Charged particle counting with non-back-thinned integrating detector and beam energy quantization

Abstract

This document provides a system / technique for facilitating charged particle counting via a non-back-side thinning integrating detector and beam energy quantization. In various embodiments, the system can access an energy-integrated image of a specimen scanned by a charged particle microscope equipped with a non-back-side thinning integrating detector. In various aspects, the system can count how many charged particle events are represented by the corresponding pixels of the energy-integrated image based on rounding the cumulative charged particle energy intensity indicated by each pixel cluster to the nearest integer multiple of the beam energy of the charged particle microscope.

Description

Background Technology

[0001] It may be difficult to accurately count how many charged particles collide with the integrating detector of a charged particle microscope. Summary of the Invention

[0002] The following is a summary to provide a basic understanding of one or more embodiments. This summary is not intended to identify key elements or to define any scope of any particular embodiment or claim. Its sole purpose is to present concepts in a simplified form as a prelude to a more detailed description that follows. In one or more embodiments described herein, devices, systems, computer implementation methods, apparatuses, or computer program products that facilitate charged particle counting through a non-back-side thinning integral detector and beam energy quantization are described.

[0003] According to one or more embodiments, a system is provided. The system may include a non-transitory computer-readable storage medium capable of storing computer-executable components. The system may also include a processor operatively coupled to and capable of executing the computer-executable components stored in the non-transitory computer-readable storage medium. In various embodiments, the computer-executable components may include a scanning component capable of accessing an energy integration image of a specimen scanned by a charged particle microscope equipped with a non-back-side thinning integrating detector. In various aspects, the computer-executable components may include a quantization component capable of counting how many charged particle events are represented by the corresponding pixels of the energy integration image based on rounding the cumulative charged particle energy intensity indicated by each pixel cluster to the nearest integer multiple of the beam energy of the charged particle microscope.

[0004] According to one or more embodiments, a computer-implemented method is provided. In various embodiments, the computer-implemented method may include accessing an energy integration image of a specimen scanned by a charged particle microscope equipped with a non-back-side thinning integrating detector by a device operatively coupled to a processor; and the device counting how many charged particle events are represented by the corresponding pixels of the energy integration image based on rounding the cumulative charged particle energy intensity indicated by each pixel cluster to the nearest integer multiple of the beam energy of the charged particle microscope.

[0005] According to one or more embodiments, a computer program product is provided for facilitating charged particle counting via a non-back-side thinning integrating detector and beam energy quantization. In various embodiments, the computer program product may include a non-transitory computer-readable storage memory having program instructions stored thereon. In various aspects, the program instructions may be processor-executable to cause the processor to access an energy integration image of a specimen scanned by an electron energy loss microscope equipped with a non-back-side thinning integrating detector. In various instances, the program instructions may be processor-executable to cause the processor to count how many electron events are represented by the corresponding pixels of the energy integration image based on rounding the cumulative electron energy intensity indicated by each pixel cluster to the nearest integer multiple of the beam energy of the electron energy loss microscope. Attached Figure Description

[0006] Various embodiments will be readily understood through the following detailed description in conjunction with the accompanying drawings. For the purpose of this description, the same reference numerals indicate the same structural elements. Embodiments are illustrated in the figures by way of example rather than limitation. The figures are not necessarily drawn to scale.

[0007] Figure 1 shows an example, non-limiting block diagram of a scientific instrument module according to various embodiments described herein.

[0008] Figure 2 shows an example, non-limiting flowchart of computer implementation methods according to the various embodiments described herein.

[0009] Figure 3 shows an example, non-limiting block diagram of a system according to one or more embodiments described herein, which facilitates charged particle counting through a non-backside thinning integral detector and beam energy quantization.

[0010] Figure 4 shows an example, non-limiting block diagram of a system including an energy integration image and beam energy according to one or more embodiments described herein, which facilitates charged particle counting through a non-backside thinning integration detector and beam energy quantization.

[0011] Figure 5 - Figure 7 An example, non-limiting block diagram illustrating how an energy integral image can be accessed according to one or more embodiments described herein.

[0012] Figure 8 shows an example, non-limiting block diagram of a system including pixel-by-pixel charged particle counting according to one or more embodiments described herein, the system facilitating charged particle counting through a non-backside thinning integral detector and beam energy quantization.

[0013] Figure 9Figure 12 shows an example, non-limiting block diagram illustrating how the pixel-by-pixel charged particle count can be determined according to one or more embodiments described herein.

[0014] Figure 13 Figure 14 shows experimental results related to an exemplary non-limiting implementation of one or more of the embodiments described herein.

[0015] Figure 15 shows a block diagram of an example, non-limiting operating environment that may facilitate one or more of the embodiments described herein.

[0016] Figure 16 illustrates an exemplary networking environment operable to implement the various implementation schemes described herein.

[0017] Figure 17 illustrates an exemplary dual-beam microscope that can be implemented according to the various embodiments described herein. Detailed Implementation

[0018] The following detailed descriptions are illustrative only and are not intended to limit the application or use of the embodiments. Furthermore, they are not intended to be construed as being limited by any express or implied information presented in the background section of the invention description or in the detailed description section.

[0019] One or more embodiments will now be described with reference to the accompanying drawings, wherein the same reference numerals are used throughout to refer to the same elements. In the following description, numerous specific details are set forth for purposes of explanation, intended to provide a more thorough understanding of the one or more embodiments. However, it will be apparent, in various cases, that one or more embodiments may be implemented without these specific details.

[0020] The various operations can be described as multiple independent actions or operations in a manner most conducive to understanding the subject matter disclosed herein. However, the order of description should not be construed as implying that these operations necessarily depend on a specific order. In particular, these operations may be performed in an order different from the order presented. The operations may be performed in an order different from the described embodiments. Various additional operations may be performed, or the operations may be omitted in additional embodiments.

[0021] While some elements may be mentioned in the singular (e.g., "a processing device"), any suitable element may be represented by multiple instances of that element, and vice versa. For example, a set of operations described as being performed by a processing device may be implemented by different operations performed by different processing devices. As used herein, unless otherwise stated, the phrase "based on" should be understood to mean "at least partially based on".

[0022] Charged particle microscopes (e.g., scanning electron microscopes (SEM), transmission electron microscopes (TEM), dual-beam microscopes, electron energy loss microscopes (EELM), energy-dispersive X-ray microscopes (EDM)) can be any suitable computerized device capable of capturing or generating microscopic or nanoscopic images of specimens in scientific, laboratory, research, or clinical operating environments. To facilitate the capture or generation of such images, charged particle microscopes can utilize complex arrangements of drivable components (e.g., ion sources, electron sources, optical lenses or apertures, optical plates or deflectors, microscope tubes, coils, heaters, coolers, fluid valves, fluid pumps, loop switches, specimen stages), sensors (e.g., ion detectors, electron detectors, voltmeters, thermistors, potentiometers, pressure gauges), or consumables (e.g., carrier liquids, calibrators, filters, reactive gases).

[0023] In various applications, charged particle microscopy (e.g., particularly EELM) can reveal material properties or compositional characteristics of specimens (such as integrated circuit chips, semiconductor wafers, sheets, biological samples, or organic samples) by irradiating the specimen with a beam of charged particles (e.g., electrons, X-ray photons) and counting how many charged particles belonging to the corresponding energy loss levels penetrate the specimen (e.g., these particles may transfer towards the specimen and thus lose their corresponding amount of energy). In some aspects, charged particle microscopy may have or possess an energy dispersive filter that can: allow only charged particles with a desired or selected energy loss to reach the detector of the charged particle microscopy; or direct or aim charged particles with different energy losses at corresponding cells of the detector (e.g., so that each cell is only struck by a single particle with a corresponding energy loss). In any case, counting how many charged particles with corresponding energy losses strike the charged particle microscopy detector can reveal valuable compositional information about the specimen through which these charged particles have penetrated.

[0024] Some charged particle microscopes are equipped with or fitted with pulse counting detectors (such as Geiger-Muller counters, scintillation detectors, and Channeltron® detectors). The hardware of the pulse counting detector is specifically designed or configured to detect or count how many individual charged particles bombard the detector.

[0025] In contrast, many other charged particle microscopes are equipped with or fitted with integrating detectors (e.g., charge-coupled devices, Faraday cup detectors). Unlike the hardware of pulse counting detectors, the hardware of integrating detectors is gated or configured to accumulate, sum, or integrate the energy of charged particles striking the detector over time. Therefore, in normal, standard, or default integrating mode operation, the integrating detector does not output or generate a charged particle count. Instead, it outputs or generates an accumulated, summed, or integrated energy signal.

[0026] To enable charged particle microscopes with integrator detectors to count charged particles, a counting mode for the integrator detector has been developed in the prior art. In counting mode, the integrator detector does not accumulate, sum, or measure the energy of the incident charged particles. Instead, in counting mode, the integrator detector operates in a binary "hit or no hit" manner. Specifically, any given cell of the integrator detector can be considered to have a sampling rate and sampling iterations. The sampling rate of a given cell can be considered to define how quickly or rapidly the given cell records or registers a signal, while the sampling iteration of a given cell can be considered to be the length of time (e.g., typically measured in microseconds or milliseconds) consumed by the given cell in recording or registering a single signal. Therefore, the sampling iteration of a given cell can be considered inversely proportional to its sampling rate (e.g., a higher sampling rate corresponds to shorter sampling iterations; conversely, a lower sampling rate corresponds to longer sampling iterations). In any case, for each sampling iteration that occurs or occurs during counting mode operation, the given cell can indicate one of two things: the given cell was hit by a charged particle; or the given cell was not hit by a charged particle. Therefore, there can exist a running count corresponding to the given cell with an initial value of zero. This run count can increment during each sampling iteration when a given cell indicates it has been struck, and can remain constant during each sampling iteration when a given cell indicates it has not been struck. After any suitable number of sampling iterations, the final value of the run count is processed by the prior art to indicate how many charged particles struck the given cell. In many cases, the counting sensitivity of a given cell can be improved if the integrating detector is thinned on its back side. In other words, the counting sensitivity of a given cell can be improved if the thickness of the integrating detector is thinner or less than the penetration depth of the charged particles striking it.

[0027] Unfortunately, existing techniques for counting charged particles using integrator detectors are susceptible to the stacking effect. The stacking effect refers to the situation where, when two or more different charged particles simultaneously, rapidly, consecutively, or separately, within the same sampling iteration (e.g., at a rate greater than the time resolution of the integrator detector), the cell may fail to independently or individually register or record these two or more different charged particles. Instead, the cell may incorrectly register or record a single charged particle.

[0028] As a non-limiting example, consider a given integrator detector unit operating on a counting modulus, which is struck sequentially by particle A and particle B. Suppose particle A strikes the given integrator detector unit during sampling iteration X and particle B strikes the given integrator detector unit during sampling iteration Y, where sampling Y occurs after or later than sampling iteration X. In this case, during sampling iteration X, the given integrator detector unit can generate or output a "striking" signal and can increment its running count by one accordingly. Furthermore, during sampling iteration Y, the given integrator detector unit can generate or output another "striking" signal and can increment its running count by one again accordingly. Note how the running count increments twice in this case: once for particle A and once for particle B. Therefore, since particles A and B occur at different sampling iterations, the given integrator detector unit is able to count them correctly. Now, instead, assume that particles A and B both strike the given integrator detector unit during sampling iteration Z. In this scenario, during sampling iteration Z, a given integrator detector unit can generate or output a "bump" signal and can accordingly increment its run count by one. Note how the run count increments only once in this case. In other words, since particles A and B occur during the same sampling iterations, the given integrator detector unit cannot count them correctly. In other words, particles A and B can be considered to be stacked on the given integrator detector unit so that the given integrator detector unit cannot accurately distinguish them and therefore cannot accurately count them. It should be noted that even if back-side thinning is being performed on the given integrator detector unit (e.g., even if it has higher sensitivity due to a thickness smaller than the penetration depth of particles A and B), the stacking problem can still occur.

[0029] This is why existing techniques for counting charged particles using integral detectors are prone to miscounting due to the accumulation effect.

[0030] Therefore, it is desirable to have a system or technology that can mitigate the accumulation effect.

[0031] The various embodiments described herein address this technical problem. One or more embodiments described herein may include systems, computer-implemented methods, apparatuses, or computer program products that facilitate charged particle counting via a non-back-thinning integrating detector and beam energy quantization. Specifically, the inventors of the various embodiments described herein recognize that prior art for counting charged particles via an integrating detector operates in a "hit-or-not-hit" binary manner when using back-thinning cells. The inventors point out that, as charged particles pass through or penetrate back into the back-thinning cells entirely or completely, these charged particles deposit an amount of energy typically ranging from a few electron volts to several thousand electron volts into these back-thinning cells. The inventors also point out that this deposited energy represents at most a few percent of the beam energy initially imparted to these charged particles by the charged particle microscope. Furthermore, the inventors realize that when implementing a non-back-thinning integrating detector (e.g., an integrating detector with a thickness greater than or equal to the penetration depth of the charged particle that hits it), each charged particle can be considered to have almost (e.g., about 98% or more) of its initial energy deposited into the non-back-thinning integrating detector of the charged particle microscope. Therefore, the inventors have conceived of various embodiments described herein, in which charged particles can be counted by quantizing the total energy deposited into each cell of a non-back-side thinning integrator. In other words, the various embodiments described herein can track or record the total energy of charged particles deposited into each cell of a non-back-side thinning integrator, rather than using a back-side thinning integrator to count charged particles in a "bumped or unbumped" binary manner. By dividing the total energy of charged particles by the beam energy of the charged particle microscope and then rounding these quotients, the total number of charged particles impacting each cell of the non-back-side thinning integrator can be accurately or reliably estimated, even in the presence of charged particle accumulation. In other words, the various embodiments described herein can be considered a sophisticated pixel processing technique that is unaffected or interfered with by accumulation effects.

[0032] The various embodiments described herein can be viewed as a computerized tool (e.g., any suitable combination of computer-executable hardware or software) that facilitates charged particle counting through a non-back-side thinning integrating detector and beam energy quantization. In various aspects, such a computerized tool may include scanning or quantization components.

