Charged particle counting by non-Bachsin integral detector and beam energy quantization

Abstract

A system and technology are provided that facilitates charged particle counting via non-Bachsin integral detectors and beam energy quantization. [Solution] In various embodiments, the system can access an energy integral image of a sample scanned by a charged particle microscope equipped with a non-Baccin integrating detector. In various forms, the system can count the number of charged particle events represented by each pixel in the energy integral image by rounding the cumulative charged particle energy intensity represented by each pixel cluster to the nearest integer multiple of the beam energy of the charged particle microscope.

Description

[Technical Field]

[0001] This application relates to a non-Bachsin integral detector and charged particle counting by beam energy quantization. [Background technology]

[0002] It is difficult to accurately count how many charged particles collide with the integrating detector of a charged particle microscope. [Overview of the project]

[0003] The following outline provides a basic understanding of one or more embodiments. This outline is not intended to identify major or important elements or to define the scope of any particular embodiment or claim. Its sole purpose is to present the concepts in a simplified form as a prelude to more detailed descriptions that will follow. One or more embodiments described herein describe devices, systems, computer implementations, apparatus, or computer program products that facilitate non-Bachsin integral detectors and charged particle counting via beam energy quantization.

[0004] A system is provided according to one or more embodiments. The system may include persistent computer-readable memory capable of storing computer-executable components. The system may further include a processor that can be operably coupled to the persistent computer-readable memory and can execute the computer-executable components stored in the persistent computer-readable memory. In various embodiments, the computer-executable components may include a scanning component that can access an energy integral image of a sample scanned with a charged particle microscope equipped with a non-bucksin integral detector. In various embodiments, the computer-executable components may include a quantization component capable of counting the number of charged particle events corresponding to each pixel of the energy integral image. This counting is based on rounding the cumulative charged particle energy intensity, represented by the cluster of each pixel, to the nearest integer multiple of the beam energy of the charged particle microscope.

[0005] According to one or more embodiments, a computer implementation method is provided. In various embodiments, the computer implementation method may include, by a device operationally connected to a processor, accessing an energy integral image of a sample scanned with a charged particle microscope equipped with a non-bucksin integrating detector, and by the device counting the number of charged particle events corresponding to each pixel of the energy integral image, based on rounding the cumulative charged particle energy intensity represented by the cluster of each pixel to the nearest integer multiple of the beam energy of the charged particle microscope.

[0006] A computer program product is provided that facilitates the counting of charged particles via a non-Bucksin integral detector and beam energy quantization, according to one or more embodiments. In various embodiments, the computer program product may include non-temporary computer-readable memory into which program instructions are incorporated. In various embodiments, the program instructions are executable by a processor, which can be used to access an energy integral image of a sample scanned with an electron energy loss microscope equipped with a non-Bucksin integral detector. In various cases, the program instructions are executable by a processor, which can be used to count the number of electron events corresponding to each pixel in the energy integral image, based on rounding the cumulative electron energy intensity represented by each pixel cluster to the nearest integer multiple of the beam energy of the electron energy loss microscope. [Brief explanation of the drawing]

[0007] Various embodiments will be readily understood in combination with the following detailed description and accompanying drawings. For the sake of this description, similar reference numerals indicate similar structural elements. Embodiments are illustrated in the drawings as examples, not as limitations. These drawings are not necessarily drawn to a specific scale. [Figure 1] Block diagrams of exemplary, non-limiting scientific instrument modules according to various embodiments described herein are shown. [Figure 2] Flowcharts of exemplary and non-limiting computer implementation methods according to various embodiments described herein are shown. [Figure 3] A block diagram of an exemplary, non-limiting system facilitating charged particle counting via a non-Bachsin integral detector and beam energy quantization is shown according to one or more embodiments described herein. [Figure 4] A block diagram of an exemplary, non-limiting system including a non-Bachsin integral detector and an energy integral image and beam energy that facilitates charged particle counting via beam energy quantization is shown according to one or more embodiments described herein. [Figure 5] An exemplary, non-limiting block diagram illustrating a method for accessing an energy integral image according to one or more embodiments described herein is shown. [Figure 6] An exemplary, non-limiting block diagram illustrating a method for accessing an energy integral image according to one or more embodiments described herein is shown. [Figure 7] An exemplary, non-limiting block diagram illustrating a method for accessing an energy integral image according to one or more embodiments described herein is shown. [Figure 8] A block diagram of an exemplary, non-limiting system, including a non-bucksin integral detector and a pixel-level charged particle counting system facilitating charged particle counting via beam energy quantization, is shown according to one or more embodiments described herein. [Figure 9] An exemplary and non-limiting block diagram illustrating a method for determining the charged particle count in pixels according to one or more embodiments described herein is shown. [Figure 10] An exemplary and non-limiting block diagram illustrating a method for determining the charged particle count in pixels according to one or more embodiments described herein is shown. [Figure 11] An exemplary and non-limiting block diagram illustrating a method for determining the charged particle count in pixels according to one or more embodiments described herein is shown. [Figure 12] An exemplary and non-limiting block diagram illustrating a method for determining the charged particle count in pixels according to one or more embodiments described herein is shown. [Figure 13] The following experimental results relate to exemplary and non-limiting examples of one or more embodiments described herein. [Figure 14] The following experimental results relate to exemplary and non-limiting examples of one or more embodiments described herein. [Figure 15] A block diagram of an exemplary, non-limiting operating environment in which one or more embodiments described herein may be realized is shown. [Figure 16]An exemplary network environment capable of executing various implementations described in this specification is shown. [Figure 17] Examples of implementable dual-beam microscopes according to various embodiments described in this specification are shown.

Mode for Carrying Out the Invention

[0008] The following detailed description is merely exemplary and is not intended to limit the embodiments and their application / use. Furthermore, there is no intention to be bound by the expressions or information presented or implied in the foregoing background or summary section or the section on the mode for carrying out the invention.

[0009] Here, one or more embodiments will be described with reference to the drawings, and like reference numerals are used throughout to refer to like elements. In the following description, many specific details are set forth for the purpose of providing a more complete understanding of one or more embodiments. However, it is clear that in various cases, one or more embodiments can be practiced without these specific details.

[0010] Various operations can be described in sequence as a plurality of individual operations or actions in a manner most beneficial for understanding the subject matter disclosed herein. However, the order of description should not be construed to mean that these operations necessarily depend on order. In particular, these operations can be executed in a different order than presented. The described operations can be executed in a different order than the described embodiments. Various additional operations can be performed, or the described operations can be omitted in additional embodiments.

[0011] Some elements may be referred to in the singular form (e.g., "processing device"), but 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 using different operations performed by different processing devices. Where used herein, the phrase "based on" should be understood to mean "at least partially based on" unless otherwise specified.

[0012] Charged particle microscopes (e.g., scanning electron microscopes (SEMs), transmission electron microscopes (TEMs), dual-beam microscopes, electron energy loss microscopes (EELMs), energy-dispersive X-ray microscopes (EDMs)) may be suitable computerized devices capable of acquiring or generating microscopic or nanoscale images of samples in scientific, laboratory, research, or clinical operating environments. To facilitate the acquisition or generation of such images, charged particle microscopes can leverage complex configurations of operating components (e.g., ion sources, electron sources, optical lenses or apertures, optical plates or deflectors, columns, coils, heaters, coolers, fluid valves, fluid pumps, circuit switches, sample stages), sensors (e.g., ion detectors, electron detectors, voltmeters, thermistors, potentiometers, pressure gauges), or consumables (e.g., carrier fluids, calibration materials, filters, reactive gases).

[0013] In various situations, charged particle microscopes (e.g., EELM in particular) can reveal the material and compositional properties of a sample (e.g., integrated circuit chips, semiconductor wafers, lamellae, biological or organic samples). This is done by irradiating the sample with a charged particle beam (e.g., electrons, X-ray photons) and counting the number of charged particles belonging to each energy loss level that pass through the sample (e.g., charged particles may transfer their respective energy to the sample and thus lose energy). In one embodiment, a charged particle microscope may have an energy dispersion filter that can: allow only charged particles with a desired or selected energy loss to reach the detector of the charged particle microscope, or direct / aim charged particles with different energy losses at each cell of the detector (e.g., so that each cell collides only with particles having a single respective energy loss). In either case, useful compositional information about the sample through which these charged particles have passed can be revealed by counting the number of charged particles with each energy loss that collide with the detector of the charged particle microscope.

[0014] Some charged particle microscopes may be equipped with pulse counting detectors (e.g., Geiger-Müller counters, scintillation detectors, Channeltron® detectors). The hardware of pulse counting detectors is specifically designed or configured to detect or count how many individual charged particles have collided.

[0015] In contrast, many other charged particle microscopes may be equipped with integrating detectors (e.g., charge-coupled elements, Faraday cup detectors). Unlike the hardware of pulse-counting detectors, the hardware of integrating detectors is specifically designed or configured to accumulate, sum, or integrate the energy of colliding charged particles over time. Therefore, when operating in the normal, standard, or default integrating mode, an integrating detector does not output or generate a count of charged particles. Instead, it outputs or generates an accumulated, summed, or integrated energy signal.

[0016] Conventional techniques have developed counting modes for integrated detectors to enable the counting of charged particles in charged particle microscopes equipped with integrated detectors. When implemented in counting mode, the integrated detector does not accumulate, sum, or measure the energy of incident charged particles. Rather, when implemented in counting mode, the integrated detector operates binaryly, either hit or not hit. In particular, any cell of an integrated detector can be considered to have a sampling rate and sampling iterations. The sampling rate of the cell can be considered to define the rate or speed at which the cell can record or register signals, while the sampling iterations of the cell can be considered to be the time required for the cell to record or register a single signal (for example, usually measured in microseconds or milliseconds). Thus, the sampling iterations can be considered inversely proportional to the sampling rate of the cell (for example, a higher sampling rate results in shorter sampling iterations, and conversely, a lower sampling rate results in longer sampling iterations). In either case, for each sampling iteration that occurs while operating in counting mode, the cell can exhibit one of the following: A charged particle collided with the cell; or a charged particle did not collide with the cell. Therefore, a running count corresponding to the cell can be established, with an initial value of 0. This running count can be increased between each sampling iteration indicating that the cell has been hit, and not increased between each sampling iteration indicating that the cell has not been hit. After a desired number of appropriate sampling iterations have elapsed, the conventional technique treats the final value of the running count as indicating the number of charged particles that collided with the cell. In many cases, back-thinning of an integrating detector can improve the counting sensitivity of the cell. In other words, if the thickness of the integrating detector is thinner than the penetration depth of the colliding charged particle, the counting sensitivity of the cell can be improved.

[0017] Unfortunately, conventional techniques for counting charged particles via integrating detectors are vulnerable to the pile-up effect. The pile-up effect refers to the following phenomenon: when two or more different charged particles collide with each other simultaneously, consecutively, in short intervals, or within the same sampling iteration (for example, at a speed exceeding the time resolution of the integrating detector), the cell may not be able to register or record these two or more different charged particles independently or individually. Instead, the cell may mistakenly register or record them as a single charged particle.

[0018] As a non-restrictive example, consider the case where particles A and B collide with the cell of an integral detector operating in counting mode. Assume that particle A collides with the integral detector cell during sampling iteration X, and particle B collides with the integral detector cell during sampling iteration Y, where sampling iteration Y follows sampling iteration X. In this case, during sampling iteration X, the integral detector cell generates or outputs a "collision detected" signal, and can increment the running count accordingly by 1. Furthermore, during sampling iteration Y, the integral detector cell generates or outputs another "collision detected" signal, and can again increment the running count accordingly by 1. Note that in this situation, the running count increments twice: once for particle A and once for particle B. Therefore, since particles A and B occurred during different sampling iterations, the integral detector cell can correctly count them. Now, assume that both particle A and particle B collided with the integral detector cell during the same sampling iteration Z. In this case, during sampling iteration Z, the integrating detector cell may generate or output a "collision" signal, thereby increasing the running count by 1. Note that in this situation, the running count only increases by 1. In other words, because particles A and B occurred during the same sampling iteration, the integrating detector cell cannot count them correctly. In other words, particles A and B can be considered piled up on the integrating detector cell, and the integrating detector cell cannot accurately distinguish them and therefore cannot count them accurately. Note that the pile-up problem can occur even if the integrating detector cell is back-thinned (for example, by having a thickness thinner than the penetration depth of particles A and B, thus increasing its sensitivity).

[0019] Thus, conventional techniques for counting charged particles via integrating detectors are prone to miscounting due to the pile-up effect.

[0020] Therefore, systems and technologies that can mitigate the pile-up effect are desirable.

[0021] Various embodiments described herein can address this technical problem. One or more embodiments described herein may include a system, computer implementation method, apparatus, or computer program product that facilitates charged particle counting via a non-Bach-Sin integral detector and beam energy quantization. In particular, the inventors of the various embodiments described herein have recognized that the prior art of charged particle counting via integral detectors operates in a binary collision-on / off manner using a Bach-Sin cell. The inventors have noted that because charged particles pass through or penetrate the Bach-Sin cell completely, these charged particles typically impart energy to the Bach-Sin cell in the range of a few electron volts to a few kiloelectron volts. The inventors have further noted that the energy thus imparted is at most only a few percent of the beam energy initially imparted to the charged particle by the charged particle microscope. Furthermore, the inventors have noticed that when a non-Bucksin integrating detector is implemented (for example, an integrating detector whose thickness is greater than or equal to the penetration depth of the colliding charged particle), each charged particle can be considered to impart almost all of its initial energy (e.g., about 98% or more) to the non-Bucksin integrating detector of the charged particle microscope. Accordingly, the inventors have devised various embodiments described herein. In these embodiments, charged particles can be counted by quantizing the total energy imparted to each non-Bucksin integrating detector cell. In other words, instead of counting charged particles in a binary collision-prevention / non-collision manner using a Bucksin integrating detector, the various embodiments described herein can track or record the total amount of charged particle energy imparted to each non-Bucksin integrating detector cell. By dividing such total charged particle energy by the beam energy of the charged particle microscope and rounding the quotient, the total number of charged particles colliding with each non-Bucksin integrating detector cell can be estimated accurately or reliably, even in the presence of a pile-up of charged particles. In other words, the various embodiments described herein can be considered sophisticated pixel processing techniques that are not adversely affected or error-prone by pile-up effects.

