the fifth logic component 110 of the module 100 may perform the fifth operation 210. The fifth operation 210 may include directing and / or causing a match of an unknown sample support to be generated with a known sample support based on the output of the fourth operation 208.

[0075] The scientific instrument methods disclosed herein may include interaction with a user entity (e.g., via a method described herein with reference to Figure 19 The user's local computing device 1920 is discussed. These interactions may include providing information to the user entity (e.g., about scientific instruments such as Figure 19 information about the operation of a scientific instrument such as a scientific instrument 1910, information about samples being analyzed or other tests or measurements being performed by the scientific instrument, information retrieved from a local or remote database, or other information) or provide a user entity with the option of inputting commands (e.g., controlling a scientific instrument such as Figure 19 In some embodiments, these interactions may be performed through a graphical user interface (GUI) that includes a display device (e.g., a graphical user interface such as a display device (e.g., a display device such as a display device shown in FIG. Figure 4 410) that provides output to a user entity and / or prompts the user entity to provide input (e.g., via a display device 410 discussed herein). Figure 4 Other I / O devices 412 discussed include one or more input devices such as a keyboard, mouse, trackpad, or touch screen. The scientific instrument system 1900 disclosed herein may include any GUI suitable for interacting with a user entity.

[0076] Next turn Figure 3 , which depicts an example GUI 300 that can be used to perform one or more methods described herein according to various embodiments described herein. As described above, the GUI 300 can be provided on a scientific instrument system (e.g., Figure 19 The computing device of the scientific instrument system 1900 discussed herein (e.g., Figure 4 The display device (eg, the computing device 400 discussed herein) Figure 4 410) and the user entity may use any suitable input device (e.g., Figure 4 Any input devices discussed, including those in the other I / O devices 412 , and input technologies (eg, cursor movement, motion capture, facial recognition, gesture detection, voice recognition, button activation, etc.) interact with the GUI 300 .

[0077] GUI 300 may include a data display area 302 , a data analysis area 304 , a scientific instrument control area 306 , and a settings area 308 . Figure 3 The specific number and arrangement of regions depicted in are merely exemplary, and any number and arrangement of regions, including any desired characteristics thereof, may be included in GUI 300 .

[0078] The data display area 302 may display data generated by a scientific instrument (e.g., Figure 18 For example, the data display area 302 may display one or more output comparison images 1106 ( Figure 11 ) and / or one or more text, graphics, charts, matrices and / or images of individual sample supports, but are not limited thereto.

[0079] The data analysis area 304 may display the results of the data analysis (e.g., the results of analyzing the data illustrated in the data display area 302 and / or other data). For example, the data analysis area 304 may display one or more results of the comparative scoring analysis (e.g., Figure 12 300 ). For example, the data analysis area 304 can display a list, flow chart, or other schematic representation of one or more scoring assessments. In one or more embodiments, the data display area 302 and the data analysis area 304 can be combined in the GUI 300 (e.g., including data output from a scientific instrument and some analysis of the data in a common graphic or area). In one or more embodiments, the data display area 302 can display an image of the sample support in which one or more non-uniformities can be highlighted by any suitable method (such as differences in color, brightness, pointers, text, etc.).

[0080] The scientific instrument control area 306 may include a section that allows a user entity to control scientific instruments (e.g., Figure 19 For example, the scientific instrument control area 306 may include one or more controls for inputting one or more metrics of interest.

[0081] The settings area 308 may include options that allow the user to physically control the features and functions of the GUI 300 (and / or other GUIs), and / or perform common computing operations with respect to the data display area 302 and the data analysis area 304 (e.g., saving data to a storage device (such as a computer program described herein)). Figure 4 For example, the settings area 308 may include one or more options to change the instantiation (such as with Figure 11 and / or Figure 12 The color, fill, or format of one or more images and / or schematics related to the graphics.

[0082] As mentioned above, the scientific instrument module 100 may be implemented by one or more computing devices. Therefore, the following discussion turns to Figure 4 , which illustrates a block diagram of a computing device 400 that can perform some or all of the scientific instrument methods disclosed herein, according to various embodiments. In one or more embodiments, the scientific instrument module 100 can be implemented by a single computing device 400 or multiple computing devices 400. Furthermore, as discussed below, the computing device 400 (or multiple computing devices 400) that implements the scientific instrument module 100 can be Figure 19 A portion of one or more of a scientific instrument 1910, a user local computing device 1920, a service local computing device 1930, or a remote computing device 1940.

[0083] Figure 4 The computing device 400 is illustrated as having multiple components, but any one or more of these components may be omitted or duplicated depending on the application and settings. As illustrated, these components may include one or more of a processor 402, a storage device 404, an interface device 406, a battery / power supply circuit 408, a display device 410, and other input / output (I / O) devices 412, as will be described below.

[0084] In one or more embodiments, one or more of the components included in computing device 400 may be attached to one or more motherboards and enclosed in a housing (e.g., comprising plastic, metal, and / or other materials). In one or more embodiments, some of these components may be fabricated onto a single system on a chip (SoC) (e.g., a SoC may include one or more processors 402 and one or more storage devices 404). Additionally, in one or more embodiments, computing device 400 may omit the processor. Figure 4 In one or more embodiments, computing device 400 may include interface circuitry (not shown) for coupling to one or more components using any suitable interface (e.g., a universal serial bus (USB) interface, a high-definition multimedia interface (HDMI) interface, a controller area network (CAN) interface, a serial peripheral interface (SPI) interface, an Ethernet interface, a wireless interface, or any other suitable interface). For example, computing device 400 may omit display device 410, but may include display device interface circuitry (e.g., a connector and driver circuitry) to which display device 410 may be coupled.

[0085] Computing device 400 may include a processor 402 (e.g., one or more processing devices). As used herein, the term "processing device" may refer to any device or portion of a device that processes electronic data from registers and / or memory to convert the electronic data into other electronic data that can be stored in registers and / or memory. Processor 402 may include one or more digital signal processors (DSPs), application-specific integrated circuits (ASICs), central processing units (CPUs), graphics processing units (GPUs), cryptographic processors (specialized processors that execute cryptographic algorithms in hardware), server processors, or any other suitable processing devices.

[0086] The computing device 400 may include a storage device 404 (e.g., one or more storage devices). The storage device 404 may include one or more memory devices, such as random access memory (RAM) (e.g., static RAM (SRAM) devices, magnetic RAM (MRAM) devices, dynamic RAM (DRAM) devices, resistive RAM (RRAM) devices, or conductive bridging RAM (CBRAM) devices), hard drive-based memory devices, solid-state memory devices, networked drives, cloud drives, or any combination of memory devices. In one or more embodiments, the storage device 404 may include memory that shares a die with the processor 402. In such embodiments, the memory may be used as a cache memory and may include, for example, embedded dynamic random access memory (eDRAM) or spin transfer torque magnetic random access memory (STT-MRAM). In one or more embodiments, the storage device 404 may include a non-transitory computer-readable medium having instructions thereon that, when executed by one or more processing devices (e.g., processor 402), cause the computing device 400 to perform any appropriate method or portion of the methods disclosed herein.

[0087] The computing device 400 may include an interface device 406 (e.g., one or more interface devices 406). The interface device 406 may include one or more communication chips, connectors, and / or other hardware and software to manage communications between the computing device 400 and other computing devices. For example, the interface device 406 may include circuitry for managing wireless communications for transmitting data to and from the computing device 400. The term "wireless" and its derivatives may be used to describe circuits, devices, systems, methods, techniques, communication channels, etc. that can transmit data through a non-solid medium using modulated electromagnetic radiation. The term does not mean that the associated device does not contain any wires, but in one or more embodiments, the associated device may not contain any wires. The circuitry included in the interface device 406 for managing wireless communications may implement any of a variety of wireless standards or protocols, including but not limited to Institute of Electrical and Electronics Engineers (IEEE) standards, including Wi-Fi (IEEE 802.11 series), IEEE 802.16 standards (e.g., IEEE 802.16-2005 amendments), Long Term Evolution (LTE) project, and any amendments, updates, and / or revisions (e.g., LTE-Advanced project, Ultra Mobile Broadband (UMB) project (also known as "3GPP2"), etc.). In one or more embodiments, the circuitry included in the interface device 406 for managing wireless communications may operate in accordance with a Global System for Mobile Communications (GSM), General Packet Radio Service (GPRS), Universal Mobile Telecommunications System (UMTS), High Speed ​​Packet Access (HSPA), Evolved HSPA (E-HSPA), or LTE network. In one or more embodiments, the circuitry included in the interface device 406 for managing wireless communications may operate in accordance with an Enhanced Data GSM Evolved In one or more embodiments, the interface device 406 may include one or more antennas (e.g., one or more antenna arrays) to receive and / or transmit wireless communications.

[0088] In one or more embodiments, interface device 406 may include a circuit for managing wired communication, such as an electrical communication protocol, an optical communication protocol, or any other suitable communication protocol. For example, interface device 406 may include a circuit for supporting communication according to Ethernet technology. In one or more embodiments, interface device 406 may support wireless and wired communication, and / or may support multiple wired communication protocols and / or multiple wireless communication protocols. For example, a first group of circuits of interface device 406 may be dedicated to short-range wireless communication such as Wi-Fi or Bluetooth, and a second group of circuits of interface device 406 may be dedicated to long-range wireless communication such as global positioning system (GPS), EDGE, GPRS, CDMA, WiMAX, LTE, EV-DO, etc. In one or more embodiments, a first group of circuits of interface device 406 may be dedicated to wireless communication, and a second group of circuits of interface device 406 may be dedicated to wired communication.

[0089] Computing device 400 may include battery / power circuitry 408. Battery / power circuitry 408 may include one or more energy storage devices (e.g., batteries or capacitors) and / or circuitry for coupling components of computing device 400 to an energy source separate from computing device 400 (e.g., AC line power).

[0090] Computing device 400 may include display device 410 (e.g., multiple display devices). Display device 410 may include any visual indicator, such as a heads-up display, a computer monitor, a projector, a touch screen display, a liquid crystal display (LCD), a light emitting diode display, or a flat panel display.

[0091] The computing device 400 may include other input / output (I / O) devices 412. For example, the other I / O devices 412 may include one or more audio output devices (e.g., speakers, headphones, earbuds, alarms, etc.), one or more audio input devices (e.g., a microphone or microphone array), a positioning device (e.g., a GPS device that communicates with a satellite-based system to receive the location of the computing device 400, as is known in the art), an audio codec, a video codec, a printer, sensors (e.g., thermocouples or other temperature sensors, humidity sensors, pressure sensors, vibration sensors, accelerometers, gyroscopes, etc.), an image capture device such as a camera, a keyboard, a cursor control device such as a mouse, stylus, trackball, or touchpad, a barcode reader, a Quick Response (QR) code reader, or a Radio Frequency Identification (RFID) reader.

[0092] The computing device 400 can have any suitable form factor suitable for its applications and settings, such as a handheld or mobile computing device (e.g., a cell phone, a smartphone, a mobile internet device, a tablet computer, a laptop computer, a netbook computer, an ultrabook computer, a personal digital assistant (PDA), an ultra-mobile personal computer, etc.), a desktop computing device, or a server computing device or other networked computing component.

[0093] Next reference Figure 5 and Figure 6 , in one or more embodiments, Figure 5 and Figure 6 The non-limiting systems 500 and / or 600 illustrated in FIG. 5 and / or their systems may also include computing environments such as those referenced herein. Figure 21 One or more computers and / or computing-based elements described in the computing environment 2100 shown in FIG. In one or more of the described embodiments, the computers and / or computing-based elements may be combined to implement the Figure 5 and / or Figure 6 and / or other figures described herein may be used with one or more of the systems, devices, components, and / or computer-implemented operations illustrated and / or described.

[0094] First turn Figure 5 , which illustrates a block diagram of an example, non-limiting system 500 that may include a sample support identification system 502 and a dual beam system 501. The sample support identification system 502 may facilitate identification of an unknown sample support grid 540 (also referred to as unknown sample support 540) as one or more known sample support grids 542 (also referred to as known sample supports 542).

[0095] In one or more embodiments, the sample support identification system 502 can be at least partially comprised by the computing device 400 .

[0096] In one or more embodiments, the dual beam system 501 can include, at least in part, a sample support identification system 502 .

[0097] It should be noted that the sample support identification system 502 is only briefly described in detail to provide an example. Figure 6 The introduction of a more complex and / or extensive sample support identification system 602 as shown in FIG. Figure 6 Non-limiting system 600 provides further details regarding processes that may be performed by one or more embodiments described herein.

[0098] Still refer to Figure 5, sample support identification system 502 may include at least memory 504, bus 505, processor 506, imaging component 510, and matching component 518. Processor 506 may be the same as, included by, or different from processor 402. Memory 504 may be the same as, included by, or different from storage device 404.

