Sample support grid recognition

Abstract

To solve problems of conventional techniques.SOLUTION: An embodiment of this specification relates to a process for sample support recognition. A system can include a memory for storing a computer executable component, and a processor for executing the computer executable component. The computer executable component can include an imaging component for capturing an image of an unknown sample support including a material layer, and a matching component for matching the unknown sample support to a known sample support on the basis of an unknown non-uniformity profile including one or more non-uniformities of the material layer in the image of the unknown sample support.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] Scientific instruments for use in materials analysis can be useful for determining the makeup and properties of unknown compositions. In one or more examples, the scientific instruments can provide high-resolution localization, manipulation, and / or analysis of samples in the range of a few hundred nanometers or less in one dimension. [Brief explanation of the drawings]

[0002] Embodiments will be readily understood from the following detailed description taken in conjunction with the accompanying drawings, in which: To facilitate this description, like reference numerals refer to like structural elements; and Embodiments are illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings. [Figure 1] 1 illustrates one or more block diagrams of exemplary scientific instruments for performing operations according to embodiments described herein. [Figure 2] 2 illustrates a flow diagram of an example method of performing operations using the scientific instrument of FIG. 1 according to one or more embodiments described herein. [Figure 3] 1 illustrates a graphical user interface (GUI) that can be used in performing one or more of the methods described herein, according to one or more embodiments described herein. [Figure 4] 1 illustrates a block diagram of an example computing device capable of performing one or more of the methods disclosed herein, in accordance with one or more embodiments described herein. [Figure 5] 1 illustrates a block diagram of an exemplary non-limiting system that can facilitate a process for sample support grid identification in accordance with one or more embodiments described herein. [Figure 6] 1 illustrates a block diagram of another exemplary non-limiting system that can facilitate a process for sample support grid identification in accordance with one or more embodiments described herein. [Figure 7] 1 provides an illustration of a pair of sample support containers according to one or more embodiments described herein. [Figure 8] 1 provides an illustration of a set of sample support grid fabrication steps according to one or more embodiments described herein. [Figure 9] 7 illustrates a schematic diagram of an imaging setup for the non-limiting system of FIG. 6, in accordance with one or more embodiments described herein. [Figure 10] 1 illustrates a diagram of an exemplary sample support grid image, according to one or more embodiments described herein. [Figure 11] 7 provides a diagram of a set of sample support grid images to illustrate a matching step that can be performed by the non-limiting system of FIG. 6 in accordance with one or more embodiments described herein. [Figure 12] 7 provides a schematic diagram further illustrating a matching step that may be performed by the non-limiting system of FIG. 6, according to one or more embodiments described herein. [Figure 13] 7 illustrates a flow diagram of one or more processes that may be performed by the automatic location system of FIG. 6 according to one or more embodiments described herein. [Figure 14] 7 illustrates a flow diagram of one or more processes that may be performed by the automatic location system of FIG. 6 according to one or more embodiments described herein. [Figure 15] 15 illustrates a continuation of the flow diagram of FIG. 14 of one or more processes that may be performed by the non-limiting system of FIG. 6, in accordance with one or more embodiments described herein. [Figure 16] 14 illustrates a flow diagram showing a further continuation of the flow diagram of FIG. 14 of one or more processes that may be performed by the non-limiting system of FIG. 6, in accordance with one or more embodiments described herein. [Figure 17] 7 illustrates another flow diagram of one or more processes that may be performed by the automatic location system of FIG. 6 according to one or more embodiments described herein. [Figure 18] 18 illustrates a continuation of the flow diagram of FIG. 17 of one or more processes that may be performed by the non-limiting system of FIG. 6, in accordance with one or more embodiments described herein. [Figure 19] 1 illustrates a block diagram of an exemplary scientific instrument system capable of performing one or more of the methods described herein, according to one or more embodiments described herein. [Figure 20] 1 illustrates a block diagram of an exemplary operating environment into which embodiments of the subject matter described herein may be incorporated. [Figure 21] 1 illustrates an exemplary schematic block diagram of a computing environment with which the subject matter described herein can at least partially interact and / or be implemented. Summary of the Invention

[0003] The following presents a summary to provide a basic understanding of one or more embodiments described herein. This summary is not intended to identify key or critical elements and / or delineate the scope of particular embodiments or the claims. Its sole purpose is to present concepts in a simplified form as a prelude to the more detailed description that is presented later. In one or more embodiments, the systems, computer-implemented methods, apparatus, and / or computer program products described herein can provide a process for recognition of a sample support grid used to support a sample with 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.

[0004] According to one embodiment, a system may include a memory that stores computer-executable components and a processor that executes 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 non-uniformity profile that includes one or more non-uniformities of the material layer in the image of the unknown sample support.

[0005] According to another embodiment, a computer-implemented method may include capturing, by a system operably 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 matching 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 the image of the known sample support.

[0006] According to yet another embodiment, a computer program product that facilitates a process for sample support recognition may comprise a computer-readable storage medium having program instructions embodied therewith, the program instructions being executable by a processor to: compare an unknown non-uniformity profile in an image of the unknown sample support, the unknown non-uniformity profile including one or more non-uniformities of the unknown sample support, with a known non-uniformity profile in an image of the known sample support; and based on a result of the comparison, identify the unknown sample support as being a known sample support.

[0007] One or more embodiments disclosed herein may achieve improved sample support grid recognition, for example, compared to conventional techniques that employ simple labeling and guesswork. Rather, one or more embodiments described herein may provide improved performance of imaging systems by providing an accepted and recognized baseline for imaging through a verified sample support grid. That is, such systems may use known dimensions and / or other measurements of a sample support grid onto which a sample of interest may be attached. Verifying that the sample support grid being used is indeed a sample support grid corresponding to such dimensions and / or measurements may reduce imaging and / or measurement errors along the line during analysis of each sample of interest.

[0008] In that regard, one or more embodiments described herein may provide verifiable tracking of one or more, such as multiple, sample support grids across various stages of manufacturing, processing, and / or preparation of the sample support grids at different locations and / or by different entities, i.e., physical characteristics of the sample support grid itself may be used to identify the sample support grid, and one or more sample support grids of interest may be recognized as appropriate (e.g., designated for use) for one or more imaging systems.

[0009] For example, based on the specific application of a light source to the sample support grid, one or more inhomogeneities in one or more materials of the sample support grid can be used to track the identity of the sample support grid (e.g., as a fingerprint or other profile), both initially and during one or more subsequent identifications. This can allow for more efficient and more accurate identification of sample supports compared to existing techniques, and / or verified recognition that a specified sample support grid is actually being used. This, in turn, can allow for more accurate placement of samples, such as slices, on and / or in the sample support compared to existing techniques.

[0010] Identification of the selected unknown sample support grid can be made increasingly efficient by using a matching process that uses one or more inhomogeneities in the material of the selected unknown sample support grid compared to one or more inhomogeneities in the image of the known sample support grid. In one or more embodiments, the comparison score generated for this comparison can be aggregated with one or more parameter scores corresponding to the 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 OF THE INVENTION

[0011] The following detailed description is merely illustrative and is not intended to limit the embodiments and / or their application or uses. Furthermore, there is no intention to be bound by any expressed or implied information presented in the Summary of the Invention section or the Detailed Description section above. One or more embodiments will now be described with reference to the drawings, wherein like reference numerals are used to refer to like elements throughout. 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. It will be apparent, however, that in various instances, one or more embodiments may be practiced without these specific details.

[0012] Various operations may be described sequentially as multiple discrete actions or operations in a manner that is most helpful for understanding the subject matter disclosed herein. However, the order of description should not be construed as implying that these operations are necessarily order dependent. In particular, these operations may be performed in an order different from the order presented. Operations described may be performed in an order different from the described embodiment. Various additional operations may be performed and / or described operations may be omitted in additional embodiments.

[0013] Now, referring to the subject of materials analysis and one or more embodiments described herein, one method of obtaining compositional imaging may be electron microscopy, in which 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, which can be detected and registered and then produce an image of the sample.

[0014] Existing techniques for this type of material analysis setup currently rely on manually implemented labeling systems for tracking the identity of different sample support grids relative to one another during one or more pre-processing steps for the sample support grid, also referred to herein as the sample support, sample grid, and / or support grid. During such material analysis, a sample of interest can be attached to the sample support. The attachment, analysis, and / or imaging of the target sample can depend, at least in part, on one or more specified parameters of the sample support, such as material composition, dimensions, surface angle, mass, etc. Thus, when a sample support is designated relative to one or more other sample supports, it may be desirable to actually use the designated sample support rather than a different sample support.

[0015] These one or more specified parameters can further be employed to identify a mounting area of ​​the sample support where it may be desirable to attach and / or otherwise position a sample of interest to be analyzed by the materials analysis system. It is noted that such a setup can be used for multiple imaging systems, such as, but not limited to, a scanning electron microscope or transmission electron microscope (S / TEM), a focused ion beam (FIB) device, and / or a dual beam system comprising both an S / TEM and a FIB device.

[0016] Existing techniques used to identify a sample support as being identical to a designated sample support include, but are not limited to, manual, slow, subjective, error-prone, and / or unverifiable. Thus, one or more errors during the material analysis steps (e.g., setup, sample application, imaging, and / or other analysis) can be caused by misidentification of the sample support and / or misidentification of a selected sample support as being the designated sample support.

[0017] Furthermore, as alluded to above, such one or more defects may be exacerbated by various stages of preparation for a set of two or more sample supports. That is, each preparation step may provide an opportunity for undesirable confusion of sample support order or labeling, such as for placement into one or more storage containers. See, for example, FIG. 7 , which illustrates an open-environment sample support container 702 and a sealed vacuum container 704, each capable of holding multiple sample supports therein. That is, sample supports may initially be placed into labeled orifices 708 of one or more sample support containers 702, 704; however, sample supports may be incorrectly repositioned and / or placed back into the wrong orifices 708; sample support containers 702, 704 may be dropped or bumped, thereby displacing one or more sample supports from one or more orifices 708; and / or sample supports may be placed into unlabeled orifices from the respective sample supports. See, for example, the combined letter and number labeling of sample support containers 702 and 704. It should be noted that any suitable labeling may be used.

[0018] To account for one or more inadequacies or deficiencies 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 recognition system to automatically recognize one or more designated sample supports as corresponding one or more known sample supports, such as against one or more data records defining one or more known sample supports. As a result, one or more embodiments described herein can achieve high information gathering on sample supports based on an automated approach, resulting in more accurate recognition of the sample support regardless of the stage of use of the sample support (e.g., pre-processing, post-processing, materials analysis setup, materials analysis, and / or post-material analysis). As used herein, the terms "identification" and "recognition" may be interchangeable. The term "recognition" is used throughout the remainder of this specification.

[0019] One or more automated sample support recognition frameworks described herein can 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 to one of the known sample supports. This can be accomplished using an automated system that defines a heterogeneity profile of one or more heterogeneities of the unknown sample support, uses the heterogeneity profile during comparison to the heterogeneity profiles of the known sample supports, generates a comparison score that defines a level of similarity of the unknown sample support to the known sample support, generates a match of the unknown sample support to the known sample support based on the comparison score, and generates a notification providing notification of the match.

[0020] To achieve matching, the automated system can identify one or more non-uniformities (e.g., scratches, blemishes, color variations, dents, channels, markings, etc.) in one or more materials and / or one or more different surfaces and / or layers of the unknown sample support. The automated system can access a data store containing one or more data records defining non-uniformity profiles of one or more known sample supports. The automated system can further identify one or more additional parameters, such as material, color, mass, and / or dimensions, of the unknown sample support and compare the one or more additional parameters with one or more corresponding additional parameters of one or more known sample supports.

[0021] The matching may be based on a threshold likelihood that the match is met, generating one or more scores, comparing the one or more scores to a threshold, comparing one or more different comparison scores to determine a highest and / or optimal score, and / or evaluating one or more other matches and / or scores against one or more other unknown sample supports in the set of unknown sample supports.

[0022] Any one or more of the foregoing steps may be based on a comparison of one or more images of the unknown sample support with one or more images of one or more known sample supports.

[0023] In one or more embodiments, the automated system can assist, such as suggest and / or control, one or more steps to facilitate the capture of one or more images. In one or more embodiments, the automated system can output suggestions for capturing one or more additional images of the unknown sample support using one or more altered conditions.

[0024] The automated system can include one or more of the scientific instrument systems, as well as related methods, computing devices, and computer-readable media described herein. For example, in one or more embodiments, the system can 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 can include an imaging component that can capture an image of the unknown sample support, a comparison component that compares the heterogeneity profile of the unknown sample support with 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 the results of the comparison.

[0025] As described above, one or more embodiments disclosed herein can achieve improved performance compared to existing approaches. For example, based on an analysis of one or more unknown inhomogeneities of an unknown sample support relative to one or more known inhomogeneities of a known sample support, a more reliable, more accurate, and less subjective matching of an image of the unknown sample to an image of a known sample, and thus an image of the unknown sample to a known sample, can be generated. This can enable more efficient and / or more accurate use of the unknown sample support during any subsequent material analysis procedures. For example, this can enable more accurate placement of a sample, such as a thin section, on and / or in the sample support compared to existing techniques. In one or more embodiments, in connection with sample placement, more efficient and / or accurate identification of the unknown sample support can reduce the amount and / or area of ​​sample substrate that needs to be removed from an identified region of the sample support (relative to preparing the sample support for sample placement) compared to existing techniques.