[0022] Various embodiments described herein can be considered as computerized tools (e.g., a suitable combination of computer-executable hardware and computer-executable software) that facilitate charged particle counting via non-Bachsin integral detectors and beam energy quantization. In various embodiments, such computerized tools may include a scanning component or a quantization component.

[0023] In various embodiments, a charged particle microscope may exist. In various aspects, the charged particle microscope may exhibit a suitable design or structure (e.g., it may be a scanning electron microscope or a dual-beam microscope). In various cases, there may be any suitable specimen (e.g., a semiconductor wafer or thin sheet) to be mounted on the charged particle microscope (e.g., currently placed on or positioned on the operational stage of the charged particle microscope). In various cases, the charged particle microscope may be equipped with a non-bucksinized or non-bucksinized integrating detector. In other words, the thickness of the integrating detector may be greater than the penetration depth corresponding to the charged particles emitted by the charged particle microscope.

[0024] In various embodiments, it may be desirable to perform electronic counting on or in relation to a sample. As described herein, computerized tools can facilitate such electronic counting.

[0025] In various embodiments, the computerized tool can electronically access the charged particle microscope. For example, the computerized tool can send electronic commands to the charged particle microscope or receive electronic data from the charged particle microscope. Thus, any component of the computerized tool can electronically interact with the charged particle microscope (e.g., operate, stop, read, write, edit, copy, operate) in any suitable manner.

[0026] In various embodiments, the scanning component of a computerized tool can be electronically controlled to cause a charged particle microscope to scan a sample using an integrating detector, thereby obtaining an energy-integrated image. In various embodiments, the charged particle microscope can perform such a scan using any appropriate beam energy (e.g., any appropriate beam voltage or beam current).

[0027] More specifically, the scanning component can electronically instruct or command the charged particle microscope to scan the sample multiple times using beam energy. Such scans can yield multiple pre-energy integral images of the sample (e.g., one pre-energy integral image per scan). In various cases, each pre-energy integral image is a two-dimensional pixel array, and each pixel in each pre-energy integral image can represent the total amount of energy imparted to the corresponding integrating detector cell of the charged particle microscope during each scan. Note that each integrating detector cell can be considered to record, register, or track the energy imparted to the detector (e.g., energy imparted to the detector), rather than the energy imparted to the sample (e.g., energy lost to the sample).

[0028] In various embodiments, the scanning component can leverage any appropriate noise threshold to transform multiple preliminary energy integral images into multiple floored energy integral images. In various cases, the noise threshold may be any appropriate scalar whose value or magnitude represents the maximum energy level known or assumed to be the upper limit of noise that may potentially affect the integral detector cell. In fact, even if no charged particles collide with the integral detector cell, the integral detector cell may register or record non-zero imputed energies due to random noise. If the registered or recorded energy level of the integral detector cell exceeds the noise threshold, the registered or recorded energy level can be considered simply not a result of noise (for example, it can be considered a result of at least one charged particle colliding with the integral detector cell). Conversely, if the registered or recorded energy level of the integral detector cell falls below the noise threshold, the registered or recorded energy level can be considered simply a result of noise (for example, it can be considered a result of no charged particles colliding with the integral detector cell). In various embodiments, if the integrated energy value of any pixel in multiple pre-energy integral images falls below the noise threshold, the scan component can replace the integrated energy value of that pixel with a value of 0. That is, the scan component can floor the integrated energy value of that pixel to 0. Conversely, if the integrated energy value of any pixel in multiple pre-energy integral images exceeds the noise threshold, the scan component can retain the integrated energy value of that pixel without changing it. After this conditional flooring operation is performed for each pixel, the multiple pre-energy integral images can be referred to as multiple floored energy integral images.

[0029] In various embodiments, the scanning component can electronically generate an energy integral image by combining or aggregating multiple floored energy integral images. For example, the energy integral image can be equal to the pixel-level sum or pixel-level average of multiple floored energy integral images. Generating an energy integral image in this manner (e.g., via flooring and aggregation) can avoid or prevent situations where low-dose charged particle counts are buried or overwhelmed by noise.

[0030] In various embodiments, the quantization component of the computerized tool can utilize the beam energy used by the charged particle microscope to generate the energy integral image to electronically calculate or compute the pixel-level charged particle count of the energy integral image.

[0031] More specifically, the quantization component can electronically apply any suitable community detection algorithm (e.g., Louvain algorithm, Girvan-Newman algorithm) to the energy integral image. In various embodiments, this allows the quantization component to identify multiple pixel clusters within the energy integral image. In various cases, each pixel cluster may be a set of spatially continuous or connected pixels having non-zero integral energy values. For any pixel cluster, the quantization component can, in various cases, sum or add the integral energy values ​​of the pixels within that cluster to obtain a cumulative integral energy value. In various cases, the quantization component can divide the cumulative energy value by the beam energy used by the charged particle microscope to obtain a quotient. In various embodiments, the quantization component can round the quotient to the nearest integer. In various cases, the nearest integer can be considered as the total number of charged particles that collided with the integral detector cell corresponding to the pixel cluster (e.g., the one storing the integral energy value). Clustering in this manner can be considered beneficial because charged particles may collide simultaneously across multiple integrating detector cells, thereby imparting energy to those cells. In other words, some integrating detector cells may absorb a portion of the initial energy of any given charged particle. Clustering as described herein helps prevent such energy from being ignored or miscounted.

[0032] Furthermore, the quantization component can, in some cases, determine how many charged particles collided with each individual pixel in the pixel cluster. In particular, for any pixel in the pixel cluster, the quantization component can obtain a ratio by dividing the integral energy value of that pixel by the cumulative integral energy value of the pixel cluster. In various embodiments, the quantization component can multiply this ratio by the total number of charged particles that collided with the integrating detector corresponding to the pixel cluster. The product resulting from such multiplication may be a scalar representing the number of individual charged particles (or a portion thereof) that collided with that pixel.

[0033] For example, suppose a charged particle microscope uses a beam energy of 100 keV. Furthermore, suppose the cumulative integrated energy value of the pixel cluster is 873 keV (or a dimensionless value corresponding to or mapped to 873 keV). Furthermore, suppose the integrated energy value of the pixel is 225 keV (or a dimensionless value corresponding to or mapped to 225 keV). In this case, the quantization component divides 873 keV by 100 keV, resulting in a quotient of 8.73. The quantization component can then round 8.73 to the nearest integer, which is 9. Thus, the quantization component can estimate or conclude that 9 charged particles collided with the integrating detector cell corresponding to the pixel cluster. Next, the quantization component divides 225 keV by 873 keV,

number

[0034] In this way, the quantization component can calculate or compute the charged particle count corresponding to each pixel in a cluster of pixels. In other words, the quantization component can calculate or compute the charged particle count corresponding to each non-zero pixel in an energy integral image.

[0035] It should be noted that such counting can be considered unimpeded or degraded by pile-up effects. That is, by quantizing (e.g., dividing) the cumulative energy value of each pixel cluster by the beam energy of the charged particle microscope, charged particles can be counted accurately or reliably regardless of when they collide with the detector, provided the detector is not buck-thin. In fact, consider two or more particles colliding with a non-buck-thin integral detector cell. The non-buck-thin integral detector cell can register or record all the energy imparted by these two or more particles. The total amount of energy registered or recorded is approximately equal to, or nearly matches, the sum of the initial energies of these two or more particles. Therefore, the total amount of energy registered or recorded does not depend on whether these two or more particles collide with the non-buck-thin integral detector cell during the same sampling iteration or during different sampling iterations. Thus, the beam energy quantization described herein can be considered a method for counting charged particles without falling into the pile-up problem.

[0036] In various embodiments, the computerized tool can perform any appropriate electronic operation with respect to the pixel-level charged particle counts calculated by the quantization component. For example, the computerized tool can display any of the pixel-level charged particle counts on any appropriate electronic display, transmit them to any appropriate computing device, or use them for any appropriate downstream analysis (e.g., compositional analysis of a sample).

[0037] The various embodiments described herein can be used with hardware or software to solve inherently highly technical problems (e.g., facilitating charged particle counting via non-Bachsin integral detectors and beam energy quantization), and are not abstract and cannot be performed by human thought alone. Furthermore, some of the processes to be performed can be carried out by specialized computers (e.g., electron microscopes such as EELM) that perform predefined operations relevant to the field of charged particle microscopy.

[0038] For example, such specific operations may include: accessing an energy integral image of a sample scanned with a charged particle microscope equipped with a non-bucksin integral detector by a device operationally connected to a processor, and the device counting the number of charged particle events corresponding to each pixel in the energy integral image based on rounding the cumulative charged particle energy intensity shown in each pixel cluster to the nearest integer multiple of the beam energy of the charged particle microscope. In various embodiments, such specific operations may further include: causing the charged particle microscope to acquire multiple preliminary energy integral images of the sample by the device, obtaining multiple floored energy integral images by assigning 0 to pixels in the multiple preliminary energy integral images where the charged particle energy intensity is below a noise threshold, obtaining an energy integral image by the device performing a pixel-level sum or average of the multiple floored energy integral images, and the device identifying each cluster by applying a community detection algorithm to the energy integral image. In various cases, for a first cluster having a first cumulative charged particle energy intensity, such specific operations may include: The device obtains a quotient by dividing the first cumulative charged particle energy intensity by the beam energy of the charged particle microscope; the device obtains a rounded quotient by rounding the quotient to the nearest integer; and the device determines that the number of charged particle events corresponding to the first cluster is equal to the rounded quotient. In various cases, if the first pixel of the first cluster has a first integrated charged particle energy intensity, such specific operations may include: the device obtains a ratio by dividing the first integrated charged particle energy intensity by the first cumulative charged particle energy intensity; the device obtains a product by multiplying that ratio by the rounded quotient; and the device determines that the number of charged particle events corresponding to the first pixel is equal to that product.

[0039] Such specific operations are essentially performed by computers. In fact, charged particle microscopes (e.g., EELMs) are highly technical computerized devices equipped with specific computerized hardware (e.g., temperature sensors, pressure sensors, voltage sensors, ion beam emitters, electron beam emitters, focusing lenses, ion detectors, electron detectors, beam apertures, fluid valves, and operable sample stages). Charged particle microscopes and the images they acquire cannot be realized in a reasonable or practical way by human thought or by pen and paper alone, without the use of computers. Furthermore, charged particle counting (e.g., electron counting) is essentially a hardware-based operation and cannot be performed in a reasonable or practical way by human thought or by simply using pen and paper alone. In fact, since subatomic particles are invisible to the naked eye, they cannot be detected or counted without special hardware (e.g., integrating detectors) specifically designed and built to physically react to the presence or absence of these subatomic particles. Discussing charged particle counting outside the context of computerization is completely meaningless.

[0040] Furthermore, the various embodiments described herein can integrate various insights related to the field of charged particle microscopy into practical applications. Conventional techniques for counting charged particles via integrating detectors are vulnerable to the pile-up effect. Specifically, if two or more charged particles collide with the integrating detector cell simultaneously, consecutively, in a short period of time, or within the same sampling iteration, the cell may not be able to distinguish or identify those two or more charged particles. In fact, the cell can be considered to be operating in a binary or binary collision-on / no collision manner. Therefore, during a sampling iteration in which the cell collides with two or more charged particles, the running count recorded by the cell may only increase by 1, even though two or more charged particles have actually collided. That is, two or more charged particles can be considered to be piled up on the cell, and the cell cannot accurately identify those two or more charged particles. Thus, the pile-up effect can cause conventional techniques to inaccurate or miscount the number of charged particles that collide with the cell, which is undesirable.

[0041] The various embodiments described herein help mitigate one or more of these technical problems by implementing non-buck-sin integral detectors and charged particle counting via beam energy quantization. In particular, the inventors recognized that the reason the prior art is affected by the pile-up effect is that it forces integral detectors to operate in a binary collision-on / non-collision non-integral mode. The inventors found that the pile-up effect can be neutralized by counting charged particles based on the residual energy retained after the charged particles interact with the sample. Specifically, each charged particle emitted from a charged particle microscope can be considered to have an initial energy equal to the beam energy used by the charged particle microscope to emit, generate, or drive that charged particle. Furthermore, each charged particle that collides with the detector cell of the charged particle microscope after passing through the sample can be considered to lose only a very small percentage of its initial energy (e.g., at most about 2%) within the sample. Therefore, unless the detector cell is buck-sin, it can be assumed that almost all of the initial energy of the charged particles (e.g., about 98% or more) is imparted to the detector cell. For this reason, when multiple charged particles collide with the same detector cell, the total energy imparted to the detector cell by all those collisions can be assumed to be approximately equal to an integer multiple of the beam energy of the charged particle microscope, where the integer multiple represents the number or number of charged particles. Furthermore, because the detector cell belongs to a non-buck-sin integrating detector, the imparted total energy can be recorded, registered, summed, or integrated. Also, the beam energy of the charged particle microscope can be assumed to be a known, selectively controllable, or otherwise easily identifiable value. Therefore, regardless of whether multiple charged particles collided with the detector cell simultaneously, consecutively in a short time, or within the same sampling iteration, the total number of charged particles that collided with the detector cell can be estimated by dividing the total integrated energy imparted to the detector cell by the beam energy.In other words, the various embodiments described herein can be considered specific pixel processing techniques that can be implemented on a charged particle microscope equipped with a non-Bucksin integrating detector, enabling accurate counting of charged particles even in the presence of pile-up effects. In other words, the various embodiments described herein can achieve higher or better charged particle counting accuracy than that achievable with conventional techniques via integrating detectors.

