A method for processing data derived from a sample

Abstract

The present invention provides a method for processing data derived from a sample, the method comprising, for calibration, processing an initial data set of elements derived from detection by a detector, the data set including elements representing interference signals and detection signals. Processing the initial data set includes adapting a distribution model to the initial data set to generate an interference distribution model, setting a signal intensity value, selecting elements within the initial data set having a magnitude greater than the signal intensity value as a set of defect candidates, adapting a distribution model to the set of defect candidates to generate a defect distribution model for the detection signal, and determining a signal intensity threshold that depends at least on the defect distribution model. This determination includes correcting the defect distribution model.

Description

Technical Field

[0001] Cross - reference to related applications

[0001] This application claims the priority of European Patent Application No. 22176199.2 filed on May 30, 2022 and European Patent Application No. 22181883.4 filed on June 29, 2022, which are incorporated herein by reference in their entirety.

[0002]

[0002] Embodiments provided herein generally relate to methods for processing data derived from samples, methods for identifying defect candidates, and evaluation systems.

Background Art

[0003]

[0003] When manufacturing semiconductor integrated circuit (IC) chips, undesirable pattern defects inevitably occur on the substrate (i.e., wafer) or mask during the manufacturing process, for example, as a result of optical effects and incidental particles, thereby reducing the yield. Therefore, monitoring the degree of undesirable pattern defects is an important process in the manufacture of IC chips. More generally, inspection and / or measurement of the surface of a substrate or other object / material is an important process during and / or after its manufacture.

[0004] [

[0004] ] Pattern inspection tools using a charged particle beam have been used to inspect an object (e.g., to detect pattern defects). These tools typically use electron microscopy techniques that use an electron optical system (e.g., within a scanning electron microscope (SEM)). In an exemplary electron optical system such as an SEM, a primary electron beam of relatively high-energy electrons is targeted at the final deceleration step in order to land on the sample with a relatively low landing energy. The electron beam is focused as a probing spot on the sample. Due to the interaction between the material structure at the probing spot and the landing electrons from the electron beam, electrons such as secondary electrons, backscattered electrons, or Auger electrons are emitted from the surface. The generated secondary electrons can be emitted from the material structure of the sample. By scanning the primary electron beam as a probing spot across the sample surface, secondary electrons can be emitted across the sample surface. By collecting these emitted secondary electrons from the sample surface, the pattern inspection tool can acquire an image representing the characteristics of the material structure of the sample surface. The intensity of the electron beam, including backscattered electrons and secondary electrons, can vary based on the characteristics of the internal and external structures of the sample, thereby indicating whether the sample has a defect.

[0005]

[0005] To identify genuine defects on an object under inspection, it is preferable to first ignore nuisance signals (e.g., caused by noise). In this way, the situation where "time and resources are wasted by performing more detailed analysis of potentially large amounts of nuisance signals" can be avoided. Nuisance signals are often identified as signals having a signal intensity below a certain threshold. Signals having a signal intensity above the threshold are considered likely to be defect signals, and this signal can then be further analyzed to determine whether a defect exists and, if so, to classify the nature of the defect. The value of the threshold is usually set by using experience through trial and error. This trial and error process can be time-consuming and it can be difficult to verify that the selected value is suitable. If the threshold is set too high, there is a risk that genuine defects may be missed due to being associated with signal intensities lower than the threshold. If the threshold is set too low, a large number of nuisance signals will be included in the dataset for further analysis. This can make the further analysis time-consuming and inefficient.

Summary of the Invention

[0006]

[0006] An object of the present disclosure is to provide a method for processing data derived from a sample, a method for identifying defect candidates, and an embodiment of an evaluation system.

[0007] According to a first aspect of the present invention, there is provided a method for processing data derived from a sample, the method comprising, for calibration, processing an initial data set of elements derived from detection by a detector, the data set including elements representing interference signals and detection signals. Processing the initial data set includes fitting a distribution model to the initial data set to generate an interference distribution model, setting a signal strength value, and selecting elements in the initial data set having a magnitude greater than the signal strength value as a set of defect candidates, fitting a distribution model to the set of defect candidates to generate a defect distribution model of the detection signal, and determining a signal strength threshold that depends at least on the defect distribution model. This determination includes correcting the defect distribution model. It is desirable that correcting is suitable for correcting the magnitude overlap between the elements representing the interference signal and the elements representing the detection signal.