[0033] In various embodiments, a charged particle microscope may be present. In various aspects, the charged particle microscope can exhibit any suitable design or construction (e.g., it can be a SEM, a TEM, a dual-beam microscope, or an EELM). In various instances, any suitable specimen (e.g., a semiconductor wafer or wafer) can be loaded into the charged particle microscope (e.g., currently located or positioned on a driveable stage of the charged particle microscope). In various cases, the charged particle microscope may include an integrating detector that has not been or has not yet been back-side thinned. In other words, the thickness of the integrating detector can be greater than any penetration depth associated with the charged particles emitted by the charged particle microscope.

[0034] In various aspects, electronic counting of specimens or relative to specimens may be required. As discussed in this article, computerized tools can facilitate such electronic counting.

[0035] In various implementations, the computerized tool can access the charged particle microscope electronically. For example, the computerized tool can send electronic commands to the charged particle microscope or receive electronic data from the charged particle microscope. Therefore, any component of the computerized tool can interact electronically with the charged particle microscope in any suitable manner (e.g., enable, disable, read, write, edit, copy, manipulate).

[0036] In various implementations, the scanning component of the computerized tool can electronically enable the charged particle microscope to scan the specimen using an integrating detector, and this scanning can produce an energy-integrated image. In various aspects, the charged particle microscope can perform this scanning using any suitable beam energy (e.g., any suitable beam voltage or beam current).

[0037] More specifically, the scanning assembly can electronically instruct or command the charged particle microscope to scan the specimen multiple times using beam energy. This scanning can produce multiple preliminary energy integrated images of the specimen (e.g., a preliminary energy integrated image / scan). In various instances, each preliminary energy integrated image can be a two-dimensional pixel array, with each pixel of each preliminary energy integrated image indicating the total amount of energy deposited into the corresponding integrating detector unit of the charged particle microscope during the corresponding scan. It is important to note that each integrating detector unit can be viewed as recording, storing, or tracking the energy deposited by the detector (e.g., energy deposited into the detector), rather than the energy deposited by the specimen (e.g., energy lost from the specimen).

[0038] In various aspects, the scanning component can convert multiple preliminary energy integration images into multiple rounded energy integration images by utilizing any suitable noise threshold. In various instances, the noise threshold can be any suitable scalar whose value or magnitude represents a maximum energy level known or identified as acting as a noise upper limit that may potentially affect any given integrator detector unit. In practice, even if a given integrator detector unit is not struck by any charged particles, it can still register or record a non-zero amount of deposited energy due to random noise. If the registered or recorded energy level of a given integrator detector unit is above the noise threshold, the registered or recorded energy level can be considered as not merely a result of noise (e.g., it can instead be considered as a result of at least one charged particle striking the given integrator detector unit). Conversely, if the registered or recorded energy level of a given integrator detector unit is below the noise threshold, the registered or recorded energy level can be considered as not merely a result of noise (e.g., it can be considered as a result of an uncharged particle striking the given integrator detector unit). In each aspect, if the integrated energy value of any given pixel in the multiple preliminary energy integration images is lower than the noise threshold, the scanning component may replace the integrated energy value of that given pixel with the value 0. That is, the scanning component may round down the integrated energy value of that given pixel to 0. Conversely, if the integrated energy value of any given pixel in the multiple preliminary energy integration images is higher than the noise threshold, the scanning component may leave the integrated energy value of that given pixel unchanged. After performing this conditional rounding operation on each pixel, the multiple preliminary energy integration images can be referred to as multiple rounded energy integration images.

[0039] In various aspects, the scanning component can electronically generate an energy integral image by combining or aggregating multiple rounded energy integral images. For example, the energy integral image can be equal to the pixel-by-pixel sum or pixel-by-pixel average of multiple rounded energy integral images. By generating the energy integral image in this way (e.g., by rounding and aggregating), situations where noise overwhelms or suppresses low-dose charged particle counts can be avoided or prevented.

[0040] In various implementations, the quantum components of the computerized tool can electronically compute or calculate pixel-by-pixel charged particle counts on the energy integral image by utilizing the beam energy of a charged particle microscope used to generate the energy integral image.

[0041] More specifically, the quantization component can electronically apply any suitable community detection algorithm (e.g., the Louvain algorithm, the Girvan-Newman algorithm) to the energy integration image. In various instances, this allows the quantization component to identify multiple pixel clusters within the energy integration image. In various instances, each pixel cluster can be a set of spatially contiguous or cohesive pixels with non-zero integrated energy values. For any given pixel cluster, the quantization component can, in various instances, sum or add the integrated energy values ​​of the pixels in the given pixel cluster to obtain a cumulative integrated energy value. In various cases, the quantization component can divide the cumulative energy value by the beam energy implemented by the charged particle microscope to obtain a quotient. In various instances, the quantization component can round this quotient to the nearest integer value. In various instances, this nearest integer value can be considered as the total number of charged particles that collide with any integration detector unit corresponding to the given pixel cluster (e.g., where its integrated energy value is stored). Clustering in this way can be considered beneficial, at least because charged particles can sometimes traverse multiple integrator detector cells at once, thus depositing their energy into more than one integrator detector cell. In other words, some integrator detector cells can sometimes absorb a portion of the initial energy of any given charged particle. Clustering as described in this paper can help ensure that these portions are not overlooked or miscounted.

[0042] Furthermore, in some cases, the quantization component can determine how many charged particles have struck each individual pixel of a given pixel cluster. Specifically, for any given pixel in a given pixel cluster, the quantization component can divide the integrated energy value of that given pixel by the cumulative integrated energy value of the given pixel cluster, thus obtaining a ratio. In various aspects, the quantization component can multiply this ratio by the total number of charged particles that struck the integral detector corresponding to the given pixel cluster. The product of this multiplication can be a scalar representing how many individual charged particles (or portions thereof) struck a given pixel.

[0043] For example, suppose a charged particle microscope uses a beam energy of 100 keV. Furthermore, suppose the cumulative integrated energy of a given pixel cluster is 873 keV (or some dimensionless value corresponding to or mapped to 873 keV). Also suppose the integrated energy of a given pixel is 225 keV (or some dimensionless value corresponding to or mapped to 225 keV). In this case, the quantization component can divide 873 keV by 100 keV, obtaining a quotient of 8.73. The quantization component can then round 8.73 to the nearest integer: 9. Therefore, the quantization component can infer or conclude that 9 charged particles have struck the integral detector unit corresponding to the given pixel cluster. Next, the quantization component can divide 225 keV by 873 keV to obtain the ratio: In various instances, the quantization component can multiply this ratio by 9, resulting in a scalar with a value of approximately 2.32. Therefore, the quantization component can infer or conclude that 2.32 charged particles have struck any integral detector unit corresponding to a given pixel.

[0044] In this way, the quantization component can compute or calculate the corresponding charged particle count for each pixel in a cluster of pixels. In other words, the quantization component can compute or calculate the corresponding charged particle count for each non-zero pixel in the energy integral image.

[0045] It is important to note that this type of counting can be considered unaffected by or unhindered by stacking effects. That is, by quantizing (e.g., dividing by) the cumulative energy value of the corresponding pixel cluster using the beam energy of the charged particle microscope, charged particles can be counted accurately or reliably regardless of when they struck the detector, provided the detector is not back-side thinned. In practice, consider two or more particles striking a non-back-side thinning integrating detector unit. This non-back-side thinning integrating detector unit can register or record all the energy deposited into the unit by these two or more particles. The total amount of registered or recorded energy can be almost or approximately equal to the sum of the initial energies of the two or more particles. Therefore, the total amount of registered or recorded energy does not depend on whether the two or more particles struck the non-back-side thinning integrating detector unit during the same or different sampling iterations. Thus, beam energy quantization as described herein can be considered a method for counting charged particles without being affected by stacking problems.

[0046] In various implementations, the computerized tool can perform any suitable electronic action on the pixel-by-pixel charged particle count calculated by the quantized components. As some examples, the computerized tool can present any pixel-by-pixel charged particle count on any suitable electronic display, can transfer any pixel-by-pixel charged particle count to any suitable computing device, or can use any pixel-by-pixel charged particle count for any suitable downstream analysis (e.g., compositional analysis of the sample).

[0047] The various implementations described herein can be used to solve problems that are highly technical (e.g., aimed at facilitating charged particle counting through non-backside thinning integrating detectors and beam energy quantization), non-abstract, and cannot be performed by humans as a set of mental actions, using hardware or software. Furthermore, some of the procedures performed can be executed by a dedicated computer (e.g., an electron microscope such as the EELM) to carry out specific actions relevant to the field of charged particle microscopy.

[0048] For example, such defining actions may include: accessing energy integral images of a specimen scanned by a charged particle microscope equipped with a non-back-side thinning energy integral detector by a device operatively coupled to the processor; and counting, by the device, how many charged particle events are represented by the corresponding pixels of the energy integral image based on rounding the cumulative charged particle energy intensity indicated by each pixel cluster to the nearest integer multiple of the beam energy of the charged particle microscope. In various aspects, such defining actions may also include: the device causing the charged particle microscope to capture multiple preliminary energy integral images of the specimen; the device assigning a value of zero to pixels in the multiple preliminary energy integral images whose charged particle energy intensity is below a noise threshold, thereby generating multiple rounded energy integral images; the device performing pixel-by-pixel summation or averaging on the multiple rounded energy integral images to obtain an energy integral image; and the device identifying corresponding clusters by applying a community detection algorithm to the energy integral images. In various instances, for a first cluster having a first cumulative charged particle energy intensity, this defining action may include: dividing the first cumulative charged particle energy intensity by the beam energy of the charged particle microscope to obtain a quotient; rounding the quotient to the nearest integer value to obtain a rounded quotient; and determining that the number of charged particle events represented by the first cluster is equal to the rounded quotient. In various cases, for a first pixel in the first cluster having a first integral charged particle energy intensity, this defining action may further include: dividing the first integral charged particle energy intensity by the first cumulative charged particle energy intensity to obtain a ratio; multiplying the ratio by the rounded quotient to obtain a product; and determining that the number of charged particle events represented by the first pixel is equal to the product.

[0049] Such constrained actions are inherently computerized. In fact, a charged particle microscope (e.g., EELM) is a high-tech computerized device containing specialized computerized hardware such as temperature sensors, pressure sensors, voltage sensors, ion beam emitters, electron beam emitters, focusing lenses, ion detectors, electron detectors, beam stops, fluid valves, and actuated specimen stages. A charged particle microscope, and the images it captures, cannot be performed by human thought or by humans using pen and paper in any reasonable or feasible way without a computer. Furthermore, charged particle counting (e.g., electron counting) is an inherently hardware-based task that cannot be performed by human thought or by humans using only pen and paper in any reasonable or feasible way. In reality, subatomic particles are invisible to the naked eye and therefore cannot be detected or counted without specialized hardware (e.g., integrating detectors) specifically designed and constructed to physically respond to the presence or absence of subatomic particles. Discussing charged particle counting outside of a computerized environment is meaningless.

[0050] Furthermore, the various embodiments described herein can integrate various teachings related to the field of charged particle microscopy into practical applications. As explained above, existing techniques for counting charged particles using an integrator detector are susceptible to a stacking effect. Specifically, when two or more charged particles simultaneously, rapidly, consecutively, or separately, strike a given integrator detector cell within the same sampling iteration, the given cell may be unable to distinguish or identify the two or more charged particles. In effect, the given cell can be considered to operate in a "strike-or-not-strike" binary or dichotomous manner. Therefore, during any sampling iteration in which the given cell is struck by two or more charged particles, any run count recorded by the given cell can only increment by one, even though the given cell is actually struck by two or more charged particles. That is, two or more charged particles can be considered to be stacked on the given cell, so the given cell cannot accurately distinguish between the two or more charged particles. Therefore, the stacking effect causes the prior art to inaccurately or incorrectly calculate how many charged particles strike a given cell, which can be potentially disadvantageous.

[0051] The various embodiments described herein can help mitigate one or more of these technical problems by enabling charged particle counting through a non-back-side thinning integrating detector and beam energy quantization. In particular, the inventors recognized that the prior art suffers from a stacking effect because it forces the integrating detector to operate in a binary "impact or no-impact" non-integrating mode. The inventors realized that the stacking effect can be counteracted by counting charged particles based on the residual energy maintained after the charged particle interacts with the specimen. Specifically, each charged particle emitted by the charged particle microscope can be considered to have an initial amount of energy equal to the beam energy used by the charged particle microscope to emit, generate, or propel the charged particles. Furthermore, each charged particle that passes through the specimen and subsequently impacts the charged particle microscope detector unit can be considered to lose only a small fraction of its initial energy in the specimen (e.g., perhaps 2% at the high-performance end). Therefore, as long as the detector unit is not back-side thinned, almost all (e.g., about 98% or more) of the initial energy of the charged particles can be considered to be deposited into the detector unit. Because of this, when multiple charged particles collide with the same detector unit, the total energy deposited into the detector unit across all such collisions may be almost an integer multiple of the beam energy of the charged particle microscope, where the integer multiple represents the number or base of these multiple charged particles. It is important to note that, being a non-back-side thinning integrating detector, the detector unit can record, store, sum, or integrate the total energy deposited into the unit. Furthermore, it is important to note that the beam energy of the charged particle microscope can be considered a known, selectively controllable, or otherwise easily identifiable value. Therefore, the total number of charged particles impacting the detector unit can thus be estimated by dividing the total integrated energy deposited into the detector unit by the beam energy, regardless of whether any of these multiple charged particles collide with the detector unit simultaneously, rapidly and continuously, or separately in the same sampling iterations. In other words, the various embodiments described herein can be considered as a specific pixel processing technique that can be implemented on a charged particle microscope with a non-back-side thinning integrating detector and allows for accurate counting of charged particles even with stacking effects. In other words, the various implementations described herein can achieve higher or better charged particle counting accuracy than existing techniques that use integrator detectors.