[0042] Furthermore, it is necessary to highlight the counterintuitive nature of the various embodiments described herein. As stated above, the central or fundamental principles of the prior art of charged particle counting via integrating detectors are: operating in a binary collision-on / no-collision counting mode rather than an integrating mode, and improving counting sensitivity by buck-thinning. The inventors have found that these central or fundamental principles of the prior art actually worsen the pile-up effect. In fact, as described herein, the inventors have found that the pile-up effect can be neutralized by deriving the charged particle count from the residual energy imparted to the non-buck-thinning integrating cell (e.g., the energy retained by the charged particle after interacting with the sample). After all, since each charged particle loses only a tiny fraction of its initial energy in the sample, each charged particle can be considered to impart almost its entire initial energy to a non-buck-thinning detector (in contrast, in a buck-thinning cell, each charged particle loses even more tiny fraction of its initial energy). Therefore, by dividing the total energy received by any non-Baccin integrating detector cell by the beam energy that drove the charged particles toward the sample, a scalar approximately equal (e.g., within rounding limits) to the total number of charged particles that collided with that non-Baccin integrating detector cell can be obtained. Counting charged particles in this manner can be considered a paradigm shift in the field of charged particle microscopy. Indeed, since the entire charged particle microscopy industry has traditionally taught counting charged particles via integrating detectors that are not Baccin-enhanced and operate in integrating mode, counting charged particles via an integrating detector that is not Baccin-enhanced and operates in integrating mode is entirely counterintuitive. In other words, leveraging the fact that the amount of total energy received by any non-Baccin integrating detector cell is approximately equal to a quantized integer multiple of the beam energy can be considered a clever, innovative, or even unconventional method for counting charged particles.

[0043] For at least the reasons stated above, the various embodiments described herein can be seen as addressing or mitigating various problems and disadvantages (e.g., pile-up effect) that arise in the prior art for facilitating charged particle counting. Accordingly, the various embodiments described herein can be seen as concrete and tangible technical improvements in the field of charged particle microscopy. Accordingly, the various embodiments described herein are certainly qualified as useful and practical applications of computers.

[0044] Furthermore, various embodiments described herein can control real-world tangible devices based on the disclosed teachings. For example, various embodiments described herein can electronically activate, deactivate, or otherwise operate real-world hardware (e.g., ion beam emitters, ion focusing lenses, transport fluid valves / pumps) of actual charged particle microscopes (e.g., SEMs, TEMs, dual-beam microscopes, EELMs).

[0045] Figure 1 shows an exemplary and non-limiting block diagram of a scientific instrument module 102 according to various embodiments described herein.

[0046] In various embodiments, the scientific instrument module 102 can be implemented by a circuit such as a programmed computing device (for example, including electrical or optical components). The logic of the scientific instrument module 102 can be contained in a single computing device or distributed across multiple computing devices in a manner that allows for communication as appropriate. Examples of computing devices that implement the scientific instrument module 102 individually or in combination are described herein with reference to Figure 15, and examples of systems or networks of interconnected computing devices in which the scientific instrument module 102 can be implemented across one or more computing devices are described herein with reference to Figure 16.

[0047] The scientific instrument module 102 may include a first logic 104 and a second logic 106. As used herein, the term “logic” may include a device that performs a set of operations associated with the logic. For example, any logic element included in the scientific instrument module 102 may be implemented by one or more computing devices and programmed with instructions that cause one or more processing units of those computing devices to perform a set of operations. In certain embodiments, a logic element may include one or more non-transient computer-readable media having instructions that, when executed by one or more processing units of one or more computing devices, cause the associated set of operations to be performed. As used herein, the term “module” refers to a collection of one or more logic elements that collectively perform a function associated with the module. Logic elements within a module may be identical or different in form. For example, some logic within a module may be implemented by a programmed general-purpose processing unit, while other logic within a module may be implemented by an application-specific integrated circuit (ASIC). As another example, different logic elements within a module may be associated with different sets of instructions executed by one or more processing units. A module may omit one or more logic elements shown in the relevant drawings. For example, if a module performs some of the operations discussed herein in relation to that module, the module may include a subset of the logic elements shown in the relevant drawings.

[0048] In various embodiments, a scientific instrument corresponding to the scientific instrument module 102 may exist. In various embodiments, the scientific instrument may be any suitable computerized device capable of electronically measuring scientifically relevant, clinically relevant, or research-relevant properties, characteristics, or attributes of an analyte (e.g., a known or unknown mixture, compound, or aggregate of substances). As an unrestricted example, the scientific instrument may be a scanning electron microscope (SEM). In this case, the scientific instrument can acquire images of the analyte to measure or determine its surface topography, surface material composition, or crystal structure. As another unrestricted example, the scientific instrument may be a electron microscope (TEM). In this case, the scientific instrument can acquire images of the inside of the analyte to measure or determine details of its internal structure. As yet another unrestricted example, the scientific instrument may be a dual-beam microscope. In this case, the scientific instrument can acquire images of the analyte in addition to being able to mill it. As yet another unrestricted example, the scientific instrument may be an electron energy loss microscope (EELM). In this case, the scientific instrument can acquire spectral images showing the electron energy loss spectrum at each physical location of the analyte. As a more general, non-limiting example, the scientific instrument may be any suitable type of charged particle microscope (for example, some types of microscopes can acquire images using ion beams other than electrons). In various cases, the scientific instrument may be equipped with a non-Bachsin integrating detector.

[0049] In various embodiments, the first logic 104 can access an energy integral image of a sample that may have been acquired or generated by a non-backsin integrating detector of a scientific instrument. More specifically, the first logic 104 can: cause a charged particle microscope to acquire multiple preliminary energy integral images of a sample; obtain multiple floored energy integral images by assigning 0 to pixels in the multiple preliminary energy integral images where the charged particle energy intensity is below a noise threshold; or obtain an energy integral image by performing a pixel-level sum or average of the multiple floored energy integral images.

[0050] In various embodiments, the second logic 106 can count the number of charged particle events (e.g., charged particle collisions) corresponding to each pixel in the energy integral image by rounding the cumulative charged particle energy intensity shown in each pixel cluster to the nearest integer multiple of the beam energy of the charged particle microscope. In various embodiments, the second logic 106 can: identify each cluster by applying a community detection algorithm to the energy integral image. In various cases, for a first cluster having a first cumulative charged particle energy intensity, the second logic 106 can: obtain a quotient by dividing the first cumulative charged particle energy intensity by the beam energy of the charged particle microscope, obtain a rounded quotient by rounding the quotient to the nearest integer, and determine that the number of charged particle events corresponding to the first cluster is equal to the rounded quotient. In various cases, if the first pixel of the first cluster has a first integrated charged particle energy intensity, the second logic 106 can: obtain a ratio by dividing the first integrated charged particle energy intensity by the first cumulative charged particle energy intensity, obtain a product by multiplying the ratio by the rounded quotient, and determine that the number of charged particle events corresponding to the first pixel is equal to that product.

[0051] Therefore, the scientific instrument module 102 can facilitate charged particle counting via a non-Bachsin integral detector and beam energy quantization.

[0052] Figure 2 shows an illustrative, non-limiting flowchart of a computer implementation method 200 according to various embodiments described herein. The operation of the computer implementation method 200 can perform appropriate actions when used in the appropriate context (for example, by any of the various modules, computing devices, or graphical user interfaces described in Figures 15 and 16, or in combination thereof). In Figure 2, the operations are shown once each and in a specific order, but the operations can be rearranged and repeated as appropriate (for example, different operations to be performed can be executed in parallel as appropriate).

[0053] In various embodiments, step 202 may include a first operation that provides access to an energy integral image of a sample scanned with a charged particle microscope equipped with a non-bucksin integrating detector by a device operationally connected to the processor. In various cases, the first logic 104 can perform or facilitate step 202.

[0054] In various embodiments, step 204 may include the device counting the number of charged particle events corresponding to each pixel in the energy integral image, based on rounding the cumulative charged particle energy intensity shown in each pixel cluster to the nearest integer multiple of the beam energy of the charged particle microscope. In various cases, the second logic 106 can perform or facilitate step 204.

[0055] Therefore, the computer implementation method 200 facilitates the counting of charged particles via a non-Bachsin integral detector and beam energy quantization.

[0056] Figure 3 shows a block diagram of an exemplary, non-limiting system that facilitates non-bucksin integral detectors and charged particle counting via beam energy quantization, according to one or more embodiments described herein.

[0057] In various embodiments, the charged particle microscope 302 may exist. In various embodiments, the charged particle microscope 302 may be configured as described above. That is, the charged particle microscope 302 may be any suitable computerized device that can electronically acquire any suitable image of any suitable analyte by utilizing its constituent hardware (e.g., electron source, anode, condenser lens, condenser aperture, scan coil, objective lens, objective aperture, deflector, capacitor, stigmeter, electron detector, X-ray detector, and operable sample stage). As an unrestricted example, the charged particle microscope 302 may be any suitable SEM. As another unrestricted example, the charged particle microscope 302 may be any suitable TEM. As yet another unrestricted example, the charged particle microscope 302 may be any suitable dual-beam microscope. As yet another unrestricted example, the charged particle microscope 302 may be any suitable EELM.

[0058] In any case, the charged particle microscope 302 may have, be equipped with, or otherwise possess an integrating detector 303. In various embodiments, the integrating detector 303 may be any suitable type of charged particle detector capable of measuring the accumulated (e.g., total or integral) energy of charged particles colliding with individual cells. As an unrestricted example, the integrating detector 303 may be any suitable type of Faraday cup detector. As another unrestricted example, the integrating detector 303 may be any suitable type of capacitive detector. As yet another unrestricted example, the integrating detector 303 may be any suitable type of semiconductor detector, such as a PIN diode. As yet another unrestricted example, the integrating detector 303 may be any suitable type of microchannel plate detector. As yet another unrestricted example, the integrating detector 303 may be any suitable type of charge-coupled device (CCD). In various embodiments, the integrating detector 303 may be non-buck-thin. In other words, the thickness of the integrating detector 303 may be greater than or equal to the penetration depth of the charged particles configured to be emitted by the charged particle microscope 302 toward the integrating detector 303.

[0059] Although not explicitly stated, the integrating detector 303 has p cells, but any appropriate positive integer

number

[0060] Although not explicitly shown in the figure, the charged particle microscope 302 may be electronically integrated with any suitable human-computer interface device located remotely or in close proximity to the charged particle microscope 302. Thus, users or technicians associated with the charged particle microscope 302 can interact with or otherwise control the charged particle microscope 302. In non-limiting examples, the human-computer interface device may be a keyboard for the charged particle microscope 302, a keypad for the charged particle microscope 302, a touchscreen for the charged particle microscope 302, or a voice command system for the charged particle microscope 302.

[0061] Although not explicitly shown in the figure, the charged particle microscope 302 may have multiple configurable operating settings. In various embodiments, each of the multiple configurable operating settings is any appropriate hardware-related or software-related characteristic of the charged particle microscope 302 that can direct, influence, or determine how the charged particle microscope 302 performs, operates, or functions with respect to a particular analyte, and can be selectively controlled, modified, adjusted, or otherwise configured by the user or technician (for example, through interaction with the human-computer interface device of the charged particle microscope 302). In a non-limiting example, one of the multiple configurable operating settings may be a user-controllable voltage setting (e.g., beam voltage) or current setting (e.g., beam current), which allows the user or technician to selectively control the electrodes of the charged particle microscope 302 and selectively increase or decrease the voltage or current applied within or by the charged particle microscope 302. As another non-limiting example, one of several configurable operating settings may be a user-controllable temperature setting, which allows the user or technician to control the heaters (e.g., stage heater, heating coil) or coolers (e.g., cooling fan, heat pump, refrigeration unit) of the charged particle microscope 302 to selectively increase or decrease the temperature inside the charged particle microscope 302 or the temperature applied by the charged particle microscope 302. As yet another non-limiting example, one of several configurable operating settings may be a user-controllable mechanical actuator setting, which allows the user or technician to control the mechanical actuators (e.g., electric motor, sample stage, aperture, fluid pump, or syringe) of the charged particle microscope 302 to selectively drive the mechanical actuators. As yet another non-limiting example, one of the multiple configurable operating settings may be a user-controllable optical setting, which allows the user or technician to control the optical elements of the charged particle microscope 302 (e.g., optical lenses, optical deflectors) and selectively change the optical properties applied by the charged particle microscope 302 (e.g., size or position of the focal spot, astigmatism, focus shift).In either case, the beam energy (which may be a function of beam voltage or beam current) can be considered one of the configurable operating settings of the charged particle microscope 302.