[0026] Thus, the embodiments disclosed herein provide improvements to scientific instrument technology (e.g., improvements in the computer technology supporting such scientific instrumentation, among other improvements), which can be used in a variety of fields, including, but not limited to, microscopy imaging, optics, signal processing, spectroscopy, and nuclear magnetic resonance (NMR). For example, in one or more embodiments, the diameter or maximum length across the surface of an exemplary sample support can range from 1 mm to about 5 mm, e.g., about 3 mm.

[0027] Various of the embodiments disclosed herein can improve upon existing approaches to achieve the technical advantages of increased contrast imaging of sample supports, narrower attachment area discrimination, and / or less subjective sample support preparation. That is, use of one or more sample support recognition frameworks discussed herein can enable automatic recognition of one or more unknown sample supports relative to one or more known sample supports. One or more frameworks used herein can employ a sample support recognition system as described herein.

[0028] The above-mentioned technical advantages are not achievable by routine and existing approaches, and all user entities of a system including such embodiments can benefit from these advantages (e.g., by assisting the user entity in performing technical tasks such as recognizing an unknown sample support as a previously recorded known sample support).

[0029] Thus, the technical features of the embodiments disclosed herein (e.g., comparison of heterogeneity profiles), as well as combinations of the features of the embodiments disclosed herein, are clearly unconventional in the fields of microscopy imaging, as well as, but not limited to, the fields of optics, signal processing, spectroscopy, and / or NMR.

[0030] As discussed further 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 do not solely involve the collection and comparison of information, but instead apply new analytical and technological techniques to alter the operation of computer analysis of material compounds. For example, based on the application of various illumination and / or sample support orientations, more efficient and / or accurate matching of unknown sample supports to known sample supports can be generated based on a computer-assisted determination of the acceptability of sample support images. Because sample support matching does not rely on manual input as in existing frameworks, these processes can all be performed automatically. Thus, the corresponding computer-directed process of sample support imaging itself can be made easier and more efficient through reduced false identifications, a lack of distinct non-uniformities, and / or a lack of subjective input, compared to one or more conventional frameworks. Thus, the non-limiting systems described herein, including the sample support recognition system 602, can be self-improving.

[0031] Thus, the present disclosure introduces functionality that neither existing computing devices nor humans can perform. Rather, such existing computing devices instead require subjective manual input to less accurately match unknown sample supports to known sample support records. The lack of accurate sample support identification, plus the time, energy, human error, and lack of automation, make it impractical to operate within existing approaches.

[0032] Accordingly, embodiments of the present disclosure may serve any of a number of technical purposes, such as controlling a particular technical system or process, determining how to control machinery from measurements, digital audio, image, or video augmentation or analysis, separating material sources in mixed signals, 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 recognition, accurate and repeatable heterogeneity profile generation, and / or accurate and repeatable analysis of multiple heterogeneity profiles relative to each other, at least partially in parallel with each other.

[0033] Accordingly, the embodiments disclosed herein provide improvements in materials analysis techniques (eg, improvements in computer techniques supporting materials analysis, among other improvements).

[0034] As used herein, the phrase "based on" should be understood to mean "based at least in part on," unless otherwise specified.

[0035] As used herein, the term "component" can refer to an atomic element, a molecular element, a phase of an atomic or molecular element, or a combination thereof.

[0036] As used herein, the terms "compound" and "precursor" may be used interchangeably.

[0037] As used herein, the term "data" can include metadata.

[0038] As used herein, the terms “entity,” “requesting entity,” and “user entity” may refer to a machine, device, component, hardware, software, smart device, party, organization, individual, and / or human being.

[0039] One or more embodiments will now be described with reference to the drawings, wherein like reference numerals are used to refer to like drawing elements throughout. 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. It will be apparent, however, that in various instances, one or more embodiments may be practiced without these specific details.

[0040] Furthermore, it should be understood that the embodiments depicted in one or more figures described herein are for illustrative purposes only, and thus the architecture of the embodiments is not limited to the systems, devices, and / or components depicted herein, nor to any particular ordering, connection, and / or coupling of the systems, devices, and / or components depicted herein.

[0041] Referring now particularly to one or more figures, initially to FIG. 1 , a block diagram of a scientific instrument module 100 for preparation and setup associated with performing material analysis operations using microscopy imaging techniques is shown, in accordance with 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 contained in a single computing device or may be distributed across multiple computing devices that communicate with each other as needed. An example of a computing device in which the scientific instrument module 100, alone or in combination, may be implemented is discussed herein with reference to the computing device 400 of FIG. 4 , and an example of a system of interconnected computing devices in which the scientific instrument module 100 may be implemented across one or more of the computing devices is discussed herein with reference to the scientific instrument system 1900 of FIG. 19 .

[0042] The scientific instrument module 100 can function in accordance with the imaging system 630. The scientific instrument module 100 can include first logic 102, second logic 104, third logic 106, fourth logic 108, and fifth logic 110. As used herein, the term “logic” can include an apparatus 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 programmed with instructions to cause one or more processing devices of the computing devices to perform a set of operations associated with the computing devices. In particular embodiments, a logic element can include one or more non-transitory computer-readable media having instructions 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 a set of operations associated with the computing devices. As used herein, the term “module” can refer to a collection of one or more logic elements that together perform a function associated with the module. Different logic elements within a module can take the same form or different forms. For example, some 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 sets of instructions executed by one or more processing devices. A module may omit one or more of the logic elements depicted in the associated figure, but, for example, a module may include a subset of the logic elements depicted in the associated figure if the module performs a subset of the operations discussed herein with reference to that module.

[0043] The first logic 102 can cause and / or direct image capture 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 between the unknown sample support and a record of a known sample support has not yet been verified. Accordingly, as used herein, the term "known sample support" refers to a sample support for which a data record has been generated that includes profiling of one or more heterogeneities and / or one or more other parameters that define the known sample support.

[0044] The second logic 104 can cause and / or direct a comparison of the unknown sample support non-uniformity profile to known sample support non-uniformities, i.e., the second logic 104 can direct a comparison of one or more non-uniformities identified from one or more sample support images output from the first logic 102.

[0045] The third logic 106 can cause and / or direct a score assignment based on the output of the second logic 104. That is, the third logic 106 can perform the generation of 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 based on the function of the first logic 102 and the second logic 104.

[0046] The fourth logic 108 may cause and / or direct an evaluation of the score assignments output by the third logic 106. The evaluating may include comparing the score resulting from the score assignments to one or more other scores and / or score thresholds.

[0047] The fifth logic 110 may cause and / or direct the generation of matches based on the output of the fourth logic 108 .

[0048] 2 illustrates a flow diagram of a method 200 of performing operations by a scientific instrument module 100, according to various embodiments. The operations of method 200 may be illustrated with reference to particular embodiments disclosed herein (e.g., the scientific instrument module 100 discussed herein with reference to FIG. 1 , the GUI 300 discussed herein with reference to FIG. 3 , the computing device 400 discussed herein with reference to FIG. 4 , and / or the scientific instrument system 1900 discussed herein with reference to FIG. 19 ), but method 200 may be used in any suitable configuration to perform any suitable operations. Although the operations are illustrated in FIG. 2 once each and in a particular order, the operations may be reordered and / or repeated as desired and appropriate (e.g., different operations performed may be performed in parallel, when suitable).

[0049] At 202, a first operation may be performed. For example, the first logic 102 of the module 100 may perform the first operation 202. The first operation 202 may include directing and / or causing an image capture of the unknown sample support grid. In one or more embodiments, the image capture may include setting and / or modifying one or more image capture angles and / or lighting conditions.

[0050] At 204, a second operation may be performed. For example, the second logic 104 of the module 100 may perform the second operation 204. The second operation 204 may include directing and / or causing a comparison of the non-uniformity profiles of the unknown sample support and at least one known sample support. That is, the comparison may include evaluating the location, size, adjacency, color, and / or any other defining aspect of one or more non-uniformities of the compared non-uniformity profiles.

[0051] At 206, a third operation may be performed. For example, the third logic 106 of the module 100 may perform the third operation 206. The third operation 206 may include directing and / or causing the assignment of a score, such as a comparison score, a parameter score, and / or an aggregate score, for the known sample support and / or for a combination of the unknown sample support and the known sample support. The comparison score may define a level of similarity between the image of the unknown sample support and the image of the known sample support, and thus may define a level of similarity between the unknown sample support and the known sample support.

[0052] A fourth operation may be performed at 208. For example, the fourth logic 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 evaluating may include comparing the score resulting from the score assignment to one or more other scores and / or score thresholds.

[0053] A fifth operation may be performed at 210. For example, the fifth logic 110 of the module 100 may perform the fifth operation 210. The fifth operation 210 may include directing and / or causing the generation of a match of the unknown sample support to the known sample support based on the output of the fourth operation 208.

[0054] Scientific instrument methods disclosed herein can include interactions with a user entity (e.g., via a user local computing device 1920, discussed herein with reference to FIG. 19 ). These interactions can include providing information to the user entity (e.g., information regarding the operation of a scientific instrument such as the scientific instrument 1910 of FIG. 19 , information regarding a sample being analyzed or other tests or measurements performed by the scientific instrument, information obtained from a local or remote database, or other information), or providing options for the user entity to enter commands (e.g., to control the operation of a scientific instrument such as the scientific instrument 1910 of FIG. 19 or to control the analysis of data generated by the scientific instrument), queries (e.g., to a local or remote database), or other information. In some embodiments, these interactions may be performed through a graphical user interface (GUI) that includes a visual display on a display device (e.g., display device 410, discussed herein with reference to FIG. 4 ) that provides output to a user entity and / or prompts the user entity to provide input (e.g., via one or more input devices, such as a keyboard, mouse, trackpad, or touchscreen included in other I / O (Input / Output) devices 412, discussed herein with reference to FIG. 4 ). The scientific instrument system 1900 disclosed herein may include any suitable GUI for interaction with a user entity.

[0055] 3, an exemplary GUI 300 is depicted that may be used in performing one or more of the methods described herein, according to various embodiments described herein. As noted above, the GUI 300 may be provided on a display device (e.g., the display device 410 discussed herein with reference to FIG. 4) of a computing device (e.g., the computing device 400 discussed herein with reference to FIG. 4) of a scientific instrument system (e.g., the scientific instrument system 1900 discussed herein with reference to FIG. 19), and a user entity may interact with the GUI 300 using any suitable input device (e.g., any of the input devices included in the other I / O devices 412 discussed herein with reference to FIG. 4) and input technique (e.g., cursor movement, motion capture, facial recognition, gesture detection, voice recognition, button actuation, etc.).

[0056] GUI 300 can include a data display area 302, a data analysis area 304, a scientific instrument control area 306, and a settings area 308. The particular number and arrangement of areas depicted in Figure 3 is merely illustrative, and any number and arrangement of areas containing any desired features can be included in GUI 300.

[0057] The data display area 302 may display data generated by a scientific instrument (e.g., the scientific instrument 1910 discussed herein with reference to FIG. 18). For example, but not limited to, the data display area 302 may display one or more output comparison images 1106 (FIG. 11) and / or one or more text, graphs, charts, matrices, and / or individual sample support images.

[0058] The data analysis area 304 can display the results of a 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 can display one or more results of a comparative score analysis (e.g., as depicted in FIG. 12 ). For example, the data analysis area 304 can display a list, flow chart, or other schematic diagram of one or more score 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., to include data output from a scientific instrument and several analyses of the data in a common graph or area). In one or more embodiments, the data display area 302 can display an image of the sample support, and one or more non-uniformities can be highlighted in any suitable manner, such as by differences in color, brightness, pointers, text, etc.

[0059] The scientific instrument control area 306 may include options that may enable a user entity to control a scientific instrument (e.g., the scientific instrument 1910 discussed herein with reference to FIG. 19 ). For example, the scientific instrument control area 306 may include one or more controls for inputting one or more metrics of interest.

[0060] Settings area 308 may include options that enable a user entity to control features and functionality of GUI 300 (and / or other GUIs) and / or perform common computing operations related to data display area 302 and data analysis area 304 (e.g., saving data on a storage device such as storage device 404 discussed herein with reference to FIG. 4, sending data to another user entity, labeling data, etc.). For example, settings area 308 may include one or more options for changing the color, fill, or format of an illustration, such as the illustration associated with one or more of the images and / or diagrams of FIG. 11 and / or 12.

[0061] As noted above, the scientific instrument module 100 can be implemented by one or more computing devices. Accordingly, considering now FIG. 4, FIG. 4 illustrates a block diagram of a computing device 400 capable of performing 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 by multiple computing devices 400. Furthermore, as discussed below, the computing device 400 (or multiple computing devices 400) implementing the scientific instrument module 100 can be part of one or more of the scientific instrument 1910, user local computing device 1920, service local computing device 1930, or remote computing device 1940 of FIG. 19 .

[0062] 4 is illustrated as having several components, any one or more of which may be omitted or duplicated as suitable for the application and setting. As illustrated, these components may include one or more of a processor 402, a storage device 404, an interface device 406, a battery / power circuitry 408, a display device 410, and other input / output (I / O) devices 412, as described below.

[0063] In one or more embodiments, one or more of the components included in computing device 400 may be mounted on one or more motherboards and housed within a housing (e.g., comprising plastic, metal, and / or other materials). In one or more embodiments, some of these components may be fabricated on a single system-on-a-chip (SoC) (e.g., an 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 one or more of the components illustrated in FIG. 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 (eg, connector and driver circuitry) to which display device 410 can be coupled.

[0064] 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 and converts 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 (dedicated processors that execute cryptographic algorithms in hardware), server processors, or any other suitable processing devices.