[0052] Furthermore, the counterintuitive features of the various embodiments described herein must be emphasized. As mentioned above, the core or basic principle of prior art for counting charged particles using an integrating detector is as follows: operating in a binary "impact or non-impact" counting mode instead of an integrating mode; and improving counting sensitivity through back-side thinning. The inventors realized that these core or basic principles of the prior art actually exacerbate the stacking effect. In fact, as described herein, the inventors realized that the stacking effect can be counteracted by deriving the charged particle count from the residual energy deposited in the non-back-side thinned integrating cell (e.g., the amount of energy retained after the charged particle interacts with the specimen). After all, since each charged particle may only lose a small percentage of its initial energy in the specimen, each charged particle can be considered to have deposited almost all of its initial energy into a detector that has not yet undergone back-side thinning (conversely, each charged particle would otherwise only lose another small amount of its initial energy in the back-side thinned cell). Therefore, dividing the total energy encountered by any given non-back-thinning integrator unit by the beam energy that propels charged particles toward the specimen yields a scalar that is almost equal to the total number of charged particles impacting that given non-back-thinning integrator unit (e.g., within its rounding error range). Counting charged particles in this way can be seen as a paradigm shift in the field of charged particle microscopy. In fact, since the entire charged particle microscopy industry traditionally teaches counting charged particles using integrators that have undergone back-thinning and are not operating in integration mode, it is entirely counterintuitive to instead count charged particles using integrators that are not back-thinned and always operate in integration mode. In other words, a clever, innovative, and unusual method of counting charged particles can be seen by utilizing the fact that the total energy encountered by any given non-back-thinning integrator unit is likely approximately equal to a quantized multiple or an integer multiple of the beam energy.

[0053] For at least the reasons stated above, the various embodiments described herein can be considered as solutions to or mitigations of various problems or drawbacks (e.g., accumulation effects) that plague existing charged particle counting techniques. Therefore, the various embodiments described herein can be seen as concrete and tangible technical improvements in the field of charged particle microscopy. Consequently, the various embodiments described herein undoubtedly qualify as useful and practical applications for computers.

[0054] Furthermore, the various embodiments described herein can control real-world physical devices based on the disclosed teachings. For example, the various embodiments described herein can electronically enable, disable, or otherwise drive real-world hardware (e.g., ion beam emitter, ion focusing lens, current-carrying valve / pump) of real-world charged particle microscopes (e.g., SEM, TEM, dual-beam microscope, EELM).

[0055] Figure 1 shows an example, non-limiting block diagram of a scientific instrument module 102 according to various embodiments described herein.

[0056] In various implementations, the scientific instrument module 102 can be implemented via a loop (e.g., including electrical or optical components), such as a programmable computing device. The logical units of the scientific instrument module 102 can be incorporated into a single computing device or, depending on the situation, distributed across multiple communicating computing devices. This document discusses examples of computing devices that can implement the scientific instrument module 102 individually or in combination with reference to Figure 15, and discusses examples of systems or networks in which the scientific instrument module 102 can be implemented across one or more computing devices with reference to Figure 16.

[0057] Scientific instrument module 102 may include a first logic unit 104 and a second logic unit 106. As used herein, a "logic unit" may include means for performing a set of operations associated with that unit. For example, any logic unit included in scientific instrument module 102 may be implemented by one or more computing devices programmed with instructions to cause one or more processing devices of the computing device to perform the associated set of operations. In one embodiment, a logic element may include one or more non-transitory computer-readable media having instructions thereon, which, when executed by one or more processing devices of one or more computing devices, cause one or more computing devices to perform the associated set of operations. As used herein, a "module" may refer to a collection of one or more logic units that collectively perform functions associated with that module. Different logic elements in a module may take the same form or may take 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 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 logical units depicted in the relevant figures; for example, when a module is to perform a subset of the operations discussed herein with reference to the module, the module may include a subset of the logical units depicted in the relevant figures.

[0058] In various embodiments, a scientific instrument corresponding to scientific instrument module 102 may be present. In various aspects, the scientific instrument can be any suitable computerized device capable of electronically measuring scientifically relevant, clinically relevant, or research-related characteristics, properties, or attributes of an analytical specimen (e.g., a known or unknown mixture, compound, or collection of substances). As a non-limiting example, the scientific instrument could be a SEM. In this case, the scientific instrument can capture images of the analytical specimen to measure or determine its surface morphology, surface material composition, or crystallographic structure. As another non-limiting example, the scientific instrument could be a TEM. In this case, the scientific instrument can capture images of the interior of the analytical specimen to measure or determine the details of its internal structure. As yet another non-limiting example, the scientific instrument could even be a dual-beam microscope. In this case, the scientific instrument can capture images of the analytical specimen in addition to grinding it. As yet another non-limiting example, the scientific instrument could be an EELM. In this case, the scientific instrument can capture spectral images of the analytical specimen, which show the electron energy loss spectrum at the corresponding physical location of the analytical specimen. As a more general, non-limiting example, a scientific instrument can be any suitable type of charged particle microscope (e.g., some types of microscopes can capture images using non-electron ion beams). In various cases, a scientific instrument can be equipped with or fitted with a non-back-side thinning integrating detector.

[0059] In various implementations, the first logic unit 104 can access an energy integration image of the specimen, wherein such an energy integration image can be captured or generated by a non-backside thinning integration detector of a scientific instrument. More specifically, the first logic unit 104 can: cause a charged particle microscope to capture multiple preliminary energy integration images of the specimen; assign a value of zero to pixels in the multiple preliminary energy integration images whose charged particle energy intensity is below a noise threshold, thereby generating multiple rounded energy integration images; or perform pixel-by-pixel summation or averaging on the multiple rounded energy integration images to obtain an energy integration image.

[0060] In various implementations, the second logic unit 106 may count how many charged particle events (e.g., charged particle impacts) are represented by the corresponding pixels of the energy integration image, based on rounding the cumulative charged particle energy intensity indicated by each pixel cluster to the nearest integer multiple of the beam energy of the charged particle microscope. In various aspects, the second logic unit 106 may: identify the corresponding clusters by applying a community detection algorithm to the energy integration image. In various instances, for a first cluster having a first cumulative charged particle energy intensity, the second logic unit 106 may: divide the first cumulative charged particle energy intensity by the beam energy of the charged particle microscope to obtain a quotient; round the quotient to the nearest integer value to obtain a rounded quotient; and determine that the number of charged particle events represented by the first cluster is equal to the rounded quotient. According to several examples, for a first pixel in a first cluster and having a first integral charged particle energy intensity, the second logic unit 106 may: divide the first integral charged particle energy intensity by the first cumulative charged particle energy intensity to obtain a ratio; multiply the ratio by the rounded quotient to obtain a product; and determine that the number of charged particle events represented by the first pixel is equal to the product.

[0061] Therefore, the scientific instrument module 102 can facilitate charged particle counting through a non-backside thinning integrating detector and beam energy quantization.

[0062] Figure 2 is an example, non-limiting flowchart of a computer implementation method 200 according to various embodiments described herein. The operation of the computer implementation method 200 can be used in any suitable environment to perform any suitable operation (e.g., it can be performed by a cooperating...). Figure 15 - Figure 16 (This can be performed or used in conjunction with any of the various modules, computing devices, or graphical user interfaces described.) The operations are shown one by one and in a specific order in Figure 2, but they can be reordered or repeated as needed and as appropriate (e.g., different operations can be performed in parallel if suitable).

[0063] In various aspects, action 202 may include performing a first operation by accessing an energy-integrated image of a specimen scanned by a charged particle microscope equipped with or fitted with a non-back-side thinning integrating detector by a device operatively coupled to the processor. In various cases, the first logic unit 104 may perform or further facilitate action 202.

[0064] In various aspects, action 204 may include, based on the device counting how many charged particle events are represented by the corresponding pixels of the energy integral image, by rounding the accumulated charged particle energy intensity indicated by each pixel cluster to the nearest integer multiple of the beam energy of the charged particle microscope. In various cases, the second logic unit 106 may perform or further facilitate action 204.

[0065] Therefore, the computer implementation method 200 can facilitate charged particle counting through a non-backside thinning integral detector and beam energy quantization.

[0066] Figure 3 shows an example, non-limiting block diagram of a system according to one or more embodiments described herein, which facilitates charged particle counting through a non-backside thinning integral detector and beam energy quantization.

[0067] In various embodiments, a charged particle microscope 302 may be present. In various aspects, the charged particle microscope 302 may be as described above. That is, the charged particle microscope 302 may be any suitable computerized device that can electronically capture any suitable image of any suitable analytical specimen using its constituent hardware (e.g., electron source, anode, condenser, condenser aperture, scanning coil, objective lens, objective aperture, deflector, condenser lens, astigmatism reducer, electron detector, X-ray detector, driveable specimen stage). As a non-limiting example, the charged particle microscope 302 may be any suitable SEM. As another non-limiting example, the charged particle microscope 302 may be any suitable TEM. As yet another non-limiting example, the charged particle microscope 302 may be any suitable dual-beam microscope. As yet another non-limiting example, the charged particle microscope 302 may be any suitable EELM.

[0068] In any case, the charged particle microscope 302 may have, be equipped with, or otherwise have an integrating detector 303. In various aspects, the integrating detector 303 may be any suitable type of charged particle detector, the individual units of which may measure the cumulative (e.g., summation or integration) energy of any charged particle impacting these units. As a non-limiting example, the integrating detector 303 may be any suitable type of Faraday cup detector. As another non-limiting example, the integrating detector 303 may be any suitable type of capacitive detector. As yet another non-limiting example, the integrating detector 303 may be any suitable type of semiconductor detector, such as a positive-intrinsic-negative (PIN) diode. As yet another non-limiting example, the integrating detector 303 may be any suitable type of microchannel planar detector. As yet another non-limiting example, the integrating detector 303 may be any suitable type of charge-coupled device. In various aspects, the integrating detector 303 may be non-backside thinned. That is, the thickness of the integrator 303 can be greater than or equal to the penetration depth of any charged particle emitted by the charged particle microscope 302 into the integrator 303.

[0069] Although not explicitly stated, for any suitable positive integer p >1, The integral detector 303 can have p Individual cells or cells composed of other elements. It should be understood that the cells of the integral detector 303 can exhibit any suitable regular or irregular shape, size, or arrangement. As a non-limiting example, the cells of the integral detector 303 can be uniform squares arranged in a linear matrix layout. As another non-limiting example, the cells of the integral detector 303 can be uniform rectangles arranged in a single row.

[0070] Although not explicitly shown in the accompanying drawings, the charged particle microscope 302 can be electronically integrated with any suitable human-machine interface device, which can be remote or local relative to the charged particle microscope 302. Therefore, users or technicians associated with the charged particle microscope 302 can interact with or separately control it. Some non-limiting examples of the human-machine interface device could be a keyboard, a keypad, a touchscreen, or a voice command system for the charged particle microscope 302.

[0071] Although not explicitly shown in the accompanying drawings, the charged particle microscope 302 may include a plurality of configurable operating settings. In various aspects, each of the plurality of configurable operating settings may be any suitable hardware-related or software-related feature of the charged particle microscope 302 that can guide, influence, or otherwise determine how the charged particle microscope 302 operates, functions, or performs relative to any given analytical specimen and can be selectively controlled, altered, adjusted, or otherwise set by a user or technician (e.g., through interaction with a human-machine interface device of the charged particle microscope 302). As a non-limiting example, any of the plurality of configurable operating settings may be a user-controllable voltage setting (e.g., beam voltage) or current setting (e.g., beam current) that allows a user or technician to selectively control the electrodes of the charged particle microscope 302 to selectively increase or decrease the voltage or current inside or applied by the charged particle microscope 302. As another non-limiting example, any of the configurable operating settings could be a user-controllable temperature setting that allows a user or technician to control the heater (e.g., stage heater, heating coil) or cooler (e.g., cooling fan, heat pump, refrigerator) of the charged particle microscope 302 to selectively raise or lower the temperature inside or applied by the charged particle microscope 302. As yet another non-limiting example, any of the configurable operating settings could be a user-controllable mechanical actuator setting that allows a user or technician to control the mechanical actuators (e.g., electric motor, specimen stage, iris aperture, fluid pump, or syringe) of the charged particle microscope 302 to selectively move the mechanical actuators. As yet another non-limiting example, any of the configurable operating settings could be a user-controllable optical setting that allows a user or technician to control the optical elements (e.g., optical lens, optical deflector) of the charged particle microscope 302 to selectively change the optical properties applied by the charged particle microscope 302 (e.g., focal size or position, astigmatism, defocus). In any case, the beam energy (which can vary with beam voltage or beam current) can be considered as one of the configurable operating settings of the charged particle microscope 302.

[0072] In various instances, the charged particle microscope 302 may be loaded with specimen 304. As a non-limiting example, specimen 304 may be currently positioned, located, or separately fixed to the specimen stage of the charged particle microscope 302, such that specimen 304 is analyzable or scannable by the charged particle microscope 302. In various cases, specimen 304 may be any suitable type of synthetic or naturally occurring sample capable of exhibiting any suitable physical, chemical, compositional, or other properties, attributes, or characteristics. In the case of synthetic specimens, specimen 304 may be fabricated using any suitable microfabrication or nanofabrication technique such as etching, milling, or deposition. As a non-limiting example, specimen 304 may be a sheet taken from a semiconductor substrate or wafer. As another non-limiting example, specimen 304 may be any other suitable integrated circuit element or printed circuit board element. However, in the case of naturally occurring specimens, specimen 304 may be an organic sample or a biological sample (e.g., a tissue sample).

[0073] In various instances, it may be necessary to count charged particles on or relative to specimen 304. In various cases, system 306, which can be electronically integrated with charged particle microscope 302 (e.g., via any suitable wired or wireless electronic connection), can achieve such counting as described herein.

[0074] In various aspects, system 306 may include processor 308 (e.g., computer processing unit, microprocessor) and non-transitory computer-readable storage 310 operatively or communicatively connected or coupled to processor 308. Non-transitory computer-readable storage 310 may store computer-executable instructions, which, once executed by processor 308, cause processor 308 or other components of system 306 (e.g., scanning component 312, quantization component 314) to perform one or more actions. In various embodiments, non-transitory computer-readable storage 310 may store computer-executable components (e.g., scanning component 312, quantization component 314), and processor 308 may execute these computer-executable components.

[0075] In various embodiments, system 306 can access charged particle microscope 302 electronically. That is, system 306 can communicate or otherwise interact with charged particle microscope 302 electronically (e.g., transmit electronic instructions or commands to charged particle microscope, receive electronic data from it). Therefore, any component of system 306 can similarly be able to interact with, communicate with, or otherwise operate charged particle microscope 302.

[0076] In various embodiments, system 306 may include scanning component 312. In various aspects, as described herein, scanning component 312 may access an energy integral image generated by charged particle microscope 302 and corresponding to specimen 304.