[0062] In various cases, the charged particle microscope 302 can be equipped with a sample 304. In non-limiting examples, the sample 304 may be placed, mounted, or otherwise fixed on the sample stage of the charged particle microscope 302 so that the sample 304 can be analyzed or scanned by the charged particle microscope 302. In various cases, the sample 304 may be any type of synthetic or natural sample that can exhibit appropriate physical, chemical, compositional, or other properties, attributes, or characteristics. In the case of a synthetic sample, the sample 304 may be fabricated by appropriate microfabrication or nanofabrication techniques such as etching, milling, or deposition. In non-limiting examples, the sample 304 may be a lamellar taken from a semiconductor substrate or wafer. In non-limiting examples, the sample 304 may be any other appropriate integrated circuit element or printed circuit board element. However, in the case of a natural sample, the sample 304 may be an organic or biological sample (e.g., a tissue sample).

[0063] In various cases, it may be desirable to count charged particles on or in the sample 304. In various cases, a system 306 that can be electronically integrated with the charged particle microscope 302 (for example, via a suitable wired or wireless electronic connection) can perform counting as described herein.

[0064] In various embodiments, the system 306 may include a processor 308 (e.g., a computer processing unit, a microprocessor) and a non-volatile computer-readable memory 310 that is operable, operable, or communicatively connected to or coupled to the processor 308. The non-volatile computer-readable memory 310 may store computer-executable instructions that, when executed by the processor 308, cause the processor 308 or other components of the system 306 (e.g., a scan component 312, a quantization component 314) to perform one or more operations. In various embodiments, the non-volatile computer-readable memory 310 may also store computer-executable components (e.g., a scan component 312, a quantization component 314), and the processor 308 can execute these computer-executable components.

[0065] In various embodiments, system 306 can access the charged particle microscope 302 electronically. That is, system 306 can communicate electronically with the charged particle microscope 302 or otherwise interact with it electronically (e.g., send electronic instructions or commands, receive electronic data). Thus, any component of system 306 can similarly interact with, communicate with, or otherwise operate the charged particle microscope 302.

[0066] In various embodiments, the system 306 may include a scanning component 312. In various forms, the scanning component 312 is generated by a charged particle microscope 302, as described herein, and can access an energy integral image corresponding to the sample 304.

[0067] In various embodiments, the system 306 may include a quantization component 314. In various cases, the quantization component 314 can identify the pixel-level charged particle count based on the energy integral image and the beam energy used by the charged particle microscope 302 when generating the energy integral image, as described herein.

[0068] It should be noted that in various cases, the scan component 312 and the quantization component 314 may be considered collectively as one or more software components 311 of the system 306. It should also be noted that in various forms, one or more software components 311 are described herein, for the sake of ease of description and illustration, primarily as comprising two components (e.g., the scan component 312 and the quantization component 314). However, one or more software components 311 are not limited to being implemented as exactly these two components in each embodiment. In fact, in some embodiments, the functions of these two components described herein may be integrated in an appropriate manner and implemented by fewer than two components (for example, in some cases, a single component may perform all the functions described herein with respect to the scan component 312 and the quantization component 314). In other embodiments, the functions of these two components described herein may be appropriately distributed, separated, divided, or subdivided and implemented by more than two components (for example, two or more components may facilitate a function that can be performed by the scanning component 312, or two or more components may facilitate a function that can be performed by the quantization component 314).

[0069] Figure 4 shows an exemplary and non-limiting block diagram of an energy integral image and beam energy that, according to one or more embodiments described herein, can facilitate charged particle counting via a non-bucksin integral detector and beam energy quantization.

[0070] In various embodiments, the scanning component 312 can electronically cause the charged particle microscope 302 to acquire or generate an energy integral image 402 of the sample 304 according to the beam energy 404. Various non-limiting embodiments are described with reference to Figures 5-7.

[0071] Figures 5–7 show exemplary, non-limiting block diagrams illustrating methods for accessing energy integral images according to one or more embodiments described herein.

[0072] First, refer to Figure 5. In various embodiments, the scan component 312 can electronically instruct or command the charged particle microscope 302 to perform multiple scans on the sample 304 according to an appropriate energy integration imaging protocol. In some embodiments, the energy integration imaging protocol may require that the configurable operating settings of the charged particle microscope 302 have or take an appropriate default value or state. In other embodiments, the energy integration imaging protocol may require that the configurable operating settings of the charged particle microscope 302 have or take an appropriate value or state selected by the user of the charged particle microscope 302 (for example, selected via the human-computer interface device of the charged particle microscope 302). In any case, the scan component 312 can cause the charged particle microscope 302 to perform multiple energy integration scans on the sample 304. The value or state that the beam energy setting of the charged particle microscope 302 has during or for such multiple scans is referred to as the beam energy 404. As a non-limiting example, the beam energy 404 may be 300 keV. In that case, during each of the multiple scans, the charged particle microscope 302 can be considered to be irradiating or bombarding the sample 304 with a charged particle beam, and each charged particle in the beam can be considered to have an initial energy of 300 keV (for example, in reality some of the charged particles in the beam have an initial energy of 300 keV). (The initial energy is slightly greater than keV, and other charged particles have initial energies slightly less than 300 keV, but these different energies average out to 300 keV).

[0073] In various configurations, by performing multiple scans on the sample 304, the charged particle microscope 302 can electronically acquire, generate, or otherwise create multiple preliminary energy integral images 502. In various cases, each of the multiple preliminary energy integral images 502 can correspond to multiple scans (for example, in a one-to-one correspondence). The multiple scans are any appropriate positive integer in the nth scan.

number

[0074] As a non-limiting example, a preliminary energy integral image 502(1) may be created, acquired, or generated by the charged particle microscope 302 during the first scan of the sample 304. As shown, the preliminary energy integral image 502(1) may consist of p pixels, from pixel 502(1)(1) to pixel 502(1)(p). In various cases, any scalar value or magnitude shown by pixel 502(1)(1) can be considered to represent the sum or integrated energy amount that collided with the first cell of the integral detector 303 during the first scan (and thus the energy amount that passed through the first physical region of the sample 304) (for example, directly in eV units or indirectly in arbitrary units). Similarly, any scalar value or magnitude shown by pixel 502(1)(p) can be considered to represent the sum or integrated energy amount that collided with the p-th cell of the integral detector 303 during the first scan (and thus the energy amount that passed through the p-th physical region of the sample 304).

[0075] As yet another non-limiting example, a preliminary energy integral image 502(n) may be created, acquired, or generated by the charged particle microscope 302 during the nth scan of sample 304. As shown, the preliminary energy integral image 502(n) may consist of p pixels, from pixel 502(n)(1) to pixel 502(n)(p). In various cases, any scalar value or magnitude shown by pixel 502(n)(1) can be considered to represent the sum or integrated energy amount that collided with the first cell of the integral detector 303 during the nth scan (and thus the energy amount that passed through the first physical region of sample 304) (for example, directly in eV units or indirectly in arbitrary units). Similarly, any scalar value or magnitude shown by pixel 502(n)(p) can be considered to represent the sum or integrated energy amount that collided with the p-th cell of the integral detector 303 during the nth scan (and thus the energy amount that passed through the p-th physical region of sample 304).

[0076] Low-dose conditions (for example, with a beam energy of 300 keV)

number

number

[0077] In various embodiments, as shown in Figure 6, the scan component 312 can perform conditional pixel-level flooring (also known as conditional pixel-level masking) on ​​multiple preliminary energy integral images 502 based on a noise threshold 602.

[0078] In various forms, the noise threshold 602 may be any suitable scalar that represents the maximum energy level known or otherwise considered to be the upper limit of noise for the integral detector 303 (for example, expressed directly in eV units or indirectly in any appropriate unit), and is indicated by its value or magnitude. In other words, if a cell of the integral detector 303 outputs an integral energy value exceeding the noise threshold 602, it can be concluded that at least some charged particles have collided with that cell. Conversely, if a cell of the integral detector 303 outputs an integral energy value less than the noise threshold 602, it can be concluded that no part of any charged particles has collided with that cell. That is, integral signals recorded by the integral detector 303 and below the noise threshold 602 can be considered to be the result of pure noise. Conversely, integral signals recorded by the integral detector 303 and above the noise threshold 602 can be considered to be the result of a charged particle collision (also called a charged particle impact or charged particle event).

[0079] In various forms, the scan component 312 can electronically floor pixels from a plurality of preliminary energy integral images 502 whose indicated value is less than the noise threshold 602. This conditional flooring may result in a plurality of floored energy integral images 604. In various cases, each of the multiple floored energy integral images 604 can correspond to a plurality of preliminary energy integral images 502 (for example, in a one-to-one correspondence). Therefore, since the plurality of preliminary energy integral images 502 have n images, the plurality of floored energy integral images 604 can similarly have n images, from floored energy integral image 604(1) to floored energy integral image 604(n). In various cases, each of the plurality of floored energy integral images 604 can be derived from each of the plurality of preliminary energy integral images 502. Therefore, each of the plurality of floored energy integral images 604 can have the same number and array of pixels (for example, p) as each of the plurality of preliminary energy integral images 502.

[0080] As a non-limiting example, the scan component 312 can convert a preliminary energy integral image 502(1) into a floored energy integral image 604(1), which may consist of p pixels, from pixel 604(1)(1) to pixel 604(1)(p). More specifically, the scan component 312 can determine whether pixel 502(1)(1) shows a summed or integrated energy value less than the noise threshold 602. If the summed or integrated energy value of pixel 502(1)(1) exceeds the noise threshold 602, the scan component 312 can make pixel 604(1)(1) show the same summed or integrated energy value as pixel 502(1)(1). On the other hand, if the sum or integrated energy value of pixel 502(1)(1) is less than the noise threshold 602, the scan component 312 may cause pixel 604(1)(1) to show a sum or integrated energy value of 0. In other words, the scan component 312 may floor pixel 502(1)(1) to 0 if pixel 502(1)(1) is less than the noise threshold 602. Similarly, the scan component 312 can determine whether pixel 502(1)(p) shows a sum or integrated energy value less than the noise threshold 602. If the sum or integrated energy value of pixel 502(1)(p) exceeds the noise threshold 602, the scan component 312 may cause pixel 604(1)(p) to show the same sum or integrated energy value as pixel 502(1)(p). On the other hand, if the sum or integrated energy value of pixel 502(1)(p) is less than the noise threshold 602, the scan component 312 may cause pixel 604(1)(p) to show a sum or integrated energy value of 0. As described above, if pixel 502(1)(p) is less than the noise threshold 602, it can be considered to floor pixel 502(1)(p) to 0.

[0081] As yet another non-limiting example, the scan component 312 can convert a preliminary energy integral image 502(n) into a floored energy integral image 604(n), which may consist of p pixels, from pixel 604(n)(1) to pixel 604(n)(p). More specifically, the scan component 312 can determine whether pixel 502(n)(1) shows a summed or integrated energy value less than the noise threshold 602. If the summed or integrated energy value of pixel 502(n)(1) exceeds the noise threshold 602, the scan component 312 can make pixel 604(n)(1) show the same summed or integrated energy value as pixel 502(n)(1). On the other hand, if the sum or integrated energy value of pixel 502(n)(1) is less than the noise threshold 602, the scan component 312 may cause pixel 604(n)(1) to show a sum or integrated energy value of 0. In other words, the scan component 312 may floor pixel 502(n)(1) to 0 if pixel 502(n)(1) is less than the noise threshold 602. Similarly, the scan component 312 can determine whether pixel 502(n)(p) shows a sum or integrated energy value less than the noise threshold 602. If the sum or integrated energy value of pixel 502(n)(p) exceeds the noise threshold 602, the scan component 312 may cause pixel 604(n)(p) to show the same sum or integrated energy value as pixel 502(n)(p). On the other hand, if the sum or integrated energy value of pixel 502(n)(p) is less than the noise threshold 602, the scan component 312 may cause pixel 604(n)(p) to show a sum or integrated energy value of 0. As described above, if pixel 502(n)(p) is less than the noise threshold 602, it can be considered to floor pixel 502(n)(p) to 0.

[0082] It should be noted that, due to this conditional pixel-level flooring (for example, under low-dose conditions), it is not always the case that any of the multiple floored energy integral images 604 appear completely noisy. Rather, many of the multiple floored energy integral images 604 may appear completely blank (e.g., black, zero only), and only a very small number of the multiple floored energy integral images 604 may have meaningful non-zero integrated energy values. In other words, due to the conditional pixel-level flooring described herein, the overwhelming noise that may have been present in the multiple preliminary energy integral images 502 can be considered absent in the multiple floored energy integral images 604.

[0083] Now, refer to Figure 7. In various embodiments, the scan component 312 can aggregate or otherwise combine multiple floored energy integral images 604 to obtain an energy integral image 402. As an unrestricted example, the energy integral image 402 may be equal to, or based on, the pixel-level sum of the multiple floored energy integral images 604. In such a case, the first pixel of the energy integral image 402 is equal to the sum of pixels 604(1)(1) through 604(n)(1), and the p-th pixel of the energy integral image 402 is equal to the sum of pixels 604(1)(p) through 604(n)(p). As yet another unrestricted example, the energy integral image 402 may be equal to, or based on, the pixel-level average of the multiple floored energy integral images 604. In such cases, the first pixel of the energy integral image 402 is equal to the average of pixels 604(1)(1) through 604(n)(1), and the p-th pixel of the energy integral image 402 is equal to the average of pixels 604(1)(p) through 604(n)(p). In either case, the energy integral image 402 can be considered unaffected by the overwhelming noise that may have been present in the multiple preliminary energy integral images 502, due to conditional pixel-level flooring based on the noise threshold 602.