[0065] Computing device 400 may include a storage device 404 (e.g., one or more storage devices). 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, network drives, cloud drives, or any combination of memory devices. In one or more embodiments, storage device 404 may include memory that shares a die with processor 402. In such embodiments, the memory may be used as cache memory and may include, for example, embedded Dynamic Random Access Memory (eDRAM) or Spin Transfer Torque Magnetic Random Access Memory (STT-MRAM). In one or more embodiments, 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 computing device 400 to perform any suitable ones of the methods or portions of those methods disclosed herein.

[0066] Computing device 400 may include interface device 406 (e.g., one or more interface devices 406). Interface device 406 may include one or more communication chips, connectors, and / or other hardware and software to manage communications between computing device 400 and other computing devices. For example, interface device 406 may include circuitry for managing wireless communications for transferring data to and from computing device 400. The term "wireless" and its derivatives may be used to describe circuits, devices, systems, methods, techniques, communication channels, etc. that can communicate data through the use of modulated electromagnetic radiation over a non-solid medium. This term does not imply that the associated devices do not include any wiring, although in one or more embodiments, the associated devices may not include any wiring. The circuitry included in interface device 406 for managing wireless communications may implement any of a number of wireless standards or protocols, including, but not limited to, Wi-Fi (IEEE 802.11 family), Institute for Electrical and Electronic Engineers (IEEE) standards including the IEEE 802.16 standard (e.g., the IEEE 802.16-2005 Amendment), the Long-Term Evolution (LTE) project with any amendments, updates, and / or revisions (e.g., the Advanced LTE project, the Ultra-Mobile Broadband (UMB) project (also referred to as "3GPP®2"), etc.).In one or more embodiments, the circuitry included in 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 interface device 406 for managing wireless communications may operate in accordance with an Enhanced Data for GSM Evolution (EDGE), GSM EDGE Radio Access Network (GERAN), Universal Terrestrial Radio Access Network (UTRAN), or Evolved UTRAN (E-UTRAN). In one or more embodiments, the circuitry included in interface device 406 for managing wireless communications may operate according to Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Digital Enhanced Cordless Telecommunications (DECT), Evolution-Data Optimized (EV-DO), and derivatives thereof, as well as any other wireless protocols designated as 3G, 4G, 5G, and beyond. In one or more embodiments, interface device 406 may include one or more antennas (e.g., one or more antenna arrays) for receiving and / or transmitting wireless communications.

[0067] In one or more embodiments, interface device 406 may include circuitry for managing wired communications, such as electrical, optical, or any other suitable communications protocol. For example, interface device 406 may include circuitry supporting communications according to Ethernet technology. In one or more embodiments, interface device 406 may support both wireless and wired communications and / or may support multiple wired and / or wireless communications protocols. For example, a first set of circuits in interface device 406 may be dedicated to short-range wireless communications, such as Wi-Fi or Bluetooth, while a second set of circuits in interface device 406 may be dedicated to long-range wireless communications, such as Global Positioning System (GPS), EDGE, GPRS, CDMA, WiMAX, LTE, EV-DO, or others. In one or more embodiments, a first set of circuits in interface device 406 may be dedicated to wireless communications, while a second set of circuits in interface device 406 may be dedicated to wired communications.

[0068] 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 (e.g., AC line power) separate from computing device 400.

[0069] Computing device 400 may include a 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 touchscreen display, a liquid crystal display (LCD), a light-emitting diode display, or a flat panel display.

[0070] Computing device 400 may include other input / output (I / O) devices 412. The other I / O devices 412 may include, for example, one or more audio output devices (e.g., speakers, headsets, earphones, alarms, etc.), one or more audio input devices (e.g., microphones or microphone arrays), a location device (e.g., a GPS device that communicates with a satellite-based system to receive the location of 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 data capture device such as a camera, a keyboard, a cursor control device (e.g., a mouse, stylus, trackball, or touchpad), a barcode reader, a Quick Response (QR) code reader, or a radio frequency identification (RFID) reader.

[0071] Computing device 400 may have any suitable form factor for its application and configuration, 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 or server computing device, or other network computing component.

[0072] 5 and 6, in one or more embodiments, the non-limiting systems 500 and / or 600 illustrated in Figures 5 and 6, and / or the systems may further comprise one or more computers and / or computing-based elements described herein with reference to a computing environment, such as the computing environment 2100 illustrated in Figure 21. In one or more described embodiments, the computers and / or computing-based elements may be used in connection with implementing one or more of the systems, devices, components, and / or computer-implemented operations shown and / or described in connection with Figures 5 and / or 6 and / or other figures described herein.

[0073] 5, the figure illustrates a block diagram of an exemplary, non-limiting system 500 that may comprise a sample support recognition system 502 and a dual beam system 501. The sample support recognition system 502 may facilitate recognizing an unknown sample support grid 540 (also referred to as an unknown sample support 540) as one or more known sample support grids 542 (also referred to as known sample supports 542).

[0074] In one or more embodiments, the sample support recognition system 502 may be included at least in part by the computing device 400 .

[0075] In one or more embodiments, the dual beam system 501 may at least partially include a sample support recognition system 502 .

[0076] It should be noted that the sample support recognition system 502 is only briefly detailed to provide an introduction to a more complex and / or more extensive sample support recognition system 602, as shown in Figure 6. That is, further details regarding processes that may be performed by one or more embodiments described herein are provided below in connection with the non-limiting system 600 of Figure 6.

[0077] 5, the sample support recognition system 502 can include at least a memory 504, a bus 505, a processor 506, an imaging component 510, and a matching component 518. The processor 506 can be the same as, included in, or different from the processor 402. The memory 504 can be the same as, included in, or different from the storage device 404.

[0078] Using the above-described components, the sample support recognition system 502 can facilitate the process of first imaging a sample support desired to be identified (e.g., an unknown sample support 540) and secondarily determining a match of a known sample support 540 to the unknown sample support 542.

[0079] Generally, the sample supports 540, 542 can be configured by a grid system including a plurality of one or more sample supports, which, as discussed above, can be used for a variety of purposes related to attachment of the sample to the sample support, to provide sample stability during imaging or other material analysis of the sample.

[0080] In general, the imaging component 510 can capture and / or direct the capture of an image 550 of an unknown sample support 540. The unknown sample support 540 can include one or more layers of material.

[0081] The matching component 518 can use the image 550 to match the unknown sample support 540 to a known sample support 542 based on an unknown non-uniformity profile 551 that includes one or more non-uniformities of a material layer in the image 550 of the unknown sample support 540. That is, the matching component 518 can match the image 550 of the unknown sample support 540 to a data record and / or an image 552 of the known sample support 542.

[0082] The imaging component 510 and the matching component 518 may be operatively coupled to a processor 506, which may be operatively coupled to a memory 504. A bus 505 may provide the operative coupling. The processor 506 may facilitate the execution of the imaging component 510 and the matching component 518. The imaging component 510 and the matching component 518 may be stored in the memory 504.

[0083] In general, the non-limiting system 500 may use any suitable communication method (e.g., electronic, telecommunications, internet, infrared, fiber, etc.) to provide communication between the sample support recognition system 502, the imaging system used by the sample support recognition system 502 to capture images of the sample support, and / or any devices associated with the user entity.

[0084] Referring now to Figure 6, a non-limiting system 600 is shown that may include a sample support recognition system 602 and an imaging system 630. Repeated descriptions of similar elements and / or processes used in each embodiment are omitted for the sake of brevity. The description of the embodiment of Figure 5 may be applicable to the embodiment of Figure 6. Similarly, the description of one embodiment of Figure 6 may be applicable to one embodiment of Figure 5.

[0085] In general, the sample support recognition system 602 can facilitate a process for recognizing an unknown sample support 640 as a known sample support 642. This process can be facilitated by generating, analyzing, and comparing heterogeneity profiles of the sample supports 640 and 642 with each other.

[0086] In one or more embodiments, the sample support recognition system 602 may be included at least in part by the computing device 400 .

[0087] In one or more embodiments, the sample support recognition system 602 may at least partially include an imaging system 630 .

[0088] One or more communications between one or more components of the non-limiting system 600 may be provided by 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 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), third Generation Partnership Project (3GPP) Long Term Evolution (LTE), third Generation Partnership Project 2 (3GPP2) Ultra-Mobile Broadband (UMB), High Speed ​​Packet Access (HSPA), Zigbee and other 802.XX wireless technologies and / or legacy telecommunications technologies, BLUETOOTH, Session Initiation Protocol (SIP), and the like. Protocol (SIP), ZIGBEE®, RF4CE protocol, WirelessHART protocol, 6LoWPAN (IPv6 over Low Power Wireless Area Networks), Z-Wave, advanced and / or adaptive network technology (ANT), ultra-wideband (UWB) standard protocol and / or other proprietary and / or non-proprietary communication protocols.

[0089] The sample support recognition system 602 may be associated with (eg, accessible via) a cloud computing environment, such as the cloud computing environment 2000 of FIG.

[0090] The sample support recognition system 602 may include multiple components, including a memory 604, a processor 606, a bus 605, an imaging component 610, a profiling 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. Using these components, the sample support recognition system 602 may acquire, compare, and analyze images of the sample support, and more particularly, may compare and analyze one or more non-uniformities identified from the images of the sample support. In this manner, the selected and / or desired sample support may be used for a subsequent material analysis procedure. That is, the sample support used in such a subsequent procedure may be verified as the sample support selected and / or desired for use in the subsequent material analysis procedure.

[0091] The processor 606, memory 604, and bus 605 of the sample support recognition system 602 will now be described. For example, in one or more embodiments, the sample support recognition system 602 may comprise 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, components associated with the sample support recognition system 602 may include one or more computer- and / or machine-readable, writable, and / or executable components and / or instructions, as described herein with or without reference to one or more figures of one or more embodiments, that may be executed by the processor 606 to provide for the execution of one or more processes defined by such components and / or instructions. In one or more embodiments, the processor 606 may include an imaging component 610, a profiling 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.

[0092] In one or more embodiments, the sample support recognition system 602 may comprise a computer-readable memory 604, which may be operatively coupled to the processor 606. The memory 604 may store computer-executable instructions that, when executed by the processor 606, cause the processor 606 and / or one or more other components of the sample support recognition system 602 (e.g., the imaging component 610, the profiling 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) to perform one or more operations. In one or more embodiments, the memory 604 may store the computer-executable components (e.g., the imaging component 610, the profiling 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).

[0093] The sample support recognition system 602 and / or its components described herein may be communicatively, electrically, operatively, optically, and / or otherwise coupled to one another via a bus 605. The bus 605 may 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 may use one or more bus architectures. One or more of these examples of the bus 605 may be used.

[0094] In one or more embodiments, the sample support recognition system 602 can be coupled (e.g., communicatively, electrically, operatively, optically, and / or with similar functionality) to one or more external systems (e.g., an electrical output generating system, one or more output target controllers, and / or output target controllers, not illustrated), sources, and / or devices (e.g., classical and / or quantum computing devices, communication devices, and / or similar devices), such as via a network. In one or more embodiments, the sample support recognition system 602 and / or one or more of the components of the non-limiting system 600 can reside in the cloud and / or can reside locally (e.g., at a designated location) within a local computing environment.

[0095] In addition to the processor 606 and / or memory 604 described above, the sample support recognition system 602 may include one or more computer and / or machine readable, writable, and / or instructions that, when executed by the executable components and / or processor 606, may provide for the performance of one or more operations defined by such components and / or instructions.

[0096] Next, one or more additional components of the sample support recognition system 602 will be described.

[0097] However, it should first be noted that in one or more embodiments, the imaging component 610, the profiling 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 one or more of the imaging component 610, the profiling 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 profiling 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 included in the high-level analysis component 603, and one or more of the functions described below of the imaging component 610, the profiling 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 performed by the high-level analysis component 603; and / or Alternatively, the imaging component 610, the profiling 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, and the high-level analysis component 603 may perform one or more of the functions described below of one or more of the omitted imaging component 610, the profiling 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.

[0098] 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 simply obtain such images from the imaging system 630.

[0099] In one or more embodiments, the imaging system 630 may be and / or comprise an optical microscope, and therefore the images 650, 652 may be optical microscope images, although other alternatives may be suitable.

[0100] 9 , in one or more embodiments, the imaging component 610 can direct the activation of the light source 902 and / or the imaging source 906 of the imaging system 630, thereby facilitating the capture of an image 650 of the sample support 640. In one or more embodiments, the orientation of the light source 902, the imaging source 906, and / or the 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, the imaging source 906, and / or the imaging platform 904 can be controlled at least in part by the imaging component 610. For example, the imaging component 610 can direct a particular color and / or brightness of the light source 902, can direct a particular 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 component 610 may otherwise suggest one or more particular settings for the light source 902, the imaging source 906, and / or the orientation of the imaging platform 904 relative to the imaging system 630 and / or the management entity.

[0101] The resulting one or more images 650, 652 may include any suitable data and / or metadata.

[0102] The imaging system 630 may comprise any suitable processor and / or memory for facilitating one or more processes, including, but not limited to, capturing one or more images of one or more sample supports, etc. In one or more embodiments, the imaging system 630 may instead use the processor 606 and / or memory 604 of the sample support recognition system 602.

[0103] Turning now to the profiling component 611, this component can generally generate and / or update a known non-uniformity profile 653 for a known sample support 640 or an unknown non-uniformity profile 651 for an unknown sample support 642. Generating can include analyzing at least one material layer of each sample support (e.g., unknown sample support 640, which is used merely as an example for one or more descriptions herein) by the profiling component 611. Based on the analysis, the profiling component 611 can define a respective non-uniformity profile that functions similar 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 respective non-uniformity profiles change over time.