[0077] In various implementations, system 306 may include quantization component 314. In various instances, as described herein, quantization component 314 may identify pixel-by-pixel charged particle counts based on an energy integral image and the beam energy used by charged particle microscope 302 to generate the energy integral image.

[0078] It should be noted that, in various instances, scanning component 312 and quantization component 314 can be collectively considered as one or more software components 311 of system 306. In several respects, it should be understood that, for ease of explanation and illustration, one or more software components 311 are generally described herein as comprising two components (e.g., scanning component 312 and quantization component 314). However, one or more software components 311 are not limited to being implemented precisely as these two components in every embodiment. In fact, in some embodiments, the functions described herein for these two components can be combined in any applicable manner so that they are implemented by or by fewer than two components (e.g., in some cases, a single component can perform all the functions described herein with respect to scanning component 312 and quantization component 314). In other embodiments, the functions described herein for these two components can instead be distributed, isolated, split, or dispersed in any applicable manner so that they are implemented by or by more than two components (e.g., two or more components may facilitate functions that can be performed by scanning component 312; two or more components may facilitate functions that can be performed by quantization component 314).

[0079] Figure 4 shows an example, non-limiting block diagram of a system including an energy integral image and beam energy according to one or more embodiments described herein, which facilitates charged particle counting through a non-backside thinning integral detector and beam energy quantization.

[0080] In various embodiments, the scanning component 312 can electronically cause the charged particle microscope 302 to capture or separately generate an energy integral image 402 of the specimen 304 based on the beam energy 404. Various non-limiting aspects are relative to... Figure 5 - Figure 7 Description.

[0081] Figure 5 Figure 7 illustrates an example, non-limiting block diagram 402, of how an energy integral image can be accessed according to one or more embodiments described herein.

[0082] First, consider Figure 5. In various embodiments, the scanning component 312 may electronically instruct or command the charged particle microscope 302 to perform multiple scans of the specimen 304 according to any suitable energy integration imaging protocol. In some aspects, the energy integration imaging protocol may require the configurable operating settings of the charged particle microscope 302 to have or adopt any suitable default value or state. In other aspects, the energy integration imaging protocol may require the configurable operating settings of the charged particle microscope 302 to have or adopt any suitable value or state selected by the user of the charged particle microscope 302 (e.g., via the human-machine interface device of the charged particle microscope 302). In any case, the scanning component 312 may cause the charged particle microscope 302 to perform multiple energy integration scans of the specimen 304. The beam energy setting of the charged particle microscope 302 during or for such multiple scans may be referred to as beam energy 404. As a non-limiting example, beam energy 404 may be 300 keV. In this context, during each of the multiple scans, the charged particle microscope 302 can be considered as irradiating or bombarding the specimen 304 with a beam of charged particles, where each charged particle in the beam can be considered to have an initial energy of 300 keV (for example, in reality, some charged particles in the beam will have an initial energy slightly higher than 300 keV, while other charged particles in the beam will have an initial energy slightly lower than 300 keV; but these varying energies can average to 300 keV).

[0083] In various respects, by performing multiple scans on specimen 304, charged particle microscope 302 can electronically capture, generate, or separately produce multiple preliminary energy integration images 502. In various instances, the multiple preliminary energy integration images 502 can each correspond to (e.g., in a one-to-one manner) multiple scan phases. Assuming for any suitable positive integer... n >1, Multiple scans by n The scan consists of: the first scan of specimen 304 to the second scan of specimen 304. n Sub-scan. In this case, multiple preliminary energy integration images 502 can be obtained from... n Image composition: Preliminary energy integration image 502(1) to preliminary energy integration image 502( n Because the integral detector 303 has p Each unit, therefore, the initial energy integral image 502 of the Duofu can be derived from each of the units. pIt consists of pixels, where each pixel can represent the sum or integral of the energy of any charged particle that struck the corresponding cell during the corresponding scan.

[0084] As a non-limiting example, the preliminary energy integration image 502(1) can be generated, captured, or produced by the charged particle microscope 302 during the first scan period of the specimen 304. As shown, the preliminary energy integration image 502(1) can be generated by... p Composed of pixels: from pixel 502(1)(1) to pixel 502(1)( p In various instances, any scalar value or magnitude indicated by pixel 502(1)(1) can be considered as representing (e.g., directly in eV, or indirectly in any unit) how much summation or integration energy struck the first cell of the integrator detector 303 during the first scan (and thus across the first physical region of the specimen 304). Similarly, pixel 502(1)( p Any scalar value or magnitude indicated can be considered as representing how much summation or integration energy impacted the integrator 303 during the first scan. p Unit (and therefore through specimen 304) p Physical region).

[0085] As another non-restrictive example, it can be seen in the... n During the secondary scan of specimen 304, a preliminary energy integration image 502 is generated, captured, or produced by charged particle microscope 302. n As shown, the preliminary energy integral image 502 ( n ) can be p Composed of 502 pixels: n (1) to pixel 502 ( n ()( p In various instances, pixel 502 ( n (1) Any scalar value or magnitude indicated can be considered as representing (e.g., again, directly in eV, or indirectly in any unit) the first scalar value. n How much summation or integration energy impacts the first cell of the integrator detector 303 during the next scan (and thus through the first physical region of the specimen 304)? Similarly, pixel 502 ( n ()( p Any scalar value or magnitude indicated can be considered as representing how much summation or integration energy impacted the integrator 303 during the first scan. p Unit (and therefore through specimen 304) p Physical region).

[0086] It should be noted that at low doses (e.g., with a beam energy of less than 300 keV), A beam of charged particles containing 60 keV or less; (a beam of charged particles), it is possible that many of the multiple preliminary energy integration images 502 appear to be nothing more than noise, and very few of the multiple preliminary energy integration images 502 have a clear, interpretable, or meaningful signal-to-noise ratio. In various cases, the scanning component 312 can handle such... Figure 6 - This type of noise is shown in Figure 7.

[0087] In various implementations, as shown in Figure 6, the scanning component 312 may perform conditional pixel-by-pixel down-rounding (also known as conditional pixel-by-pixel masking) on ​​multiple preliminary energy integration images 502 based on a noise threshold 602.

[0088] In all respects, the noise threshold 602 can be any suitable scalar whose value or amplitude indicates the maximum energy level known or otherwise determined to be the upper limit of noise relative to the integrator 303 (e.g., directly expressed in eV, or indirectly expressed in any unit as appropriate). In other words, when the cell output of the integrator 303 is greater than the integrated energy value of the noise threshold 602, it can be concluded that the cell has been struck by at least a portion of a charged particle. Conversely, when the cell output of the integrator 303 is less than the integrated energy value of the noise threshold 602, it can be concluded that the cell has not been struck by any portion of any charged particle. That is, any integrated signal recorded by the integrator 303 and below the noise threshold 602 can be considered as the result of pure noise. Conversely, any integrated signal recorded by the integrator 303 and above the noise threshold 602 can be considered as the result of a charged particle impact (also referred to as a charged particle collision or charged particle event).

[0089] In various aspects, the scanning component 312 can electronically perform down-rounding on pixels in the plurality of preliminary energy integration images 502 whose indicated values ​​are below the noise threshold 602. This conditional down-rounding can generate a plurality of down-rounded energy integration images 604. In various instances, the plurality of down-rounded energy integration images 604 can each correspond to (e.g., correspond one-to-one) the plurality of preliminary energy integration images 502. Therefore, since the plurality of preliminary energy integration images 502 can have n A single image, multiple rounded-down energy integral images 604 can also have nImage: Rounded-down energy integral image 604(1) to rounded-down energy integral image 604( n In various cases, each image in the plurality of rounded-down energy integral images 604 can be derived from each corresponding image in the plurality of preliminary energy integral images 502. Therefore, each image in the plurality of rounded-down energy integral images 604 can have the same number of pixels and arrangement as each image in the plurality of preliminary energy integral images 502 (e.g., p ).

[0090] As a non-limiting example, scanning component 312 can convert the initial energy integral image 502(1) into a rounded-down energy integral image 604(1), and therefore the rounded-down energy integral image 604(1) can be obtained from... p Composed of pixels: from pixel 604(1)(1) to pixel 604(1)( p More specifically, scanning component 312 can determine whether pixel 502(1)(1) indicates a summation or integration energy value less than the noise threshold 602. If the summation or integration energy value of pixel 502(1)(1) is greater than the noise threshold 602, scanning component 312 can cause pixel 604(1)(1) to indicate the same summation or integration energy value as pixel 502(1)(1). On the other hand, if the summation or integration energy value of pixel 502(1)(1) is less than the noise threshold 602, scanning component 312 can instead cause pixel 604(1)(1) to indicate a summation or integration energy value of 0. In other words, in response to pixel 502(1)(1) being less than the noise threshold 602, scanning component 312 can round pixel 502(1)(1) down to 0. In a similar manner, scanning component 312 can determine whether pixel 502(1)(1) indicates a summation or integration energy value less than the noise threshold 602. p Does it indicate the summation or integration energy value less than the noise threshold of 602? If pixel 502(1)( p If the summation or integral energy value of ) is greater than the noise threshold 602, then the scanning component 312 can make pixel 604(1)( p Indicator and pixel 502(1)( p The same summation or integral energy value. On the other hand, if pixel 502(1)( p If the summation or integral energy value of ) is less than the noise threshold 602, then the scanning component 312 can instead make pixel 604(1)( p The summation or integration energy value is 0. As mentioned above, this can be seen as a response to pixel 502(1)( pIf the noise threshold is less than 602, then pixel 502(1)( p Round down to 0.

[0091] As another non-limiting example, scanning component 312 can generate a preliminary energy integration image 502 ( n Convert to a floor-rounded energy integral image 604. n Therefore, the energy integral image is rounded down to 604. n ) can be made by p Composed of 604 pixels: n (1) to pixel 604 ( n ()( p More specifically, scanning component 312 can determine pixel 502 (). n (1) Whether to indicate the summation or integral energy value less than the noise threshold of 602. If pixel 502 ( n If the summation or integration energy value of (1) is greater than the noise threshold 602, then the scanning component 312 can make pixel 604 ( n (1) Indicator and pixel 502 ( n (1) The same summation or integral energy value. On the other hand, if pixel 502 ( n If the summation or integration energy value of (1) is less than the noise threshold 602, then the scanning component 312 can instead make pixel 604 ( n (1) Indicates that the summation or integration energy value is 0. In other words, in response to pixel 502 ( n (1) If the noise threshold is less than 602, the scanning component 312 can scan pixel 502 ( n (1) Round down to 0. Similarly, scanning component 312 can determine pixel 502 ( n ()( p Does it indicate the summation or integral energy value less than the noise threshold of 602? If pixel 502 ( n ()( p If the summation or integration energy value of pixel 604 is greater than the noise threshold 602, then the scanning component 312 can make pixel 604 ( n ()( p Indicator and pixel 502 ( n ()( p The same summation or integral energy value. On the other hand, if pixel 502 ( n ()( p If the summation or integration energy value of pixel 604 is less than the noise threshold 602, then the scanning component 312 can instead make pixel 604 ( n()( p The summation or integration energy value is 0. As mentioned above, this can be considered a response to pixel 502 ( n ()( p If the value is less than the noise threshold of 602, then pixel 502 ( n ()( p Round down to 0.

[0092] It is important to note that, due to this conditional pixel-by-pixel rounding, it is possible that (e.g., in low-dose conditions) none of the multiple rounded energy integral images 604 appear to be entirely noise. Conversely, it is possible that a large number of the multiple rounded energy integral images 604 appear completely blank (e.g., black, nothing but zero) and a very small number of the multiple rounded energy integral images 604 may have meaningful, non-zero integral energy values. In other words, due to the conditional pixel-by-pixel rounding described herein, any overwhelming noise that may already exist in the multiple preliminary energy integral images 502 can be considered absent in the multiple rounded energy integral images 604.

[0093] Now, consider Figure 7. In various embodiments, scanning component 312 may aggregate or otherwise combine multiple rounded-down energy integral images 604 to produce an energy integral image 402. As a non-limiting example, energy integral image 402 may be equal to or otherwise based on the total number of pixels of the multiple rounded-down energy integral images 604. In this case, the first pixel of energy integral image 402 may be equal to pixels 604(1)(1) to pixels 604( n The sum of (1), and the first energy integral image 402. p One pixel can be equal to 604 pixels (1)( p ) to pixel 604 ( n ()( p The sum of ) . As another non-limiting example, the energy integral image 402 may be equal to or otherwise based on the pixel-wise average of multiple rounded-down energy integral images 604. In this case, the first pixel of the energy integral image 402 may be equal to pixels 604(1)(1) to pixels 604( n (1) average value, and the first energy integral image 402 p One pixel can be equal to 604 pixels (1)( p ) to pixel 604 ( n ()( pThe average value of ). In any case, due to the conditional pixel-by-pixel rounding based on the noise threshold 602, the energy integration image 402 can be considered unaffected by any overwhelming noise that may be present in the multiple preliminary energy integration images 502.

[0094] Figure 8 shows an example, non-limiting block diagram of a system comprising multiple pixel-by-pixel charged particle counts according to one or more embodiments described herein, the system facilitating charged particle counting via a non-back-side thinning integral detector and beam energy quantization.

[0095] In various implementations, the quantization component 314 can electronically calculate, compute, or separately estimate a plurality of pixel-by-pixel charged particle counts 802 based on the energy integral image 402 and the beam energy 404. Non-limiting aspects are described with respect to Figures 9-12.

[0096] Figure 9 Figure 12 shows an example, non-limiting block diagram illustrating how multiple pixel-by-pixel charged particle counts 802 can be determined according to one or more embodiments described herein.

[0097] First, refer to Figure 9. In various implementations, the quantization component 314 can apply any suitable community detection algorithm to the pixels of the energy integral image 402. As a non-limiting example, the community detection algorithm could be the Louvain algorithm. As another non-limiting example, the community detection algorithm could be the Girvan-Newman algorithm. As yet another non-limiting example, the community detection algorithm could be the Kernighan-Lin algorithm. As yet another non-limiting example, the community detection algorithm could be a spectral clustering algorithm. As yet another non-limiting example, the community detection algorithm could be an infomap algorithm. As yet another non-limiting example, the community detection algorithm could be a label propagation algorithm. As yet another non-limiting example, the community detection algorithm could be an edge betweenness centrality algorithm. As yet another non-limiting example, the community detection algorithm could be a random block model algorithm. As yet another non-limiting example, the community detection algorithm could be a faction filtering algorithm. As yet another non-limiting example, the community detection algorithm could be a random walk algorithm. In any case, applying the community detection algorithm to the energy integral image 402 can result in the identification of multiple pixel clusters 902.