[0084] Figure 8 shows an exemplary and non-limiting block diagram of a multi-pixel-level charged particle counting system that, according to one or more embodiments described herein, can facilitate charged particle counting via a non-bucksin integral detector and beam energy quantization.

[0085] In various embodiments, the quantization component 314 can electronically calculate, compute, or otherwise electronically estimate a plurality of pixel-level charged particle counts 802 based on the energy integral image 402 and the beam energy 404. Non-limiting embodiments are described with reference to Figures 9-12.

[0086] Figures 9–12 show illustrative and non-limiting block diagrams illustrating how a multi-pixel-based charged particle count 802 may be determined according to one or more embodiments described herein.

[0087] First, refer to Figure 9. In various embodiments, the quantization component 314 can apply a suitable community detection algorithm to the pixels of the energy integral image 402. As an unrestricted example, the community detection algorithm may be the Louvain algorithm. As yet another unrestricted example, the community detection algorithm may be the Girvan-Newman algorithm. As yet another unrestricted example, the community detection algorithm may be the Kernighan-Lin algorithm. As yet another unrestricted example, the community detection algorithm may be a spectral clustering algorithm. As yet another unrestricted example, the community detection algorithm may be the infomap algorithm. As yet another unrestricted example, the community detection algorithm may be a label propagation algorithm. As yet another unrestricted example, the community detection algorithm may be an edge centrality algorithm. As yet another unrestricted example, the community detection algorithm may be a stochastic block model algorithm. As yet another unrestricted example, the community detection algorithm may be a creek percolation algorithm. As yet another unrestricted example, the community detection algorithm may be a walk trap algorithm. In either case, applying the community detection algorithm to the energy integral image 402 may allow for the identification of multiple pixel clusters 902.

[0088] In various embodiments, the multiple pixel clusters 902 have m clusters of any appropriate positive integer

number

[0089] Next, consider FIG. 10. FIG. 10 shows pixel cluster 902(r) of the plurality of pixel clusters 902, where r is any suitable positive integer [Number] corresponding thereto. As shown, pixel cluster 902(r) can be composed of a total of q r pixels for any suitable positive integer q r and ranges from non-zero pixel 902(r)(1) to non-zero pixel 902(r)(q r ).

[0090] In various forms, the pixels of pixel cluster ۹۰۲(r) can be collectively considered as representing a cluster 1002 of integrated charged particle energy intensity. As a non-limiting example, any value or magnitude indicated by non-zero pixel 902(r)(1) may be referred to as integrated charged particle energy intensity 1002(1). As a further non-limiting example, any value or magnitude indicated by non-zero pixel 902(r)(q r ) may be referred to as integrated charged particle energy intensity 1002(q r ). In various cases, integrated charged particle energy intensities 1002(1) to 1002(q r ) can be collectively considered as a cluster 1002 of integrated charged particle energy intensity.

[0091] In various cases, the quantization component 314 can sum or add up clusters 1002 of integrated charged particle energy intensities. In various forms, the scalar resulting from such summing or addition may be called the cumulative integrated charged particle energy intensity 1004.

[0092] Next, refer to Figure 11. In various embodiments, the quantization component 314 can divide the cumulative integrated charged particle energy intensity 1004 by the beam energy 404. To perform this division, it should be understood or recognized that the beam energy 404 is expressed or formatted in the same units as the cumulative integrated charged particle energy intensity 1004. As an unrestricted example, assume that the cells of the integral detector 303 directly measure the energy in eV units. In such a case, the beam energy 404 can also be expressed in eV units. As yet another unrestricted example, assume that the cells of the integral detector 303 indirectly measure the energy using arbitrary units correlated or mapped to eV. In some of such cases, the beam energy 404 can also be expressed in those arbitrary units. However, in some other such cases, the beam energy 404 is expressed in eV, and the cumulative integrated charged particle energy intensity 1004 can be converted or rewritten to eV.

[0093] In any case, the quotient 1102 can be obtained by dividing the cumulative integrated charged particle energy intensity 1004 by the beam energy 404. In various forms, the quantization component 314 can numerically round the quotient 1102 to the nearest integer. In various cases, this nearest integer is sometimes called the cumulative charged particle count 1104. In other words, the cumulative charged particle count 1104 may be equal to, or based on, the following formula:

number

[0094] It should be noted that the cumulative charged particle count 1104 can be calculated even when two or more charged particles collide with a cell corresponding to a pixel cluster 902(r) simultaneously, consecutively, or pile up with each other within the same sampling iteration. In other words, the accuracy of the cumulative charged particle count 1104 can be considered not to be reduced or degraded by the pile-up effect.

[0095] Next, refer to Figure 12. In various embodiments, the quantization component 314 can calculate or compute a set of pixel-level charged particle counts 1202 based on clusters 1002 of integrated charged particle energy intensities, cumulative integrated charged particle energy intensities 1004, and cumulative charged particle counts 1104. Specifically, for each integrated charged particle energy intensity within clusters 1002 of integrated charged particle energy intensities, the quantization component 314 can: divide the integrated charged particle energy intensity by the cumulative integrated charged particle energy intensity 1004 to obtain a ratio, and then multiply that ratio by the cumulative charged particle counts 1104. The result of this multiplication is a value corresponding to each of the pixel-level charged particle counts 1202.

[0096] As a non-restrictive example, consider the integrated charged particle energy intensity 1002(1). In various forms, 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 cases, the quantization component 314 can obtain a first product by multiplying this first ratio by the cumulative charged particle count 1104. In various cases, this first product may be called the pixel-level charged particle count 1202(1). In various forms, the pixel-level charged particle count 1202(1) is a scalar whose value or magnitude can indicate the number of charged particles that collided with any cell of the integral detector 303 corresponding to a non-zero pixel 902(r)(1) (for example, the one that stores its integrated energy measurement). Note that the pixel-level charged particle count 1202(1) may not be an integer. This is because, at the moment a charged particle collides with the integrating detector 303, it may span two or more cells (for example, 30% of the charged particle may collide with the first cell and the remaining 70% with an adjacent cell).

[0097] As yet another non-restrictive example, consider the integrated charged particle energy intensity 1002(q r Consider the following. In various forms, the quantization component 314 has an integrated charged particle energy intensity of 1002(q r By dividing ) by the cumulative integrated charged particle energy intensity of 1004, the q r The ratio can be calculated. Furthermore, in various cases, the quantization component 314 can calculate the q r By multiplying the ratio by the cumulative charged particle count of 1104, the q r The product of can be obtained. In various cases, the q r The product of these is the charged particle count per pixel, 1202(q r It is sometimes called ). In various forms, the charged particle count per pixel is 1202 (q r ) is a scalar whose value or magnitude is a non-zero pixel 902(r)(q rThis can indicate the number of charged particles that collided with any cell of the integral detector 303 corresponding to (for example, the cell that stores the integrated energy measurement). In the above, the number of charged particles per pixel is 1202 (q r ) may not be an integer. This is because the charged particle may span two or more cells at the moment it collides with the integrated detector 303.

[0098] In various cases, the charged particle count per pixel is 1202(1) to the charged particle count per pixel is 1202(q r Up to this point, they can be considered to form a set of pixel-level charged particle counts 1202. In various forms, the set of pixel-level charged particle counts 1202 can be considered to belong to, or constitute at least a part of, a group of pixel-level charged particle counts 802. Furthermore, if the cumulative charged particle count 1104 is not degraded by the pile-up effect, then each of the pixel-level charged particle counts 1202 can similarly be considered not to be degraded by the pile-up effect.

[0099] In various embodiments, the quantization component 314 can repeat the operations described above with respect to Figures 9-12 for each of the multiple pixel clusters 902. By performing these operations for each of the multiple pixel clusters 902, the quantization component 314 can fully position, fully calculate, or fully compute the multiple pixel-level charged particle counts 802. In other words, by performing these operations for each of the multiple pixel clusters 902, the quantization component 314 can determine the number of charged particle collisions or events represented by each non-zero pixel in the energy integral image 402 (for example, a pixel floored to 0 can be considered to have 0 charged particle collisions or events).

[0100] In various embodiments, the quantization component 314 can electronically perform any appropriate operation based on a plurality of pixel-level charged particle counts 1202. As an unrestricted example, the quantization component 314 can electronically display any of the plurality of pixel-level charged particle counts 1202 on a suitable computer screen or electronic display. As yet another unrestricted example, the quantization component 314 can electronically transmit any of the plurality of pixel-level charged particle counts 1202 to a suitable other computing device. As yet another unrestricted example, the quantization component 314 can electronically apply a suitable downstream or follow-up analysis technique to any of the plurality of pixel-level charged particle counts 1202 (for example, there may be a processing technique configured to receive a charged particle count as input and generate as output a compositional or chemical prediction about the sample from which the charged particle count originates).

[0101] Figures 13-14 show experimental results related to exemplary and non-limiting examples of one or more embodiments described herein.

[0102] To implement the various embodiments described herein, it is necessary to obtain the beam energy 404. As previously stated, the beam energy 404 may be a controllable, configurable, or otherwise selectable setting or parameter of the charged particle microscope 302, if it is directly expressed in at least eV units. However, the integrating detector 303 is often configured to output measurements in arbitrary units (e.g., detector number (DN)) rather than eV units. In such cases, how the arbitrary units map to or correlate with eV units may initially be unclear. Therefore, in such cases, the various embodiments described herein can be implemented by first creating a calibration curve such that the curve shows measurements in specific arbitrary units corresponding to the arbitrary eV value for which the beam energy 404 is set, and then performing quantization as described above. Non-limiting forms of such calibration are described with respect to Figures 13-14.

[0103] Figure 13 shows the calibration curve 1300. The calibration curve 1300 was created by irradiating the charged particle microscope 302 with the integrating detector 303 with a beam energy of 404 multiple times before the sample 304 was loaded (for example, so that the integrating detector 303 ultimately records tens of thousands or hundreds of thousands of charged particle events). For each energy integral image generated during these multiple scans, the pixels of the energy integral image were clustered (as described above), and the cumulative energy amount shown by each cluster in arbitrary units was recorded. This cumulative energy amount is represented on the horizontal axis of the calibration curve 1300. The vertical axis of the calibration curve 1300 represents the number of times each cluster obtained during these multiple scans showed its respective cumulative energy value. For example, as shown, approximately 35,000 clusters obtained during these multiple scans showed a cumulative energy value slightly above 500 DN. As another example, approximately 115,000 clusters obtained during these multiple scans showed a cumulative energy value of approximately 3700 DN. As another example, the approximately 10,000 clusters obtained during the multiple scans showed a cumulative energy value of approximately 7400 DN. Note the large peak at 3700 DN. Also note the subsequent peak at 7400 DN. When the beam energy 404 corresponds to a low dose (for example, below any appropriate threshold), it is expected that there will be far more individual charged particle events (e.g., single-particle collisions) than pile-up events. Based on this expectation, and given that 7400 DN is twice that of 3700 DN, it can be estimated that a single charged particle event corresponds to 3700 DN. In other words, it can be concluded that when a single charged particle with beam energy 404 collides with the cell of the integral detector 303, an amount of energy is accumulated in the cell that produces an output of approximately 3700 DN. Therefore, two piled-up charged particles correspond to approximately 7400 DN (for example, 2 × 3700), three piled-up charged particles correspond to approximately 11,100 DN (for example, 3 × 3700), and so on.The portion of the calibration curve preceding the large peak at 3700DN can be considered primarily a result of noise and may therefore be discarded or ignored. Thus, if the beam energy 404 is found to correlate or map to the detector output 3700DN (in this non-limiting example), the detector output 3700DN can be used in the aforementioned multiplication and division operations to calculate the number of charged particle collisions or events shown in any energy integral image (e.g., pixels of energy integral image 402).

[0104] Figure 14 shows graphs 1402 and 1404.

[0105] Graph 1402 is a non-restrictive example of a single acquisition frame obtained by the charged particle microscope 302. The horizontal axis of Graph 1402 represents the pixel identifier or pixel index, and the vertical axis represents the intensity shown by each pixel in DN units. According to the non-restrictive example shown in Graph 1402, the 650th pixel of any energy integral image represented in Graph 1402 shows an energy level of approximately 7400 D.N., and the 950th pixel of the same image shows an energy level of approximately 3700 D.N. Therefore, it can be concluded that two charged particles collided with the 650th pixel and a single charged particle collided with the 950th pixel. As mentioned above, energy dispersion filters may be implemented to ensure that only particles with a specific uniform or constant energy loss collide with the integral detector 303. Therefore, if an energy dispersion filter is implemented for any energy integral image corresponding to Graph 1402, the two particles that collide with pixel 650 and the one particle that collide with pixel 950 can all be considered to have the same energy loss (for example, as having any energy loss that is allowed or not denied by the energy dispersion filter). In that case, any cumulative particle count corresponding to that energy loss can be increased by 3 (for example, by 2 for pixel 650 and by 1 for pixel 950). This increment can also be made if the two particles that collide with pixel 650 are piled up (for example, occurring within the same sampling iteration). In this way, an accurate particle count that is not hindered by pile-up effects can be calculated for any energy loss level or energy loss bin.

[0106] In various forms, the particle count for each energy loss can be visually represented in a separate graph, such as Graph 1404. Specifically, the horizontal axis of Graph 1404 represents the energy loss level or energy loss bin, and the vertical axis represents the particle count corresponding to each energy loss level or bin. In a non-restrictive example of Graph 1404, the specific charged particle being counted is an electron. In some cases, an energy dispersion filter can be implemented to guide or target charged particles with specific energy losses to their respective pixels (for example, only particles with the first energy loss can collide with the first pixel, only particles with the second energy loss can collide with the second pixel, and only particles with the third energy loss can collide with the third pixel). In such cases, each point in Graph 1404 can be considered to be based on data integrated or accumulated by each pixel, or to be composed of each pixel. As a non-restrictive example, as shown in Graph 1404, one peak or point can be considered to be based on data acquired by pixel 650, and another peak or point can be considered to be based on data acquired by pixel 950.