[0104] For example, the profiling component 611 may identify a first inhomogeneity 1010 in the images 650, 652 based on a variation in contrast between boundaries or regions 1014 that bound the inhomogeneity 1010, the boundaries or regions 1014 having different contrast. Accordingly, recognition of one or more inhomogeneities 1010 may be recorded in respective inhomogeneity profiles 651, 653.

[0105] Referring to Figures 8 and 10 in addition to Figure 6, the profiling component 611 can use image recognition software to identify one or more non-uniformities 1010, such as one or more blemishes, marks, markings, defects, bumps, depressions, scratches, channels, dents, raised areas and / or other optical defects, in at least one material layer of each sample support being analyzed.

[0106] For example, in one or more embodiments, image recognition can include image processing to remove pixels corresponding to the image background to reduce unused information. A processing script, such as a Python script, can then be employed to locate circular structures or structures of other types and / or shapes. In one or more embodiments, a processing script, such as a Python script, can be employed to dilute the structures, which can result in pixels of the image outside (e.g., at the boundary) of the structures being darkened. This can provide a brightness difference between the first region 1012 and the second region 1014, in one or more embodiments.

[0107] The one or more inhomogeneities can be located anywhere on any surface of any material layer of the sample support 640, 642. For example, referring to FIG. 10 , which shows a top view of the plane PP ( FIG. 8 ), the one or more inhomogeneities 1010 can be located in one or more first regions, which are peripherally located first regions 1012 of the partially cylindrical sample support 640, 642. However, it will be understood that the sample support 640, 642 can have any suitable shape. Additionally and / or alternatively, in one or more embodiments, the one or more inhomogeneities 1010 can be located in a central second region 1014. However, it will be understood that due to the nature of the manufacturing process performed to fabricate the sample support 640, 642 (e.g., as shown in FIG. 8 ), the majority of the inhomogeneities 1010 resulting from the manufacturing process can be located in the first peripherally located regions of the material layer.

[0108] As an aside, note that the square features in FIG. 10 may be openings 1016 that extend through one or more layers of the sample supports 640,642.

[0109] The one or more non-uniformities may result from various processes. For example, in one or more embodiments, the non-uniformity 1010 may result from one or more manufacturing processes shown in Figure 8 and described further below. Additionally and / or alternatively, in one or more embodiments, the at least one non-uniformity 1010 may be intentionally marked on the surface of the material layer of the sample support 640, 642.

[0110] The one or more non-uniformities 1010 can be located in one or more different layers of the sample support 640, 642. Similarly, these one or more non-uniformities can be described within the sample support 640, 642 and / or on the outer surface of the sample support 640, 642. For example, in one or more embodiments, the one or more non-uniformities 1010 can be located in 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 support 640 / 642. For example, these non-uniformities 1010 can result from an etching process and / or any other manufacturing process. In one particular example, the one or more non-uniformities 1010 can occur in the silicon frame layer 812, such as a result of an etching process of the initial manufacturing process 802.

[0111] In one or more embodiments, the one or more non-uniformities 1010 are visible through one or more overlay layers (e.g., silicon nitride membrane layer 814 and / or first additional layer 816) covering one or more other layers (e.g., silicon frame layer 812 and / or silicon nitride membrane layer 814), allowing for capture of the one or more non-uniformities in images 650, 652, for example.

[0112] Additionally and / or alternatively, one or more non-uniformities 1010 can be disposed on the outer cylindrical surface S (FIG. 8) of the substrate layer 812.

[0113] In one or more embodiments, one or more non-uniformities 1010 may be intentionally marked on one or more surfaces, such as the second central region 1014 .

[0114] It will thus be appreciated that the imaging component 610 can capture images of one or more different surfaces and / or layers of the sample support 640, 642 having one or more different non-uniformities. Similarly, it will be appreciated that the profiling component 611 can generate non-uniformity profiles 651, 653 for any one or more different surfaces and / or layers of the sample support 640, 642.

[0115] In this regard, the recording component 612 can generate and / or update data records (e.g., data records 644) associated with the sample supports for which heterogeneity profiles have been generated and / or updated by the profiling component 611. This recording process can include tagging, marking, and / or otherwise writing data to the respective data records 644, such as via appropriate write operations, to define the respective heterogeneity profiles and identify each heterogeneity profile as corresponding to the respective sample support for which analysis was performed by the profiling component 611.

[0116] Additionally and / or alternatively, the recording component 612 may perform associated write operations for any other secondary parameters, which may comprise, but are not limited to, descriptive aspects and / or dimensions of the sample support of interest, to further populate the associated data record 644 as provided in the metadata of the data record 644.

[0117] The data records 644 may be provided in any suitable format (e.g., log, table, matrix, data, metadata, etc.) and may be stored in any suitable location (e.g., but not limited to, memory 604) communicatively accessible by the sample support recognition system 602.

[0118] 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 profiling component 611, the recording component 612, and / or the acquisition component 614 can be performed outside of the sample support recognition system 602 and / or the non-limiting system 600.

[0119] Reference is now made to Figure 8, which illustrates an exemplary manufacturing process for a sample support. The description of schematic diagram 800 is provided to define at least a first set of exemplary opportunities in which the non-limiting system 600 may be used. As shown in Figure 8, schematic flow diagram 800 illustrates an exemplary manufacturing process for a sample support, including identification of various steps performed and / or material layers 812-820 added.

[0120] During an initial manufacturing stage 802, a base grid 832 (also referred to herein as a sample support 834) can be manufactured, where the base grid 832 includes a first silicon (Si) substrate layer 812 and a silicon nitride membrane layer 814 (such as SiN) applied to the silicon substrate layer 812, such as by etching the base grid 832. This initial manufacturing stage 802 can be performed, for example, at a base grid manufacturer and / or at a site corresponding to the operator of the non-limiting system 600. As used herein, "corresponding" can refer to the same site as the non-limiting system 600 and / or a site owned, used, and / or associated with the operator.

[0121] The base grid 832 can then be transmitted to a second processing site, such as, without limitation, a site corresponding to the administrator of the system 600. This receipt can provide a first opportunity to profile the sample supports (at the time including only the base grid 832) for initial identification. For example, the imaging component 610 and the profiling component 611 can be used as described above. The result of that use can 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 because the non-uniformity profile is known for each of the known sample supports 642 and each of the known sample supports 642 is returned to an appropriately labeled orifice 708 of the sample support vessel 702). It is understood that any reference made herein to a sample support vessel 702 can alternatively be associated with a vacuum-sealed sample support vessel 704, which can also have a set of labeled orifices 708, as shown in FIG. 7 .

[0122] In connection with receiving the base grid 832 / known sample support 642 , the pre-processing step 804 may include the application of a first additional layer 816 , such as by a coating process, resulting in the sample support 834 .

[0123] At the end of each step (e.g., between steps 802-808), the base grid 832 / sample support 834 and multiple other etched and / or otherwise partially processed base grids 832 / sample supports 834 can be inserted into orifices of the sample support container 702 to prepare them for shipment. The orifices 708 can be labeled to identify the multiple base grids 832 / sample supports 834, as shown in FIG. 7 . After this process is complete, the base grids 832 / sample supports 834 can be returned to the sample support container 702 with the intent to return the sample supports to their respective labeled orifices 708. However, this does not always occur, thus providing yet another opportunity to use the non-limiting system 600 for recognition of the base grids 832 / sample supports 834 upon return to a management entity of the non-limiting system 600, as described below. That is, when the base grid 832 / sample support 834 is removed from the sample support container 702, direct identification is lost because the base grid 832 / sample support 834 are often not individually labeled. As a result, continued successful identification is based on indirect tracking other than through the use of the sample support container 702.

[0124] The next processing stage can be performed at the same processing location or at a different processing location. The next stage can be a 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 with the intention of returning the sample supports to their respective labeled orifices 708. Alternatively, the sample supports 834 can be switched to a vacuum-sealable sample support container 704 having the same labeling method for the corresponding orifices 708.

[0125] The next processing stage can be performed at the same processing location or at a different 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 with the intention of returning the sample supports to their respective labeled orifices 708. Alternatively, the sample supports 834 can be switched to a vacuum-sealable sample support container 704 having the same labeling method for the corresponding orifices 708.

[0126] In view of the above, it will be appreciated that one or more base grids 832 / sample supports 834 may be misinserted into the wrong orifice 708 of the same or different sample containers 702 / 704, lost, and / or otherwise damaged during any one or more of the above-described processing steps 802-808. Indeed, as noted above, when a base grid 832 / sample support 834 is removed from the initial sample support container 702, direct identification may be lost because the base grids 832 / sample supports 834 are often not individually labeled. As a result, continued successful identification is based on indirect tracking other than through the use of the sample support container 702, which is often subjective and prone to error.

[0127] Thus, upon receipt by an administrative entity or other location with access to the sample support recognition system 600, the plurality of base grids 832 / sample supports 834 within the vacuum-sealed sample support container 704 can be identified as unknown sample supports 640. Each of these unknown sample supports 640 is therefore desirably recognized as one of the known sample supports 642 that have been previously profiled.

[0128] As described above, one or more processes may be performed by the imaging component 610 and / or the profiling component 611 to generate respective data records 644 defining the unknown sample supports 640. In addition to the processes described above, the data records 644 may include an indication of the orifice 708 in which each sample support 640 was positioned upon receipt after post-processing. This step may be applied to any use of the system 600, without limitation. That is, the orifice label may provide further evidence (e.g., a surrogate parameter) that may be used by the matching component 618 to generate matches 656, as described below.

[0129] The images 650, 652 can be retrieved, received, transmitted, and / or otherwise acquired by the acquisition component 614 for use by one or more other components of the sample support recognition system 602 to ultimately generate a match 656 of the unknown sample support 640 to the known sample support 642.

[0130] Using one or more images 650 of an unknown sample support 640 of interest (e.g., an unknown sample support for which recognition is desired and / or directed) and one or more images 652 of one or more known sample supports 640, the comparison component 616 can generally output and / or otherwise generate a comparison score 654 corresponding to the unknown sample support 642 and therefore corresponding to at least one image 650 of the unknown sample support 640.

[0131] 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 heterogeneity profile 651 associated with the unknown sample support 640 of interest with at least one known heterogeneity profile 613 associated with a known sample support.

[0132] This comparison may be a result of and / or may use contrast in one or more images 650, 652, thereby varying the brightness or darkness of different aspects of one or more images 650, 652. For example, in one or more embodiments, one or more non-uniformities 1010 may have a lower brightness and a central region 1014 may have a higher brightness. In one or more other embodiments, one or more non-uniformities 1010 may have a higher brightness and a central region 1014 may have a lower brightness. In one or more embodiments, one or more non-uniformities 1010 may have a range of brightnesses. Note that while the non-uniformities 1010 are shown as being darker and having a lower brightness, this is merely an example for ease of viewing. Indeed, in use, the opposite contrast may result from dark-field microscopic imaging performed and / or guided by the imaging component 610 (e.g., when one or more inhomogeneities 1010 have a greater brightness than the surrounding central region 1014 that surrounds and bounds the one or more inhomogeneities 1010).

[0133] For example, comparing can include identifying one or more sections and using subsequent analysis on all sections as a whole, and / or on one or more sections at a time, and / or at least partially in parallel with one another. After selecting a first section of one of the images 650, 652, the comparison component 616 can use image recognition techniques to search for the most similar region of the other of the images 650, 652. In one or more embodiments, sectioning can be applied to the other of the images 650, 652 based on the identification of the region of the other of the images 650, 652. In one or more other embodiments, sectioning can already be applied to the other of the images 650, 652. In one or more other embodiments, sectioning can already be applied to the other of the images 650, 652, but can be modified based on the identification of the region of the other of the images 650, 652.

[0134] In another example, the comparison component 616 may analyze a first of the images 650, 652 based on the pixelation of the image, such as analyzing one or more groups of pixels at a time and matching such groups to the most similar group of one or more pixels in the other of the images 650, 652.

[0135] In one or more embodiments, such pixel-based searches can be employed for the entire image, or for less than the entire image, for example, for only all or a portion of the pixels that contain the non-uniformity 1010 (e.g., including pixels having greater or less brightness and / or contrast than determined).

[0136] In another example, the comparison component 616 can identify a first non-uniformity 1010 in a first one of the images 650, 652 based on a variation in contrast between the boundaries or regions 1014 that bound the non-uniformity 1010 and that have a different contrast than the boundaries or regions 1014. Thus, analysis of the first one 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 one of the images 650, 652. It is understood that in one or more embodiments, the non-uniformity profile 651, 653 used can have one or more non-uniformities 1010 pre-defined and / or pre-recognized. Thus, recognition of such one or more non-uniformities 1010 can additionally and / or alternatively be performed by the profiling component 611.

[0137] It should be understood that any one or more of the techniques described above and / or similar techniques for image recognition known to those skilled in the art may be used by comparison component 616.

[0138] For example, to provide an explanation of the above, attention is directed to Figure 11. Referring to Figure 11, various diagrams of comparison stages that may be performed by comparison component 616, such as using one or more image recognition techniques, are shown.

[0139] For example, an unknown image and a known image may first be acquired from acquisition component 614. The first captured image 1102 is an unknown image 650 of unknown sample support A (640). The second captured image 1104 is a known image 652 of known sample support X (642).

[0140] Next, comparison image 1106 illustrates the comparison process based on a pixel-based or non-uniformity-based approach. As shown in comparison image 1106, a first unknown sample support B is compared to a first known sample support Y. Also, a second unknown sample support C is compared to a second known sample support Z. Identification lines 1112 indicate matching of aspects of the first image (e.g., non-uniformities and / or groups of one or more pixels) with aspects of the second image (e.g., non-uniformities and / or groups of one or more pixels).