[0098] In various implementation schemes, for any suitable positive integer m < p Multiple pixel clusters 902 can include m Cluster: Pixel cluster 902(1) to pixel cluster 902(p In various respects, each of the plurality of pixel clusters 902 may be a group of pixels in the energy integration image 402, the pixels being adjacent, contiguous, or separately spatially continuous and each indicating a non-zero summation or integration energy value. As a non-limiting example, for any suitable positive integer... q 1. Pixel cluster 902(1) can be derived from... q One total pixel consists of: non-zero pixel 902(1)(1) to non-zero pixel 902(1)( q 1). As its name suggests, each pixel within pixel cluster 902(1) can have or exhibit a non-zero summation or integral energy value or intensity. Furthermore, this type of... q A single pixel can form a spatially contiguous set, group, or region within the energy integral image 402. In other words, each pixel within a pixel cluster 902(1) can be physically adjacent to, physically adjacent to, or otherwise a physical neighbor of at least one other pixel within the pixel cluster 902(1). In some cases, no pixel within a pixel cluster 902(1) can be physically adjacent to, physically adjacent to, or otherwise a physical neighbor of any pixel belonging to any other pixel cluster among multiple pixel clusters 902. As another non-limiting example, for any suitable positive integer... q m Pixel cluster 902 ( m ) can be made by q m Composed of a total of 902 non-zero pixels. m (1) to non-zero pixel 902 ( m ()( q m As mentioned above, pixel cluster 902 ( m Each pixel within a given area can have or exhibit a non-zero summation or integral energy value or intensity, and this type of... q m Each pixel can form a spatially continuous set, group, or region within the energy integral image 402 (e.g., pixel cluster 902). m Internally, pixels can be physically adjacent, physically connected, or separate from pixel cluster 902. m At least one physical neighbor of another pixel within the pixel cluster 902. m No pixel can be physically adjacent to, physically adjacent to, or separately be a physical neighbor of any pixel belonging to any other pixel cluster in multiple pixel clusters 902.

[0099] Now, consider Figure 10, which for any suitable positive integer... Displaying pixel clusters 902 from multiple pixel clusters 902 (r As shown, for any suitable positive integer... q r Pixel cluster 902 ( r ) can be made by q r Composed of a total of 902 non-zero pixels. r (1) to non-zero pixel 902 ( r ()( q r ).

[0100] In all aspects, pixel cluster 902 ( r Pixels of type 902 can be considered as a cluster that collectively indicates the energy intensity of the integrated charged particle, 1002. As a non-limiting example, non-zero pixels 902 (… r (1) Any value or magnitude indicated can be referred to as the integral charged particle energy intensity 1002(1). As another non-limiting example, non-zero pixel 902( r ()( q r Any value or magnitude indicated can be called the integral charged particle energy intensity 1002 ( q r In all respects, the integrated charged particle energy intensity 1002(1) to the integrated charged particle energy intensity 1002( q r They can be collectively regarded as a cluster of integral charged particles with an energy intensity of 1002.

[0101] In various instances, the quantization component 314 can sum or add together clusters of integrated charged particle energy intensities 1002. In various respects, the scalar resulting from such summation or addition can be referred to as the cumulative integrated charged particle energy intensities 1004.

[0102] Next, consider Figure 11. In various embodiments, the quantization component 314 can divide the accumulated integrated charged particle energy intensity 1004 by the beam energy 404. It should be understood, or further understood, that for the convenience of this division, the beam energy 404 can be expressed or formatted in the same units as the accumulated integrated charged particle energy intensity 1004. As a non-limiting example, suppose the cells of the integrator detector 303 measure energy directly in eV. In such a case, the beam energy 404 can also be expressed in eV. As another non-limiting example, suppose the cells of the integrator detector 303 instead measure energy indirectly using arbitrary units associated with or mapped to eV. In some of these cases, the beam energy 404 can also be expressed in such arbitrary units. However, in other cases, the beam energy 404 can be expressed in eV, and the accumulated integrated charged particle energy intensity 1004 can be converted or rewritten in eV.

[0103] In any case, dividing the cumulative integrated charged particle energy intensity 1004 by the beam energy 404 yields a quotient 1102. In various instances, the quantization component 314 can numerically round the quotient 1102 to the nearest integer. In various instances, this nearest integer can be referred to as the cumulative charged particle count 1104. In other words, the cumulative charged particle count 1104 can be equal to or based on the following expression: in i For the summation index, where E i Indicates pixel cluster 902 ( r ) i The integral charged particle energy intensity of each pixel, where B The beam energy is 404. In any case, since the integrator 303 can be non-backside thinned, the cumulative charged particle count 1104 can be an integer representing the total number of charged particles that have struck the integrator 303 in relation to the pixel cluster 902. r Any unit that corresponds to (for example, where its integral energy value is stored).

[0104] It should be noted that even if the impact is with pixel cluster 902 ( r The cumulative charged particle count 1104 can also be calculated if two or more charged particles in the corresponding unit appear simultaneously, appear rapidly and consecutively, or accumulate within the same sampling iteration. That is, the accuracy of the cumulative charged particle count 1104 can be considered unaffected by the accumulation effect.

[0105] Now, consider Figure 12. In various embodiments, the quantization component 314 can operate on or compute a pixel-by-pixel charged particle count set 1202 based on the integrated charged particle energy intensity cluster 1002, the cumulative integrated charged particle energy intensity 1004, and the cumulative charged particle count 1104. Specifically, for each given integrated charged particle energy intensity in the integrated charged particle energy intensity cluster 1002, the quantization component 314 can: divide the given integrated charged particle energy intensity by the cumulative integrated charged particle energy intensity 1004 to obtain a ratio; and multiply the ratio by the cumulative charged particle count 1104. The result of this multiplication can be one of the corresponding values ​​in the pixel-by-pixel charged particle count set 1202.

[0106] As a non-limiting example, consider the integrated charged particle energy intensity 1002(1). In various aspects, the quantization component 314 can calculate a first ratio by dividing the integrated charged particle energy intensity 1002(1) by the cumulative integrated charged particle energy intensity 1004. Furthermore, in various instances, the quantization component 314 can multiply this first ratio by the cumulative charged particle count 1104 to obtain a first product. In various instances, this first product can be referred to as the per-pixel charged particle count 1202(1). In various aspects, the per-pixel charged particle count 1202(1) can be a scalar whose value or magnitude indicates how many charged particles have struck the non-zero pixel 902(1) in the integral detector 303. r (1) Any single cell corresponding to (e.g., storing its integrated energy measurement therein). It should be noted that the per-pixel charged particle count 1202(1) may not be an integer, because any charged particle may cross two or more cells at the moment it hits the integral detector 303 (e.g., it is possible that 30% of the charged particles hit the first cell and the remaining 70% of the charged particles hit the adjacent cells).

[0107] As another non-limiting example, consider the integral charged particle energy intensity 1002 ( q r In various aspects, the quantization component 314 can be achieved by integrating the charged particle energy intensity 1002 (…). q r Divide by the cumulative integral charged particle energy intensity 1004 to calculate the first... q r Ratio. Furthermore, in various instances, the quantization component 314 can convert this first... q r Multiplying the ratio by the cumulative charged particle count of 1104, we get the first...q r Product. In various cases, this product... q r The product can be called the per-pixel charged particle count 1202 ( q r In all aspects, the per-pixel charged particle count is 1202 (). q r It can be a scalar, whose value or magnitude indicates how many charged particles have collided with the integrator detector 303 and the non-zero pixel 902. r ()( q r This corresponds to any single cell (e.g., where its integrated energy measurement is stored). As described above, the per-pixel charged particle count 1202 ( q r It may not be an integer, because any charged particle may cross two or more units at the instant it impacts the integrator detector 303.

[0108] In all aspects, the per-pixel charged particle count 1202(1) to the per-pixel charged particle count 1202( q r These can be collectively considered to form a pixel-by-pixel charged particle count set 1202. In various respects, this pixel-by-pixel charged particle count set 1202 can be considered to belong to or at least partially constitute a plurality of pixel-by-pixel charged particle counts 802. It should be noted that since the cumulative charged particle count 1104 may not be affected by the stacking effect, each pixel charged particle count set 1202 can also be considered to be unaffected by the stacking effect.

[0109] In various implementations, the quantization component 314 can be repeatedly applied to each of the multiple pixel clusters 902. Figure 9 - The above operations are described in Figure 12. By performing such operations on each of the multiple pixel clusters 902, the quantization component 314 can fully fill, fully compute, or fully calculate the multiple pixel-by-pixel charged particle counts 802. In other words, by performing such operations on each of the multiple pixel clusters 902, the quantization component 314 can determine how many charged particle impacts or events are represented by each non-zero pixel of the energy integral image 402 (e.g., a pixel rounded down to 0 can be considered to represent 0 charged particle impacts or events).

[0110] In various embodiments, the quantization component 314 can electronically perform any suitable action based on a plurality of pixel-by-pixel charged particle counts 1202. As a non-limiting example, the quantization component 314 can electronically present any of the plurality of pixel-by-pixel charged particle counts 1202 on any suitable computer screen or electronic display. As another non-limiting example, the quantization component 314 can electronically transfer any count value of the plurality of pixel-by-pixel charged particle counts 1202 to any other suitable computing device. As yet another non-limiting example, the quantization component 314 can electronically apply any suitable downstream or subsequent analytical technique to any of the plurality of pixel-by-pixel charged particle counts 1202 (e.g., there may be processing techniques configured to receive charged particle counts as input and produce compositional or chemical predictions about these charged particle counts from any specimen therefrom as output).

[0111] Figure 13 Figure 14 shows experimental results related to an exemplary non-limiting implementation of one or more of the embodiments described herein.

[0112] To implement the various embodiments described herein, a beam energy 404 should be obtained. As mentioned above, when directly expressed at least in eV, the beam energy 404 can be a controllable, configurable, or alternatively optional setting or parameter of the charged particle microscope 302. However, it is often the case that the integrating detector 303 is configured to output measurements in arbitrary units (e.g., detector number (DN)) rather than in eV units. In these cases, it may not be initially known how these arbitrary units map to or relate to eV units. Therefore, in such cases, the various embodiments described herein can be achieved by first generating a calibration curve indicating which particular arbitrary unit measurement corresponds to any eV value to which the beam energy 404 is set, and subsequently quantized as described above. Non-limiting aspects of this calibration are described with respect to Figures 13-14.

[0113] Figure 13 depicts calibration curve 1300. Calibration curve 1300 is generated by irradiating an integrating detector 303 with beam energy 404 during a large-scale scan before the charged particle microscope 302 is loaded with specimen 304 (e.g., so that the integrating detector 303 ultimately records tens or hundreds of thousands of charged particle events). For each energy integration image generated during such a large-scale scan, the pixels of the energy integration image are clustered (as described above), and the cumulative energy is recorded in arbitrary units for each cluster. These cumulative energy amounts are represented by the horizontal axis of calibration curve 1300. The vertical axis of calibration curve 1300 represents how many clusters acquired during this large-scale scan exhibit the corresponding cumulative energy value. For example, it can be seen that approximately 35,000 clusters acquired during this large-scale scan exhibit a cumulative energy value slightly higher than 500 DN. As another example, nearly 115,000 clusters acquired during this large-scale scan exhibit a cumulative energy value of approximately 3,700 DN. As yet another example, the nearly 10,000 clusters obtained during this large-scale scan exhibit a cumulative energy value of approximately 7400 DN. Note the significant peak at 3700 DN. Note the subsequent peak at 7400 DN. Given that the beam energy 404 is consistent with a low dose (e.g., below any suitable threshold), it can be expected that there will be far more individual charged particle events (e.g., single-particle impacts) than accumulation events. Due to this expectation and because 7400 DN is a second multiple of 3700 DN, it can be inferred that individual charged particle events are associated with 3700 DN. In other words, it can be concluded that when a single charged particle with beam energy 404 impacts a cell of the integrator detector 303, it deposits a certain amount of energy into that cell, causing the cell to output approximately 3700 DN. Therefore, two stacks of charged particles would correspond to approximately 7400 DN (e.g., 2 × 3700), three stacks of charged particles would correspond to approximately 11100 DN (e.g., 3 × 3700), and so on. It should be noted that the portion of the calibration curve preceding the peak at 3700 DN can be considered primarily a result of noise and can therefore be discarded or ignored. Thus, once the beam energy 404 is determined to be correlated with or mapped to the detector output 3700 DN (in this non-limiting example), the detector output 3700 DN can be used for the aforementioned multiplication and division operations to count how many charged particle impacts or events are represented by pixels of any desired energy integration image (e.g., by pixels of energy integration image 402).

[0114] Figure 14 depicts Chart 1402 and Chart 1404.

[0115] Figure 1402 is a non-limiting example of a single acquisition frame captured by the charged particle microscope 302. The horizontal axis of Figure 1402 represents the pixel identifier or pixel index, while the vertical axis of Figure 1402 represents the intensity indicated by the corresponding pixel in DN. According to the non-limiting example shown in Figure 1402, the 650th pixel in any energy integration image represented by Figure 1402 exhibits an energy level of approximately 7400 DN, while the 950th pixel of the same energy integration image exhibits an energy level of approximately 3700 D.N. Therefore, it can be concluded that the 650th pixel was struck by two charged particles, while the 950th pixel was struck by only a single charged particle. As mentioned above, energy dispersion filters can sometimes be used in such a way that only particles with a specific, uniform, or constant energy loss are allowed to strike the integrator detector 303. Therefore, if an energy dispersion filter is applied to any energy integral image corresponding to Figure 1402, then the two particles hitting the 650th pixel and the one hitting the 950th pixel can be considered to have the same energy loss as each other (e.g., because the energy dispersion filter allows or does not reject all particles with any energy loss). In this case, any running particle count corresponding to this energy loss can be incremented by 3 (e.g., by 2 for the 650th pixel and by 1 for the 950th pixel). It should be noted that this increment can occur regardless of whether the two particles hitting the 650th pixel are stacked (e.g., whether they appear within the same sampling iteration). In this way, an accurate particle count unaffected by stacking effects can be calculated for each desired energy loss level or energy loss interval.