[0107] These experimental results help demonstrate that the various embodiments described herein can count charged particles more accurately and reliably than conventional techniques. Therefore, the various embodiments described herein certainly constitute concrete and tangible technical improvements in the field of charged particle microscopy.

[0108] In various forms, the various embodiments described herein can be implemented in dual-use charged particle microscopy scenarios. As a non-limiting example, when the beam energy 404 corresponds to a high dose (e.g., above any appropriate threshold), system 306 can facilitate charged particle counting using a pulse counting method, and when the beam energy 404 corresponds to a low dose (e.g., below any appropriate threshold), system 306 can facilitate charged particle counting using the integral and quantization methods described herein.

[0109] In various cases, machine learning algorithms or models can be implemented in any suitable manner to facilitate the relevant aspects described herein. To facilitate the aforementioned machine learning aspects of the various embodiments described herein, consider the following description of artificial intelligence (AI). The various embodiments described herein can utilize artificial intelligence to facilitate the automation of one or more functions or actions. Components can use various AI-based schemes to implement the various embodiments / examples disclosed herein. To provide or assist in many of the decisions described herein (e.g., decisions, confirmations, inferences, calculations, predictions, forecasts, estimations, derivations, foresights, detections, computations), components described herein can examine all or a subset of data to which they are granted access and infer or determine the state of a system or environment from a set of observations captured through events or data. Decisions can be used to identify specific situations or actions, and can also, for example, generate probability distributions between states. Decisions can be probabilistic, i.e., they compute probability distributions for interesting states based on considerations of data and events. The term "decision" may also refer to techniques used to construct higher-level events from a set of events or data.

[0110] Such decisions may result in the construction of new events or behaviors from an observed set of events or stored event data, regardless of whether the events are temporally close and correlated, and regardless of whether the events and data come from one or more event and data sources. The components disclosed herein relate to various classification schemes or systems (e.g., support vector systems, neural networks, expert systems, Bayesian belief networks, fuzzy logic, data fusion engines, etc.) that are explicitly trained (e.g., through training data) and implicitly trained (e.g., through observation of behavior, preferences, historical information, external information, etc.) to perform actions automatically or determined in relation to the claimed subject. Thus, many functions, evaluations, or decisions can be automatically learned and performed using classification schemes or systems.

[0111] The classifier takes an input attribute vector, z=(z1, z2, z3, z 4、 zn) 、 The input can be mapped to a degree of confidence that it belongs to a class, such as f(z) = confidence(class). In such classifications, probabilistic or statistical analysis (e.g., factoring the utility and cost of the analysis) can be used to determine the actions to be taken automatically. Support vector machines (SVMs) can be an example of a classifier that can be used. SVMs work by finding a hypersurface in the input space, which attempts to separate trigger criteria from non-trigger events. Intuitively, this correctly classifies test data that is similar to but not identical to the training data. Other direct and indirect model classification approaches can be used, including, for example, naive Bayes, Bayesian networks, decision trees, neuronetworks, fuzzy logic models, or probabilistic classification models that provide various independence patterns. Classifications used herein also include statistical regressions used to develop priority models.

[0112] To provide additional context to the various embodiments described herein, Figure 15 and the following discussion are intended to provide a brief general description of a suitable computing environment 1500 that can implement various embodiments of the embodiments described herein. Although the embodiments have been described 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 embodiments can also be implemented in combination with other program modules or as a combination of hardware and software.

[0113] Generally, a program module includes routines, programs, components, data structures, etc., that perform a specific task or implement a specific abstract data type. Furthermore, those skilled in the art will recognize that the methods of the present invention can be implemented in single-processor or multi-processor computer systems, minicomputers, mainframe computers, Internet of Things (IoT) devices, distributed computing systems, and other computer system configurations including personal computers, portable computing devices, microprocessor-based electronic devices, programmable home appliances, etc., each of which can be operably coupled to one or more related devices.

[0114] The illustrated embodiments of the embodiments described herein can also be practiced in a distributed computing environment in which specific tasks are performed by remote processing devices linked via a communication network. In a distributed computing environment, program modules can be located on both local and remote memory storage devices.

[0115] Computing devices typically include a variety of media, which may include computer-readable storage media, machine-readable storage media, or communication media, and these two terms are used herein to distinguish them from one another as follows: Computer-readable storage media or machine-readable storage media can be any available storage media that can be accessed by a computer, and include both volatile and non-volatile media, removable and non-removable media. By example, but not by limitation, computer-readable storage media or machine-readable storage media can be implemented in relation to any method or technique for storing information such as computer-readable or machine-readable instructions, program modules, structured data, or unstructured data.

[0116] Computer-readable storage media may include, but are not limited to, random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CDROM), digital versatile discs (DVD), Blu-ray discs (BD) or other optical disc storage devices, magnetic cassettes, magnetic tapes, magnetic disk storage devices or other magnetic storage devices, solid-state drives or other solid-state storage devices, or other tangible or non-temporary media that can be used to store desired information. In this regard, the terms “tangible” or “non-temporary” as used herein for storage, memory, or computer-readable media are understood to exclude only transient signals that propagate on their own as modifiers, and do not waive any rights to all standard storage, memory, or computer-readable media that do not propagate on their own.

[0117] 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, for various operations with respect to the information stored on the medium.

[0118] Communication media typically include any information distribution or transmission medium that embodies computer-readable instructions, data structures, program modules, or other structured or unstructured data in the form of data signals, such as modulated data signals, such as carrier waves or other transmission mechanisms. The term “modulated data signal” refers to a signal having one or more characteristics that are set or modified to encode information in one or more signals. By example, but not limited to, communication media include wired media such as wired networks or direct wired connections, as well as wireless media such as acoustic, RF, infrared, and other wireless media.

[0119] Referring again to Figure 15, an exemplary environment 1500 for implementing the various embodiments described herein includes a computer 1502, which includes a processing unit 1504, system memory 1506, and a system bus 1508. The system bus 1508 connects system components (including, but not limited to, 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 employed as the processing unit 1504.

[0120] The system bus 1500 can be one of several types of bus structures that can be further interconnected to a memory bus (with or without a memory controller), a peripheral bus, and a local bus using any of the various commercially available bus architectures. The system memory 1506 includes ROM 1510 and RAM 1512. The basic input / output system (BIOS) can be stored in non-volatile memory (e.g., ROM, erasable programmable read-only memory (EPROM), EEPROM), and the BIOS includes basic routines that help transfer information between elements within the computer 1502, for example, during startup. RAM 1512 may also include high-speed RAM (e.g., static RAM) for caching data.

[0121] Computer 1502 further includes an internal hard disk drive (HDD) 1514 (e.g., EIDE, SATA) and one or more external storage devices 1516 (e.g., magnetic floppy disk drive (FDD) 1516, memory stick or flash drive reader, memory card reader, etc.). It also includes a drive 1520, for example, a solid-state drive or optical disc drive, which can read from and write to discs 1522 such as CD-ROM discs, DVDs, BDs, etc. Alternatively, when a solid-state drive is involved, discs 1522 are not included unless they are separate. Although the internal HDD 1514 is shown to be located within computer 1502, the internal HDD 1514 can also be configured for external use within a suitable chassis (not shown). Furthermore, although not shown in environment 1500, a solid-state drive (SSD) may be used in addition to or instead of the HDD 1514. The HDD 1514, external storage device 1516, and drive 1520 may be connected to the system bus 1508 by the HDD interface 1524, external storage interface 1526, and drive interface 1528, respectively. The interface 1524 for external drive implementation may include at least one or both of the Universal Serial Bus (USB) and the Institute of Electrical and Electronics Engineers (IEEE) 1394 interface technology. Other external drive connection technologies are within the considerations of the embodiments described herein.

[0122] Drives and their associated computer-readable storage media provide non-volatile storage such as data, data structures, and computer-executable instructions. In the case of computer 1502, drives and storage media accommodate the storage of any data in an appropriate digital format. While the above description of computer-readable storage media refers to each type of storage device, it should be understood by those skilled in the art that other types of computer-readable storage media, whether currently existing or to be developed in the future, can also be used in the exemplary operating environment, and furthermore, any such storage media may contain computer-executable instructions for carrying out the methods described herein.

[0123] Numerous program modules can be stored in the drive and RAM 1512 (including the operating system 1530, one or more application programs 1532, other program modules 1534, and program data 1536). All or part of the operating system, applications, modules, or data can 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.

[0124] Computer 1502 may optionally include emulation techniques. For example, a hypervisor (not shown) or other intermediary may emulate a hardware environment for operating system 1530, and the emulated hardware may optionally differ from the hardware shown in Figure 15. In such embodiments, operating system 1530 may comprise one VM from a plurality of virtual machines (VMs) hosted on computer 1502. Furthermore, operating system 1530 may provide a runtime environment (e.g., the Java runtime environment or the .NET framework) to application 1532. The runtime environment is a consistent execution environment that enables application 1532 to run on any operating system that includes the runtime environment. Similarly, operating system 1530 may support containers, and application 1532 may take the form of a container, which is a lightweight, standalone executable software package containing, for example, code, runtime, system tools, system libraries, and configuration for the application.

[0125] Furthermore, computer 1502 can be enabled by security modules such as a Trusted Platform Module (TPM). For example, in a TPM, the boot component then hashs the next boot component in time and waits for the result to match a secure value before loading the next boot component. This process can be performed at any layer of the code execution stack of computer 1502, for example, at the application run level or the operating system (OS) kernel level, thereby enabling security at any level of code execution.

[0126] The user may input commands and information to the computer 1502 via one or more wired / wireless input devices, such as a keyboard 1538, a touchscreen 1540, and a pointing device such as a mouse 1542. Other input devices (not shown) may include a microphone, an infrared (IR) remote control, a radio frequency (RF) remote control, or other remote control, a joystick, a virtual reality controller or virtual reality headset, a gamepad, a stylus pen, an image input device such as a camera(s), a gesture sensor input device, a visual-motor sensor input device, an emotion or face detection device, and a biometric input device such as a fingerprint or iris scanner. These and other input devices are often connected to the processing unit 1504 via an input device interface 1544 which can be coupled to the system bus 1508, but may also be connected via other interfaces such as a parallel port, an IEEE 1394 serial port, a game port, a USB port, an IR interface, or a BLUETOOTH® interface.

[0127] Monitor 1546 or other types of display devices may also be connected to the system bus 1508 via an interface such as a video adapter 1548. In addition to monitor 1546, the computer typically includes other peripheral output devices (not shown), such as speakers and printers.

[0128] Computer 1502 may operate in a network environment using logical connections via wired or wireless communication to one or more remote computers, such as remote computer 1550. The remote computer 1550 could be a workstation, server computer, router, personal computer, portable computer, microprocessor-based entertainment appliance, peer device, or other common network node, generally including many or all of the elements described for computer 1502, but for brevity, only the memory / storage device 1552 is illustrated. The illustrated logical connections 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 enterprises, facilitating enterprise-scale computer networks such as intranets, all of which can connect to global communication networks such as the Internet.

[0129] When used in a LAN network environment, computer 1502 may connect to local network 1554 via a wired or wireless network interface or adapter 1558. Adapter 1558 can facilitate wired or wireless communication with LAN 1554, and LAN 1554 may also include a wireless access point (AP) placed on it to communicate with adapter 1558 in wireless mode.

[0130] When used in a WAN networking environment, computer 1502 may include a modem 1560 or connect to a communication server on WAN 1556 via other means for establishing communication on WAN 1556, such as the Internet. The modem 1560 may be an internal or external wired or wireless device and may connect to the system bus 1508 via an input device interface 1544. In a network environment, program modules or parts thereof shown in relation to computer 1502 may be stored in the remote memory / storage device 1552. It will be understood that the illustrated network connection is illustrative and other means for establishing communication links between computers may be used.

[0131] Whether used in a LAN or WAN networking environment, computer 1502 can access, in addition to or instead of, the external storage device 1516 described above, a cloud storage system or other network-based storage system, which includes, but is not limited to, a network virtual machine that provides one or more modes of storing or processing information. Generally, the connection between computer 1502 and the cloud storage system can be established via LAN 1554 or WAN 1556, for example, by an adapter 1558 or modem 1560, respectively. When computer 1502 is connected to the relevant cloud storage system, the external storage interface 1526 can, with the help of the adapter 1558 or modem 1560, manage the storage provided by the cloud storage system in the same way as other types of external storage. For example, the external storage interface 1526 can be configured to provide access to the cloud storage sources as if those sources were physically connected to computer 1502.

[0132] Computer 1502 may be capable of communicating with any wireless device or entity configured to operate wirelessly, such as printers, scanners, desktop or portable computers, portable data assistants, communication satellites, any equipment or location associated with wirelessly discoverable tags (e.g., kiosks, newspaper stands, store shelves, etc.), and telephones. This may include Wireless Fidelity (Wi-Fi) and Bluetooth® wireless technologies. Thus, communication can be a predefined structure similar to existing networks, or simply ad-hoc communication between at least two devices.