[0099] Using the components described above, the sample support identification system 502 can facilitate the process of first imaging a sample support desired for identification (eg, unknown sample support 540 ) and second determining a match of a known sample support 542 corresponding to the unknown sample support 540 .

[0100] Typically, the sample supports 540, 542 can be comprised of a grid system comprising a plurality of one or more sample supports, which, as described above, can be attached to the sample supports for various purposes relative to the sample, such as to provide stability of the sample during imaging or other material analysis of the sample.

[0101] In general, imaging assembly 510 can capture and / or direct the capture of an image 550 of an unknown sample support 540. Unknown sample support 540 can include one or more layers of material.

[0102] Matching component 518 can use image 550 to match unknown sample support 540 to known sample support 542 based on unknown inhomogeneity profile 551 including one or more inhomogeneities of a material layer in image 550 of unknown sample support 540. That is, matching component 518 can match image 550 of unknown sample support 540 to a data record and / or image 552 of a known sample support 542.

[0103] The imaging component 510 and the matching component 518 can be operatively coupled to the processor 506, which is operatively coupled to the memory 504. The bus 505 can provide the operative coupling. The processor 506 can facilitate the execution of the imaging component 510 and the matching component 518. The imaging component 510 and the matching component 518 can be stored at the memory 504.

[0104] In general, the non-limiting system 500 may employ any suitable communication method (e.g., electronic, telecommunication, internet, infrared, fiber optic, etc.) to provide communication between the sample support identification system 502, an imaging system employed by the sample support identification system 502 to capture images of the sample support, and / or any device associated with a user entity.

[0105] Next turn Figure 6, illustrates a non-limiting system 600 that may include a sample support identification system 602 and an imaging system 630. For the sake of brevity, repeated descriptions of similar elements and / or processes employed in corresponding embodiments are omitted. Figure 5 The description of the embodiments may be applicable to Figure 6 Similarly, with respect to Figure 6 The description of the embodiments may be applicable to Figure 5 implementation plan.

[0106] Generally, the sample support identification system 602 can facilitate a process for identifying an unknown sample support 640 as a known sample support 642. This process can be facilitated by generating, analyzing, and comparing non-uniformity profiles of the sample supports 640 and 642 to each other.

[0107] In one or more embodiments, the sample support identification system 602 can be at least partially comprised by the computing device 400 .

[0108] In one or more embodiments, the sample support identification system 602 can include, at least in part, an imaging system 630 .

[0109] One or more communications between one or more components of the non-limiting system 600 may be provided via wired and / or wireless means, including but not limited to using a cellular network, a wide area network (WAN) (e.g., the Internet), and / or a local area network (LAN). Suitable wired or wireless technologies for supporting communications may include, but are not limited to, Wireless Fidelity (Wi-Fi), Global System for Mobile Communications (GSM), Universal Mobile Telecommunications System (UMTS), Worldwide Interoperability for Microwave Access (WiMAX), Enhanced General Packet Radio Service (Enhanced GPRS), 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE), 3rd Generation Partnership Project 2 (3GPP2) Ultra Mobile Broadband (UMB), High Speed ​​Packet Access (HSPA), Zigbee and other 802.XX wireless technologies and / or traditional telecommunication technologies, Session Initiation Protocol (SIP), RF4CE protocol, WirelessHART protocol, 6LoWPAN (IPv6 over Low Power Wireless Area Network), Z-Wave, Advanced and / or Adaptive Networking Technology (ANT), Ultra-Wideband (UWB) standard protocol and / or other proprietary and / or non-proprietary communication protocols.

[0110] The sample support identification system 602 can be integrated with a cloud computing environment such as Figure 20 The cloud computing environment 2000 is associated with the cloud computing environment 2000, such as being accessible via the cloud computing environment.

[0111] Sample support identification system 602 may include multiple components. These components may include memory 604, processor 606, bus 605, imaging component 610, profile analysis component 611, recording component 612, acquisition component 614, comparison component 616, matching component 618, update component 620, and / or notification component 622. Using these components, sample support identification system 602 may acquire, compare, and analyze images of sample supports, and more specifically, compare and analyze one or more non-uniformities identified from the images of the sample supports. In this manner, a selected and / or desired sample support may be used in a subsequent material analysis procedure. That is, the sample support employed in such a subsequent procedure may be verified as the sample support selected and / or desired for use in the subsequent material analysis procedure.

[0112] The following discussion turns to the processor 606, memory 604, and bus 605 of the sample support identification system 602. For example, in one or more embodiments, the sample support identification system 602 can include a processor 606 (e.g., a computer processing unit, a microprocessor, a classical processor, a quantum processor, and / or the like). In one or more embodiments, as described herein with or without reference to one or more figures of one or more embodiments, components associated with the sample support identification system 602 can include one or more computer and / or machine readable, writable, and / or executable components and / or instructions that can be executed by the processor 606 to provide performance of one or more processes defined by such components and / or instructions. In one or more embodiments, the processor 606 can include an imaging component 610, a profile analysis component 611, a recording component 612, an acquisition component 614, a comparison component 616, a matching component 618, an update component 620, and / or a notification component 622.

[0113] In one or more embodiments, the sample support identification system 602 can include a computer-readable memory 604 that can be operably connected to a processor 606. The memory 604 can store computer-executable instructions that, when executed by the processor 606, can cause the processor 606 and / or one or more other components of the sample support identification system 602 (e.g., an imaging component 610, a profiling component 611, a recording component 612, an obtaining component 614, a comparing component 616, a matching component 618, an updating component 620, and / or a notification component 622) to perform one or more actions. In one or more embodiments, the memory 604 can store computer-executable components (e.g., an imaging component 610, a profiling component 611, a recording component 612, an obtaining component 614, a comparing component 616, a matching component 618, an updating component 620, and / or a notification component 622).

[0114] The sample support identification system 602 and / or its components as described herein can be communicatively, electrically, operatively, optically, and / or otherwise coupled to each other via a bus 605. The bus 605 can include one or more of a memory bus, a memory controller, a peripheral bus, an external bus, a local bus, a quantum bus, and / or another type of bus that can employ one or more bus architectures. One or more of these examples of the bus 605 can be employed.

[0115] In one or more embodiments, the sample support identification system 602 can be coupled (e.g., communicatively, electrically, operatively, optically, and / or the like) to one or more external systems (e.g., an unillustrated electrical output generation system, one or more output targets, and / or output target controllers), sources, and / or devices (e.g., classical and / or quantum computing devices, communication devices, and / or the like), such as via a network. In one or more embodiments, one or more of the components of the sample support identification system 602 and / or the non-limiting system 600 can reside in the cloud and / or can reside locally in a local computing environment (e.g., at a designated location).

[0116] In addition to the processor 606 and / or memory 604 described above, the sample support identification system 602 may also include one or more computer and / or machine readable, writable and / or executable components and / or instructions that, when executed by the processor 606, may provide for the performance of one or more operations defined by such components and / or instructions.

[0117] The discussion next turns to one or more additional components of the sample support identification system 602 .

[0118] However, first, it should be noted that in one or more embodiments, the imaging component 610, the profile analysis component 611, the recording component 612, the acquisition component 614, the comparison component 616, the matching component 618, the update component 620 and / or the notification component 622 can be implemented independently without requiring one or more other components of the imaging component 610, the profile analysis component 611, the recording component 612, the acquisition component 614, the comparison component 616, the matching component 618, the update component 620 and / or the notification component 622. Additionally and / or alternatively, the imaging component 610, the profile analysis component 611, the recording component 612, the obtaining component 614, the comparison component 616, the matching component 618, the updating component 620, and / or the notification component 622 may be included by the high-level analysis component 603, one or more of the following functions of the imaging component 610, the profile analysis component 611, the recording component 612, the obtaining component 614, the comparison component 616, the matching component 618, the updating component 620, and / or the notification component 622 may be performed by the high-level analysis component 603, and / or The imaging component 610, the profile analysis component 611, the recording component 612, the acquisition component 614, the comparison component 616, the matching component 618, the update component 620 and / or the notification component 622 may be omitted, wherein the high-level analysis component 603 performs one or more of the following functions of one or more of the omitted imaging components 610, the profile analysis component 611, the recording component 612, the acquisition component 614, the comparison component 616, the matching component 618, the update component 620, the matching component 618, the update component 620 and / or the notification component 622.

[0119] Turning now to the components, the imaging component 610 can capture and / or direct the capture of one or more images 650 of one or more unknown sample supports 640 and / or one or more images 652 of one or more known sample supports 642. In one or more embodiments, the imaging component 610 can direct the imaging system 630 to capture the images. In one or more embodiments, the imaging component can obtain such images solely from the imaging system 630.

[0120] In one or more embodiments, the imaging system 630 can be and / or include an optical microscope, and thus the images 650, 652 can be optical microscope images, although another alternative may be suitable.

[0121] For example, see Figure 9In one or more embodiments, the imaging assembly 610 can direct the activation of the light source 902 and / or imaging source 906 of the imaging system 630 to facilitate the capture of an image 650 of the sample support 640. In one or more embodiments, the orientation of the light source 902, imaging source 906, and / or imaging platform 904 supporting the sample support 640 can be controlled manually and / or by the imaging system 630. In one or more embodiments, the orientation of the light source 902, imaging source 906, and / or imaging platform 904 can be at least partially controlled by the imaging assembly 610. For example, the imaging assembly 610 can direct a specific color and / or brightness of the light source 902, can direct a specific energy level or amount of the imaging source 906, and / or can direct the movement of the imaging platform 904. In one or more embodiments, the imaging assembly 610 can otherwise recommend one or more specific settings for the orientation of the light source 902, imaging source 906, and / or imaging platform 904 to the imaging system 630 and / or to an administrative entity.

[0122] The resulting one or more images 650 , 652 may include any suitable data and / or metadata.

[0123] The imaging system 630 may include any suitable processor and / or memory for facilitating one or more processes, including but not limited to capturing one or more images, such as images of one or more sample supports. In one or more embodiments, the imaging system 630 may alternatively employ the processor 606 and / or memory 604 of the sample support recognition system 602.

[0124] Turning next to the profile analysis component 611, the component can generally generate and / or update a known non-uniformity profile 653 for a known sample support 642 or an unknown non-uniformity profile 651 for an unknown sample support 640. This generation can include analyzing, by the profile analysis component 611, at least one material layer of a corresponding sample support (e.g., the unknown sample support 640, which will be used merely as an example for one or more descriptions herein). Based on this analysis, the profile analysis component 611 can define a corresponding non-uniformity profile, which is analogous to a microscopic fingerprint of the sample support. These non-uniformity profiles can be compared to one another to determine a match between one or more images 650 of the unknown sample support and one or more images 652 of the known sample support, regardless of whether the corresponding non-uniformity profiles change over time.

[0125] For example, the contour analysis component 611 can identify a first non-uniformity 1010 of the images 650, 652 based on a contrast change between the non-uniformities 1010 having a different contrast than the boundaries or regions 1014 defined by the boundaries or regions 1014. Thus, the identification of one or more non-uniformities 1010 can be recorded at the corresponding non-uniformity contours 651, 653.

[0126] refer to Figure 8 and Figure 10 ,Apart from Figure 6 Additionally, the profile analysis component 611 can employ image recognition software to identify one or more non-uniformities 1010, such as one or more flaws, marks, indicia, damages, bumps, depressions, scratches, channels, indentations, raised areas, and / or other optical defects in at least one material layer of the corresponding sample support being analyzed.

[0127] For example, in one or more embodiments, image recognition may include image processing to remove pixels corresponding to the image background to reduce unused information. A processing script, such as a Python script, may then be used to locate circular structures or other types and / or shapes of structures. In one or more embodiments, a processing script, such as a Python script, may be used to thin out the structure, which may result in darkening of pixels in the image outside of the structure (e.g., bounding the structure). In one or more embodiments, this may provide a brightness difference between the first region 1012 and the second region 1014.

[0128] One or more non-uniformities may be located anywhere on any surface of any material layer of the sample supports 640, 642. For example, referring to the example plane PP ( Figure 8 ) of the floor plan Figure 10 , one or more non-uniformities 1010 can be located at one or more first regions that are peripheral first regions 1012 of the partially cylindrical sample supports 640, 642. However, it should be understood that the sample supports 640, 642 can have any suitable shape. Additionally and / or alternatively, in one or more embodiments, one or more non-uniformities 1010 can be located at a central second region 1014. However, it should be understood that due to the manufacturing process performed to manufacture the sample supports 640, 642 (e.g., Figure 8 ), most of the non-uniformity 1010 resulting from the manufacturing process may be located at a first region of the material layer located at the periphery.

[0129] By the way, it should be noted that Figure 10 The square aspects in FIG. 6A may be holes 1016 extending through one or more layers of the sample supports 640 , 642 .