[0141] It will be appreciated that one or more of the captured image 1102, the recorded image 1104, and / or the 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 displayed on a display device 410 of a computing device 400 associated with the user entity.

[0142] Based on the comparison, the comparison component 616 can generate a comparison score 654 that provides a value defining the level of similarity between the pair of compared images (e.g., between the pair of compared sample supports).

[0143] Additionally and / or alternatively, the comparison component 616 can generate a parameter score 1202 that provides a 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 something other than the comparison performed by the comparison component 616. For example, the comparison component 616 can compare any other parameters (e.g., material composition, dimensions, surface angle, mass, etc.) that can be obtained from the respective non-uniformity profiles 651, 653 corresponding to the pair of sample supports being compared.

[0144] Additionally and / or alternatively, the comparison component 616 can generate an aggregate score 1204 that provides an aggregate 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 something other than the comparison 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.

[0145] It will be appreciated that more than one unknown image 650 of an unknown sample support 642 and / or more than one known image 652 of a known sample support 640 may be used by the comparison component 616. Accordingly, a compared imaged pair (e.g., a pair of sample supports being compared accordingly) may have more than one comparison score 654, parameter score 1202 and / or aggregate score 1204 associated therewith.

[0146] It will further be appreciated that two or more known sample supports 642 associated with one or more known images 652 can be used by the comparison component 616. Thus, an unknown image 650 (e.g., therefore an unknown sample support 640) can be associated with a first group of one or more scores based on a comparison with a first known image 652 of a first known sample support 642, and with additional groups of one or more scores based on a comparison with additional known images 652 of other known sample supports 642.

[0147] Based on one or more of the above processes that can be performed by the comparison component 616, the matching component 618 can match the unknown sample support 650 to the known sample support 652 generally based on the aforementioned heterogeneity profiles 651, 653, and more particularly based on one or more of the aforementioned scores (e.g., comparison score, parameter score and / or aggregate score) based on the aforementioned heterogeneity profiles 651, 653.

[0148] For example, in one or more embodiments, the matching component 618 can match the unknown sample support 640 of interest to the selected known sample support 640 based on the comparison score 654, parameter score 1202 and / or aggregate score 1204 associated with the known sample support 640 that has the 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 642.

[0149] 12, known sample support X may be associated with a comparison score I that contains the highest value compared to other comparison scores II and III. As a result of this score comparison, a match 656 may be output by matching component 618 that associates unknown sample support 640 with known sample support 642.

[0150] In one or more embodiments, one or more of the comparison score 654, parameter score 1202, and / or aggregate score 1204 associated with the same known sample support 642 can be used to determine a match 656 by the matching component 618. For example, if a first known sample support 642 can be associated with the highest comparison score 654, but a second known sample support 642 can be associated with the highest aggregate score 1204, a deviation between the first and second highest scores (e.g., one or more of the comparison score 654, parameter score 1202, and / or aggregate score 1204) can be determined by the matching component 618. Thus, a first deviation between the first and second highest comparison scores 654 can be greater than a second deviation between the first and second highest aggregate scores 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 matching 656.

[0151] In one or more embodiments, the matching component 618 can compare the comparison score 654 to a comparison score threshold 1216, the parameter score 1202 to a parameter score threshold 1212, and / or the aggregate score 1204 to an aggregate score threshold 1214. For example, as a result of one or more of the score comparisons described above, a probabilistic match 1210 can instead be output by the matching component 618. The scores used for the probabilistic match 1210 (e.g., of the comparison score 654, the parameter score 1202, and / or the aggregate score 1204) can be compared to respective thresholds (e.g., the respective comparison threshold 1216, the parameter threshold 1212, and / or the aggregate threshold 1214).

[0152] If a probabilistic match 1210 is generated that meets the respective threshold, the likely match 1210 may be identified as a match 656 and output by the matching component 618 from the non-limiting system 600 to the display device 410 of the device 400, for example.

[0153] In one or more embodiments, if a match is not generated that meets the respective one or more thresholds used, the matching component 618 can provide one or more suggestions and / or take one or more actions. The one or more suggestions can include capturing a new image, changing the lighting for the capture, changing the orientation of the sample support for the capture, changing the contrast of the captured image, and / or suggesting the use of manual review 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 instructing the imaging component 610 to automatically capture another image, such as when the sample support remains on the imaging platform 904.

[0154] Further, we now describe one or more processes that may be performed by the matching component 618 with respect to a requested recognition of a set of two or more unknown sample supports 640. For example, if it is desired to perform recognition for two or more unknown sample supports 642 that are known to correspond to a group of two or more known sample supports 640, the matching component 618 may perform one or more additional processes. As one non-limiting example, a second match 656 for a second unknown sample support 640 may be generated based on the generation of a first match 656 for a first unknown sample support 640. That is, in one or more cases, the second unknown sample support 640 may associate a temporary match 1210 with two or more known sample supports 642, but not a final match 656. By a process of elimination, the generation of the first match 656 may enable the removal of one of the temporary matches 1210 and the generation of a second match 656 for the second unknown sample support 642.

[0155] It should be noted that this process of using one or more temporary matchings 1210 and / or final matchings 656 to determine one or more other temporary matchings 1210 and / or final matchings 656 can be performed using various comparison and / or elimination processes, if suitable.

[0156] Once a match 656 is generated and / or identified by the matching component 618, the updating component 620 can update the respective data record 644 associated with one of the unknown sample support 640 or known sample support 642 that corresponds to the match 656. This updating can include writing data to the respective data record 644, such as via tagging, marking, and / or appropriate write operations, to identify the unknown sample support 640 and the known sample support 642 as being recognized as the same. The updating can additionally and / or alternatively include data and / or metadata corresponding to any one or more of the respective comparison scores, parameter scores, aggregate scores, and / or threshold comparisons.

[0157] Additionally, in connection with the generation of the match 656 by the matching component 618, and separately from or at least partially in parallel with the update, the notification component 622 may generate a notification 658 generally defining that the match 656 has been generated. This notification 658 may include any suitable type of data, metadata, images, audio, text, etc., and may be published, transmitted, and / or otherwise made available to devices associated with an administrative entity of the non-limiting system 600, for example.

[0158] It will be appreciated that in one or more embodiments, any one or more of the processes described above as being performed by the non-limiting system 600 may be performed automatically in a continuous manner.

[0159] As an overview of the above-mentioned components and their functions, reference is now made to Figure 13, which shows a flow diagram of an example non-limiting method 1300 that can facilitate a sample support recognition process according to one or more embodiments described herein, such as non-limiting system 600 of Figure 6. Although non-limiting method 1300 is described with respect to non-limiting system 600 of Figure 6, non-limiting method 1300 may also be applicable to other systems described herein, such as non-limiting system 500 of Figure 5. Repeated descriptions of similar elements and / or processes used in each embodiment are omitted for the sake of brevity.

[0160] At 1302, the non-limiting method 1300 can include capturing, by a system operably coupled to a processor (e.g., imaging component 610 coupled to processor 506), an image (e.g., an image of unknown sample support grid 640) of an unknown sample support (e.g., unknown sample support grid 650) including a material layer (e.g., material layers 812-820).

[0161] At 1304, the non-limiting method 1300 can include determining whether an unknown sample support image has been obtained by the system (e.g., matching component 618). If yes, the non-limiting method 1300 can proceed to step 1306. If no, the non-limiting method can return to step 1302 to obtain one or more additional images.

[0162] In 1306, the non-limiting method 1300 can include matching, by a system (e.g., matching component 618), the unknown sample support to the known sample support based on an unknown non-uniformity profile (e.g., unknown non-uniformity profile 651) that includes one or more non-uniformities (e.g., non-uniformity 1010) of a material layer in the image of the unknown sample support.

[0163] As another overview of the above-mentioned components and their functions, reference is now made to Figures 14-16, which illustrate a flow diagram of an example non-limiting method 1400 that can facilitate a sample support recognition process according to one or more embodiments described herein, such as non-limiting system 600 of Figure 6. Although non-limiting method 1400 is described with respect to non-limiting system 600 of Figure 6, non-limiting method 1200 may also be applicable to other systems described herein, such as non-limiting system 500 of Figure 5. Repeated descriptions of similar elements and / or processes used in each embodiment are omitted for the sake of brevity.

[0164] At 1402, the non-limiting method 1400 can include capturing, by a system operably coupled to a processor (e.g., imaging component 610 coupled to processor 506), an image (e.g., an image of unknown sample support grid 640) of an unknown sample support (e.g., unknown sample support grid 650) including a material layer (e.g., material layers 812-820).

[0165] At 1404, the non-limiting method 1400 can include capturing, by a system (e.g., imaging component 610), an image of an unknown sample support that includes a set of peripherally located first regions having greater brightness and bounding a central second region having lesser brightness.

[0166] At 1406, the non-limiting method 1400 can include capturing, by the system, an image that includes a first region of one or more unknown inhomogeneities (eg, inhomogeneity 1010).

[0167] 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 with one or more known non-uniformities of the known sample support (e.g., known non-uniformities 1110).

[0168] At 1410, the non-limiting method 1400 can include generating, by the system (eg, the comparison component 616), a comparison score (eg, the comparison score 654) based on the comparison.

[0169] 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 inhomogeneities of the unknown sample support and one or more known inhomogeneities of the known sample support.

[0170] At 1414, the non-limiting method 1400 can 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 with one or more known non-uniformities in the image of the known sample support (e.g., an image of the known sample support grid 652).

[0171] At 1416, the non-limiting method 1400 can include matching the unknown sample support to the known sample support based on a comparison score that includes a highest value compared to one or more other comparison scores generated by comparison against another image of another unknown sample support or another known sample support by the system (e.g., matching component 618).

[0172] At 1418, the non-limiting method 1400 can include determining, by the system (e.g., matching component 618), whether the comparison score meets the comparison score threshold (e.g., comparison threshold 1216). If no, the non-limiting method 1400 can return to step 1402 to capture one or more additional images of the unknown sample support. If yes, the non-limiting method can proceed to step 1420.

[0173] At 1420, the non-limiting method 1400 can include aggregating, by the system (e.g., matching component 618), comparison cores having parameter scores (e.g., parameter scores 1202) based on a comparison of secondary parameters of the unknown sample support associated with one or more other than unknown inhomogeneities to corresponding secondary parameters of the known sample support associated with one or more other than known inhomogeneities.

[0174] At 1422, the non-limiting method 1400 can include matching the unknown sample support to the known sample support by the system (e.g., matching component 618) based on an aggregate score resulting from the aggregation, including a highest value compared to one or more other aggregate scores generated by comparison and aggregation against another image of another unknown sample support or another known sample support.

[0175] At 1424, the non-limiting method 1400 can include determining, by the system (e.g., matching component 618), whether the aggregate score meets a comparison score threshold. If no, the non-limiting method 1400 can return to step 1402 to capture one or more additional images of the unknown sample support. If yes, the non-limiting method can proceed to step 1426.

[0176] At 1426, the non-limiting method 1400 can include updating, by the system (e.g., update component 620), a data record (e.g., data record 644) associated with the known sample to reference the unknown sample.

[0177] At 1428, the non-limiting method 1400 can include generating, by the system (e.g., notification component 622), a notification (e.g., notification 658) defining whether a match was generated corresponding to the unknown sample support.

[0178] As another overview of the above-mentioned components and their functions, reference is now made to Figures 17 and 18, which show a flow diagram of another example non-limiting method 17 that can facilitate a sample support recognition process according to one or more embodiments described herein, such as non-limiting system 600 of Figure 6. Although non-limiting method 1700 is described with respect to non-limiting system 600 of Figure 6, non-limiting method 1700 may also be applicable to other systems described herein, such as non-limiting system 500 of Figure 5. Repeated descriptions of similar elements and / or processes used in each embodiment are omitted for the sake of brevity.

[0179] In 1702, the non-limiting method 1700 can include comparing, by a processor executing program instructions contained on a computer-readable storage medium, an unknown non-uniformity profile (e.g., unknown non-uniformity profile 651) including one or more non-uniformities (e.g., non-uniformities 1010) of the unknown sample support (e.g., unknown sample support grid 640) in an image of the unknown sample support (e.g., image 652 of unknown sample support grid 642) with a known non-uniformity profile (e.g., known non-uniformity profile) of an image of the known sample support (e.g., image 653 of known sample support grid 650).

[0180] At 1704, the non-limiting method 1700 can include comparing, by a processor, an image of the unknown sample support that includes a set of peripherally located first regions having greater brightness and bounding a central second region having lesser brightness.

[0181] At 1706, the non-limiting method 1700 can include comparing, by the processor, images of the unknown sample support that include a first region that includes one or more unknown inhomogeneities.

[0182] At 1708, the non-limiting method 1700 can include comparing, by a processor, an image of the unknown sample support, which is an optical microscope image, with one or more non-uniformities of the unknown sample support disposed in an interior material layer of the unknown sample support.

[0183] At 1710, the non-limiting method 1700 may include generating, by the processor, a comparison score (e.g., comparison score 654) based on the comparison.

[0184] At 1712, the non-limiting method 1700 can include generating, by the processor, a comparison score comprising a value defining a level of similarity between the unknown heterogeneity profile of the unknown sample support and the known heterogeneity profile of the known sample support.

[0185] At 1714, the non-limiting method 1700 can include identifying, by the processor, the unknown sample support as being a known sample support based on the results of the comparison.