[0133] Figure 16 is a schematic block diagram of an exemplary computing environment 1600 in which the disclosed subject matter can interact. The sample computing environment 1600 includes one or more clients 1610. Clients 1610 may be hardware or software (e.g., threads, processes, computing devices). The sample computing environment 1600 further includes one or more servers 1630. Servers 1630 may also be hardware or software (e.g., threads, processes, computing devices). Servers 1630 may house threads for performing transformations, for example, by employing one or more embodiments described herein. One possible communication between clients 1610 and servers 1630 may be in the form of data packets adapted to be transmitted between two or more computer processes. The sample computing environment 1600 includes a communication framework 1650 which may be used to facilitate communication between clients 1610 and servers 1630. Clients 1610 are operably connected to one or more client data stores 1620 which may be used to store information local to client 1610. Similarly, server 1630 is operablely connected to one or more server datastores 1640 that can be used to store information local to server 1630.

[0134] Figure 17 shows examples of non-limiting apparatus for carrying out the various embodiments described herein. Figure 17 shows a non-limiting example of a dual-beam system 1710 in which a vertically mounted scanning electron microscope (SEM) column and a focused ion beam (FIB) column are mounted at an angle of approximately 52° from the vertical. Such dual-beam systems are commercially available, for example, from FEI Company, Hillsboro, Oregon, the assignee of this application. On the other hand, Figure 17 shows examples of suitable microscope hardware that can implement the various embodiments described herein, and it should be understood that there are no limitations to such microscope hardware. 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 example of a charged particle microscope 302 or any other scientific instrument described above.

[0135] The scanning electron microscope 1741 may be equipped with a dual-beam system 1710, along with a power supply and control unit 1745. The 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 spot by a focusing lens 1756 and an objective lens 1758. The electron beam 1743 can be scanned two-dimensionally over any suitable specimen by a deflection coil 1760. The operation of the focusing lens 1756, the objective lens 1758, or the deflection coil 1760 can be controlled by the power supply and control unit 1745.

[0136] The electron beam 1743 can be focused onto a substrate 1722 which may be located on a movable XY stage 1725 in a lower chamber 1726. When electrons in the electron beam 1743 collide with the substrate 1722, secondary electrons can be emitted. These secondary electrons can be detected by a secondary electron detector 1740, as will be 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, can collect electrons that have passed through the sample mounted in the TEM sample holder 1724, as described above.

[0137] The dual-beam system 1710 may further include a focused ion beam (FIB) system 1711, which comprises a vacuum chamber having an upper neck 1712, and a focusing column 1716 including an ion source 1714 and an extraction electrode and electrostatic optics can be placed within the upper neck 1712. The axis of the focusing column 1716 can be tilted 52° (or any other suitable angular displacement) from the axis of the electron column. The ion column 1712 may include an ion source 1714, an extraction electrode 1715, a focusing element 1717, a deflection element 1720, and a focused ion beam 1718. The focused ion beam 1718 can pass from the ion source 1714 through the focusing column 1716 and between electrostatic deflection means schematically indicated by reference numeral 1720 toward the substrate 1722, the electrostatic deflection means may include, for example, a semiconductor device located on a movable XY stage 1725 in a lower chamber 1726.

[0138] The movable XY stage 1725 can be moved vertically (along the Z axis) in the horizontal plane (along the X and Y axes). The movable XY stage 1725 can be tilted at an angle of approximately 60° and rotated around 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. The door 1761 can be opened to insert the substrate 1722 onto the XY stage 1725 and, if an internal gas supply reservoir is used, to service it. The door 1761 is interlocked so that it cannot be opened when the system is under vacuum.

[0139] The ion pump 1768 can be used to create a vacuum in the neck section 1712. The chamber 1726 can be vacuumed using a turbomolecular and mechanical pump system 1730 under the control of a vacuum controller 1732. Such a vacuum system can create a vacuum of approximately 1 × 10⁻¹⁶ units within the chamber 1726. 7 Torr~5×10 -4 A vacuum can be provided between Torr. When etching aid gases, etching delay gases, or deposition precursor gases are used, the chamber background pressure is typically about 1 × 10⁻⁶. -5 It's okay to raise it up to Torr.

[0140] The high-voltage power supply 1734 can supply an appropriate acceleration voltage to the electrodes in the focusing column 1716, thereby energizing the focused ion beam 1718. When it strikes the substrate 1722, the material can be sputtered (i.e., physically ejected) from the sample. Alternatively, the focused ion beam 1718 can decompose a precursor gas to deposit the material.

[0141] A high-voltage power supply 1734 can be connected to an ion source 1714 (which may be a liquid metal ion source) and a suitable electrode in the ion beam focusing column 1716 to form an ion beam 1718 of approximately 1 keV to 60 keV, which can then be directed to the sample. A deflection controller and amplifier 1736, operating according to a given pattern provided by a pattern generator 1738, can be coupled to a deflection element 1720 (which may be a deflection plate), thereby allowing the focused ion beam 1718 to be manually or automatically controlled to track a corresponding pattern on the upper surface of the substrate 1722. In some systems, the deflection element 1720 can be placed in front of the final lens. A beam blanking electrode (not shown) in the ion beam focusing column 1716 causes the focused ion beam 1718 to collide with a blanking aperture (not shown) instead of the substrate 1722 when a blanking controller (not shown) applies a blanking voltage to the blanking electrode.

[0142] The ion source 1714 can provide, for example, a gallium metallic ion beam. In other examples, the ion source 1714 may be a plasma ion source that extracts ions from the generated plasma. The source can focus a beam of sub-1 / 10 micrometer width on the substrate 1722 for the purpose of modifying the substrate 1722 by ion milling, enhanced etching, material deposition, or imaging the substrate 1722.

[0143] Everhart, used to detect secondary ions or electron emission. A charged particle detector 1740, such as a Thornley or multi-channel plate, can be connected to a video circuit 1742 that can supply 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 may be coaxial with the ion beam and may include holes that allow the ion beam to pass through. In other embodiments, secondary particles are collected through a final lens and can then be deflected from the axis for collection.

[0144] The micromanipulator 1747 can precisely move objects within a vacuum chamber. The micromanipulator 1747 may include a precision electric motor 1748 located outside the vacuum chamber to provide X, Y, Z, and theta control of a portion 1749 located within the vacuum chamber. Different end effectors can be attached to the micromanipulator 1747 for manipulating small objects. In the embodiments described herein, the end effector may be a thin probe 1750.

[0145] The gas delivery system 1746 can extend into the lower chamber 1726 to introduce and guide gas vapor toward the substrate 1722. U.S. Patent No. 5,851,413, Casella et al., “Gas Delivery Systems for A suitable gas delivery system is described in “Particle Beam Processing” 1746. Another gas delivery system is described in U.S. Patent No. 5,435,850, Rasmussen, “Gas Injection System,” which is transferred to the assignee of the present invention. For example, iodine can be delivered to enhance etching, or metal-organic compounds can be delivered to deposit metals.

[0146] The system controller 1719 can control the operation of various parts of the dual-beam system 1710. Through the system controller 1719, the user can scan the focused ion beam 1718 or electron beam 1743 in a desired manner via commands entered into any suitable user interface (not shown). Alternatively, the system controller 1719 may control the dual-beam system 1710 according to programmed instructions stored in memory 1721. In various embodiments, any one of one or more software components 311 may be implemented by the system controller 1719 or otherwise executed.

[0147] Various embodiments may be systems, methods, apparatus, or computer program products in any possible level of technical detail. A computer program product may include a computer-readable recording medium (or media) containing computer-readable program instructions for causing a processor to execute aspects of various embodiments. A computer-readable storage medium may be a tangible device capable of holding and storing instructions used by an instruction execution device. A computer-readable storage medium may be, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the above. A non-exhaustive list of more specific examples of computer-readable storage mediums may also include: portable computer diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disk read-only memory (CD-ROM), digital multipurpose disks (DVDs), memory sticks, floppy disks, mechanically encoded devices such as punched cards or grooved raised structures on which instructions are recorded, or any suitable combination of the above. The computer-readable storage media used herein should not be interpreted as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses passing through fiber optic cables), or transient signals themselves, such as electrical signals transmitted through wires.

[0148] The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, or a wireless network. The network may include copper transmission cables, optical transmission fibers, wireless transmissions, routers, firewalls, switches, gateway computers, or edge servers. A network adapter card or network interface within each computing / processing device receives computer-readable program instructions from the network and transfers them for storage on a computer-readable storage medium within each computing / processing device. Computer-readable program instructions for performing the operations of various embodiments may be assembler instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state 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 and C++, and procedural programming languages ​​such as the C programming language or similar programming languages. Computer-readable program instructions can run entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter scenario, the remote computer can connect to the user's computer via any type of network, including a local area network (LAN) or wide area network (WAN), or it can connect to an external computer (for example, via the Internet using an Internet service provider).In some embodiments, for example, electronic circuits including programmable logic circuits, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs) can execute computer-readable program instructions by personalizing the electronic circuit using state information of computer-readable program instructions to implement various embodiments.

[0149] This specification describes various embodiments of methods, apparatus (systems), and computer program products with reference to flowcharts or block diagrams of various embodiments. It will be understood that each block in a flowchart or block diagram, as well as combinations of blocks within a flowchart or block diagram, can be implemented by computer-readable program instructions. These computer-readable program instructions are provided to the processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, and the instructions executed through the processor of the computer or other programmable data processing device manufacture a machine such that it creates means for implementing the functions / operations specified in one or more blocks of the flowchart or block diagram. These computer-readable program instructions can also be stored in computer-readable storage media that cause a computer, a programmable data processing device, or other device to function in a particular way, thereby including a product in which a computer-readable storage medium having instructions stored therein contains instructions that implement the functions / operations specified in one or more blocks of the flowchart or block diagram. These computer-readable program instructions can also be loaded into a computer, other programmable data processing device, or other device to generate a computer-executed process through a series of actions performed on the computer, other programmable device, or other device, thereby enabling the instructions executed on the computer, other programmable device, or other device to perform functions / actions specified in one or more blocks of a flowchart or block diagram.

[0150] The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of a system, method, or computer program product in various embodiments. In this regard, each block in a flowchart or block diagram may represent a module, segment, or part of an instruction, and contains one or more executable instructions for implementing a specified logic function(s). In some alternative implementations, the functions described within a block may be performed in an order different from that shown in the diagram. For example, two consecutively shown blocks may be executed substantially simultaneously, or blocks may sometimes be executed in reverse order, depending on the functionality involved. It should also be noted that each block in a block diagram or flowchart, and any combination of blocks in a block diagram or flowchart, may be implemented by a dedicated hardware-based system that performs a specified function or operation, or a combination of dedicated hardware and computer instructions.

[0151] While the subject matter has been described above in the general context of computer executable instructions for computer program products running on one or more computers, those skilled in the art will recognize that the disclosure can, or may, be implemented in combination with other program modules. Generally, a program module comprises routines, programs, components, data structures, etc., that perform a specific task or implement a specific abstract data type. Furthermore, those skilled in the art will understand that various embodiments can be implemented in single-processor or multi-processor computer systems, minicomputing devices, mainframe computers, and other computer system configurations, including computers, portable computing devices (e.g., PDAs, mobile phones), microprocessor-based or programmable consumer or industrial electronic devices. Each illustrated embodiment can also be implemented in a distributed computing environment where tasks are performed by remote processing devices linked via a communication network. However, not all embodiments of the disclosure may be implementable on a standalone computer. In a distributed computing environment, program modules can reside on both local and remote memory storage devices.

[0152] As used in this application, terms such as “component,” “system,” “platform,” and “interface” refer to or may include computer-related entities or entities relating to operating devices having one or more specific functionalities. Entities disclosed herein may be hardware, a combination of hardware and software, software, or running software. 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. Exemplarily, both an application running on a server and the server itself may be components. One or more components may reside within a process or execution thread, and a component may be localized to one computer or distributed across two or more computers. As another example, each component may run from various computer-readable media storing various data structures. A component may communicate via local or remote processes according to signals containing one or more data packets (for example, data from one component interacting with other systems via signals over a network such as a local system, a distributed system, or the Internet). As another example, a component may be a device having a specific functionality provided by mechanical parts operated by electrical or electronic circuits, which in turn operate on software or firmware applications executed by a processor. In such a case, the processor may be internal or external to the device and may execute at least a portion of the software or firmware application. As yet another example, a component may be a device that provides a specific functionality through electronic components without mechanical parts, and the electronic components may include a processor or other means for executing software or firmware that at least partially grants the functionality of the electronic components.In one embodiment, the component can emulate electronic components via a virtual machine, for example, within a cloud computing system.

[0153] Furthermore, the term “or” is used here with the intention of meaning an inclusive “or,” rather than an exclusive “or.” That is, unless otherwise explicitly stated or evident from the context, “X uses A or B” is used with the intention of meaning all natural inclusive interpretations. That is, whether X uses A, X uses B, or X uses both A and B, the expression “X uses A or B” applies in all cases. The terms “and / or” as used herein are intended to have the same meaning as “or.” Furthermore, the articles “a” and “an” as used herein and in accompanying drawings should generally be interpreted as meaning “one or more,” unless otherwise explicitly stated or evident from the context. Where used herein, the terms “example” or “exemplary” are used to mean serving as an example, case, or explanation. To avoid misunderstanding, the subject matter described herein is not limited by such examples. Furthermore, any embodiment or design described herein as “example” or “exemplary” should not necessarily be construed as being preferable or advantageous to other embodiments or designs, nor should it be meant to exclude equivalent exemplary structures and techniques known to those skilled in the art.