[0130] The one or more non-uniformities may be generated by various processes. For example, in one or more embodiments, the non-uniformity 1010 may be generated by Figure 8 Additionally and / or alternatively, in one or more embodiments, at least one non-uniformity 1010 can be purposefully marked onto the surface of the material layer of the sample supports 640 , 642 .

[0131] One or more non-uniformities 1010 can be located at one or more different layers of the sample supports 640, 642. Similarly, these one or more non-uniformities can be discussed within the sample supports 640, 642 and / or at the outer surfaces of the sample supports 640, 642. For example, in one or more embodiments, the one or more non-uniformities 1010 can be located at the silicon nitride membrane layer 814 and / or the silicon substrate layer 812 (also referred to herein as the silicon frame layer 812) of the sample supports 640 / 642. For example, these non-uniformities 1010 can be generated by an etching process and / or by any other manufacturing process. In one specific example, the one or more non-uniformities 1010 can be generated at the silicon frame layer 812, such as as a result of an etching process during the initial manufacturing process 802.

[0132] In one or more embodiments, the one or more non-uniformities 1010 may be visible through one or more covering layers (e.g., the silicon nitride membrane layer 814 and / or the first additional layer 816) covering one or more other layers (e.g., the silicon frame layer 812 and / or the silicon nitride membrane layer 814) to allow capture of the one or more non-uniformities in the images 650, 652.

[0133] Additionally and / or alternatively, one or more non-uniformities 1010 may be provided on the outer cylindrical surface S ( Figure 8 )superior.

[0134] In one or more embodiments, one or more non-uniformities 1010 can be purposefully marked at one or more surfaces, such as at the second central region 1014 .

[0135] Thus, it will be appreciated that the imaging assembly 610 can capture images of one or more different surfaces and / or layers of the sample supports 640, 642 having one or more different non-uniformities. Likewise, it will be appreciated that the profiling assembly 611 can generate non-uniformity profiles 651, 653 relative to any one or more different surfaces and / or layers of the sample supports 640, 642.

[0136] In this regard, the recording component 612 can generate and / or update a data record (e.g., data record 644) associated with the sample support for which the non-uniformity profile is generated and / or updated by the profile analysis component 611. The recording process can include tagging, marking, and / or otherwise writing data to the corresponding data record 644, such as via an appropriate writing action, to define the corresponding non-uniformity profile and identify the corresponding non-uniformity profile as corresponding to the corresponding sample support for which the analysis is performed by the profile analysis component 611.

[0137] Additionally and / or alternatively, the record component 612 may perform associated writing actions for any other secondary parameters, which may include, but are not limited to, descriptive aspects and / or dimensions of the sample support of interest, for further populating the associated data record 644, such as provided at the metadata of the data record 644.

[0138] Data record 644 may be provided in any suitable format (e.g., log, table, matrix, data, metadata, etc.) and may be stored at any suitable location communicatively accessible to sample support identification system 602 (e.g., in memory 604, but not limited thereto).

[0139] It should be noted that in one or more embodiments, any one or more of the processes described above and / or below as being performed by the imaging component 610, the profile analysis component 611, the recording component 612, and / or the acquisition component 614 may be performed external to the sample support identification system 602 and / or the non-limiting system 600.

[0140] The discussion that follows is directed to illustrating an exemplary manufacturing process for a sample support. Figure 8 A description of schematic 800 is provided to define at least a first set of example opportunities in which non-limiting system 600 may be employed. Figure 8 As shown, schematic flow chart 800 illustrates an exemplary process for fabricating a sample support, including identifying various procedures performed and / or material layers 812 - 820 added.

[0141] During the initial manufacturing phase 802, a base grid 832 (also referred to herein as a sample support 834) may be manufactured, wherein the base grid 832 includes a first silicon (Si) substrate layer 812 and a silicon nitride membrane layer 814 (such as Si3N4) applied to the silicon substrate layer 812, such as by etching the base grid 832. For example, this initial manufacturing phase 802 may be performed at a base grid manufacturer and / or at a site corresponding to an administrator of the non-limiting system 600. As used herein, "corresponding" may mean at the same site as the non-limiting system 600 and / or at a site owned, used, and / or associated with the administrator.

[0142] The base grid 832 may then be sent to a second processing site, such as at a site corresponding to an administrator of the non-limiting system 600. Such receipt may provide a first opportunity to profile analyze the sample supports (at this point comprising only the base grid 832) for initial identification. For example, the imaging component 610 and the profile analysis component 611 may be employed, as described above. The result of their use may be the generation of a set of known non-uniformity profiles 652 for the set of base grids 832 (also identified as known sample supports 642, since the non-uniformity profile will be known for each known sample support in the known sample supports 642, where each known sample support in the known sample supports 642 is returned to an appropriately labeled orifice 708 of the sample support container 702). It should be understood that any references made herein to the sample support container 702 may alternatively be associated with a vacuum-sealed sample support container 704, which may also have a set of labeled orifices 708, such as Figure 7 shown.

[0143] With respect to receiving base grid 832 / known sample support 642 , pre-processing stage 804 may include applying a first additional layer 816 , such as by a coating process, thereby producing sample support 834 .

[0144] At the conclusion of each step (e.g., between steps 802 and 808), to prepare the base grid 832 / sample support 834 and a plurality of other etched and / or otherwise partially processed base grids 832 / sample supports 834 for transport, these base grids 832 / sample supports 834 can be inserted into the apertures of the sample support container 702. The apertures 708 can be labeled, e.g., Figure 7, to identify a plurality of base grids 832 / sample supports 834. After this process is complete, the base grids 832 / sample supports 834 can be returned to the sample support container 702 with the goal of returning the sample supports to their corresponding labeled orifices 708. However, this does not always occur, and thus provides another opportunity to use the non-restrictive system 600 to identify the base grids 832 / sample supports 834 upon return to the entity managing the non-restrictive system 600, as will be described below. That is, once the base grids 832 / sample supports 834 are removed from the sample support container 702, direct identification is lost because the base grids 832 / sample supports 834 are typically not individually labeled. As a result, continued successful identification is based on indirect tracking rather than through the use of the sample support container 702.

[0145] The next processing stage can be performed at the same processing location or at another processing location. The next stage can be the main processing stage 806, which can include one or more processing steps, such as baking. After one or more processing steps, the sample supports 834 can be returned to the sample support container 702 in order to return the sample supports to their corresponding marked orifices 708. Alternatively, the sample supports 834 can be switched to vacuum sealable sample support containers 704 having the same marking method for the corresponding orifices 708.

[0146] The next processing stage can be performed at the same processing location or at another processing location. The next stage can be a post-processing stage 808, which can include one or more post-processing steps, such as cleaning. Again, after post-processing, the sample supports 834 can be returned to the sample support container 702 in order to return the sample supports to their corresponding marked orifices 708. Alternatively, the sample supports 834 can be switched to vacuum-sealable sample support containers 704 having the same marking method for the corresponding orifices 708.

[0147] In view of the foregoing, it will be appreciated that during any one or more of the above-described processing stages 802 through 808, one or more base grids 832 / sample supports 834 may have been misinserted into the wrong aperture 708 of the same or different sample container 702 / 704, lost, and / or otherwise damaged. Indeed, as noted above, once the base grids 832 / sample supports 834 are removed from the initial sample support container 702, direct identification may be lost because the base grids 832 / sample supports 834 are typically not individually labeled. As a result, continued successful identification is based on indirect tracking, which is often subjective and error-prone, rather than through the use of the sample support container 702.

[0148] Thus, when received at an administrative entity or other location with access to the sample support identification system 600, the plurality of base grids 832 / sample supports 834 in the vacuum-sealed sample support container 704 may be identified as unknown sample supports 640. Consequently, it is desirable that each of these unknown sample supports 640 be identified as one of the previously profiled known sample supports 642.

[0149] As described above, one or more processes can be performed by the imaging component 610 and / or the profile analysis component 611 to generate corresponding data records 644 defining the unknown sample supports 640. In addition to the processes described above, the data records 644 can also include an indication of the orifice 708 at which each sample support 640 was located upon receipt after post-processing. This step can be applied to any use of the non-limiting system 600. That is, the orifice label can provide another piece of evidence (e.g., an alternative parameter) that can be used by the matching component 618 to generate a match 656, as will be described below.

[0150] To ultimately generate a match 656 of the unknown sample support 640 to the known sample support 642 , the images 650 , 652 may be retrieved, received, sent, and / or otherwise obtained by the acquisition component 614 for use by one or more other components of the sample support identification system 602 .

[0151] Using one or more images 650 of an unknown sample support 640 of interest (e.g., an unknown sample support for which identification is desired and / or guided) and one or more images 652 of one or more known sample supports 642, the comparison component 616 can generally output and / or otherwise generate a comparison score 654 corresponding to the unknown sample support 640 and, therefore, to at least one image 650 of the unknown sample support 640.

[0152] For example, the comparison component 616 can compare an unknown image 650 of an unknown sample support 640 with a known image 652 of a known sample support 642. That is, based on the images 650, 652, the comparison component 616 can compare an unknown non-uniformity profile 651 associated with the unknown sample support 640 of interest with at least one known non-uniformity profile 613 associated with a known sample support.

[0153] This comparison can be the result of one or more images 650, 652 and / or employing contrast in one or more images 650, 652, which can result in varying brightness or darkness of different aspects of one or more images. For example, in one or more embodiments, one or more non-uniformities 1010 can have a smaller brightness, and a central region 1014 can have a greater brightness. In one or more other embodiments, one or more non-uniformities 1010 can have a greater brightness, and a central region 1014 can have a smaller brightness. In one or more embodiments, one or more non-uniformities 1010 can have varying brightness. It should be noted that the non-uniformities 1010 are illustrated as being darker and having a lower brightness as an example for ease of viewing. In practice, in use, an opposite contrast can be produced by darkfield microscopy imaging performed and / or directed by the imaging assembly 610 (e.g., where one or more non-uniformities 1010 have a greater brightness than a surrounding central region 1014 that surrounds and defines the one or more non-uniformities 1010).

[0154] For example, the comparison may include the identification of one or more portions and the use of subsequent analysis of all portions as a whole and / or one or more portions at a time and / or at least partially in parallel with each other. After selecting a first portion of one of the images 650, 652, the comparison component 616 employing image recognition technology may search for a most similar region of the other image 650, 652. In one or more embodiments, based on the identification of the region of the other image 650, 652, the partitioning of the other image 650, 652 may be applied. In one or more other embodiments, the partitioning may already be applied to the other image 650, 652. In one or more other embodiments, the partitioning may already be applied to the other image 650, 652, but may be revised based on the identification of the region of the other image 650, 652.

[0155] In another example, the comparison component 616 can analyze a first image in the images 650 , 652 based on pixelation of the images, such as analyzing a group of one or more pixels at a time and matching such groups to the most similar group of one or more pixels of the other image in the images 650 , 652 .

[0156] In one or more embodiments, this pixel-based search may be used for the entire image or for less than all of the image, such as only for all or a portion of the pixels that include the non-uniformity 1010 (e.g., including pixels having a determined greater or lesser brightness and / or contrast).

[0157] In another example, the comparison component 616 can identify a first non-uniformity 1010 for the first of the images 650, 652 based on a contrast change between the boundary or region 1014 defining a non-uniformity 1010 having a different contrast than the boundary or region 1014. Thus, the analysis of the first of the images 650, 652 can proceed to analyzing all or a portion of the non-uniformity 1010 of the sample support in the first of the images 650, 652. It should be understood that in one or more embodiments, the employed non-uniformity profile 651, 653 can have one or more pre-defined and / or pre-identified non-uniformities 1010. Thus, the identification of such one or more non-uniformities 1010 can additionally and / or alternatively be performed by the profile analysis component 611.

[0158] It should be understood that any one or more of the above-described techniques and / or similar techniques for image recognition known to those of ordinary skill in the art can be employed by the comparison component 616 .

[0159] For example, to provide an illustration of the above, focus on Figure 11 See also Figure 11 , illustrates various examples of comparison stages that may be performed by comparison component 616, such as employing one or more image recognition techniques.

[0160] For example, unknown and known images may first be obtained from acquisition component 614. The first captured image 1102 is an unknown image 650 of unknown sample support A (640). The second recorded image 1104 is a known image 652 of known sample support X (642).

[0161] Next, comparison image 1106 illustrates the comparison process according to a pixel-based or non-uniformity-based approach. As shown in comparison image 1106, a first unknown sample support B has been compared with a first known sample support Y. Similarly, a second unknown sample support C has been compared with a second known sample support Z. Identification line 1112 indicates a match between aspects of the first image (e.g., non-uniformity and / or groups of one or more pixels) and aspects of the second image (e.g., non-uniformity and / or groups of one or more pixels).

[0162] It should be understood that one or more of the captured image 1102, recorded image 1104 and / or comparison image 1106, and / or similar images including the identification line 1112 may be displayed to a user entity of the non-limiting system 600, such as at a display device 410 of a computing device 400 associated with the user entity.