[0186] At 1716, the non-limiting method 1700 can include identifying, by the processor, the unknown sample support relative to the known sample support based on a comparison score that includes a highest value compared to one or more other comparison scores generated by comparison against another image of another unknown sample support or another known sample support.

[0187] At 1718, non-limiting method 1700 can include determining, by the processor, whether the comparison score meets a comparison score threshold (e.g., comparison threshold 1216). If no, non-limiting method 1400 can return to step 1702 to capture one or more additional images of the unknown sample support. If yes, non-limiting method can proceed to step 1720.

[0188] At 1720, the non-limiting method 1700 can include determining, by the processor, a match between the second known sample support and the second unknown sample support based on the results of the comparison.

[0189] Additional Overview For ease of explanation, computer-implemented and non-computer-implemented methodologies provided herein are depicted and / or described as a series of acts. It should be understood that the present invention is not limited by the illustrated acts and / or by the order of the acts; for example, acts may occur in one or more orders and / or simultaneously, as well as with other acts not presented and described herein. Furthermore, not all illustrated acts may be utilized to implement computer-implemented and non-computer-implemented methodologies in accordance with the described subject matter. Additionally, computer-implemented and non-computer-implemented methodologies may alternatively be represented as a series of interrelated states via state diagrams or events. Additionally, the computer-implemented methodologies described below and throughout this specification may be stored on an article of manufacture for transmission and transfer to a computer. The term article of manufacture, as used herein, is intended to encompass a computer program accessible from any computer-readable device or storage medium.

[0190] Systems and / or devices are described (and / or further described) herein with respect to interactions between one or more components. Such systems and / or components may include components or subcomponents specified herein, one or more of the specified components and / or subcomponents, and / or additional components. Subcomponents may be implemented as components 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. Components may interact with one or more other components not specifically described herein for the sake of brevity, but known by those skilled in the art.

[0191] In summary, one or more systems, computer program products, and / or computer-implemented methods provided herein relate to a process for sample support recognition. The system may include a memory that stores computer-executable components and a processor that executes 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 non-uniformity profile that includes one or more non-uniformities of the material layer in the image of the unknown sample support.

[0192] One or more embodiments disclosed herein may achieve improved sample support grid recognition, for example, compared to conventional techniques that employ simple labeling and guesswork. Rather, one or more embodiments described herein may provide improved performance of imaging systems by providing an accepted and recognized baseline for imaging through a verified sample support grid. That is, such systems may use known dimensions and / or other measurements of a sample support grid onto which a sample of interest may be attached. Verifying that the sample support grid being used is indeed a sample support grid corresponding to such dimensions and / or measurements may reduce imaging and / or measurement errors along the line during analysis of each sample of interest.

[0193] In that regard, one or more embodiments described herein may provide verifiable tracking of one or more, such as multiple, sample support grids across various stages of manufacturing, processing, and / or preparation of the sample support grids at different locations and / or by different entities, i.e., physical characteristics of the sample support grid itself may be used to identify the sample support grid, and one or more sample support grids of interest may be recognized as appropriate (e.g., designated for use) for one or more imaging systems.

[0194] For example, based on the specific application of a light source to the sample support grid, one or more inhomogeneities in one or more materials of the sample support grid can be used to track the identity of the sample support grid (e.g., as a fingerprint or other profile), both initially and during one or more subsequent identifications. This can allow for more efficient and more accurate identification of sample supports compared to existing techniques, and / or verified recognition that a specified sample support grid is actually being used. This, in turn, can allow for more accurate placement of samples, such as slices, on and / or in the sample support compared to existing techniques.

[0195] Identification of the selected unknown sample support grid can be made increasingly efficient by using a matching process that uses one or more inhomogeneities in the material of the selected unknown sample support grid compared to one or more inhomogeneities in the image of the known sample support grid. In one or more embodiments, the comparison score generated for this comparison can be aggregated with one or more parameter scores corresponding to the 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.

[0196] Indeed, in view of one or more embodiments described herein, a practical application of one or more of the systems, computer-implemented methods, and / or computer program products described herein may be the ability to automatically match an image of an unknown sample support to an image of a known sample support, thereby matching the known sample support to a record defining at least the known sample support. This recognition may be performed for the purpose of verifying one or more characteristics of a sample support that is being used and / or is being used for a materials analysis imaging system, such as a S / TEM or FIB device. This recognition may also be performed efficiently and accurately without manual input into the decision-making process, thus reducing the time, effort, manual input, and / or one or more errors that result from conventional frameworks for sample support recognition.

[0197] Furthermore, the accurate non-uniformity identification that can be performed by one or more embodiments described herein allows the sample support to be more accurately positioned relative to the imaging column or other aspects of such an imaging system, and / or the target sample to be more accurately attached to the attachment area of ​​the sample support.

[0198] Furthermore, one or more embodiments described herein can be used in real-world systems based on the disclosed teachings. For example, as noted above, precise localization of a sample support being imaged, such as by recognizing the sample support according to the framework described herein, can directly result in more accurate identification of the attachment regions of the sample support and / or more accurate physical placement of the physical sample on the sample support, including the attachment regions, compared to existing techniques. This real-world result can be enabled by the process of one or more embodiments described herein, which relates to profiling one or more unknown inhomogeneities of an unknown sample support, comparing the resulting unknown inhomogeneity profile with known inhomogeneity profiles of known samples, and subsequently matching an image of the unknown sample support with an image of the known sample support based on the comparison. That is, one or more embodiments described herein can provide high accuracy in computer-aided recognition of unknown sample supports. Therefore, the embodiments disclosed herein can provide improvements to scientific instrument technology (e.g., improvements to the computer technology supporting such scientific instrumentation, among other improvements).

[0199] Embodiments herein can provide scaled recognition 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 for a requested recognition can be performed at least partially in parallel with a set of processes for another requested recognition, or at least partially in parallel with a set of processes for the same requested recognition (e.g., when two or more unknown sample support images for the same unknown sample support are analyzed at least partially in parallel with one another). As an example of the latter, as described above, one image can have a first darkness level and a second image can have a second darkness level.

[0200] Systems and / or devices are described (and / or further described) herein with respect to interactions between one or more components. Such systems and / or components may include components or subcomponents specified herein, one or more of the specified components and / or subcomponents, and / or additional components. Subcomponents may be implemented as components 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. Components may interact with one or more other components not specifically described herein for the sake of brevity, but known by those skilled in the art.

[0201] One or more embodiments described herein may, in one or more embodiments, be inherently and / or inseparably linked 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 for more efficient and even more feasible execution of programs and / or program instructions, such as in connection with automated sample support recognition, compared to existing systems and / or techniques that use manual and / or computer-assisted approaches. Systems, computer-implemented methods, and / or computer program products that provide the performance of these processes are highly useful in the field of materials analysis, such as materials analysis using dual-beam systems, and cannot be equally practically implemented, in a reasonable manner, outside of a computing environment.

[0202] One or more embodiments described herein may employ hardware and / or software to solve problems that are highly technical, not abstract, and cannot be implemented as a series of mental acts by a human. For example, one or more embodiments described herein may provide a process so that a single human, or even thousands of humans, cannot efficiently, accurately, and / or effectively automatically identify the non-uniformity profile of a sample support grid, particularly a sample support grid having a circumference in the range of about 5 mm to about 2 mm, e.g., about 3 mm. That is, regardless of the use of a microscope and manual observation approach, a human cannot digitize the observed results and compare such results with computer data as can be implemented by one or more embodiments described herein as a series of mental acts and / or with pen and paper.

[0203] In one or more embodiments, one or more of the processes described herein may be executed by one or more special-purpose computers (e.g., a special-purpose processing unit, a special-purpose classical computer, a special-purpose quantum computer, a special-purpose classical / quantum hybrid system, and / or another type of special-purpose computer) to perform defined tasks associated with one or more of the techniques described above. One or more embodiments described herein and / or components thereof may be used to solve new problems that arise through the use of the above-referenced technological advancements, quantum computing systems, cloud computing systems, computer architectures, and / or other techniques.

[0204] One or more embodiments described herein may be fully operational to perform one or more other functions (e.g., fully powered on, fully running, and / or another function) while performing one or more of the operations described herein.

[0205] To provide a further overview, a list of embodiments and their features is provided below.

[0206] A system comprising: a memory that stores computer-executable components; and a processor that executes the computer-executable components stored in the memory, the computer-executable components comprising: 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 non-uniformity profile that includes one or more non-uniformities in the material layer in the image of the unknown sample support.

[0207] The system of the preceding paragraph, 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, the comparison score including a value that defines 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.

[0208] The system of any of the preceding paragraphs, further comprising the matching component further matching the unknown sample support to the known sample support based on the comparison score that has the highest value compared to one or more other comparison scores generated by the comparison component for another image of another unknown sample support or another known sample support.

[0209] The system of any of the preceding paragraphs, 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 less brightness, the first regions defining an unknown heterogeneity profile.

[0210] The system of any of the preceding paragraphs, wherein the image of the unknown sample support is an optical microscope image and the layer of material comprises a silicon frame.

[0211] The system of any of the preceding paragraphs, further comprising an acquisition component that acquires a set of images of the known sample support, including an image of the known sample support.

[0212] The system of any of the preceding paragraphs, further comprising a notification component that generates a notification defining whether a match has been generated corresponding to the unknown sample support.

[0213] A computer-implemented method comprising: 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 matching 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 the image of the known sample support.

[0214] The computer-implemented method of the preceding paragraph, further comprising: comparing, by the system, one or more unknown non-uniformities of the image of the unknown sample support with one or more known non-uniformities of the known sample support; and generating, by the system, a comparison score based on the comparison, the comparison score comprising 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.

[0215] The computer-implemented method of any of the preceding paragraphs, wherein the matching further includes matching the unknown sample support to the known sample support based on a comparison score that has the highest value compared to one or more other comparison scores generated by comparing against another image of another unknown sample support or another known sample support.

[0216] The computer-implemented method of any of the preceding paragraphs, wherein the matching further includes aggregating, by the system, comparison cores having parameter scores based on a comparison of secondary parameters of the unknown sample support associated with other than the one or more unknown inhomogeneities with corresponding secondary parameters of known sample supports associated with other than the one or more known inhomogeneities, and matching, by the system, the unknown sample support to the known sample support based on an aggregated score resulting from the aggregation, the aggregated score including the highest value compared to one or more other aggregated scores generated by comparing and aggregating another image of another unknown sample support or another known sample support.

[0217] 10. The computer-implemented method of any of the preceding paragraphs, 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, the first regions being one or more unknown inhomogeneities.

[0218] The computer-implemented method of any of the preceding paragraphs, further comprising updating, by the system, data records associated with the known samples to reference the unknown samples.

[0219] The computer-implemented method of any of the preceding paragraphs, further comprising generating, by the system, a notification defining whether a match has been generated corresponding to the unknown sample support.

[0220] 1. A computer program product that facilitates a process for sample support recognition, 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 an unknown non-uniformity profile, including one or more non-uniformities of the unknown sample support, in an image of the unknown sample support with known non-uniformity profiles of an image of a known sample support, and based on a result of the comparison, identify the unknown sample support as being a known sample support.

[0221] 10. The computer program product of claim 9, wherein the program instructions are further executable by the processor to cause the processor to generate a comparison score based on the comparison, the comparison score comprising a value defining a level of similarity between the unknown heterogeneity profile of the unknown sample support and the known heterogeneity profile of the known sample support.

[0222] The computer program product of any of the preceding paragraphs, wherein the program instructions are executable by a processor and further cause the processor to match the unknown sample support to a known sample support based on a comparison score that includes the highest value compared to one or more other comparison scores generated by comparing against another image of another unknown sample support or another known sample support.

[0223] 10. The computer program product of any of the preceding paragraphs, 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, the first regions defining an unknown heterogeneity profile.

[0224] 10. The computer program product of any of the preceding paragraphs, wherein the image of the unknown sample support is an optical microscope image, and wherein the one or more non-uniformities of the unknown sample support are provided in an interior material layer of the unknown sample support.

[0225] The computer program product of any of the preceding paragraphs, wherein the program instructions are further executable by a processor to cause the processor to determine a match between the second unknown sample support and the second known sample support based on the results of the comparison.

[0226] Scientific Instrument System Description Referring now to Figure 19, a detailed description of additional context for one or more embodiments described herein in Figures 1-18 is provided. 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 illustrates a block diagram of an exemplary scientific instrument system 1900 capable of performing one or more of the scientific instrument methods or other methods disclosed herein, in accordance with various embodiments described herein. The scientific instrument modules and methods disclosed herein (e.g., scientific instrument module 100 of Figure 1 and method 200 of Figure 2) may be implemented by one or more of the scientific instrument 1910, user local computing device 1920, service local computing device 1930, and / or remote computing device 1940 of the scientific instrument system 1900.

[0227] 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 embodiments of computing device 400 discussed herein with reference to FIG. 4, 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 take the form of any suitable one or more of the embodiments of computing device 400 discussed herein with reference to FIG. 4.

[0228] One or more of the scientific instruments 1910, user local computing devices 1920, service local computing devices 1930, and / or remote computing devices 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 form of processor 402 discussed herein with reference to FIG. 4. The processing devices 1902 included in different ones of the scientific instruments 1910, user local computing devices 1920, service local computing devices 1930, and / or remote computing devices 1940 may take the same form or different forms. The storage device 1904 may take any suitable form, including any form of storage device 404 discussed herein with reference to FIG. 4. The storage devices 1904 included in different ones of the scientific instruments 1910, user local computing devices 1920, service local computing devices 1930, and / or remote computing devices 1940 may take the same form or different forms. The interface device 1906 may take any suitable form, including any of the forms of the interface device 406 discussed herein with reference to Figure 4. The interface devices 1906 included in different ones 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 take the same or different forms.