[0154] The disclosures herein describe non-limiting examples. For ease of explanation and commentary, various parts of the disclosures herein use the terms “each,” “each,” or “all” when describing various examples. The use of the terms “each,” “each,” or “all” is not limited. In other words, where the disclosures herein present a description that applies to “each,” “each,” or “all” of a particular object or component, it should be understood that this is a non-limiting example, and it should be further understood that in various other examples, such descriptions may apply to less than “each,” “each,” or “all” of that particular object or component.

[0155] As used herein, the term “processor” can refer to substantially any computing processing unit or device, including but not limited to single-core processors, single processors with software multithreading capabilities, multi-core processors, multi-core processors with software multithreading capabilities, multi-core processors with hardware multithreading technology, and parallel platforms. Furthermore, a processor can refer to integrated circuits, application-specific integrated circuits (ASICs), digital signal processors (DSPs), field-programmable gate arrays (FPGAs), programmable logic controllers (PLCs), composite programmable logic devices (CPLDs), discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. Additionally, a processor may, but is not limited to, utilize nanoscale architectures such as molecular and quantum dot-based transistors, switches, or gates to optimize space use or to improve the performance of user devices. A processor can be implemented as a combination of computing processing units. In this specification, terms such as “memory,” “storage,” “datastore,” “data storage,” “database,” and substantially any other information storage component relating to the operation and function of a component are used to refer to an entity embodied in “memory component,” “memory,” or a component containing memory. It should be understood that the memory or memory component described herein may be either volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. Non-volatile memory may include, but is not limited to, read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EEPROM), electrically erasable ROM (EPROM), flash memory, or non-volatile random-access memory (RAM) (for example, ferroelectric RAM (eRAM)).Volatile memory may include RAM, for example, which can function as external cache memory. RAM comes in many forms, including, but is not limited to, synchronous RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data-rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), sync-link DRAM (SLDRAM), direct Rambus RAM (DRRAM), direct Rambus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM). Furthermore, the memory components of the systems or computer implementations described herein are intended to include, but are not limited to, these and any other suitable types of memory.

[0156] The foregoing includes only examples of systems and computer implementations. Naturally, it is not possible to describe all possible combinations of components or computer implementations for the purpose of illustrating this disclosure, but many more combinations and permutations of the disclosure are possible. Furthermore, to the extent that terms such as “includes,” “has,” and “possesses” are used in the detailed description, claims, appendices, and / or drawings, such terms are intended to be as comprehensive as the term “comprising,” since “comprising” is used as a transitional term in the claims.

[0157] The descriptions of various embodiments are presented for illustrative purposes only and are not intended to be exhaustive or to limit the embodiments disclosed herein. It will be apparent that many modifications and changes are possible without departing from the scope and spirit of the embodiments described. The terminology disclosed herein has been selected to best describe the principles of the embodiments, their practical application or technological improvement over the technology available on the market, or to enable those skilled in the art to understand the embodiments disclosed herein.

[0158] Various non-limiting forms are illustrated in the following examples.

[0159] Example 1: The system may consist of the following components: a processor that runs computer-executable components stored in non-temporary computer-readable memory, the computer-executable components being a scanning component that can access an energy integral image of a sample scanned by a charged particle microscope which may have a non-bucksin integral detector, and a quantization component that can count the number of charged particle events represented by each pixel in the energy integral image based on rounding the cumulative charged particle energy intensity shown for each pixel cluster to the nearest integer multiple of the beam energy of the charged particle microscope.

[0160] Example 2: Any prior example system may be implemented so that the scanning component allows the charged particle microscope to acquire multiple preliminary energy integral images of the sample.

[0161] Example 3: Any prior example system may be implemented such that the scanning component can assign zeros to pixels in multiple preliminary energy integral images where the charged particle energy intensity is below a noise threshold, thereby obtaining multiple floored energy integral images.

[0162] Example 4: Any prior example system may be implemented so that the scanning component can perform a pixel-level summation or averaging of multiple floored energy integral images, thereby obtaining an energy integral image.

[0163] Example 5: Any prior example system can be implemented so that the quantization component can identify each cluster by applying a community detection algorithm to the energy integral image.

[0164] Example 6: Any prior example system may be implemented such that, for a first cluster having a first cumulative charged particle energy intensity, the quantization component performs the following: obtains a quotient by dividing the first cumulative charged particle energy intensity by the beam energy of the charged particle microscope; obtains a rounded quotient by rounding the quotient to the nearest integer; and determines that the number of charged particle events corresponding to the first cluster is equal to the rounded quotient.

[0165] Example 7: Any prior example system may be implemented such that, for a first pixel in a first cluster, and if the pixel has a first integrated charged particle energy intensity, the quantization component can perform the following: obtain a ratio by dividing the first integrated charged particle energy intensity by the first cumulative charged particle energy intensity; obtain a product by multiplying the ratio by the rounded quotient; and determine that the number of charged particle events corresponding to the first pixel is equal to the product.

[0166] Example 8: Any prior example system can be implemented such that the charged particle microscope is an electron energy loss microscope.

[0167] In various embodiments, any one or more combinations of Examples 1 to 8 can be implemented.

[0168] Example 9: A computer implementation method which may include: accessing an energy integral image of a sample scanned by a charged particle microscope, which may be equipped with a non-bucksin integrating detector, by a device operably connected to a processor; and counting the number of charged particle events represented by each pixel of the energy integral image by the device, based on rounding the cumulative charged particle energy intensity represented for each pixel cluster to the nearest integer multiple of the beam energy of the charged particle microscope.

[0169] Example 10: A computer implementation of any prior example further includes: the device causing the charged particle microscope to acquire multiple preliminary energy integral images of the sample.

[0170] Example 11: A computer implementation of any prior example further includes: obtaining a plurality of floored energy integral images by the device by assigning zeros to pixels in the plurality of preliminary energy integral images where the charged particle energy intensity is less than a noise threshold.

[0171] Example 12: A computer implementation of any prior example further includes: obtaining the energy integral image by performing a pixel-level summation or averaging of the plurality of floored energy integral images using the device.

[0172] Example 13: A computer implementation of any prior example further includes: identifying each of the clusters by applying a community detection algorithm to the energy integral image using the device.

[0173] Example 14: A computer implementation of any prior example may further include, for a first cluster having a first cumulative charged particle energy intensity, the following: the device obtains a quotient by dividing the first cumulative charged particle energy intensity by the beam energy of a charged particle microscope; the device obtains a rounded quotient by rounding the quotient to the nearest integer; and the device determines that the number of charged particle events corresponding to the first cluster is equal to the rounded quotient.

[0174] Example 15: A computer implementation of any prior example may further include, for a first pixel in a first cluster, and if the pixel has a first integrated charged particle energy intensity, the following: the device divides the first integrated charged particle energy intensity by the first cumulative charged particle energy intensity to obtain a ratio; the device multiplies the quotient rounded to the ratio 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.

[0175] Example 16: A computerized implementation of any prior example may be carried out such that the charged particle microscope is an electron energy loss microscope.

[0176] In various embodiments, any one or more combinations of Examples 9-16 can be implemented.

[0177] Example 17: A computer program product for facilitating charged particle counting via a non-Bucksin integrating detector and beam energy quantization may include non-transient computer-readable memory in which the program instructions are embodied. In various embodiments, the program instructions are executable by a processor, which may perform the following: access an energy integral image of a sample scanned by an electron energy loss microscope that may be equipped with a non-Bucksin integrating detector, and count the number of electron events represented by each pixel in the energy integral image, based on rounding the cumulative electron energy intensity represented by each pixel cluster to the nearest integer multiple of the beam energy of the electron energy loss microscope.

[0178] Example 18: Any prior example computer program product may be implemented such that program instructions cause a processor to perform the following: obtain multiple preliminary energy integral images of a sample using an electron energy loss microscope; obtain multiple floored energy integral images by assigning zeros to pixels in the multiple preliminary energy integral images where the electron energy intensity is below a noise threshold; and obtain an energy integral image by performing a pixel-level sum or average of the multiple floored energy integral images.

[0179] Example 19: Any prior computer program product may be implemented such that, for a first cluster having a first cumulative electron energy intensity, program instructions cause the processor to perform the following: divide the first cumulative electron energy intensity by the beam energy of an electron energy loss microscope to obtain a quotient; round the quotient to the nearest integer to obtain a rounded quotient; and determine that the number of electron events represented by the first cluster is equal to the rounded quotient.

[0180] Example 20: Any prior computer program product may be implemented such that, for a first pixel in a first cluster, and if the pixel has a first electron energy intensity, the program instruction can be further made executable to cause the processor to: divide the first electron energy intensity by the first cumulative electron energy intensity to obtain a ratio; multiply by the rounded quotient of the ratio to obtain a product; and determine that the number of electron events represented by the first pixel is equal to the product.

[0181] In various embodiments, any one or more combinations of Examples 17-20 can be implemented.

[0182] In various embodiments, any combination or multiple combinations of Examples 1 to 20 can be implemented.

Claims

1. It is a system, A processor that executes computer executable components stored in non-temporary computer-readable memory, wherein the computer executable components are: A scanning component that accesses the energy integral image of a sample scanned by a charged particle microscope equipped with a non-Baccin integrating detector, and The system includes a quantization component that counts the number of charged particle events represented by each pixel in the energy integral image, based on rounding the cumulative charged particle energy intensity shown for each pixel cluster to the nearest integer multiple of the beam energy of the charged particle microscope. system.

2. The system according to claim 1, wherein the scanning component causes the charged particle microscope to acquire a plurality of preliminary energy integral images of the sample.

3. The system according to claim 2, wherein the scanning component obtains a plurality of floored energy integral images by assigning zero to pixels in the plurality of preliminary energy integral images where the charged particle energy intensity is less than a noise threshold.

4. The system according to claim 3, wherein the scanning component obtains the energy integral image by performing a pixel-level sum or average of the plurality of floored energy integral images.

5. The system according to claim 4, wherein the quantization component identifies each cluster by applying a community detection algorithm to the energy integral image.

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

7. If the first pixel of the first cluster has a first integrated charged particle energy intensity, the quantization component is: The first integrated charged particle energy intensity is divided by the first cumulative charged particle energy intensity to obtain a ratio. By multiplying the aforementioned ratio by the aforementioned rounded quotient, the product is obtained. The system according to claim 6, wherein it is determined that the number of charged particle events indicated by the first pixel is equal to the product.

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

9. A computer implementation method, Accessing energy integral images of a sample scanned by a charged particle microscope equipped with a non-Baccin integrating detector via a device operablely connected to the processor, and The device includes counting the number of charged particle events represented by each pixel in the energy integral image, based on rounding the cumulative charged particle energy intensity represented for each cluster of pixels to the nearest integer multiple of the beam energy of the charged particle microscope, Computer implementation method.

10. Furthermore, the computer implementation method according to claim 9, further comprising using the device to cause the charged particle microscope to acquire a plurality of preliminary energy integral images of the sample.

11. Furthermore, the computer implementation method according to claim 10, comprising obtaining a plurality of floored energy integral images by assigning zeros to pixels in the plurality of preliminary energy integral images where the charged particle energy intensity is less than a noise threshold.

12. Furthermore, the computer implementation method according to claim 11, comprising obtaining the energy integral image by performing a pixel-level summation or averaging of the plurality of floored energy integral images using the device.

13. Furthermore, the computer implementation method according to claim 12 further includes identifying each cluster by applying a community detection algorithm to the energy integral image using the device.

14. Furthermore, for the first cluster having the first cumulative charged particle energy intensity, The device divides the first cumulative 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, and obtains the rounded quotient, and The computer implementation method according to claim 13, comprising determining by the device that the number of charged particle events represented by the first cluster is equal to the rounded quotient.

15. Furthermore, if the first pixel of the first cluster has a first integrated charged particle energy intensity, The device divides the first integrated 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 the product, and The computer implementation method according to claim 14, comprising determining by the device that the number of charged particle events indicated by the first pixel is equal to the product.

16. The computer implementation method according to claim 9, wherein the charged particle microscope is an electron energy loss microscope.

17. A computer program for facilitating charged particle counting via a non-bucksin integral detector and beam energy quantization, comprising a non-transient computer-readable memory in which program instructions are embodied, wherein the program instructions are executable by a processor, and the processor Accessing energy integral images of a sample scanned by an electron energy loss microscope equipped with a non-Baccin integrating detector, and The system is configured to count the number of electronic events represented by each pixel in the energy integral image, based on rounding the cumulative electron energy intensity shown for each pixel cluster to the nearest integer multiple of the beam energy of the electron energy loss microscope. Computer program.

18. The program instruction is given to the processor, The electron energy loss microscope is used to acquire multiple preliminary energy integral images of the sample. In the aforementioned multiple preliminary energy integral images, by assigning zero to pixels whose electron energy intensity is less than the noise threshold, multiple floored energy integral images are obtained, and The computer program according to claim 17, which is executable to perform the task of obtaining the energy integral image by performing a pixel-level sum or average of the plurality of floored energy integral images.

19. For a first cluster having a first electron energy intensity, the program instruction further provides the processor with: The first cumulative electron energy intensity is divided by the beam energy of the electron energy loss microscope to obtain the quotient. The aforementioned quotient is rounded to the nearest integer value, and the rounded quotient is obtained. The computer program according to claim 18, which is executable to perform the task of determining whether the number of charged particle events represented by the first cluster is equal to the rounded quotient.

20. If the first pixel of the first cluster has a first electron energy intensity, the program instruction further instructs the processor to: The first electron energy intensity is divided by the first cumulative electron energy intensity to obtain a ratio. The ratio is multiplied by the rounded quotient to obtain the product, and The computer program according to claim 19, which is executable to cause the computer program to determine that the number of electronic events indicated by the first pixel is equal to the product.

Images