[0163] Based on the comparison, the comparison component 616 can generate a comparison score 654 that provides a value defining a level of similarity between the pair of compared images (eg, between the pair of compared sample supports).

[0164] Additionally and / or alternatively, the comparison component 616 can generate a parameter score 1202 that provides a value for defining a level of similarity between parameters of the pair of compared images (e.g., between the pair of compared sample supports) based on comparisons other than the comparison performed by the comparison component 616. For example, the comparison component 616 can compare any other parameters (e.g., such as material composition, size, surface angle, mass, and / or the like) that can be obtained from the respective non-uniformity profiles 651, 653 corresponding to the pair of compared sample supports.

[0165] Additionally and / or alternatively, the comparison component 616 can generate an aggregate score 1204 that provides an aggregate value defining a level of similarity between parameters of the pair of compared images (e.g., between the pair of compared sample supports) based on comparisons other than the comparisons performed by the comparison component 616. The aggregate value can be based on an aggregation, such as a sum, of the comparison score 654 and at least one parameter score 1202.

[0166] It should be understood that the comparison component 616 can employ more than one unknown image 650 of an unknown sample support 640 and / or more than one known image 652 of a known sample support 642. Thus, a pair of compared images (e.g., and thus a pair of compared sample supports) can have more than one comparison score 654, parameter score 1202, and / or aggregate score 1204 associated therewith.

[0167] It should also be understood that the comparison component 616 can employ more than one known sample support 642 having associated therewith one or more known images 652. Thus, an unknown image 650 (e.g., and therefore an unknown sample support 640) can have associated therewith a first set of one or more scores based on comparison with a first known image 652 of a first known sample support 642 and an additional set of one or more scores based on comparison with additional known images 652 of other known sample supports 642.

[0168] Based on the above-described one or more processes that can be performed by the comparison component 616, the matching component 618 can generally match the unknown sample support 650 with the known sample support 652 based on the above-described non-uniformity profiles 651, 653 and more specifically based on one or more of the above-described scores (e.g., comparison scores, parameter scores and / or aggregate scores) based on the above-described non-uniformity profiles 651, 653.

[0169] For example, in one or more embodiments, the matching component 618 may match the unknown sample support 642 of interest to a selected known sample support 640 based on a comparison score 654, parameter score 1202, and / or aggregate score 1204 associated with the known sample support 640 that includes a highest value compared to one or more other comparison scores 654, parameter scores 1202, and / or aggregate scores 1204 associated with one or more other known sample supports 640.

[0170] That is, see Figure 12 , the known sample support X can have a comparison score I associated with it that includes the highest value compared to the other comparison scores II and III. As a result of this score comparison, the matching component 618 can output a match 656 that associates the unknown sample support 640 with the known sample support 642.

[0171] In one or more embodiments, the matching component 618 can employ one or more of the comparison score 654, the parameter score 1202, and / or the aggregate score 1204 associated with the same known sample support 642 to determine a match 656. For example, the deviation between the first highest score and the second highest score (e.g., one or more of the comparison score 654, the parameter score 1202, and / or the aggregate score 1204) can be determined by the matching component 618, such as in the case where the first known sample support 642 can have the highest comparison score 654 associated with it, but the second known sample support 642 can have the highest aggregate score 1204 associated with it. Thus, the first deviation between the first highest comparison score and the second highest comparison score 654 can be greater than the second deviation between the first highest aggregate score and the second highest aggregate score 1204, and thus the first known sample support 642 (associated with the highest comparison score 654) can be matched to the unknown sample support 640 via the match 656.

[0172] In one or more embodiments, the matching component 618 can compare the comparison score 654 to the comparison score threshold 1216, the parameter score 1202 to the parameter score threshold 1212, and / or the aggregate score 1204 to the aggregate score threshold 1214. For example, as a result of one or more of the score comparisons discussed above, a possible match 1210 can instead be output by the matching component 618. The scores for the possible match 1210 (e.g., the comparison score 654, the parameter score 1202, and / or the aggregate score 1204) can be compared to corresponding thresholds (e.g., the corresponding comparison threshold 1216, the parameter threshold 1212, and / or the aggregate threshold 1214).

[0173] Where a possible match 1210 is generated that satisfies the respective thresholds, the possible match 1210 may be identified as a match 656 and output from the non-limiting system 600 by the matching component 618 , such as to the display device 410 of the device 400 .

[0174] In one or more embodiments, in the event that no match is generated that satisfies the corresponding one or more thresholds employed, the matching component 618 can provide one or more suggestions and / or take one or more actions. The one or more suggestions can include suggesting capturing a new image, changing the lighting used for capturing, changing the orientation of the sample support used for capturing, changing the contrast of the captured image, and / or using manual inspection by a user entity or other entity. The one or more actions can include issuing one or more suggestions (e.g., to the display device 410 of the device 400), and / or directing the imaging component 610 to automatically capture another image, such as with the sample support remaining at the imaging platform 904.

[0175] Furthermore, the discussion now turns to one or more processes that may be performed by the matching component 618 with respect to a requested identification of a set of two or more unknown sample supports 640. For example, when it is desired to perform identification for more than one unknown sample support 640 that is known to correspond to a set of more than one known sample support 642, the matching component 618 may perform one or more additional processes. As just one non-limiting example, based on the generation of a first match 656 with respect to a first unknown sample support 640, a second match 656 with respect to a second unknown sample support 640 may be generated. That is, in one or more instances, the second unknown sample support 640 may have associated with it provisional matches 1210 for two or more known sample supports 642, but no final match 656. By a process of elimination, the generation of the first match 656 may allow one of the provisional matches 1210 to be removed and the second match 656 to be generated for the second unknown sample support 642.

[0176] It should be noted that this process of using one or more provisional matches 1210 and / or final matches 656 to determine one or more other provisional matches 1210 and / or final matches 656 may be performed using varying comparison and / or elimination processes, where appropriate.

[0177] When the matching component 618 generates and / or otherwise identifies a match 656, the updating component 620 can update the corresponding data record 644 associated with one of the unknown sample support 640 or the known sample support 642 corresponding to the match 656. Such an update can include tagging, marking, and / or otherwise writing data to the corresponding data record 644, such as via an appropriate write action, to identify the unknown sample support 640 and the known sample support 642 as being identified as being the same. Additionally and / or alternatively, the update can include data and / or metadata corresponding to any one or more of a corresponding comparison score, a parameter score, an aggregate score, and / or a threshold comparison.

[0178] Furthermore, with respect to the matching component 618 generating a match 656, whether separately from or at least partially in parallel with the updating, the notification component 622 can generally generate a notification 658 defining the generated match 656. The notification 658 can include any suitable type of data, metadata, images, audio, text, and / or the like, and can, for example, be published, transmitted, and / or otherwise made available to a device associated with an administrative entity of the non-limiting system 600.

[0179] It should be understood that in one or more embodiments, any one or more of the processes discussed above as being performed by the non-limiting system 600 may be performed continuously and automatically.

[0180] As an overview of the above components and their functions, refer to Figure 13 , illustrating a method according to one or more embodiments described herein (such as Figure 6 Flowchart of an example non-limiting method 1300 of a process for facilitating identification of a sample support, according to a non-limiting system 600 of FIG. Figure 6 The non-limiting method 1300 is described with respect to the non-limiting system 600 of FIG. 1 , but the non-limiting method 1300 may also be applicable to other systems described herein, such as Figure 5 For the sake of brevity, repeated descriptions of similar elements and / or processes employed in corresponding embodiments are omitted.

[0181] At 1302, non-limiting method 1300 may include capturing, by a system operatively coupled to a processor (e.g., imaging assembly 610 coupled to processor 506), an image (e.g., image 650 of the unknown sample support grid) of an unknown sample support (e.g., unknown sample support grid 640) comprising material layers (e.g., material layers 812 to 820).

[0182] At 1304, the non-limiting method 1300 may include determining, by the system (e.g., the matching component 618), whether an unknown sample support image has been acquired. If so, the non-limiting method 1300 may proceed to step 1306. If not, the non-limiting method may continue back to step 1302 to acquire one or more additional images.

[0183] At 1306, non-limiting method 1300 may include matching, by a system (e.g., matching component 618), an unknown sample support to a known sample support based on an unknown inhomogeneity profile (e.g., unknown inhomogeneity profile 651) that includes one or more inhomogeneities (e.g., inhomogeneity 1010) of a material layer in an image of the unknown sample support.

[0184] As another overview of the above components and their functions, refer to Figures 14 to 16 , illustrating a method according to one or more embodiments described herein (such as Figure 6 Flowchart of an example non-limiting method 1400 of a process for facilitating identification of a sample support, according to a non-limiting system 600 of FIG. Figure 6 The non-limiting method 1400 is described with respect to the non-limiting system 600 of FIG. 1 , but the non-limiting method 1200 may also be applicable to other systems described herein, such as Figure 5 For the sake of brevity, repeated descriptions of similar elements and / or processes employed in corresponding embodiments are omitted.

[0185] At 1402, non-limiting method 1400 may include capturing, by a system operatively coupled to a processor (e.g., imaging assembly 610 coupled to processor 506), an image (e.g., image 650 of the unknown sample support grid) of an unknown sample support (e.g., unknown sample support grid 640) comprising material layers (e.g., material layers 812 to 820).

[0186] At 1404, non-limiting method 1400 may include capturing, by a system (e.g., imaging assembly 610), an image of an unknown sample support comprising a set of peripherally located first regions having greater brightness and defining a central second region having less brightness.

[0187] At 1406 , the non-limiting method 1400 may include capturing, by the system, an image including first regions of one or more unknown non-uniformities (eg, non-uniformities 1010 ).

[0188] At 1408 , the non-limiting method 1400 can include comparing, by the system, one or more unknown non-uniformities of the image of the unknown sample support to one or more known non-uniformities of the known sample support (eg, the known non-uniformities 1110 ).

[0189] At 1410 , the non-limiting method 1400 can include generating, by the system (eg, comparison component 616 ), a comparison score (eg, comparison score 654 ) based on the comparison.

[0190] At 1412 , non-limiting method 1400 can include generating, by the system (e.g., comparison component 616 ), a comparison score comprising a value defining a level of similarity between one or more unknown non-uniformities of the unknown sample support and one or more known non-uniformities of the known sample support.

[0191] At 1414, non-limiting method 1400 may include matching, by a system (e.g., matching component 618), the unknown sample support to a known sample support based on matching one or more unknown non-uniformities of the material layer in the image of the unknown sample support to one or more known non-uniformities in an image of a known sample support (e.g., image 652 of the known sample support grid).

[0192] At 1416, non-limiting method 1400 may include matching, by a system (e.g., matching component 618), the unknown sample support to a known sample support based on a comparison score that includes a highest value compared to one or more other comparison scores generated by the comparison relative to another image of another unknown sample support or another known sample support.

[0193] At 1418, the non-limiting method 1400 may include determining, by the system (e.g., the matching component 618), whether the comparison score satisfies a comparison score threshold (e.g., the comparison threshold 1216). If not, the non-limiting method 1400 may continue back to step 1402 to capture one or more additional images of the unknown sample support. If yes, the non-limiting method may proceed to step 1420.

[0194] At 1420, non-limiting method 1400 can include aggregating, by the system (e.g., matching component 618), the comparison score with a parameter score (e.g., parameter score 1202) based on a comparison of secondary parameters of the unknown sample support that are unrelated to one or more unknown non-uniformities with corresponding secondary parameters of the known sample support that are unrelated to one or more known non-uniformities.

[0195] At 1422, non-limiting method 1400 may include matching, by a system (e.g., matching component 618), the unknown sample support to the known sample support based on an aggregate score generated by aggregation, the aggregate score comprising a highest value compared to one or more other aggregate scores generated by the comparison and aggregation relative to another image of another unknown sample support or another known sample support.

[0196] At 1424, the non-limiting method 1400 may include determining, by the system (e.g., the matching component 618), whether the aggregate score satisfies the comparison score threshold. If not, the non-limiting method 1400 may continue back to step 1402 to capture one or more additional images of the unknown sample support. If yes, the non-limiting method may proceed to step 1426.

[0197] At 1426 , non-limiting method 1400 can include updating, by the system (eg, update component 620 ), a data record associated with the known sample (eg, data record 644 ) to reference the unknown sample.

[0198] At 1428 , the non-limiting method 1400 can include generating, by the system (eg, notification component 622 ), a notification (eg, notification 658 ) defining whether the match corresponds to an unknown sample support.

[0199] As another overview of the above components and their functions, refer to Figure 17 and Figure 18 , illustrating a method according to one or more embodiments described herein (such as Figure 6 A flow chart of another example non-limiting method 17 of a process for identifying a sample support is provided, for example, by a non-limiting system 600 of FIG. Figure 6 The non-limiting method 1700 is described with respect to the non-limiting system 600 of FIG. 1 , but the non-limiting method 1700 may also be applicable to other systems described herein, such as Figure 5 For the sake of brevity, repeated descriptions of similar elements and / or processes employed in corresponding embodiments are omitted.