[0229] The scientific instrument 1910, the user local computing device 1920, the service local computing device 1930, and / or the remote computing device 1940 can communicate with other elements of the scientific instrument system 1900 via communication paths 1908. As shown, the communication paths 1908 can communicatively couple the interface devices 1906 of different elements of the scientific instrument system 1900 (e.g., according to any of the communication techniques discussed herein with reference to the interface device 406 of the computing device 400 of FIG. 4 ) and can be wired or wireless communication paths. While the particular scientific instrument system 1900 depicted in FIG. 19 includes communication paths between each pair of the scientific instrument 1910, the user local computing device 1920, the service local computing device 1930, and the remote computing device 1940, this “fully connected” implementation is merely illustrative, and various embodiments can omit various ones of the communication paths 1908. For example, in one or more embodiments, the service local computing device 1930 may omit the direct communication path 1908 between its interface device 1906 and the interface device 1906 of the scientific instrument 1910, and instead may 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.

[0230] Scientific instrument 1910 may include any suitable scientific instrument, such as a separation or MS instrument, or other instrument that facilitates material analysis.

[0231] The user local computing device 1920 may be a computing device near 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 may also be near the scientific instrument 1910, but need not be. For example, a user local computing device 1920 associated with a home, office, or other building associated with a user entity may be remote from but in communication with the scientific instrument 1910 such that the user entity can control and / or access data from the scientific instrument 1910 using the user local computing device 1920. In one or more embodiments, the user local computing device 1920 may be a laptop, smartphone, or tablet device. In one or more embodiments, the user local computing device 1920 may be a portable computing device. In one or more embodiments, the user local computing device 1920 may be deployed in the field.

[0232] The service local computing device 1930 may be a computing device (e.g., according to any of the embodiments of computing device 400 discussed herein) that is proximate to an entity that provides services to the scientific instrument 1910. For example, the service local computing device 1930 may be proximate 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 may 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., results of self-diagnostic tests 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 discussed above) and transmit data to the scientific instrument 1910, the user local computing device 1920, and / or the remote computing device 1940 (e.g., to update programmed instructions such as firmware in the scientific instrument 1910, to initiate the performance of a test or calibration sequence in the scientific instrument 1910, to update programmed 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 communicate with the service local computing device 1930 to utilize the scientific instrument 1910 or the user local computing device 1920 to report problems 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.

[0233] The remote computing device 1940 may be a computing device that is remote from the scientific instrument 1910 and / or from the user 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 may be included in a data center or other large-scale server environment. In one or more embodiments, the remote computing device 1940 may include network-attached storage (e.g., as part of the storage device 1904). The remote computing device 1940 may store data generated by the scientific instrument 1910, perform analysis of the data generated by the scientific instrument 1910 (e.g., according to programmed instructions), facilitate communications between the user local computing device 1920 and the scientific instrument 1910, and / or facilitate communications between the service local computing device 1930 and the scientific instrument 1910.

[0234] In one or more embodiments, one or more of the elements of the scientific instrument system 1900 illustrated in FIG. 19 may be omitted. Furthermore, in one or more embodiments, more than one of various elements of the elements of the scientific instrument system 1900 of FIG. 19 may be present. 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 different locations). In another example, the scientific instrument system 1900 may include multiple scientific instruments 1910 that are all in communication with a service local computing device 1930 and / or a remote computing device 1940. In such an embodiment, 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 "broadcast" to the multiple scientific instruments 1910 simultaneously. Different scientific instruments 1910 in the scientific instrument system 1900 can be located near each other (e.g., in the same room) or far 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 instruments 1910 can be connected to an Internet-of-Things (IoT) stack that enables command and control of the scientific instruments 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 is in communication with the scientific instruments 1910 through an intervening remote computing device 1940. In one or more embodiments, the scientific instruments 1910 can be sold by a manufacturer together with one or more associated user local computing devices 1920 as part of a local scientific instrument computing unit 1912.

[0235] In one or more embodiments, different ones of the scientific instruments 1910 included in the scientific instrument system 1900 may be different types of scientific instruments 1910. For example, one scientific instrument 1910 may be an EDS instrument and another scientific instrument 1910 may be an analytical instrument that analyzes the results of the EDS instrument. In some such embodiments, the remote computing device 1940 and / or the user local computing device 1920 may combine data from the different types of scientific instruments 1910 included in the scientific instrument system 1900.

[0236] Example Operating Environment 20 is a schematic block diagram of an operating environment 2000 with which the described subject matter can interact. The operating environment 2000 includes one or more remote components 2010. The remote components 2010 can be hardware and / or software (e.g., threads, processes, computing devices). In one or more embodiments, the remote components 2010 can be a distributed computer system connected via a communications framework 2040 to local autoscaling components and / or programs that use resources of the distributed computer system. The communications framework 2040 can include wired network devices, wireless network devices, mobile devices, wearable devices, radio access network devices, gateway devices, femtocell devices, servers, etc.

[0237] The operating environment 2000 also includes one or more local components 2020. The local components 2020 can be hardware and / or software (e.g., threads, processes, computing devices). In one or more embodiments, the local components 2020 can include auto-scaling components and / or programs that communicate / use remote resources 2010 and 2020, such as those connected to a remotely located distributed computing system via a communications framework 2040.

[0238] Possible communication between the remote component 2010 and the local component 2020 can be in the form of data packets adapted to be transmitted between two or more computer processes. Another possible communication between the remote component 2010 and the local component 2020 can be in the form of circuit-switched data adapted to be transmitted between two or more computer processes in radio time slots. The operating environment 2000 includes a communication framework 2040 that can be used to facilitate communication between the remote component 2010 and the local component 2020 and can include an air interface, such as an interface for a UMTS network over an LTE network. The remote component 2010 can include a hard drive, solid-state drive, Subscriber Identity Module (SIM) card, electronic SIM card, or other storage device that can be used to store information on the remote component 2010 side of the communication framework 2040. The local component 2020 may be operatively connected to one or more remote data stores 2050, such as a SIM, eSIM, device memory, etc. Similarly, the local component 2020 may be operatively connected to one or more local data stores 2030 that may be used to store information on the local component 2020 side of the communication framework 2040.

[0239] Exemplary Computing Environment To provide additional context for the various embodiments described herein, Figure 21 and the following discussion are intended to provide a brief, general description of a suitable computing environment 2100 in which various embodiments of the embodiments discussed herein may be implemented. While 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 combination with other program modules and / or as a combination of hardware and software.

[0240] Generally, program modules include routines, programs, components, data structures, etc. that perform tasks or implement abstract data types. Furthermore, the methods may 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, and personal computers, handheld computing devices, microprocessor-based or programmable consumer electronics, and the like, each of which may be operatively coupled to one or more associated devices.

[0241] The illustrated embodiments of the present specification may 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.

[0242] Although a computing device typically includes a variety of media, which may include a computer-readable storage medium, a machine-readable storage medium, and / or a communication medium, these two terms are used differently herein as follows. A computer-readable storage medium or a machine-readable storage medium may be any available storage medium that can be accessed by a computer, and includes both volatile and nonvolatile media, removable and non-removable media. By way of example and not limitation, a computer-readable storage medium or a machine-readable storage medium may be implemented in connection with any method or technology for storing information, such as computer-readable or machine-readable instructions, program modules, structured data, or unstructured data.

[0243] A computer-readable storage medium may include, but is 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 disk read only memory (CD ROM), digital versatile disk (DVD), Blu-ray Disc (BD) or other optical disk storage, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage device, solid state drive or other solid state storage device, or other tangible and / or non-transitory medium 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 medium herein exclude only the propagating transitory signal itself as a modifier, and do not disclaim any right to all standard storage, memory, or computer-readable medium that do not merely propagate the transitory signal itself.

[0244] The computer-readable storage medium can be accessed by one or more local or remote computing devices for various operations on the information stored by the medium, for example, via access requests, queries, or other data retrieval protocols.

[0245] 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 characteristics set or changed in such a manner as to encode information in the signal or 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.

[0246] 21 , an exemplary computing environment 2100 in which one or more embodiments described herein can 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 a variety of commercially available processors. Dual microprocessors and other multi-processor architectures can also be used as the processing unit 2104.

[0247] The system bus 2108 can be any of several types of bus structures that can be further interconnected to 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 a ROM 2110 and a RAM 2112. The basic input / output system (BIOS) can be stored in non-volatile memory such as a ROM, erasable programmable read-only memory (EPROM), or EEPROM, and contains the basic routines that help to transfer information between elements within the computer 2102, such as during start-up. The RAM 2112 can also include a high-speed RAM such as static RAM for caching data.

[0248] The 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.). While the internal HDD 2114 is illustrated as being located within the computer 2102, the internal HDD 2114 may also be configured for external use in a suitable chassis (not shown). Additionally, although not shown in the computing environment 2100, a solid state drive (SSD) may be used in addition to or in place of the HDD 2114.

[0249] 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 disk drive that can read from and write to removable media such as CD-ROM disks, DVDs, BDs, etc.). The external storage device 2116 may be facilitated by a networked virtual machine. The HDD 2114, the external storage device 2116, and the storage device (e.g., drive) 2121 can be connected to the system bus 2108 by an HDD interface 2124, an external storage interface 2126, and a drive interface 2128, respectively.

[0250] The drives and their associated computer-readable storage media provide non-volatile storage of data, data structures, computer-executable instructions, etc. In the case of computer 2102, the drives and storage media accommodate the storage of any data in a suitable digital format. Although the above description of computer-readable storage media refers to each type of storage device, other types of computer-readable storage media, whether currently existing or developed in the future, may also be used in the exemplary operating environment, and further, any such storage media may contain computer-executable instructions for performing the methods described herein.

[0251] Several 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, applications, modules, and / or data may also be cached in RAM 2112. The systems and methods described herein can be implemented using various commercially available operating systems or combinations of operating systems.

[0252] Computer 2102 may optionally include emulation technology. For example, a hypervisor (not shown) or other intermediary may emulate the hardware environment of operating system 2130, where the emulated hardware may optionally differ from the hardware illustrated in FIG. 21 . In such an embodiment, operating system 2130 may include one of multiple virtual machines (VMs) hosted on computer 2102. Additionally, operating system 2130 may provide a runtime environment, such as the Java Runtime Environment or the .NET Framework, for applications 2132. A runtime environment is a consistent execution environment that allows applications 2132 to run on any operating system that includes the runtime environment. Similarly, operating system 2130 may support containers, where applications 2132 may be in the form of a container, which is a lightweight, standalone executable package of software that includes, for example, code, runtime, system tools, system libraries, and settings for the application.

[0253] Additionally, computer 2102 can be enabled with a security module such as a trusted processing module (TPM). For example, in a TPM, a boot component can hash the boot component in time and wait for the result to match a secure value before loading the next boot component. This process can occur at any layer within the code execution stack of computer 2102, and can be applied at the application execution level or the operating system (OS) kernel level, thereby enabling security at any level of code execution.

[0254] A user entity can enter commands and information into the computer 2102 through one or more wired / wireless input devices, such as a keyboard 2138, a touch screen 2140, and a pointing device such as a mouse 2142. Other input devices (not shown) can include a microphone, an infrared (IR) remote control, a radio frequency (RF) remote control, or other remote controls, a joystick, a virtual reality controller and / or headset, a game pad, a stylus pen, an image input device such as a camera, a gesture sensor input device, a visual motion sensor input device, an emotion or face detection device, a biometric input device such as a fingerprint or iris scanner, etc. These and other input devices are often connected to the processing unit 2104 via an input device interface 2144, which can be coupled to the system bus 2108, but can also be connected by other interfaces, such as a parallel port, an IEEE 1394 serial port, a game port, a USB port, an IR interface, a BLUETOOTH interface, etc.

[0255] A monitor 2146 or other type of display device can 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, etc.

[0256] The computer 2102 can operate in a networked environment using wired and / or wireless logical connections to one or more remote computers, such as a remote computer 2150. The remote computer 2150 may be a workstation, a server computer, a router, a personal computer, a portable computer, a microprocessor-based entertainment device, a peer device, or other common network node, and typically includes many or all of the elements described relative to the computer 2102, although for simplicity, only a memory / storage device 2152 is illustrated. The depicted logical connections include wired / wireless connections to a local area network (LAN) 2154 and / or a wide area network (e.g., a wide area network (WAN) 2156). Such LAN and WAN networking environments are commonplace in offices and businesses and facilitate enterprise-wide computer networks, such as intranets, all of which can connect to a global communications network, e.g., the Internet.

[0257] When used in a LAN networking environment, the computer 2102 can be connected to the local network 2154 via a wired and / or wireless communication network interface or adapter 2158. The adapter 2158 can facilitate wired or wireless communication to the LAN 2154, and the LAN can also include a wireless access point (AP) disposed thereon for communicating with the adapter 2158 in a wireless mode.

[0258] When used in a WAN networking environment, the computer 2102 may include a modem 2160 or may be connected to a communications server on the WAN 2156 via other means for establishing communications over the WAN 2156, such as via the Internet. The modem 2160 may be internal or external, and a wired or wireless device, and may be connected to the system bus 2108 via the input device interface 2144. 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, and other means of establishing a communications link between computers may be used.

[0259] When used in either a LAN or WAN networking environment, computer 2102 can access a cloud storage system or other network-based storage system in addition to, or in place of, external storage device 2116, as described above. Generally, the connection between computer 2102 and the cloud storage system can be established via LAN 2154 or WAN 2156, respectively, by, for example, adapter 2158 or modem 2160. Upon connecting computer 2102 to an associated cloud storage system, external storage interface 2126, with the aid of adapter 2158 and / or modem 2160, can manage the storage provided by the cloud storage system in the same way as 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.