[0116] In various aspects, this particle count divided by energy loss can be collectively visualized in separate charts, such as Chart 1404. Specifically, the horizontal axis of Chart 1404 represents the energy loss level or energy loss range, while the vertical axis of Chart 1404 represents the particle count at the corresponding energy loss level or range. In a non-limiting example of Chart 1404, the specific charged particle being counted is an electron. In some cases, energy dispersion filters can be used to direct or aim charged particles with specific energy losses at corresponding pixels (e.g., so that a first pixel can be hit only by particles with a first energy loss; a second pixel can be hit only by particles with a second energy loss; and a third pixel can be hit only by particles with a third energy loss). In such cases, each point in Chart 1404 can be considered as being filled from or by any data integrated or accumulated over the corresponding pixel. Figure 1404 shows a non-limiting example in which one peak or point can be considered as derived from data captured based on the 650th pixel and another peak or point can be considered as derived from data captured based on the 950th pixel.

[0117] These experimental results help demonstrate that the various implementation schemes described herein allow for more accurate or reliable counting of charged particles than existing techniques. Therefore, the various implementation schemes described herein undoubtedly constitute concrete and tangible technical improvements in the field of charged particle microscopy.

[0118] In various respects, the various embodiments described herein can be implemented in dual-purpose charged particle microscopy scenarios. As a non-limiting example, when the beam energy 404 meets the high dose (e.g., above any suitable threshold), system 306 can promote charged particle counting in a pulse counting manner, while when the beam energy 404 meets the low dose (e.g., below any suitable threshold), system 306 can instead promote charged particle counting in an integral and quantized manner as described herein.

[0119] In various instances, machine learning algorithms or models can be implemented in any suitable manner to facilitate any suitable aspect described herein. To facilitate some of the aforementioned machine learning aspects of various implementations, consider the following discussion of artificial intelligence (AI). The various implementations described herein may employ artificial intelligence to facilitate the automation of one or more features or functions. These components may employ various AI-based schemes to perform the various implementations / examples disclosed herein. To provide or assist the numerous determinations described herein (e.g., determining, ascertaining, inferring, calculating, predicting, estimating, deducing, predicting, detecting, computing), the components described herein may examine the entirety or a subset of data authorized to their access and may provide inference about the system or environment or determine its state from a set of observations captured, such as by events or data. For example, determining a probability distribution that can be used to identify a specific context or action, or to generate states. Determination can be probabilistic; that is, calculating the probability distribution of states of interest based on considerations of data and events. Determination can also refer to techniques used to compose higher-level events from a set of events or data.

[0120] Such determinations can lead to the construction of new events or actions from a set of observed events or stored event data, regardless of whether the events are closely related in time or whether the events and data come from one or more event and data sources. The components disclosed herein can employ various classification schemes or systems (e.g., support vector machines, neural networks, expert systems, Bayesian belief networks, fuzzy logic, data fusion engines, etc.) involving the execution of automated or determined actions related to the claimed subject matter. Therefore, classification schemes or systems can be used to automatically learn and execute multiple functions, actions, or determinations.

[0121] The classifier can take the input attribute vector z = (z1, z2, z3, z4, ... z n This maps the input to the confidence level of belonging to a certain category, such as based on f(z) = confidence ( classThis classification can employ probabilistic analysis or statistical analysis (e.g., considering utility and cost in the analysis) to determine the actions that should be automated. Support Vector Machines (SVMs) are a possible example of a classifier that can be used. SVMs operate by finding a hypersurface in the space of possible inputs, where the hypersurface attempts to separate triggering conditions from non-triggering events. Intuitively, this makes the classification correct for test data that is close to but not exactly the same as the training data. Other directed and non-directed model classification methods include, for example, Naive Bayes, Bayesian networks, decision trees, neural networks, fuzzy logic models, or probabilistic classification models that provide different independent patterns, any of which can be employed. Classification as used in this paper also includes statistical regression for developing prioritization models.

[0122] To provide additional context for the various implementations described herein, Figure 15 and the following discussion are intended to briefly and generally describe suitable computing environments 1500 in which the various implementations described herein can be implemented. Although the implementations have been described above in the general context of computer-executable instructions that can run on one or more computers, those skilled in the art will recognize that these implementations can also be implemented in combination with other program modules or as a combination of hardware and software.

[0123] Typically, program modules include routines, programs, components, data structures, etc., that perform specific tasks or implement specific abstract data types. Furthermore, those skilled in the art will understand that the method of this invention can be implemented in conjunction 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, etc., each of which can be operatively coupled to one or more related devices.

[0124] The implementation schemes shown in this paper can also be implemented in a distributed computing environment, where certain tasks are performed by remote processing devices linked via a communication network. In a distributed computing environment, program modules can reside on both local and remote memory storage devices.

[0125] Computing devices typically include various media, which may include computer-readable storage media, machine-readable storage media, or communication media, these terms being used interchangeably herein. A computer-readable storage media or a machine-readable storage media can be any available storage medium accessible to a computer, including volatile and non-volatile media, removable and non-removable media. By way of example and not limitation, a computer-readable storage media or a machine-readable storage media can be implemented in conjunction with any information storage method or technology, such as computer-readable or machine-readable instructions, program modules, structured data, or unstructured data.

[0126] Computer-readable storage media may include, but are not limited to, random access memory (RAM), read-only memory (ROM), electrically erasable read-only memory (EEPROM), flash memory or other memory technologies, read-only optical disc storage (CD-ROM), digital versatile optical disc (DVD), Blu-ray disc (BD) or other optical disc storage, cassette tape, magnetic tape, disk storage or other magnetic storage devices, solid-state drives or other solid-state storage devices, or other tangible and / or non-transitory media that can be used to store desired information. In this regard, the terms “tangible” or “non-transitory” as used herein for storage, memory, or computer-readable media shall be understood to exclude only the propagation of transient signals themselves as a modifier, and shall not waive the rights to all standard storage devices, memory, or computer-readable media that do not merely propagate transient signals themselves.

[0127] Computer-readable storage media can be accessed by one or more local or remote computing devices, for example, by access requests, queries or other data retrieval protocols, to perform various operations on the information stored in the media.

[0128] Communication media typically embody computer-readable instructions, data structures, program modules, or other structured or unstructured data in data signals (such as modulated data signals, e.g., carrier waves or other transmission mechanisms), and include any medium for information transmission or delivery. The term "modulated data signal" or a signal refers to a signal whose one or more characteristics are set or altered in such a way that information is encoded in one or more signals. By way of example and not limitation, communication media include wired media (such as wired networks or direct wired connections) and wireless media (such as acoustic, RF, infrared, and other wireless media).

[0129] Referring again to Figure 15, an exemplary environment 1500 for implementing various embodiments of the aspects described herein includes a computer 1502, which includes a processing unit 1504, a system memory 1506, and a system bus 1508. The system bus 1508 couples system components, including but not limited to the system memory 1506, to the processing unit 1504. The processing unit 1504 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 1504.

[0130] System bus 1508 can be any of a variety of bus architectures, which can be further interconnected with memory buses (with or without memory controllers), peripheral buses, and local buses using any of a variety of commercially available bus architectures. System memory 1506 includes ROM 1510 and RAM 1512. The Basic Input / Output System (BIOS) can be stored in non-volatile memory, such as ROM, erasable programmable read-only memory (EPROM), or EEPROM, where the BIOS contains basic routines that facilitate the transfer of information between internal components of computer 1502, such as during startup. RAM 1512 may also include high-speed RAM, such as static RAM for caching data.

[0131] Computer 1502 also includes an internal hard disk drive (HDD) 1514 (e.g., EIDE, SATA), one or more external storage devices 1516 (e.g., floppy disk drive (FDD) 1516, memory stick or flash drive reader, memory card reader, etc.), and a drive 1520, such as a solid-state drive or optical disc drive, which can read from or write to disk 1522 (e.g., CD-ROM, DVD, BD, etc.). Alternatively, in cases involving solid-state drives, disk 1522 may not be included unless provided separately. Although the internal HDD 1514 is shown as being located inside computer 1502, the internal HDD 1514 may also be configured for external use in a suitable chassis (not shown). Furthermore, although not shown in environment 1500, solid-state drives (SSDs) other than or alternative to HDD 1514 may be used. HDD 1514, external storage device 1516, and drive 1520 can be connected to system bus 1508 via HDD interface 1524, external storage interface 1526, and drive interface 1528, respectively. Interface 1524 for the external drive implementation may include at least one or both of Universal Serial Bus (USB) and Institute of Electrical and Electronics Engineers (IEEE) 1394 interface technologies. Other external drive connection technologies are also within the scope of the embodiments described herein.

[0132] The drive and its associated computer-readable storage medium provide non-volatile storage of data, data structures, computer-executable instructions, etc. For computer 1502, the drive and storage medium support storing any data in a suitable digital format. Although the description of computer-readable storage media above refers to corresponding types of storage devices, those skilled in the art will understand that other types of computer-readable storage media, whether existing or developed in the future, may also be used in the example operating environment, and further, any such storage medium may contain computer-executable instructions for performing the methods described herein.

[0133] The driver and RAM 1512 can store multiple program modules, including an operating system 1530, one or more application programs 1532, other program modules 1534, and program data 1536. All or part of the operating system, application programs, modules, or data can also be cached in RAM 1512. The systems and methods described herein can be implemented using various commercially available operating systems or combinations of operating systems.

[0134] Computer 1502 may optionally include emulation technology. For example, a virtual machine monitor (not shown) or other intermediary may virtualize the hardware environment of operating system 1530, and the virtual hardware may optionally be different from the hardware shown in Figure 15. In such an embodiment, operating system 1530 may include one of a plurality of virtual machines (VMs) hosted on computer 1502. Furthermore, operating system 1530 may provide a runtime environment for application 1532, such as the Java Runtime Environment or the .NET Framework. A runtime environment is a consistent execution environment that allows application 1532 to run on any operating system that includes that runtime environment. Similarly, operating system 1530 may support containers, and application 1532 may be in the form of a container, which is a lightweight, standalone, executable software package that includes, for example, application code, runtime, system tools, system libraries, and settings.

[0135] Furthermore, computer 1502 can be equipped with a security module, such as a Trusted Processing Module (TPM). For example, with the help of a TPM, the boot component hashes the next boot component over time and waits for the result to match a security value before loading the next boot component. This process can occur at any layer of the computer 1502's code execution stack, for example, at the application execution level or the operating system (OS) kernel level, thus achieving security at any level of code execution.

[0136] Users can input commands and information into computer 1502 through one or more wired / wireless input devices, such as keyboard 1538, touchscreen 1540, and pointing devices (such as mouse 1542). Other input devices (not shown) may include microphones, infrared (IR) remote controls, radio frequency (RF) remote controls or other remote controls, joysticks, virtual reality controllers or virtual reality headsets, game controllers, styluses, image input devices (e.g., cameras), gesture sensor input devices, visual motion sensor input devices, emotion or face detection devices, biometric input devices (e.g., fingerprint or iris scanners), etc. These and other input devices are typically connected to processing unit 1504 via input device interface 1544, which can be coupled to system bus 1508, but may also be connected via other interfaces, such as parallel ports, IEEE 1394 serial ports, game ports, USB ports, IR interfaces, BLUETOOTH® interfaces, etc.

[0137] Monitor 1546 or other types of display devices can also be connected to system bus 1508 via an interface (such as video adapter 1548). In addition to monitor 1546, computers typically include other peripheral output devices (not shown), such as speakers, printers, etc.

[0138] Computer 1502 can operate in a networked environment that uses logical connections to one or more remote computers (such as remote computer 1550) via wired or wireless communication. Remote computer 1550 can be a workstation, server computer, router, personal computer, laptop computer, microprocessor-based entertainment device, peer-to-peer device, or other common network node, and typically includes many or all of the elements described relative to computer 1502; however, for simplicity, only memory / storage device 1552 is shown. The logical connections shown include wired / wireless connections to a local area network (LAN) 1554 or a larger network (e.g., a wide area network (WAN) 1556). Such LAN and WAN networking environments are common in offices and companies and are advantageous for enterprise-wide computer networks (such as intranets) that can all connect to global communication networks (such as the Internet).

[0139] When used in a LAN networking environment, computer 1502 can connect to local network 1554 via a wired or wireless communication network interface or adapter 1558. Adapter 1558 facilitates wired or wireless communication with LAN 1554, which may also include a wireless access point (AP) configured thereon to communicate wirelessly with adapter 1558.

[0140] When used in a WAN networking environment, computer 1502 may include modem 1560, or may otherwise be connected to a communication server on WAN 1556 to establish communication via WAN 1556 (e.g., via the Internet). Modem 1560 may be built-in or external, and may be a wired or wireless device, connected to system bus 1508 via input device interface 1544. In a networking environment, program modules described with respect to computer 1502 or parts thereof may be stored in remote memory / storage device 1552. It should be understood that the network connection shown is an example, and other methods of establishing inter-computer communication links may be used.

[0141] When used in a LAN or WAN network environment, computer 1502 can access cloud storage systems or other network-based storage systems, such as, but not limited to, network virtual machines that provide storage or processing of one or more aspects of information, in addition to external storage device 1516 as described above or as an alternative to it. Typically, the connection between computer 1502 and the cloud storage system can be established via LAN 1554 or WAN 1556, for example, via adapter 1558 or modem 1560, respectively. Once computer 1502 is connected to the relevant cloud storage system, external storage interface 1526 can manage the storage provided by the cloud storage system in the same way as other types of external storage, with the assistance of adapter 1558 or modem 1560. For example, external storage interface 1526 can be configured to provide access to cloud storage sources as if these sources were physically connected to computer 1502.

[0142] Computer 1502 is operable to communicate with any wireless device or entity operable in a wireless communication manner (e.g., printer, scanner, desktop or portable computer, portable data assistant, communications satellite, any device or location associated with a wirelessly detectable tag (e.g., kiosks, newsstands, store shelves, etc.) and telephone). This can include Wi-Fi and BLUETOOTH® wireless technologies. Therefore, communication can be a predefined structure like a traditional network, or simply ad hoc communication between at least two devices.