[0200] At 1702, non-limiting method 1700 may include comparing, by a processor executing program instructions included by a computer-readable storage medium, an unknown inhomogeneity profile (e.g., unknown inhomogeneity profile 651) at an image (e.g., image 650 of the unknown sample support grid) of an unknown sample support (e.g., unknown sample support grid 640) that includes one or more inhomogeneities (e.g., nonuniformity 1010) of the unknown sample support to a known inhomogeneity profile (e.g., known inhomogeneity profile 653) at an image (e.g., image 652 of the known sample support grid 642).

[0201] At 1704, non-limiting method 1700 can include comparing, by a processor, an image of an unknown sample support including a set of peripherally located first regions having greater brightness and defining a central second region having lesser brightness.

[0202] At 1706 , non-limiting method 1700 can include comparing, by a processor, images of the unknown sample support including first regions that include one or more unknown inhomogeneities.

[0203] At 1708 , the non-limiting method 1700 can include comparing, by a processor, the image of the unknown sample support that is an optical microscope image to one or more non-uniformities of the unknown sample support provided at an interior material layer of the unknown sample support.

[0204] At 1710 , the non-limiting method 1700 may include generating, by a processor, a comparison score (eg, the comparison score 654 ) based on the comparison.

[0205] At 1712 , the non-limiting method 1700 can include generating, by a processor, a comparison score comprising a value defining a level of similarity between an unknown non-uniformity profile of an unknown sample support and a known non-uniformity profile of a known sample support.

[0206] At 1714 , the non-limiting method 1700 can include identifying, by the processor, the unknown sample support as a known sample support based on a result of the comparison.

[0207] At 1716, non-limiting method 1700 may include identifying, by the processor, the unknown sample support as a known sample support based on a comparison score that includes a highest value compared to one or more other comparison scores generated by the comparison relative to another image of another unknown sample support or another known sample support.

[0208] At 1718, the non-limiting method 1700 may include determining, by the processor, whether the comparison score satisfies a comparison score threshold (e.g., comparison threshold 1216). If not, the non-limiting method 1400 may continue back to step 1702 to capture one or more additional images of the unknown sample support. If yes, the non-limiting method may proceed to step 1720.

[0209] At 1720 , the non-limiting method 1700 can include determining, by the processor, a match of the second unknown sample support to the second known sample support based on the results of the comparison.

[0210] Additional Overview

[0211] For simplicity of explanation, the computer-implemented and non-computer-implemented methods provided herein are depicted and / or described as a series of actions. It should be understood that the present invention is not limited by the illustrated actions and / or action sequences, and for example, actions can occur in one or more sequences and / or simultaneously, and occur together with other actions not presented and described herein. In addition, not all illustrated actions can be used to implement the computer-implemented and non-computer-implemented methods according to the described subject matter. In addition, the computer-implemented and non-computer-implemented methods can alternatively be represented as a series of interrelated states via state diagrams or events. In addition, the computer-implemented methods described hereinafter and throughout this specification can be stored in an article of manufacture so that the computer-implemented methods are transported and transferred to a computer. The term "article of manufacture" as used herein is intended to encompass a computer program that can be accessed from any computer-readable device or storage medium.

[0212] This document has (and / or will further) describe systems and / or devices with respect to interactions between one or more components. Such systems and / or components may include those components or subcomponents specified therein, one or more of the specified components and / or subcomponents, and / or additional components. Subcomponents may be implemented as components that are communicatively coupled to other components rather than being included within a parent component. One or more components and / or subcomponents may be combined into a single component that provides aggregate functionality. These components may interact with one or more other components, which are not specifically described herein for brevity, but are known to those skilled in the art.

[0213] In summary, one or more systems, computer program products, and / or computer-implemented methods provided herein relate to a process for sample support identification. A system may include a memory storing computer-executable components and a processor executing the computer-executable components. The computer-executable components may include an imaging component that captures an image of an unknown sample support including a material layer; and a matching component that matches the unknown sample support to a known sample support based on an unknown inhomogeneity profile in the image of the unknown sample support including one or more inhomogeneities of the material layer.

[0214] For example, one or more embodiments disclosed herein can achieve improved sample support grid identification compared to conventional techniques that employ only labeling and guesswork. Conversely, one or more embodiments described herein can provide improved performance of imaging systems by providing an approved and recognized baseline for imaging using a validated sample support grid. That is, such a system can employ known dimensions and / or other measurements of a sample support grid to which a sample of interest can be attached. Verifying that the sample support grid being used is indeed the sample support grid corresponding to such dimensions and / or measurements can reduce imaging and / or measurement errors during subsequent analysis of the corresponding sample of interest.

[0215] In conjunction with the foregoing, one or more embodiments described herein can provide verifiable tracking of one or more (e.g., multiple) sample support grids at different locations and / or by different entities across various stages of their manufacture, processing, and / or preparation. That is, physical properties of the sample support grids themselves can be employed to identify the sample support grids, thereby providing for identifying one or more sample support grids of interest as the correct sample support grids (e.g., those designated for use) with respect to one or more imaging systems.

[0216] For example, based on a designated application of a light source to a sample support grid, one or more non-uniformities of one or more materials of the sample support grid can be used to track (e.g., as a fingerprint or other profile) the identification of the sample support grid, both initially and during one or more subsequent identification periods. This can allow for more efficient and accurate identification of sample supports and / or verified confirmation that a designated sample support grid was indeed employed, compared to prior art techniques. Furthermore, this can allow for more accurate placement of samples, such as thin layers, on and / or at the sample support, compared to prior art techniques.

[0217] By using a matching process that utilizes one or more inhomogeneities of the material of the selected unknown sample support grid compared to one or more inhomogeneities of an image of a known sample support grid, identification of the selected unknown sample support grid can be made increasingly more efficient. In one or more embodiments, a comparison score generated for this comparison can be aggregated with one or more parameter scores corresponding to a comparison of one or more secondary parameters of the selected unknown sample support grid with one or more corresponding secondary parameters of the known sample support grid.

[0218] In practice, in view of one or more embodiments described herein, practical applications of one or more systems, computer-implemented methods, and / or computer program products described herein can be capable of automatically matching an image of an unknown sample support with an image of a known sample support, thereby providing a match of the known sample support with at least a record defining the known sample support. This identification can be performed for the purpose of verifying one or more characteristics of a sample support to be used and / or in use with respect to a materials analysis imaging system (such as an S / TEM or FIB apparatus). This identification can also be performed efficiently and accurately without requiring manual input into the decision-making process, thereby reducing the time, effort, manual input, and / or one or more errors caused by conventional frameworks for sample support identification.

[0219] Further due to the accurate non-uniformity identification that can be performed by one or more embodiments described herein, the sample support can be more precisely positioned relative to the imaging column or other aspects of such an imaging system, and / or the sample of interest can be attached with more precise attachment to the attachment area of ​​the sample support.

[0220] Furthermore, based on the disclosed teachings, one or more of the embodiments described herein can be employed in real-world systems. For example, as described above, accurate positioning of an imaged sample support (such as due to identification of the sample support by the framework discussed herein) can directly lead to more accurate identification of the attachment area of ​​the sample support and / or more accurate physical placement of a physical sample at the sample support including the attachment area, as compared to the prior art. This real-world result can be made possible because the process of one or more of the embodiments described herein involves profile analysis of one or more unknown non-uniformities of an unknown sample support, comparison of the resulting unknown non-uniformity profiles with known non-uniformity profiles of a known sample, and subsequent matching of an image of the unknown sample support with an image of a known sample support based on the comparison. That is, one or more of the embodiments described herein can provide a high degree of accuracy in computer-aided identification of an unknown sample support. Thus, the embodiments disclosed herein can provide improvements to scientific instrumentation technology (e.g., improvements in computer technology supporting such scientific instrumentation, among other improvements).

[0221] Embodiments herein can provide for scaled identification of two or more sample supports and / or two or more sample support images at least partially in parallel with one another. For example, a set of processes associated with a requested identification can be performed at least partially in parallel with a set of processes associated with another requested identification, or even at least partially in parallel with a set of processes associated with the same requested identification (e.g., such as where two or more unknown sample support images of the same unknown sample support are analyzed at least partially in parallel with one another). As an example of the latter, one image can have a first darkness level and a second image can have a second darkness level, as discussed above.

[0222] This document has (and / or will further) describe systems and / or devices with respect to interactions between one or more components. Such systems and / or components may include those components or subcomponents specified therein, one or more of the specified components and / or subcomponents, and / or additional components. Subcomponents may be implemented as components that are communicatively coupled to other components rather than being included within a parent component. One or more components and / or subcomponents may be combined into a single component that provides aggregate functionality. These components may interact with one or more other components, which are not specifically described herein for brevity, but are known to those skilled in the art.

[0223] One or more embodiments described herein may, in one or more embodiments, be inherently and / or unavoidably related to computer technology and cannot be implemented outside of a computing environment. For example, one or more processes performed by one or more embodiments described herein may provide program and / or program instruction execution more efficiently and more feasible than existing systems and / or techniques that use manual methods and / or computer-assisted methods, such as with respect to automatic sample support identification. Systems, computer-implemented methods, and / or computer program products that provide the performance of these processes have an important role in the field of materials analysis (such as in materials analysis using dual-beam systems) and cannot be implemented equally feasibly in a reasonable manner outside of a computing environment.

[0224] One or more embodiments described herein can employ hardware and / or software to solve problems that are highly technical, non-abstract, and cannot be performed by a human through a set of mental activities. For example, a human, or even thousands of humans, cannot efficiently, accurately, and / or effectively automatically identify the non-uniform contours of a sample support grid, particularly a sample support grid having a perimeter in the range of about 5 mm to about 2 mm (such as about 3 mm), but one or more embodiments described herein can provide such a process. That is, whether using a microscope or manual observation methods, a human cannot digitize the observed results and compare them to computer data, but one or more embodiments described herein can perform this as a set of mental activities and / or with pen and paper.

[0225] In one or more embodiments, one or more of the processes described herein can be performed by one or more special-purpose computers (e.g., special-purpose processing units, special-purpose classical computers, special-purpose quantum computers, special-purpose hybrid classical / quantum systems, and / or another type of special-purpose computer) to perform defined tasks associated with one or more of the above-described technologies. One or more embodiments described herein and / or components thereof can be used to solve new problems arising from advances in the above-described technologies, the adoption of quantum computing systems, cloud computing systems, computer architectures, and / or other technologies.

[0226] One or more embodiments described herein may be fully operable to perform one or more other functions (eg, fully powered on, fully executed, and / or another function) while also performing one or more of the one or more operations described herein.

[0227] To provide additional overview, a list of embodiments and their features is provided next.

[0228] A system comprising: a memory storing computer-executable components; and a processor executing the computer-executable components stored in the memory, wherein the computer-executable components include: an imaging component that captures an image of an unknown sample support including a material layer; and a matching component that matches the unknown sample support to a known sample support based on an unknown inhomogeneity profile in the image of the unknown sample support including one or more inhomogeneities of the material layer.

[0229] The system as described in the preceding paragraph further includes a comparison component that compares the unknown non-uniformity profile of the image of the unknown sample support with the known non-uniformity profile of the image of the known sample support and generates a comparison score based on the comparison, wherein the comparison score includes a value defining a level of similarity between the unknown non-uniformity profile of the unknown sample support and the known non-uniformity profile of the known sample support.

[0230] The system of any preceding paragraph, further comprising: wherein the matching component also matches the unknown sample support to the known sample support based on the comparison score, the comparison score comprising a highest value compared to one or more other comparison scores generated by the comparison component relative to another image of another unknown sample support or another known sample support.

[0231] A system as described in any preceding paragraph, wherein the image of the unknown sample support includes a set of peripherally located first regions having greater brightness and bounding a central second region having lesser brightness, and wherein the first regions define the unknown non-uniformity profile.

[0232] The system of any preceding paragraph, wherein the image of the unknown sample support is an optical microscope image, and the layer of material comprises a silicon frame.

[0233] The system of any preceding paragraph, further comprising an acquisition component that acquires a set of images of a known sample support, including the image of the known sample support.

[0234] The system of any preceding paragraph, further comprising: a notification component that generates a notification defining whether the match corresponds to the unknown sample support.

[0235] A computer-implemented method includes capturing, by a system operatively coupled to a processor, an image of an unknown sample support including a layer of material; and matching, by the system, the unknown sample support to a known sample support based on a matching of one or more unknown inhomogeneities of the layer of material in the image of the unknown sample support with one or more known inhomogeneities in an image of the known sample support.