[0260] The computer 2102 may be operable to communicate with any wireless device or entity operatively arranged for wireless communication, such as a printer, a scanner, a desktop and / or portable computer, a portable data assistant, a communications satellite, any equipment or location associated with a radio-detectable tag (e.g., a kiosk, a newsstand, a store shelf, etc.), and a telephone. This may include Wireless Fidelity (Wi-Fi) and BLUETOOTH® wireless technologies. Thus, communication may be in a defined structure similar to an existing network, or simply ad-hoc communication between at least two devices.

[0261] Additional Information The embodiments described herein may be directed to one or more of a system, a method, an apparatus, and / or a computer program product at any possible level of technical detail of integration. The computer program product may include a computer-readable storage medium having computer-readable program instructions for causing a processor to execute aspects of one or more embodiments described herein. The computer-readable storage medium may be a tangible device capable of holding and storing instructions for use by an instruction-execution device. The computer-readable storage medium may 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 thereof. A non-exhaustive list of more specific examples of computer-readable storage media can also include: portable computer diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital versatile disk (DVD), memory stick, floppy disk, mechanically encoded devices such as punch cards or ridge-in-groove structures having instructions recorded thereon, and / or any suitable combination of the foregoing. As used herein, computer-readable storage media should not be construed as being ephemeral signals per se, 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 passing through fiber optic cables), and / or electrical signals transmitted over wires.

[0262] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device and / or to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network may include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing / processing device. The computer-readable program instructions for carrying out the operations of one or more embodiments described herein may be source code and / or object code written in any combination of one or more programming languages, including assembler instructions, Instruction-Set-Architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, configuration data for integrated circuits, and / or object-oriented programming languages ​​such as Smalltalk, C++, and / or procedural programming languages ​​such as the "C" programming language and / or similar programming languages. The computer-readable program instructions may execute entirely on the computer, partially on the computer, as a standalone software package, partially on the computer, and / or partially on a remote computer, or entirely on a remote computer and / or server. In the latter scenario, the remote computer may be connected to the computer via any type of network, including a local area network (LAN) and / or a wide area network (WAN), and / or may be connected to an external computer (e.g., via the Internet using an Internet Service Provider).In one or more embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGAs), and / or programmable logic arrays (PLAs), can execute computer-readable program instructions by utilizing state information of the computer-readable program instructions to personalize the electronic circuitry to perform aspects of one or more embodiments described herein.

[0263] Aspects of one or more embodiments described herein will be described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to one or more embodiments described herein. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks 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 apparatus to generate a machine such that the instructions, executed by the processor of the computer or other programmable data processing apparatus, create means for implementing the function(s) / act(s) specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can be stored on a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and / or other device to function in a particular manner, whereby the computer-readable storage medium having instructions stored therein can include an article of manufacture containing instructions that can implement aspects of the function(s) / act(s) specified in one or more blocks of the flowchart and / or block diagram. The computer-readable program instructions may also be loaded into a computer, other programmable data processing apparatus, and / or other device to cause a series of operational acts to be executed on the computer, other programmable apparatus, and / or other device to generate a computer-implemented process, whereby the instructions executing on the computer, other programmable apparatus, and / or other device implement the functions / acts specified in one or more blocks of the flowcharts and / or block diagrams.

[0264] The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and / or operation of possible implementations of systems, computer-implementable methods, and / or computer program products according to one or more embodiments described herein. In this regard, each block in a flowchart or block diagram may represent a module, segment, and / or portion of an instruction, which comprises one or more executable instructions for implementing the specified logical function(s). In one or more alternative implementations, the functions noted in the blocks may occur out of the order noted in the figures. For example, two blocks shown in succession may be executed substantially concurrently and / or the blocks may sometimes be executed in the reverse order, depending on the functionality involved. It should also be noted that each block of the block diagrams and / or flowchart illustrations, and / or combinations of blocks in the block diagrams and / or flowchart illustrations, may be implemented by a special-purpose hardware-based system capable of performing the specified functions and / or acts and / or executing one or more combinations of special-purpose hardware and / or computer instructions.

[0265] While the subject matter has been described above in the general context of computer-executable instructions for a computer program product executed on one and / or more computers, those skilled in the art will recognize that one or more embodiments herein can also be implemented, at least in part, in parallel with one or more other program modules. Generally, program modules include routines, programs, components, and / or data structures that perform particular tasks and / or implement particular abstract data types. Furthermore, the computer-implemented methods described above can be practiced with single-processor and / or multiprocessor computer systems, minicomputing devices, mainframe computers, and other computer system configurations, including computers, handheld computing devices (e.g., PDAs, phones), and / or microprocessor-based or programmable consumer and / or industrial electronic devices. The illustrated aspects can also be practiced in distributed computing environments where tasks are performed by remote processing devices linked through a communications network. However, one or more, if not all, aspects of one or more embodiments described herein can be practiced on stand-alone computers. In a distributed computing environment, program modules can be located in both local and remote memory storage devices.

[0266] As used herein, the terms “component,” “system,” “platform,” and / or “interface” can refer to and / or include computer-related entities or entities associated with an operating machine having one or more particular functions. The entities described herein can be hardware, a combination of hardware and software, software, or software in execution. For example, a component can be, but is not limited to, a process running on a processor, a processor, an object, an executable, a thread of execution, a program, and / or a computer. By way of illustration, both an application running on a server and the server can be a component. One or more components can reside within a process and / or thread of execution, but a component can also be localized on one computer and / or distributed between two or more computers. In another example, each component can execute from various computer-readable media having various data structures stored thereon. Components may communicate via local and / or remote processes, such as according to signals comprising one or more data packets (e.g., data from a local system, another component in a distributed system, and / or one component interacting across a network such as the Internet with other systems via signals). As another example, a component may be a device having a particular functionality provided by mechanical parts operated by electrical or electronic circuits operated by software and / or firmware applications executed by a processor. In such cases, the processor may be internal and / or external to the device and may execute at least a portion of the software and / or firmware applications.As yet another example, a component may be a device that provides a particular functionality through electronic components without mechanical parts, but the electronic component may include a processor and / or other means for executing software and / or firmware that at least partially provides the functionality of the electronic component. In one aspect, a component may emulate the electronic component, for example, via a virtual machine in a cloud computing system.

[0267] Additionally, the term "or" is intended to mean an inclusive "or" rather than an exclusive "or." That is, unless otherwise specified or clear from context, "X employs A or B" is intended to mean all natural inclusive permutations. That is, if X employs A, then X employs B, or if X employs both A and B, then "X employs A or B" is satisfied under any of the foregoing cases. Furthermore, the articles "a" and "an" as used in this specification and the accompanying drawings should generally be interpreted to mean "one or more" unless otherwise specified or clear from context to refer to the singular form. As used herein, the terms "example" and / or "exemplary" are utilized to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter described herein is not limited by such examples. Additionally, 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, and is not meant to exclude equivalent exemplary structures and techniques known to those skilled in the art.

[0268] As used herein, the term "processor" may refer to substantially any computing processing unit and / or device, including, but not limited to, a single-core processor, a single processor with software multithreading execution capabilities, a multi-core processor, a multi-core processor with software multithreading execution capabilities, a multi-core processor with hardware multithreading techniques, a parallel platform, and / or a parallel platform with distributed shared memory. Additionally, 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, discrete hardware components, and / or any combination thereof designed to perform the functions described herein. Furthermore, a processor may utilize nanoscale architectures, such as, but not limited to, molecular and quantum dot-based transistors, switches, and / or gates, to optimize space usage and / or enhance the performance of associated equipment. A processor may be implemented as a combination of computing processing units.

[0269] As used herein, the terms "store," "storage," "data store," and "data storage" are used interchangeably. Terms such as "memory component," "database," and substantially any other information storage component associated with the operation and functionality of a component are utilized to refer to a "memory component," an entity embodied in a "memory," or a component that includes a memory. The memory and / or memory components described herein may be either volatile or nonvolatile memory, or may include both volatile and nonvolatile memory. By way of example, and not limitation, nonvolatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable ROM (EEPROM), flash memory, and / or nonvolatile random access memory (RAM) (e.g., ferroelectric RAM (FeRAM)). Volatile memory may include, for example, RAM, which may function as external cache memory. By way of example, and not limitation, RAM is available in many forms, such as synchronous RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), Direct Rambus RAM (DRRAM), Direct Rambus Dynamic RAM (DRDRAM), and / or Rambus Dynamic RAM (RDRAM). Additionally, 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.

[0270] What has been described above includes merely example 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, to the extent that terms such as "includes," "has," "possesses," and the like are used in the detailed description, claims, appendices, and / or drawings, such terms are intended to be as inclusive as the term "comprising," as "comprising" is interpreted when used as a transitional term in a patent claim.

[0271] 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.

[0272] The descriptions of various embodiments are presented for illustrative purposes and are not intended to be exhaustive or limited to the embodiments described herein. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein has been selected to best explain the principles of the embodiments, practical applications and / or technical improvements over technology found in the market, and / or to enable those skilled in the art to understand the embodiments described herein.

Claims

1. 1. A system comprising: a memory for storing computer-executable components; a processor for executing the computer-executable components stored in the memory; the computer-executable components: an imaging component that captures an image of the unknown sample support including the layer of material; a matching component that matches the unknown sample support to a known sample support based on an unknown non-uniformity profile that includes one or more non-uniformities of the material layer in the image of the unknown sample support. system.

2. a comparison component that compares the unknown non-uniformity profile of the image of the unknown sample support with known non-uniformity profiles of the image of the known sample support and generates a comparison score based on the comparison; the comparison score comprises a value defining a level of similarity between the unknown heterogeneity profile of the unknown sample support and the known heterogeneity profile of the known sample support. The system of claim 1 .

3. 3. The system of claim 2, wherein the matching component further comprises matching the unknown sample support to the known sample support based on the comparison score having the highest value compared to one or more other comparison scores generated by the comparison component for another image of another unknown sample support or another known sample support.

4. the image of the unknown sample support includes a set of peripherally located first regions having greater brightness bounding a central second region having lesser brightness; The system of claim 1 , wherein the first region defines the unknown heterogeneity profile.

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

6. The system of claim 2 , further comprising an acquisition component that acquires a set of images of a known sample support, the acquisition component including the images of the known sample support.

7. The system of claim 1 , further comprising a notification component that generates a notification defining whether the match was generated corresponding to the unknown sample support.

8. 1. 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; and matching, by the system, the unknown sample support to the known sample support based on matching one or more unknown inhomogeneities of the material layer in the image of the unknown sample support with one or more known inhomogeneities in the image of the known sample support. Computer-implemented methods.

9. 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; generating, by the system, a comparison score based on the comparing; the comparison score comprises a value defining a level of similarity between the one or more unknown inhomogeneities of the unknown sample support and the one or more known inhomogeneities of the known sample support.

9. The computer-implemented method of claim 8.

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

11. The matching aggregating, by the system, the comparison scores into a parameter score based on a comparison of a secondary parameter of the unknown sample support associated with other than the one or more unknown inhomogeneities with a corresponding secondary parameter of the known sample support associated with other than the one or more known inhomogeneities; and further comprising: matching, by the system, the unknown sample support to the known sample support based on an aggregate score resulting from the aggregation, the aggregate score having a highest value compared to one or more other aggregate scores generated by the comparison and aggregation for another image of another unknown sample support or another known sample support.

10. The computer-implemented method of claim 9.

12. the image of the unknown sample support includes a set of peripherally located first regions having greater brightness bounding a central second region having lesser brightness; the first region is the one or more unknown heterogeneities; 9. The computer-implemented method of claim 8.

13. 10. The computer-implemented method of claim 9, further comprising updating, by the system, a data record associated with the known sample to reference the unknown sample.

14. The computer-implemented method of claim 8 , further comprising generating, by the system, a notification defining whether the match was generated corresponding to the unknown sample support.

15. 1. A computer program product facilitating a process for sample support recognition, comprising: The computer program product comprises a computer-readable storage medium having program instructions embodied thereon, the program instructions being executable by a processor, the processor comparing, by the processor, an unknown non-uniformity profile comprising one or more non-uniformities of the unknown sample support in the image of the unknown sample support with known non-uniformity profiles of images of known sample supports; and identifying, by the processor, the unknown sample support as the known sample support based on the results of the comparison. Computer program products.

16. The program instructions are further executable by the processor to cause the processor to: causing the processor to generate a comparison score based on the comparing; the comparison score comprises a value defining a level of similarity between the unknown heterogeneity profile of the unknown sample support and the known heterogeneity profile of the known sample support.

16. A computer program product according to claim 15.

17. The program instructions are executable by the processor, causing the processor to: causing the processor to match the unknown sample support to the known sample support based on the comparison score that has the highest value compared to one or more other comparison scores generated by the comparison to another image of another unknown sample support or another known sample support; 17. A computer program product according to claim 16.

18. the image of the unknown sample support includes a set of peripherally located first regions having greater brightness bounding a central second region having lesser brightness; The computer program product of claim 15 , wherein the first region defines the unknown heterogeneity profile.

19. 16. 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 non-uniformities of the unknown sample support are provided in an interior material layer of the unknown sample support.

20. The program instructions are further executable by the processor to cause the processor to: causing the processor to determine a match between the second unknown sample support and the second known sample support based on the results of the comparison; 17. A computer program product according to claim 16.

Images