[0143] Figure 16 is a schematic block diagram of a sample computing environment 1600 that can interact with the disclosed subject matter. The sample computing environment 1600 includes one or more clients 1610. Clients 1610 can be hardware or software (e.g., threads, processes, computing devices). The sample computing environment 1600 also includes one or more servers 1630. Servers 1630 can also be hardware or software (e.g., threads, processes, computing devices). For example, server 1630 can accommodate threads to perform transformations by employing one or more embodiments as described herein. One possible communication between client 1610 and server 1630 can be in the form of data packets suitable for transmission between two or more computer processes. The sample computing environment 1600 includes a communication framework 1650 that can be used to facilitate communication between client 1610 and server 1630. Client 1610 is operatively connected to one or more client data storage devices 1620 that can be used to store local information on client 1610. Similarly, server 1630 is operatively connected to one or more server data storage devices 1640 that can be used to store local information on server 1630.

[0144] Examples, non-limiting apparatuses for implementing the various embodiments described herein are shown in Figure 17. Figure 17 illustrates a non-limiting embodiment of a dual-beam system 1710 having a vertically mounted scanning electron microscope (SEM) tube and a focused ion beam (FIB) tube mounted at an angle of approximately 52 degrees from the vertical direction. Such dual-beam systems are commercially available, for example, from the assignee of this application, FEI Company, Hillsboro, Oregon. Although Figure 17 Embodiments of suitable microscope hardware that can be used to implement the various embodiments described herein are shown, but it should be understood that such microscope hardware is non-limiting. In other words, the various embodiments described herein can be implemented in combination with any other suitable type of microscope hardware. The dual-beam system 1710 is a non-limiting embodiment of the charged particle microscope 302 or any other scientific device described above.

[0145] A scanning electron microscope 1741, along with a power supply and control unit 1745, can be provided with a dual-beam system 1710. An electron beam 1743 can be emitted from the cathode 1752 by applying a voltage between the cathode 1752 and the anode 1754. The electron beam 1743 can be focused into a fine point by a condenser lens 1756 and an objective lens 1758. The electron beam 1743 can be scanned in two dimensions on any suitable specimen using a deflection coil 1760. The operation of the condenser lens 1756, the objective lens 1758, or the deflection coil 1760 can be controlled by the power supply and control unit 1745.

[0146] An electron beam 1743 can be focused onto a substrate 1722 mounted on a movable XY stage 1725 within the lower chamber 1726. When electrons in the electron beam 1743 strike the substrate 1722, secondary electrons are emitted. These secondary electrons can be detected by a secondary electron detector 1740, as discussed below. A scanning transmission electron microscope (STEM) detector 1762, located below the transmission electron microscope (TEM) sample holder 1724 and the movable XY stage 1725, collects electrons transmitted through the sample mounted on the TEM sample holder 1724, as discussed above.

[0147] The dual-beam system 1710 may also include a focused ion beam (FIB) system 1711, which includes a vacuum chamber having an upper neck portion 1712, within which an ion source 1714 and a focusing tube 1716 including extraction electrodes and an electrostatic optical system are located. The axis of the focusing tube 1716 may be tilted 52 degrees (or any other suitable angular displacement) from the axis of the electron tube. The ion tube 1712 may include the ion source 1714, extraction electrodes 1715, focusing element 1717, deflection element 1720, and focused ion beam 1718. The focused ion beam 1718 may pass from the ion source 1714 through the focusing column 1716 and between the electrostatic deflection devices schematically shown at number 1720 toward a substrate 1722, which may include, for example, semiconductor devices located on a movable XY stage 1725 within the lower chamber 1726.

[0148] The movable XY stage 1725 is movable horizontally (along the X and Y axes) and vertically (along the Z axis). The movable XY stage 1725 can tilt approximately sixty (60) degrees and rotate about the Z axis. In some embodiments, a separate TEM sample stage (not shown) may be used. Such a TEM sample stage is movable along the X, Y, and Z axes. Door 1761 can be opened for inserting substrate 1722 onto the movable XY stage 1725, or, if used, for servicing the internal gas supply reservoir. Door 1761 can be interlocked so that it cannot be opened if the system is under vacuum.

[0149] An ion pump 1768 can be used to evacuate the neck section 1712. The chamber 1726 can be evacuated under the control of a turbomolecular and mechanical pumping system 1730 and a vacuum controller 1732. This vacuum system can provide a vacuum level of approximately 1 x 10⁻⁶ within the chamber 1726. -7 With 5 x 10 -4 The vacuum between the chambers. If an etching assist gas, etching delay gas, or deposition precursor gas is used, the chamber background pressure can be increased, typically to about 1 x 10⁻⁶. -5 Entrust.

[0150] A high-voltage power supply 1734 can provide an appropriate accelerating voltage to the electrodes in the focusing cylinder 1716 to excite the focused ion beam 1718. When the ion beam strikes the substrate 1722, material may be sputtered (i.e., physically ejected) from the sample. Alternatively, the focused ion beam 1718 can break down the precursor gas to deposit material.

[0151] A high-voltage power supply 1734 can be connected to an ion source 1714 (which may be a liquid metal ion source) and appropriate electrodes in the ion beam focusing tube 1716 to form an ion beam 1718 of approximately 1 keV to 60 keV and guide it to the sample. A deflection controller and amplifier 1736, operating according to a pattern provided by a pattern generator 1738, can be coupled to a deflection element 1720 (which may be a deflection plate), thereby allowing manual or automatic control of the focused ion beam 1718 to depict a corresponding pattern on the upper surface of the substrate 1722. In some systems, the deflection element 1720 may be positioned before the final stage lens. When a blanking voltage is applied to the blanking electrode by a blanking controller (not shown), a beam blanking electrode (not shown) inside the ion beam focusing tube 1716 can cause the focused ion beam 1718 to strike the blanking stop (not shown) instead of the substrate 1722.

[0152] For example, ion source 1714 can provide a beam of gallium metal ions. In other examples, ion source 1714 can be a plasma ion source that extracts ions from a generated plasma. This source can be focused into a beam sub-1 / 10 of a micrometer wide at substrate 1722 for use in modifying substrate 1722 by means of ion milling, enhanced etching, material deposition, or for imaging purposes of substrate 1722.

[0153] A charged particle detector 1740, such as an Everhart Thornley or multichannel board, for detecting secondary ion or electron emissions can be connected to video circuitry 1742, which can provide drive signals to a video monitor 1744 and receive deflection signals from a system controller 1719. The position of the charged particle detector 1740 within the lower chamber 1726 can vary in different embodiments. For example, the charged particle detector 1740 can be coaxial with the ion beam and include an aperture allowing the ion beam to pass through. In other embodiments, secondary particles can be collected by a final-stage lens and then deflected off-axis for collection.

[0154] Micromanipulator 1747 can accurately move an object inside a vacuum chamber. Micromanipulator 1747 may include a precision electric motor 1748 located outside the vacuum chamber to provide X, Y, Z, and θ control of a portion 1749 located inside the vacuum chamber. Micromanipulator 1747 may be equipped with different end effectors for manipulating small objects. In the various embodiments described herein, the end effector may be a fine probe 1750.

[0155] Gas delivery system 1746 may extend into lower chamber 1726 for introducing and guiding gaseous vapor to substrate 1722. U.S. Patent No. 5,851,413, "Gas Delivery Systems for Particle Beam Processing" by Casella et al., assigned to the assignee of this invention, describes a suitable gas delivery system 1746. Another gas delivery system is described in U.S. Patent No. 5,435,850, "Gas Injection System" by Rasmussen, also assigned to the assignee of this invention. For example, iodine may be delivered to enhance etching, or organometallic compounds may be delivered to deposit metal.

[0156] System controller 1719 can control the operation of various parts of dual-beam system 1710. Through system controller 1719, a user can cause the focused ion beam 1718 or electron beam 1743 to scan in a desired manner by inputting commands into any suitable user interface (not shown). Alternatively, system controller 1719 can control dual-beam system 1710 according to program instructions stored in memory 1721. In various embodiments, one or more software components 311 may be implemented in or separately executed by system controller 1719.

[0157] Various implementations can be systems, methods, apparatus, or computer program products at any possible level of technical detail integration. A computer program product can include a computer-readable storage medium (or media) having computer-readable program instructions thereon to cause a processor to execute aspects of various implementations. A computer-readable storage medium can be a tangible device capable of retaining and storing instructions for use by an instruction execution device. A computer-readable storage medium can be, for example, but not limited to, electronic storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of computer-readable storage media may also include: portable computer floppy disks, 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 optical disc read-only storage media (CD-ROM), digital versatile optical disc (DVD), memory sticks, floppy disks, mechanical encoding devices such as punched cards or recessed protrusions on which instructions are recorded, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium must not be interpreted as a transient signal itself, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse passing through an optical fiber cable), or an electrical signal transmitted through a metallic wire.

[0158] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to a corresponding computing / processing device or downloaded via a network (e.g., the Internet, a local area network, a wide area network, or a wireless network) to an external computer or external storage device. This network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, 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 them to a computer-readable storage medium within the corresponding computing / processing device. The computer-readable program instructions used to perform operations in various implementation schemes may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, integrated circuit configuration data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​(such as Smalltalk, C++, etc.) or procedural programming languages ​​(such as the "C" programming language or similar programming languages). Computer-readable program instructions can be executed as a standalone software package, entirely on a user's computer, partially on a user's computer, partially on a user's computer and partially on a remote user's computer, or entirely on a remote computer or server. In the latter case, the remote computer can be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., via the Internet provided by an Internet service provider). In some implementations, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs) can be personalized by utilizing the state information of the computer-readable program instructions to execute the computer-readable program instructions intended to perform various aspects.

[0159] This document describes various aspects with reference to flowchart illustrations or block diagrams of methods, apparatus (systems), and computer program products according to various embodiments. It should be understood that each block in the flowchart illustration or block diagram, and combinations of blocks in the flowchart illustration or block diagram, can be implemented by computer-readable program instructions. These computer-readable program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus to produce a machine, thereby creating a manner by which the instructions, executed by the processor of the computer or other programmable data processing apparatus, are used to implement the functions / actions specified in the flowchart or block diagram blocks. These computer-readable program instructions can also be stored in a computer-readable storage medium that can instruct a computer, programmable data processing apparatus, or other apparatus to operate in a particular manner, such that the computer-readable storage medium in which the instructions are stored constitutes an article of manufacture containing instructions for implementing aspects of the functions / actions specified in the flowchart or block diagram blocks and / or blocks. Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus or other apparatus to cause a series of operations to be performed on the computer, other programmable apparatus or other apparatus to produce a computer-implemented process, thereby implementing the functions / actions specified in the flowchart or block diagram blocks or blocks.

[0160] The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments. In this respect, each block in a flowchart or block diagram may represent a module, segment, or portion of instructions, which includes one or more executable instructions for implementing a specified logical function. In some alternative embodiments, the functions indicated in the blocks may not occur in the order shown in the figures. For example, two consecutively shown blocks may actually be executed substantially simultaneously, or these blocks may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented by a system based on dedicated hardware, which performs the specified function or action, or by a combination of dedicated hardware and computer instructions.

[0161] Although the subject matter has been described above in the general context of computer-executable instructions for a computer program product running on one or more computers, those skilled in the art will recognize that this disclosure can also be implemented in conjunction with other program modules. Typically, program modules include routines, programs, components, data structures, etc., that perform specific tasks or implement specific abstract data types. Furthermore, those skilled in the art will recognize that various aspects can be implemented in conjunction with other computer system configurations, including single-processor or multi-processor computer systems, small computing devices, mainframe computers, and computers, handheld computing devices (e.g., PDAs, telephones), or microprocessor-based or programmable consumer and / or industrial electronic products, etc. The aspects shown can also be implemented in a distributed computing environment, where tasks are performed by remote processing devices linked via a communication network. However, some (if not all) aspects of this disclosure can be implemented on a standalone computer. In a distributed computing environment, program modules can reside on both local and remote memory storage devices.

[0162] As used herein, the terms “component,” “system,” “platform,” “interface,” etc., may refer to or include computer-related entities or entities associated with an operational machine having one or more specific functions. Entities disclosed herein may be hardware, a combination of hardware and software, software, or software in execution. For example, a component may be, but is not limited to, a process running on a processor, a processor, an object, an executable file, an execution thread, a program, or a computer. As an example, an application running on a server and the server itself can both be components. One or more components may reside in a process or execution thread, and components may be located on a single computer or distributed across two or more computers. In another example, a corresponding component may be executed from various computer-readable media on which various data structures are stored. These components may communicate via local or remote processes, such as according to signals having one or more data packets (e.g., data from a component that interacts with another component in a local system, a distributed system, or across a network such as the Internet via signals). As another example, a component may be a device having specific functions provided by mechanical parts operated by electrical or electronic circuitry, the device being operated by a software or firmware application executed by a processor. In this case, the processor may be located internally or externally to the device and may execute at least a portion of the software or firmware application. As yet another example, a component can be a device that provides a specific function through electronic components without mechanical parts, wherein the electronic components may include a processor or other means for executing software or firmware that at least partially endows the electronic components with function. In one aspect, a component can simulate an electronic component via a virtual machine (e.g., within a cloud computing system).

[0163] Furthermore, the term "or" is intended to mean inclusive "or" rather than exclusive "or". That is, unless otherwise specified or clearly apparent from the context, "X adopts A or B" means any naturally inclusive permutation. That is, if X adopts A; X adopts B; or X adopts both A and B, then "X adopts A or B" is satisfied in any of the foregoing cases. As used herein, the term "and / or" is intended to have the same meaning as "or". Furthermore, unless otherwise specified or clearly apparent from the context involving the singular form, the article "a" as used in the subject matter description and accompanying drawings should generally be interpreted as meaning "one or more". As used herein, the terms "example" or "exemplary" are used to indicate that something is used as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited to such examples. Moreover, any aspect or design described herein as an "example" or "exemplary" is not necessarily construed as preferred or superior to other aspects or designs, nor does it imply exclusion of equivalent exemplary structures and techniques known to those skilled in the art.

[0164] The disclosure herein describes non-limiting examples. For ease of description or explanation, the terms “each,” “every,” or “all” are used in discussions of various examples throughout the disclosure. Such use of the terms “each,” “every,” or “all” is not restrictive. In other words, when the disclosure herein provides a description of “each,” “every,” or “all” applicable to a particular object or component, it should be understood that this is a non-limiting example, and it should also be understood that in various other examples, such a description may apply to fewer than “each,” “all,” or “all” of that particular object or component.