[0236] The computer-implemented method as described in the preceding paragraphs further includes: comparing, by the system, the one or more unknown non-uniformities of the image of the unknown sample support with the one or more known non-uniformities of the known sample support; and generating, by the system, a comparison score based on the comparison, wherein the comparison score includes a value defining a level of similarity between the one or more unknown non-uniformities of the unknown sample support and the one or more known non-uniformities of the known sample support.

[0237] A computer-implemented method as described in any preceding paragraph, wherein the matching further comprises matching, by the system, the unknown sample support with the known sample support based on the comparison score, the comparison score comprising a highest value compared to one or more other comparison scores generated by the comparison relative to another image of another unknown sample support or another known sample support.

[0238] A computer-implemented method as described in any preceding paragraph, wherein the matching further comprises: aggregating, by the system, the comparison score with a parameter score based on a comparison of secondary parameters of the unknown sample support that are unrelated to the one or more unknown non-uniformities with corresponding secondary parameters of the known sample support that are unrelated to the one or more known non-uniformities; and matching, by the system, the unknown sample support with the known sample support based on an aggregated score generated by the aggregation, the aggregated score comprising a highest value compared to one or more other aggregated scores generated by the comparison and aggregation relative to another image of another unknown sample support or another known sample support.

[0239] A computer-implemented method as described in any preceding paragraph, wherein the image of the known sample support includes a set of peripherally located first regions having greater brightness and bounding a central second region having lesser brightness, and wherein the first regions are the one or more unknown inhomogeneities.

[0240] The computer-implemented method of any preceding paragraph, further comprising updating, by the system, a data record associated with the known sample to reference the unknown sample.

[0241] The computer-implemented method of any preceding paragraph, further comprising generating, by the system, a notification defining whether the match is generated corresponding to the unknown sample support.

[0242] A computer program product facilitates a process for sample support identification, the computer program product comprising a computer-readable storage medium having program instructions embodied therein, the program instructions being executable by a processor to cause the processor to: compare, by the processor, an unknown inhomogeneity profile at an image of an unknown sample support including one or more inhomogeneities of the unknown sample support with a known inhomogeneity profile of an image of a known sample support; and based on a result of the comparison, identify, by the processor, the unknown sample support as the known sample support.

[0243] A computer program product as described in the preceding paragraph, wherein the program instructions are further executable by the processor to cause the processor to: generate a comparison score based on the comparison, wherein the comparison score includes a value defining a level of similarity between the unknown non-uniformity profile of the unknown sample support and the known non-uniformity profile of the known sample support.

[0244] A computer program product as described in any preceding paragraph, wherein the program instructions are executable by the processor to further cause the processor to: match the unknown sample support with the known sample support based on the comparison score, the comparison score comprising a highest value compared to one or more other comparison scores generated by the comparison relative to another image of another unknown sample support or another known sample support.

[0245] A computer program product as described in any preceding paragraph, wherein the image of the unknown sample support includes a set of peripherally located first regions having greater brightness and bounding a central second region having lesser brightness, and wherein these first regions define the unknown non-uniformity profile.

[0246] The computer program product of any preceding paragraph, wherein the image of the unknown sample support is an optical microscope image, and the one or more inhomogeneities of the unknown sample support are provided at an interior material layer of the unknown sample support.

[0247] A computer program product as described in any preceding paragraph, wherein the program instructions are further executable by the processor to cause the processor to: determine, by the processor, a match of a second unknown sample support to a second known sample support based on the result of the comparing.

[0248] Scientific Instrument System Description

[0249] Next turn Figure 19 , provides the Figures 1 to 18 Detailed description of additional context for one or more embodiments described in . One or more computing devices implementing any of the scientific instrument modules or methods disclosed herein may be part of a scientific instrument system. Figure 19 A block diagram of an example scientific instrument system 1900 is illustrated, in accordance with various embodiments described herein, in which one or more of the scientific instrument methods or other methods disclosed herein may be performed. Figure 1 The scientific instrument module 100 and Figure 2 The method 200) may be implemented by one or more of the scientific instrument 1910, the user local computing device 1920, the service local computing device 1930, and / or the remote computing device 1940 of the scientific instrument system 1900.

[0250] Any of the scientific instrument 1910, the user local computing device 1920, the service local computing device 1930, and / or the remote computing device 1940 may include any of the aforementioned references. Figure 4Any of the embodiments of the computing device 400 discussed herein, and any of the scientific instrument 1910, the user local computing device 1920, the service local computing device 1930, and / or the remote computing device 1940 may employ any of the embodiments described herein. Figure 4 Any suitable form of one or more of the discussed embodiments of computing device 400 .

[0251] One or more of the scientific instrument 1910, the user local computing device 1920, the service local computing device 1930, and / or the remote computing device 1940 may include a processing device 1902, a storage device 1904, and / or an interface device 1906. The processing device 1902 may take any suitable form, including any suitable form described herein. Figure 4 The processor 402 discussed herein may be in the form of any processor. The processing devices 1902 in different devices, including the scientific instrument 1910, the user local computing device 1920, the service local computing device 1930, and / or the remote computing device 1940, may be in the same form or in different forms. The storage device 1904 may be in any suitable form, including those described herein. Figure 4 The storage devices 1904 discussed herein may be in the form of any storage device. The storage devices 1904 in different devices, including the scientific instrument 1910, the user local computing device 1920, the service local computing device 1930, and / or the remote computing device 1940, may take the same form or different forms. The interface device 1906 may take any suitable form, including those described herein. Figure 4 The interface devices 1906 discussed herein may be in the form of any of the interface devices 406. The interface devices 1906 in different devices included in the scientific instrument 1910, the user local computing device 1920, the service local computing device 1930, and / or the remote computing device 1940 may take the same form or different forms.

[0252] The scientific instrument 1910, the user local computing device 1920, the service local computing device 1930, and the remote computing device 1940 can communicate with other elements of the scientific instrument system 1900 via a communication path 1908. The communication path 1908 can communicatively couple an interface device 1906 of different elements of the scientific instrument system 1900, as shown, and can be a wired or wireless communication path (e.g., according to the reference herein). Figure 4 any of the communication technologies discussed above for the interface device 406 of the computing device 400). Figure 191900 includes a communication path between each pair of devices among the scientific instrument 1910, the user local computing device 1920, the service local computing device 1930, and the remote computing device 1940, but such a "fully connected" implementation is merely illustrative, and in various embodiments, various communication paths in the communication paths 1908 may be omitted. For example, in one or more embodiments, the service local computing device 1930 may omit a direct communication path 1908 between its interface device 1906 and the interface device 1906 of the scientific instrument 1910, and may instead communicate with the scientific instrument 1910 via the communication path 1908 between the service local computing device 1930 and the user local computing device 1920 and / or the communication path 1908 between the user local computing device 1920 and the scientific instrument 1910.

[0253] Scientific instrument 1910 may include any suitable scientific instrument, such as a separation or MS instrument, or other instrument that facilitates analysis of materials.

[0254] The user-local computing device 1920 can be a computing device local to a user of the scientific instrument 1910 (e.g., according to any of the embodiments of the computing device 400 discussed herein). In one or more embodiments, the user-local computing device 1920 can also be local to the scientific instrument 1910, but this is not necessarily the case; for example, a user-local computing device 1920 associated with a home, office, or other building associated with a user entity can be remote from the scientific instrument 1910 but in communication with it such that the user entity can use the user-local computing device 1920 to control and / or access data from the scientific instrument 1910. In one or more embodiments, the user-local computing device 1920 can be a laptop, smartphone, or tablet device. In one or more embodiments, the user-local computing device 1920 can be a portable computing device. In one or more embodiments, the user-local computing device 1920 can be deployed in the field.

[0255] The service local computing device 1930 can be a computing device local to the entity serving the scientific instrument 1910 (e.g., according to any of the embodiments of the computing device 400 discussed herein). For example, the service local computing device 1930 can be a device local to the manufacturer of the scientific instrument 1910 or a third-party service company. In one or more embodiments, the service local computing device 1930 can communicate with the scientific instrument 1910, the user local computing device 1920, and / or the remote computing device 1940 (e.g., via a direct communication path 1908 or via multiple "indirect" communication paths 1908, as discussed above) to receive data regarding the operation of the scientific instrument 1910, the user local computing device 1920, and / or the remote computing device 1940 (e.g., self-test results of the scientific instrument 1910, calibration coefficients used by the scientific instrument 1910, measurements of sensors associated with the scientific instrument 1910, etc.). In one or more embodiments, the service local computing device 1930 can communicate with the scientific instrument 1910, the user local computing device 1920, and / or the remote computing device 1940 (e.g., via a direct communication path 1908 or via multiple "indirect" communication paths 1908, as described above) to send data to the scientific instrument 1910, the user local computing device 1920, and / or the remote computing device 1940 (e.g., to update programming instructions (such as firmware) in the scientific instrument 1910 to initiate execution of a test or calibration sequence in the scientific instrument 1910, to update programming instructions (such as software) in the user local computing device 1920 or the remote computing device 1940, etc.). A user entity of the scientific instrument 1910 can utilize the scientific instrument 1910 or the user local computing device 1920 to communicate with the service local computing device 1930 to report a problem with the scientific instrument 1910 or the user local computing device 1920, to request a technician visit to improve the operation of the scientific instrument 1910, to order consumables or replacement parts associated with the scientific instrument 1910, or for other purposes.

[0256] The remote computing device 1940 can be a computing device remote from the scientific instrument 1910 and / or the user's local computing device 1920 (e.g., according to any of the embodiments of the computing device 400 discussed herein). In one or more embodiments, the remote computing device 1940 can be included in a data center or other large-scale server environment. In one or more embodiments, the remote computing device 1940 can include network attached storage (e.g., as part of the storage device 1904). The remote computing device 1940 can store data generated by the scientific instrument 1910, perform analysis of data generated by the scientific instrument 1910 (e.g., according to programmed instructions), facilitate communication between the user's local computing device 1920 and the scientific instrument 1910, and / or facilitate communication between the service local computing device 1930 and the scientific instrument 1910.

[0257] In one or more embodiments, the Figure 19 One or more of the elements of the scientific instrument system 1900 illustrated in FIG. Additionally, in one or more embodiments, there may be Figure 19 1900 ). For example, the scientific instrument system 1900 may include multiple user-local computing devices 1920 (e.g., different user-local computing devices 1920 associated with different user entities or located in different locations). In another example, the scientific instrument system 1900 may include multiple scientific instruments 1910, all of which communicate with a service local computing device 1930 and / or a remote computing device 1940; in such embodiments, the service local computing device 1930 may monitor these multiple scientific instruments 1910, and the service local computing device 1930 may cause updates or other information to be "broadcasted" to the multiple scientific instruments 1910 simultaneously. The different scientific instruments 1910 in the scientific instrument system 1900 may be close to each other (e.g., in the same room) or far away from each other (e.g., on different floors of a building, in different buildings, in different cities, etc.). In one or more embodiments, the scientific instrument 1910 can be connected to an Internet of Things (IoT) stack that allows command and control of the scientific instrument 1910 through web-based applications, virtual or augmented reality applications, mobile applications, and / or desktop applications. Any of these applications can be accessed by a user entity operating a user-local computing device 1920 that communicates with the scientific instrument 1910 through an intermediary remote computing device 1940. In one or more embodiments, the scientific instrument 1910 can be sold by a manufacturer along with one or more associated user-local computing devices 1920 as part of a local scientific instrument computing unit 1912.

[0258] In one or more embodiments, the different scientific instruments 1910 included in the scientific instrument system 1900 can be different types of scientific instruments 1910; for example, one scientific instrument 1910 can be an EDS device, while another scientific instrument 1910 can be an analysis device that analyzes the results of the EDS device. In some such embodiments, the remote computing device 1940 and / or the user's local computing device 1920 can combine data from the different types of scientific instruments 1910 included in the scientific instrument system 1900.

[0259] Sample operating environment

[0260] Figure 202000 is a schematic block diagram of an operating environment 2000 with which the subject matter can interact. Operating environment 2000 includes one or more remote components 2010. Remote components 2010 can be hardware and / or software (e.g., threads, processes, computing devices). In one or more embodiments, remote components 2010 can be distributed computer systems that are connected to local autoscaling components and / or programs that use resources of the distributed computer systems via a communication framework 2040. Communication framework 2040 can include wired network devices, wireless network devices, mobile devices, wearable devices, radio access network devices, gateway devices, femtocell devices, servers, and the like.

[0261] Operating environment 2000 also includes one or more local components 2020. Local components 2020 can be hardware and / or software (e.g., threads, processes, computing devices). In one or more embodiments, local components 2020 can include an autoscaling component connected to a remote distributed computing system via communication framework 2040 and / or programs that transfer / use remote resources 2010 and 2020.