[0165] As used herein, the term "processor" can refer to substantially any computing processing unit or device, including but not limited to a single-core processor; a single processor with software multithreading capabilities; a multi-core processor; a multi-core processor with software multithreading capabilities; a multi-core processor with hardware multithreading technology; a parallel platform; or a parallel platform with distributed shared memory. Furthermore, a processor can refer to an integrated circuit, application-specific integrated circuit (ASIC), digital signal processor (DSP), field-programmable gate array (FPGA), programmable logic controller (PLC), complex programmable logic device (CPLD), discrete gate or transistor logic, discrete hardware components, or any combination thereof, designed to perform the functions described herein. Additionally, processors can utilize nanoscale architectures, such as, but not limited to, molecular and quantum dot-based transistors, switches, or gates, to optimize space utilization or enhance the performance of user devices. Processors can also be implemented as a combination of computing processing units. In this disclosure, terms such as "storage," "memory," "data storage," "database," and any other information storage component related to the operation and function of a component are used to refer to a "memory component," an entity embodied in "memory," or a component containing memory. It should be understood that the memory or memory component described herein may be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. By way of illustration and not limitation, non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable ROM (EEPROM), flash memory, or non-volatile random access memory (RAM) (e.g., ferroelectric RAM (FeRAM)). Volatile memory may include RAM, for example, which may act as external cache memory. By way of illustration and not limitation, RAM may be provided in various 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), or Rambus dynamic RAM (RDRAM). Furthermore, the memory components of the systems or computer implementation methods disclosed herein are intended to include, but are not limited to, these or any other suitable types of memory.

[0166] The foregoing description includes only examples of systems and computer-implemented methods. It is certainly impossible to describe every conceivable combination of components or computer-implemented methods for the purposes of describing this disclosure, but many further combinations and arrangements of the contents of this disclosure are possible. Furthermore, the terms “comprising,” “having,” “possessing,” etc., are used to such an extent in the detailed description, claims, appendices, or drawings that such terms are intended to be inclusive in a manner similar to the term “comprising,” as interpreted when “comprising” is used as a transitional word in the claims.

[0167] Descriptions of various embodiments have been presented for illustrative purposes and are not intended to be exhaustive or limited to the disclosed embodiments. Many modifications and variations will be apparent without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical applications, or technical improvements to existing technologies in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

[0168] Various non-limiting aspects are described in the following embodiments.

[0169] Example 1: A system may include: a processor capable of executing computer-executable components stored in a non-transitory computer-readable memory, wherein the computer-executable components may include: a scanning component capable of scanning an energy integral image of a specimen by a charged particle microscope equipped with a non-back-side thinning integrating detector; and a quantization component capable of counting how many charged particle events are represented by the corresponding pixels of the energy integral image based on rounding the cumulative charged particle energy intensity indicated by each pixel cluster to the nearest integer multiple of the beam energy of the charged particle microscope.

[0170] Example 2: Any of the aforementioned examples can be implemented, wherein the scanning component enables the charged particle microscope to capture multiple preliminary energy integration images of the specimen.

[0171] Example 3: Any of the aforementioned examples can be implemented, wherein the scanning component can assign zero to pixels in multiple preliminary energy integration images whose charged particle energy intensity is below a noise threshold, thereby generating multiple rounded-down energy integration images.

[0172] Example 4: Any of the aforementioned examples can be implemented, wherein the scanning component can perform pixel-by-pixel summation or averaging on multiple rounded energy integral images to obtain an energy integral image.

[0173] Example 5: Any of the aforementioned examples can be implemented, wherein the quantization component can identify the corresponding clusters by applying a community detection algorithm to the energy integral image.

[0174] Example 6: Any of the aforementioned examples can be implemented, wherein, for a first cluster having a first cumulative charged particle energy intensity, the quantization component: divides the first cumulative charged particle energy intensity by the beam energy of the charged particle microscope to obtain a quotient; rounds the quotient to the nearest integer value to obtain a rounded quotient; and determines that the number of charged particle events represented by the first cluster is equal to the rounded quotient.

[0175] Example 7: Any of the aforementioned examples can be implemented, wherein, for a first pixel in the first cluster and having a first integral charged particle energy intensity, the quantization component can: divide the first integral charged particle energy intensity by the first cumulative charged particle energy intensity to obtain a ratio; multiply the ratio by the rounded quotient to obtain a product; and determine that the number of charged particle events represented by the first pixel is equal to the product.

[0176] Example 8: Any of the aforementioned examples can be implemented, wherein the charged particle microscope can be an electron energy loss microscope.

[0177] In various implementation schemes, any combination or combination of embodiments 1-8 can be implemented.

[0178] Example 9: A computer-implemented method may include: accessing an energy integral image of a specimen by a device operatively coupled to a processor, the energy integral image being scanned by a charged particle microscope that may be equipped with a non-backside thinning integral detector; and the device counting how many charged particle events are represented by the corresponding pixels of the energy integral image based on rounding the cumulative charged particle energy intensity indicated by each pixel cluster to the nearest integer multiple of the beam energy of the charged particle microscope.

[0179] Example 10: A computer implementation of any of the foregoing examples can be implemented, the method further comprising: using the device to enable a charged particle microscope to capture multiple preliminary energy integration images of the specimen.

[0180] Example 11: A computer implementation of any of the foregoing examples can be implemented, the method further comprising: assigning a value of zero to pixels in a plurality of preliminary energy integration images whose charged particle energy intensity is below a noise threshold, thereby generating a plurality of rounded-down energy integration images.

[0181] Example 12: A computer implementation method of any of the foregoing examples can be implemented, the method further comprising: performing pixel-by-pixel summation or averaging on multiple rounded energy integral images by the device to obtain an energy integral image.

[0182] Example 13: A computer implementation of any of the foregoing examples can be implemented, the method further comprising: the device identifying corresponding clusters by applying a community detection algorithm to an energy integral image.

[0183] Example 14: A computer implementation of any of the foregoing examples can be implemented, wherein for a first cluster having a first cumulative charged particle energy intensity, the method further includes: dividing the first cumulative charged particle energy intensity by the beam energy of a charged particle microscope by a device to obtain a quotient; rounding the quotient to the nearest integer value by the device to obtain a rounded quotient; and determining by the device that the number of charged particle events represented by the first cluster is equal to the rounded quotient.

[0184] Example 15: A computer implementation of any of the foregoing examples can be implemented, wherein for a first pixel in a first cluster and having a first integrated charged particle energy intensity, the method further includes: dividing the first integrated charged particle energy intensity by a first cumulative charged particle energy intensity by the device to obtain a ratio; multiplying the ratio by the rounded quotient by the device to obtain a product; and determining by the device that the number of charged particle events represented by the first pixel is equal to the product.

[0185] Example 16: A computer implementation of any of the foregoing examples can be implemented, wherein the charged particle microscope can be an electron energy loss microscope.

[0186] In various implementation schemes, any combination or combination of embodiments 1-16 can be implemented.

[0187] Example 17: A computer program product for facilitating charged particle counting via a non-back-side thinning integrating detector and beam energy quantization may include a non-transitory computer-readable storage device having program instructions stored thereon. In various aspects, these program instructions are executable by a processor to: access an energy integration image of a specimen, the energy integration image being scanned by an electron energy loss microscope that may be equipped with a non-back-side thinning integrating detector; and count how many electronic events are represented by the corresponding pixels of the energy integration image, based on rounding the cumulative electron energy intensity indicated by each pixel cluster to the nearest integer multiple of the beam energy of the electron energy loss microscope.

[0188] Example 18: A computer program product of any of the foregoing examples can be implemented, wherein the program instructions are further executable to cause the processor to: cause the electron energy loss microscope to capture multiple preliminary energy integration images of the specimen; assign a value of zero to pixels in the multiple preliminary energy integration images whose electron energy intensity is below a noise threshold, thereby generating multiple rounded energy integration images; and perform pixel-by-pixel summation or averaging on the multiple rounded energy integration images to obtain an energy integration image.

[0189] Example 19: A computer program product of any of the foregoing examples can be implemented, wherein, for a first cluster having a first cumulative electron energy intensity, program instructions can be further executable to cause the processor to: divide the first cumulative electron energy intensity by the beam energy of the electron microscope to obtain a quotient; round the quotient to the nearest integer value to obtain a rounded quotient; and determine that the number of electron events represented by the first cluster is equal to the rounded quotient.

[0190] Example 20: A computer program product of any of the foregoing examples can be implemented, wherein, for a first pixel in a first cluster and having a first electronic energy intensity, program instructions can be further executable to cause the processor to: divide the first electronic energy intensity by the first electronic energy intensity to obtain a ratio; multiply the ratio by the rounded quotient to obtain a product; and determine that the number of electronic events represented by the first pixel is equal to the product.

[0191] In various implementation schemes, any one or more combinations of embodiments 17-20 can be implemented.

[0192] In various implementation schemes, any one or more combinations of Examples 1-20 can be implemented.

Claims

1. A system comprising: A processor that executes a computer-executable component stored in a non-transitory computer-readable storage memory, wherein the computer-executable component includes: A scanning component that accesses an energy integral image of a specimen scanned by a charged particle microscope equipped with a non-back-side thinning integrating detector; and A quantization component that counts how many charged particle events are represented by the corresponding pixels of the energy integral image, based on rounding the cumulative charged particle energy intensity indicated by each pixel cluster to the nearest integer multiple of the beam energy of the charged particle microscope.

2. The system of claim 1, wherein the scanning component enables the charged particle microscope to capture multiple preliminary energy integration images of the specimen.

3. The system according to claim 2, wherein the scanning component assigns a value of zero to pixels in multiple preliminary energy integration images whose charged particle energy intensity is lower than a noise threshold, thereby generating multiple rounded energy integration images.

4. The system of claim 3, wherein the scanning component performs pixel-by-pixel summation or averaging on multiple rounded energy integral images to obtain an energy integral image.

5. The system of claim 4, wherein the quantization component identifies the corresponding clusters by applying a community detection algorithm to an energy integral image.

6. The system according to claim 5, wherein, For the first cluster having a first cumulative charged particle energy intensity, the quantized component: Divide the first cumulative charged particle energy intensity by the beam energy of the charged particle microscope to obtain a quotient; Round the quotient to the nearest integer value to obtain the rounded quotient; and The number of charged particle events represented by the first cluster is determined to be equal to the quotient of the rounding.

7. The system according to claim 6, wherein, For a first pixel in the first cluster and having a first integral charged particle energy intensity, the quantized component: Divide the first integral charged particle energy intensity by the first cumulative charged particle energy intensity to obtain a ratio; Multiply the ratio by the rounded quotient to obtain a product; and The number of charged particle events represented by the first pixel is determined to be equal to the product.

8. The system of claim 1, wherein the charged particle microscope is an electron energy loss microscope.

9. A computer implementation method, comprising: Access to energy-integrated images of specimens scanned by a charged particle microscope equipped with a non-back-side thinning integrating detector via a device operably coupled to the processor; and The device counts how many charged particle events are represented by the corresponding pixels of the energy integral image, based on rounding the cumulative charged particle energy intensity indicated by each pixel cluster to the nearest integer multiple of the beam energy of the charged particle microscope.

10. The computer implementation method according to claim 9, further comprising: The device enables the charged particle microscope to capture multiple preliminary energy integration images of the specimen.

11. The computer implementation method according to claim 10, further comprising: The device assigns a value of zero to pixels in multiple preliminary energy integration images whose charged particle energy intensity is lower than the noise threshold, thereby generating multiple rounded-down energy integration images.

12. The computer implementation method according to claim 11, further comprising: The device performs pixel-by-pixel summation or average averaging on multiple rounded energy integral images to obtain an energy integral image.

13. The computer implementation method according to claim 12, further comprising: The device identifies the corresponding clusters by applying a community detection algorithm to the energy integral image.

14. The computer implementation method according to claim 13, further comprising, for a first cluster having a first cumulative charged particle energy intensity: The device divides the first accumulated charged particle energy intensity by the beam energy of the charged particle microscope to obtain a quotient. The device rounds the quotient to the nearest integer value, thus obtaining a rounded quotient. and The device determines that the number of charged particle events represented by the first cluster is equal to the quotient of the rounding.

15. The computer implementation method according to claim 14, further comprising, for a first pixel in the first cluster and having a first integral charged particle energy intensity: The device divides the first integral charged particle energy intensity by the first cumulative charged particle energy intensity to obtain a ratio. The device multiplies the ratio by the rounded quotient to obtain a product; and The device determines that the number of charged particle events represented by the first pixel is equal to the product.

16. The system of claim 9, wherein the charged particle microscope is an electron energy loss microscope.

17. A computer program product for promoting charged particle counting via a non-back-side thinning integral detector and beam energy quantization, the computer program product comprising a non-transitory computer-readable storage thereon having program instructions executable by a processor to cause the processor to: Access to energy-integrated images of specimens scanned by an electron energy loss microscope equipped with a non-back-side thinning integrator; and The number of electron events represented by the corresponding pixels in the energy integral image is counted by rounding the cumulative electron energy intensity indicated by each pixel cluster to the nearest integer multiple of the beam energy of the electron energy loss microscope.

18. The computer program product of claim 17, wherein the program instructions are further executable to cause the processor to: The electron energy loss microscope captures multiple preliminary energy integration images of the specimen. Pixels in multiple preliminary energy integration images whose electron energy intensity is below the noise threshold are assigned a value of zero, thereby generating multiple rounded energy integration images; and The energy integral image is obtained by performing pixel-by-pixel summation or averaging on multiple rounded energy integral images.

19. The computer program product according to claim 18, wherein, For a first cluster having a first accumulated electron energy intensity, the program instructions are further executable to cause the processor to: Divide the first cumulative electron energy intensity by the beam energy of the electron energy loss microscope to obtain a quotient; Round the quotient to the nearest integer value to obtain the rounded quotient; as well as The number of electronic events represented by the first cluster is determined to be equal to the quotient of the rounding.

20. The computer program product according to claim 19, wherein, For a first pixel in the first cluster and having a first electron energy intensity, the program instructions are further executable to cause the processor to: Divide the first electron energy intensity by the first cumulative electron energy intensity to obtain a ratio; Multiply the ratio by the rounded quotient to obtain a product; as well as The number of electronic events represented by the first pixel is determined to be equal to the product.

Images