[0262] One possible communication between remote component 2010 and local component 2020 can take the form of data packets suitable for transmission between two or more computer processes. Another possible communication between remote component 2010 and local component 2020 can take the form of circuit-switched data, which is suitable for transmission between two or more computer processes in a radio time slot. Operating environment 2000 includes a communication framework 2040, which can be used to facilitate communication between remote component 2010 and local component 2020 and can include an air interface, such as a UMTS network interface via an LTE network. Remote component 2010 can be operably connected to one or more remote data repositories 2050, such as a hard drive, solid-state drive, subscriber identity module (SIM) card, electronic SIM (eSIM), device memory, etc., which can be used to store information on the remote component 2010 side of the communication framework 2040. Similarly, local component 2020 can be operably connected to one or more local data repositories 2030, which can be used to store information on the local component 2020 side of the communication framework 2040.

[0263] Sample computing environment

[0264] To provide additional context for the various embodiments described herein, Figure 21The following discussion is intended to provide a brief, general description of a suitable computing environment 2100 in which various embodiments of the embodiments described herein may be implemented. Although the embodiments have been described above in the general context of computer-executable instructions that may be executed on one or more computers, those skilled in the art will recognize that the embodiments may also be implemented in conjunction with other program modules and / or as a combination of hardware and software.

[0265] Generally, program modules include routines, programs, components, data structures, etc. that perform tasks or implement abstract data types. Furthermore, the methods can be practiced with other computer system configurations, including single-processor or multi-processor computer systems, minicomputers, mainframe computers, Internet of Things (IoT) devices, distributed computing systems, as well as personal computers, handheld computing devices, microprocessor-based or programmable consumer electronics, etc., each of which can be operatively coupled to one or more associated devices.

[0266] The embodiments illustrated herein can also be practiced in distributed computing environments where certain tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

[0267] Computing devices typically include various media, which may include computer-readable storage media, machine-readable storage media, and / or communication media, the two terms being used differently herein as follows. A computer-readable storage medium or machine-readable storage medium can be any available storage medium that can be accessed by a computer, and includes both volatile and non-volatile media, removable and non-removable media. By way of example and not limitation, a computer-readable storage medium or machine-readable storage medium can be implemented in conjunction with any method or technology for storing information, such as computer-readable or machine-readable instructions, program modules, structured data, or unstructured data.

[0268] Computer-readable storage media may include, but are not limited to, random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD ROM), digital versatile disc (DVD), Blu-ray disc (BD) or other optical disc storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, solid-state drives or other solid-state storage devices, or other tangible and / or non-transitory media that can be used to store the desired information. In this regard, the terms "tangible" or "non-transitory" as applied to storage, memory, or computer-readable media herein only exclude the propagation of transient signals themselves as a modifier and do not disclaim all rights to standard storage, memory, or computer-readable media that are not merely propagating transient signals themselves.

[0269] Computer-readable storage media can be accessed by one or more local or remote computing devices, eg, via access requests, queries, or other data retrieval protocols, for various operations regarding the information stored by the media.

[0270] Communication media typically embodies computer-readable instructions, data structures, program modules, or other structured or unstructured data in a data signal (such as a modulated data signal, such as a carrier wave or other transport mechanism), and includes any information delivery or transmission media. The term "modulated data signal" or signal refers to a signal that has one or more of its characteristics set or changed so as to encode information in one or more signals. By way of example, and not limitation, communication media includes wired media (such as a wired network or direct-wired connection) and wireless media (such as acoustic, RF, infrared, and other wireless media).

[0271] Still refer to Figure 21 , an example computing environment 2100 in which one or more embodiments described herein may be implemented includes a computer 2102, which includes a processing unit 2104, a system memory 2106, and a system bus 2108. The system bus 2108 couples system components, including but not limited to the system memory 2106, to the processing unit 2104. The processing unit 2104 can be any of various commercially available processors. Dual microprocessors and other multi-processor architectures can also be used as the processing unit 2104.

[0272] The system bus 2108 can be any of several types of bus structures, which can further interconnect with a memory bus (with or without a memory controller), a peripheral bus, and a local bus using any of a variety of commercially available bus architectures. The system memory 2106 includes ROM 2110 and RAM 2112. A basic input / output system (BIOS), containing the basic routines that help transfer information between elements within the computer 2102, such as during startup, can be stored in nonvolatile memory (such as ROM, erasable programmable read-only memory (EPROM), or EEPROM). RAM 2112 can also include high-speed RAM, such as static RAM for caching data.

[0273] Computer 2102 also includes an internal hard disk drive (HDD) 2114 (e.g., EIDE, SATA) and may include one or more external storage devices 2116 (e.g., a magnetic floppy disk drive (FDD) 2116, a memory stick or flash drive reader, a memory card reader, etc.). Although internal HDD 2114 is illustrated as being located within computer 2102, internal HDD 2114 may also be configured for use externally in a suitable chassis (not illustrated). Additionally, although not shown in computing environment 2100, a solid-state drive (SSD) may be used in addition to or in place of HDD 2114.

[0274] Other internal or external storage devices may include at least one other storage device 2121 having a storage medium 2122 (e.g., a solid-state storage device, a non-volatile memory device, and / or an optical drive that can read from or write to removable media (such as CD-ROM disks, DVDs, BDs, etc.)). External storage device 2116 may be facilitated by a network virtual machine. HDD 2114, external storage device 2116, and storage device (e.g., drive) 2121 may be connected to system bus 2108 via HDD interface 2124, external storage interface 2126, and drive interface 2128, respectively.

[0275] The drives and their associated computer-readable storage media provide non-volatile storage of data, data structures, computer-executable instructions, and the like. The drives and storage media accommodate the storage of any data in a suitable digital format for the computer 2102. Although the above description of computer-readable storage media refers to corresponding types of storage devices, other types of storage media (whether currently existing or developed in the future) that can be read by a computer may also be used in the example operating environment, and further, any such storage media may contain computer-executable instructions for performing the methods described herein.

[0276] A number of program modules may be stored in the drives and RAM 2112, including an operating system 2130, one or more application programs 2132, other program modules 2134, and program data 2136. All or portions of the operating system, application programs, modules, and / or data may also be cached in RAM 2112. The systems and methods described herein may be implemented using various commercially available operating systems or combinations of operating systems.

[0277] The computer 2102 may optionally include emulation technology. For example, a hypervisor (not shown) or other middleware may emulate the hardware environment for the operating system 2130, and the emulated hardware may optionally be different from the hardware of the operating system 2130. Figure 21 21. The illustrated hardware. In this embodiment, the operating system 2130 may comprise one of a plurality of virtual machines (VMs) hosted at the computer 2102. In addition, the operating system 2130 may provide a runtime environment, such as a Java runtime environment or a .NET framework, for the application 2132. The runtime environment is a consistent execution environment that allows the application 2132 to run on any operating system that includes a runtime environment. Similarly, the operating system 2130 may support containers, and the application 2132 may be in the form of containers, which are lightweight, independent, executable software packages that include, for example, the application's code, runtime, system tools, system libraries, and settings.

[0278] Furthermore, the computer 2102 may be equipped with a security module, such as a Trusted Processing Module (TPM). For example, using a TPM, the boot component hashes the next boot component in time and waits for the hash to match a secure value before loading the next boot component. This process can be performed at any layer in the code execution stack of the computer 2102, for example, at the application execution level or the operating system (OS) kernel level, thereby achieving security for code execution at any level.

[0279] A user entity may enter commands and information into the computer 2102 through one or more wired / wireless input devices (e.g., a keyboard 2138, a touch screen 2140, and a pointing device such as a mouse 2142). Other input devices (not shown) may include a microphone, an infrared (IR) remote control, a radio frequency (RF) remote control, or other remote control, a joystick, a virtual reality controller and / or a virtual reality headset, a game controller, a stylus, an image input device (e.g., a camera), a gesture sensor input device, a visual movement sensor input device, an emotion or facial detection device, a biometric input device (e.g., a fingerprint or iris scanner), etc. These and other input devices are typically connected to the processing unit 2104 through an input device interface 2144, which may be coupled to the system bus 2108, but may also be connected through other interfaces such as a parallel port, an IEEE 1394 serial port, a game port, a USB port, an IR interface, a memory card ... Interfaces, etc.

[0280] A monitor 2146 or other type of display device may also be connected to the system bus 2108 via an interface, such as a video adapter 2148. In addition to the monitor 2146, computers typically include other peripheral output devices (not shown), such as speakers, printers, and the like.

[0281] Computer 2102 can operate in a network environment, using logical connections to one or more remote computers (such as remote computer 2150) via wired and / or wireless communications. Remote computer 2150 can be a workstation, server computer, router, personal computer, portable computer, microprocessor-based entertainment device, peer device, or other common network node, and typically includes many or all of the elements described with respect to computer 2102, but only memory / storage device 2152 is illustrated for simplicity. The depicted logical connections include wired / wireless connections to a local area network (LAN) 2154 and / or a larger network (e.g., wide area network (WAN) 2156). Such LAN and WAN networking environments are common in offices and companies and facilitate the establishment of enterprise-wide computer networks (such as intranets), all of which can be connected to a global communication network (e.g., the Internet).

[0282] When used in a LAN networking environment, the computer 2102 can be connected to the local network 2154 through a wired and / or wireless communication network interface or adapter 2158. The adapter 2158 can facilitate wired or wireless communication with the LAN 2154, which can also include a wireless access point (AP) provided thereon for communicating with the adapter 2158 in a wireless mode.

[0283] When used in a WAN networking environment, the computer 2102 may include a modem 2160 or may be connected to a communication server on the WAN 2156 via other means for establishing communications over the WAN 2156 (such as over the Internet). The modem 2160 may be connected to the system bus 2108 via the input device interface 2144 and may be internal or external and may be a wired or wireless device. In a networked environment, program modules depicted relative to the computer 2102 or portions thereof may be stored in the remote memory / storage device 2152. The network connections shown are examples only, and other means of establishing a communications link between the computers may be used.

[0284] When used in a LAN or WAN networking environment, computer 2102 can access cloud storage systems or other network-based storage systems in addition to or as an alternative to the external storage devices 2116 described above. Typically, the connection between computer 2102 and the cloud storage system can be established over LAN 2154 or WAN 2156, for example, via adapter 2158 or modem 2160, respectively. When computer 2102 is connected to an associated cloud storage system, external storage interface 2126 can manage the storage provided by the cloud storage system with the assistance of adapter 2158 and / or modem 2160, just as it would other types of external storage. For example, external storage interface 2126 can be configured to provide access to cloud storage sources as if those sources were physically connected to computer 2102.

[0285] The computer 2102 is operable to communicate with any wireless device or entity operatively arranged in wireless communication (e.g., printers, scanners, desktop and / or portable computers, portable data assistants, communication satellites, any equipment or location associated with a wirelessly detectable tag (e.g., information kiosks, newsstands, store shelves, etc.), and telephones). This may include Wireless Fidelity (Wi-Fi) and Wireless technology. Therefore, the communication can be a defined structure like existing networks, or simply an ad hoc communication between at least two devices.

[0286] Additional Information

[0287] The embodiments described herein can be directed to one or more of the systems, methods, devices and / or computer program products at any possible level of technical detail integration. A computer program product can include a computer-readable storage medium (or multiple media) having a computer-readable program instruction thereon, which is used to cause a processor to perform various aspects of one or more embodiments described herein. A computer-readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. A computer-readable storage medium can be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a superconducting storage device and / or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of computer-readable storage media can also include the following: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device (such as a punched card or a raised structure in a groove with instructions recorded thereon) and / or any suitable combination of the foregoing. Computer-readable storage media as used herein should not be interpreted as transient signals themselves, such as radio waves and / or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides and / or other transmission media (e.g., light pulses transmitted through fiber optic cables), and / or electrical signals sent through wires.

[0288] Computer-readable program instructions as herein described can be downloaded to corresponding computing / processing equipment and / or downloaded to external computer or external storage device via network (for example, the Internet, local area network, wide area network and / or wireless network) from computer-readable storage medium.The network can comprise copper transmission cable, light transmission fiber, wireless transmission, router, firewall, switch, gateway computer and / or edge server.Network adapter card or the network interface in each computing / processing equipment receive computer-readable program instructions from network, and forward computer-readable program instructions to be stored in the computer-readable storage medium in corresponding computing / processing equipment.The computer-readable program instructions for performing the operation of one or more embodiments as herein described can be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, configuration data for integrated circuit and / or source code and / or object code written in any combination of one or more programming languages ​​(including object-oriented programming languages, such as Smalltalk, C++ etc.) and / or process programming languages ​​(such as " C " programming language and / or similar programming languages). Computer readable program instructions can be executed entirely on a computer, partially on a computer, as an independent software package, partially on a computer and / or partially on a remote computer or entirely on a remote computer and / or server. In the latter case, the remote computer can be connected to the computer and / or can be connected to an external computer (for example, using an internet service provider via the internet) through any type of network (including a local area network (LAN) and / or a wide area network (WAN)). In one or more embodiments, an electronic circuit (including for example a programmable logic circuit, a field programmable gate array (FPGA) and / or a programmable logic array (PLA)) can execute computer readable program instructions to personalize the electronic circuit by utilizing the state information of the computer readable program instructions to perform the various aspects of one or more embodiments described herein.

[0289] Reference is made to the flowchart illustrations and / or block diagrams of the methods, devices (systems) and computer program products according to one or more embodiments described herein to describe various aspects of one or more embodiments described herein. It should be understood that each box in the flowchart illustrations and / or block diagrams and the combination of boxes in the flowchart illustrations and / or block diagrams can be implemented by computer-readable program instructions. These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer and / or other programmable data processing device to produce a machine so that the instructions executed by the processor of the computer or other programmable data processing device can create a component for implementing the function / action specified in one or more boxes of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium, which can guide a computer, a programmable data processing device and / or other equipment to operate in a specific manner, and the computer-readable storage medium storing instructions can include a manufactured product, which includes instructions for various aspects of the function / action specified in one or more boxes of the flowchart and / or block diagram. The computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus and / or other devices to cause a series of operational actions to be performed on the computer, other programmable apparatus and / or other devices, thereby producing a computer-implemented process, so that the instructions executed on the computer, other programmable apparatus and / or other devices implement the functions / actions specified in one or more boxes of the flowchart and / or block diagram.

[0290] The flow chart and block diagram in the figure illustrate the possible architecture, function and / or operation of the system, computer-implemented method and / or computer program product according to one or more embodiments described herein. In this regard, each box in the flow chart or block diagram can represent a module, segment and / or partial instruction, which includes one or more executable instructions for realizing the specified logical function. In one or more alternative embodiments, the function marked in the box may not occur in the order marked in the figure. For example, two consecutively displayed boxes can be executed substantially simultaneously, and / or can sometimes be executed in the opposite order, depending on the function involved. It should also be noted that each box in the block diagram and / or flow chart illustration, and / or the combination of the boxes in the block diagram and / or flow chart illustration can be implemented by a dedicated hardware system, which can perform specified functions and / or actions, and / or perform one or more combinations of dedicated hardware and / or computer instructions.

[0291] Although the subject matter has been described above in the general context of the computer executable instructions of the computer program product running on a computer and / or a computer, it will be appreciated by those skilled in the art that one or more embodiments herein may also be implemented at least in part in parallel with one or more other program modules. In general, a program module includes routines, programs, components and / or data structures that perform specific tasks and / or implement specific abstract data types. In addition, the aforementioned computer-implemented method can be put into practice with other computer system configurations, including single-processor and / or multi-processor computer systems, small computing devices, mainframe computers, and computers, handheld computing devices (e.g., PDAs, phones) and / or based on microprocessors or programmable consumer and / or industrial electronic devices. The illustrated aspects may also be put into practice in a distributed computing environment, where tasks are performed by a remote processing device connected via a communication network. However, one or more (if not all) aspects of one or more embodiments described herein may be put into practice on a stand-alone computer. In a distributed computing environment, a program module may be located in both a local memory storage device and a remote memory storage device.

[0292] As used in this application, the terms "component", "system", "platform" and / or "interface" may refer to and / or may include computer-related entities or entities related to an operating machine having one or more specific functions. The entities described herein may be hardware, a combination of hardware and software, software, or software in execution. For example, a component may be, but is not limited to, a process, a processor, an object, an executable program, a thread of execution, a program, and / or a computer running on a processor. As an example, both an application running on a server and a server may be components. One or more components may reside within a process and / or thread of execution, and a component may be located on a computer and / or distributed between two or more computers. For another example, corresponding components may be executed from various computer-readable media having various data structures stored thereon. These components may communicate via local and / or remote processes, such as according to signals having one or more data packets (e.g., data from a component interacts with another component in a local system, a distributed system, and / or interacts with other systems across a network such as the Internet). As another example, a component may be a device having a specific functionality provided by a mechanical component operated by an electrical or electronic circuit, which is operated by software and / or firmware applications executed by a processor. In such cases, the processor can be internal and / or external to the device and can execute at least a portion of the software and / or firmware application. As another example, the component can be a device that provides specific functionality through electronic components without mechanical parts, where the electronic components can include a processor and / or other components for executing the software and / or firmware that at least partially imparts the functionality to the electronic components. In one aspect, the component can emulate the electronic component via a virtual machine within a cloud computing system, for example.

[0293] Furthermore, the term "or" is intended to mean an inclusive "or" rather than an exclusive "or". That is, unless specified otherwise, or clear from the context, "X employs A or B" is intended to mean any of the natural inclusive permutations. That is, if X employs A; X employs B; or X employs both A and B, then "X employs A or B" is satisfied under any of the foregoing instances. Furthermore, unless specified otherwise, or clear from the context to refer to a singular form, the articles "a" and "an" as used in this specification and the drawings should generally be interpreted to mean "one or more". As used herein, the terms "example" and / or "exemplary" are used to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter described herein is not limited to such examples. Furthermore, any aspect or design described herein as "example" and / or "exemplary" is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to exclude equivalent exemplary structures and techniques known to those of ordinary skill in the art.

[0294] As used in this specification, the term "processor" may refer to substantially any computational processing unit and / or device, including but not limited to a single-core processor; a single processor with software multi-threaded execution capability; a multi-core processor; a multi-core processor with software multi-threaded execution capability; a multi-core processor with hardware multi-threading technology; a parallel platform; and / or a parallel platform with distributed shared memory. In addition, a processor may refer to an integrated circuit, an application-specific integrated circuit (ASIC), a digital signal processor (DSP), a field programmable gate array (FPGA), a programmable logic controller (PLC), a complex programmable logic device (CPLD), discrete gate or transistor logic components, discrete hardware components, and / or any combination thereof, designed to perform the functions described herein. In addition, the processor may utilize nanoscale architectures, such as, but not limited to, transistors, switches, and / or gates based on molecules and quantum dots, in order to optimize space usage and / or enhance the performance of related equipment. The processor may be implemented as a combination of computational processing units.

[0295] In this document, terms such as "repository," "storage device," "data repository," "data storage device," "database," and substantially any other information storage component related to the operation and functionality of the component are used to refer to a "memory component," an entity embodied in "memory," or a component that includes a memory. The memory and / or memory components described herein can be volatile memory or non-volatile memory, or can include both volatile memory and non-volatile memory. By way of example and not limitation, non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable ROM (EEPROM), flash memory, and / or non-volatile random access memory (RAM) (e.g., ferroelectric RAM (FeRAM)). Volatile memory can include RAM, which can, for example, act as external cache memory. By way of example and not limitation, RAM can take a variety of forms, such as synchronous RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), direct Rambus RAM (DRRAM), direct Rambus dynamic RAM (DRDRAM), and / or Rambus dynamic RAM (RDRAM). In addition, the memory components of the systems and / or computer-implemented methods described herein are intended to include, but are not limited to, these and / or any other suitable types of memory.

[0296] The foregoing includes only examples of systems and computer-implemented methods. Of course, it is not possible to describe every conceivable combination of components and / or computer-implemented methods for purposes of describing one or more embodiments, but one of ordinary skill in the art will recognize that many further combinations and / or permutations of one or more embodiments are possible. Furthermore, with respect to the use of the terms "including," "having," "having," and the like in the detailed description, claims, appendices, and / or drawings, these terms are intended to be inclusive in a manner similar to how the term "comprising" is interpreted when used as a transitional word in a claim.

[0297] The description of various embodiments may use the phrases "an embodiment," "various embodiments," "one or more embodiments," and / or "some embodiments," each of which may refer to one or more of the same or different embodiments.

[0298] The descriptions of various embodiments have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodiments described herein. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is selected to best explain the principles of the embodiments, practical applications, and / or technical improvements over commercially available technologies, and / or to enable others of ordinary skill in the art to understand the embodiments described herein.

Claims

1. A system, comprising: a memory storing computer-executable components; and a processor that executes the computer-executable components stored in the memory, wherein the computer-executable components include: an imaging assembly that captures an image of an unknown sample support including a layer of material; and A matching component matches the unknown sample support to a known sample support based on an unknown inhomogeneity profile in the image of the unknown sample support including one or more inhomogeneities of the material layer.

2. The system according to claim 1, further comprising: a comparison component that compares the unknown non-uniformity profile of the image of the unknown sample support with the known non-uniformity profile of the image of the known sample support and generates a comparison score based on the comparison, Wherein the comparison score comprises a value defining a level of similarity between the unknown non-uniformity profile of the unknown sample support and the known non-uniformity profile of the known sample support.

3. The system according to claim 2, further comprising: Wherein the matching component further matches the unknown sample support to the known sample support based on the comparison score, the comparison score comprising a highest value compared to one or more other comparison scores generated by the comparison component relative to another image of another unknown sample support or another known sample support.

4. The system according to claim 1, wherein the image of the unknown sample support comprises a set of peripheral first regions having a greater brightness and delimiting a central second region having a lesser brightness, and The first region defines the unknown non-uniformity profile.

5. The system of claim 1, wherein the image of the unknown sample support is an optical microscope image and the material layer comprises a silicon frame.

6. The system according to claim 2, further comprising: An acquisition component acquires a set of images of a known sample support, including the image of the known sample support.

7. The system according to claim 1, further comprising: A notification component generates a notification defining whether the match corresponds to the unknown sample support.

8. A computer-implemented method, comprising: capturing, by a system operatively coupled to a processor, an image of an unknown sample support including a layer of material; as well as The unknown sample support is matched, by the system, to a known sample support based on a match of one or more unknown non-uniformities of the material layer in the image of the unknown sample support with one or more known non-uniformities in an image of a known sample support.

9. The computer-implemented method of claim 8, further comprising: comparing, by the system, the one or more unknown non-uniformities of the image of the unknown sample support to the one or more known non-uniformities of the known sample support; as well as generating, by the system, a comparison score based on the comparison, Wherein the comparison score comprises a value defining a level of similarity between the one or more unknown non-uniformities of the unknown sample support and the one or more known non-uniformities of the known sample support.

10. The computer-implemented method of claim 9, Wherein the matching further comprises matching, by the system, the unknown sample support with the known sample support based on the comparison score, the comparison score comprising a highest value compared to one or more other comparison scores generated by the comparison relative to another image of another unknown sample support or another known sample support.

11. The computer-implemented method of claim 9, The matching also includes: aggregating, by the system, the comparison score with a parameter score based on a comparison of secondary parameters of the unknown sample support that are unrelated to the one or more unknown non-uniformities with corresponding secondary parameters of the known sample support that are unrelated to the one or more known non-uniformities, and The unknown sample support is matched, by the system, to the known sample support based on an aggregated score generated by the aggregation, the aggregated score comprising a highest value compared to one or more other aggregated scores generated by the comparison and aggregation relative to another image of another unknown sample support or another known sample support.

12. The computer-implemented method of claim 8, wherein the image of the unknown sample support comprises a set of peripheral first regions having a greater brightness and delimiting a central second region having a lesser brightness, and The first region is the one or more unknown inhomogeneities.

13. The computer-implemented method of claim 9, further comprising: A data record associated with the known sample is updated by the system to reference the unknown sample.

14. The computer-implemented method of claim 8, further comprising: A notification is generated by the system defining whether the match corresponds to the unknown sample support.

15. A computer program product that facilitates a process for sample support identification, the computer program product comprising a computer-readable storage medium having program instructions embodied therein and executable by a processor to cause the processor to: comparing, by the processor, an unknown inhomogeneity profile at an image of an unknown sample support including one or more inhomogeneities of the unknown sample support with known inhomogeneity profiles of images of known sample supports; and Based on a result of the comparison, the unknown sample support is identified by the processor as the known sample support.

16. The computer program product of claim 15, wherein the program instructions are further executable by the processor to cause the processor to: generating, by the processor, a comparison score based on the comparison, Wherein the comparison score comprises a value defining a level of similarity between the unknown non-uniformity profile of the unknown sample support and the known non-uniformity profile of the known sample support.

17. The computer program product of claim 16, wherein the program instructions are executable by the processor to cause the processor to: The unknown sample support is matched, by the processor, to the known sample support based on the comparison score comprising a highest value compared to one or more other comparison scores generated by the comparison relative to another image of another unknown sample support or another known sample support.

18. The computer program product according to claim 15, wherein the image of the unknown sample support comprises a set of peripheral first regions having a greater brightness and delimiting a central second region having a lesser brightness, and The first region defines the unknown non-uniformity profile.

19. The computer program product of claim 15, wherein the image of the unknown sample support is an optical microscope image, and the one or more inhomogeneities of the unknown sample support are provided at an interior material layer of the unknown sample support.

20. The computer program product of claim 16, wherein the program instructions are further executable by the processor to cause the processor to: A match of a second unknown sample support to a second known sample support is determined by the processor based on the result of the comparison.

Images