[0008] According to a second aspect of the present invention, there is provided a method for processing data derived from a sample, the method comprising, for calibration, processing an initial data set of elements derived from detection by a detector, the data set including elements representing interference signals and detection signals. Processing the initial data set includes fitting a distribution model to the initial data set to generate an interference distribution model, setting a signal strength value, and selecting elements in the initial data set having a magnitude greater than the signal strength value as a set of defect candidates, fitting a distribution model to the set of defect candidates to generate a defect distribution model of the detection signal, determining a signal strength threshold that depends at least on the defect distribution model, and determining the relationship between the capture rate and the signal strength threshold.

[0009] According to a third aspect of the present invention, there is provided a method for processing data derived from a sample, the method including processing an initial data set of elements derived from detections by a detector. The data set includes elements representing interference signals and defect signals. The interference distribution includes elements representing interference signals having a magnitude interference range. The defect distribution includes elements representing defect signals having a magnitude defect range. The interference range overlaps the defect range. The interference range overlaps the defect range within the overlap. At least one element of the defect range has a magnitude that exceeds the upper limit of the magnitude interference range.

[0010] According to a fourth aspect of the present invention, there is provided a method for identifying defect candidates, the method including processing a data set of elements derived from detections by a detector, the data set including elements representing interference signals and detection signals. The capture threshold relationship between the capture rate and the signal intensity threshold is calibrated by using the initial data set. The processing includes selecting a capture rate and setting a signal intensity threshold based on the capture threshold relationship, and processing the data set by using the signal intensity threshold to select elements representing detection signals.

[0011] According to a fifth aspect of the present invention, there is provided an evaluation system for identifying defect candidates in inspection data derived from a sample. The evaluation system includes a detector and a processor. The detector is configured to generate detection signals representing one or more characteristics of the sample. The processor is configured to process a data set of elements derived from detections by the detector, the data set including elements representing interference signals and detection signals, by selecting a capture rate and setting a signal intensity threshold based on the capture threshold relationship between the capture rate and the signal intensity threshold (the capture relationship calibration has been previously calibrated by the initial data set), and processing the data set by using the signal intensity threshold to select elements representing detection signals.

[0012]

[0012] The above and other aspects of the present disclosure will become more apparent from the description of the exemplary embodiments in conjunction with the accompanying drawings.

Brief Description of the Drawings

[0013]

Figure 1

[0013] It is a schematic diagram showing an exemplary electron beam inspection apparatus.

Figure 2

[0014] It is a schematic diagram showing an exemplary multi-beam charged particle evaluation system which is a part of the exemplary electron beam inspection apparatus of FIG. 1.

Figure 3

[0015] It is a schematic diagram of an exemplary multi-beam charged particle evaluation system according to an embodiment.

Figure 4

[0016] It is a schematic diagram of an exemplary multi-beam charged particle evaluation system according to an embodiment.

Figure 5

[0017] It is an exemplary histogram of signal intensity data derived from a sample.

Figure 6A

[0018] It is an exemplary graphical representation of the total distribution model, the actual distribution of the initial data set, the defect distribution model, and the interference distribution model.

Figure 6B

[0018] It is an enlarged view of a region of FIG. 6A.

Figure 7A

[0019] It is an exemplary plot of the interference rate against the capture rate.

Figure 7B

[0019] It is an exemplary plot of the capture rate against the signal intensity threshold.

Figure 8A

[0020] It is another example of comparing the corrected distribution model with the corresponding actual data.

Figure 8B

[0020] It is another example of comparing the corrected distribution model with the corresponding actual data.

Figure 9

[0021] It is a graphical representation of the distribution of an exemplary initial data set including a model of the distribution of interference signals.

Figure 10A

[0022] A graphic representation of the distribution of two additional exemplary initial data sets, including a corresponding model of the distribution of interference signals having various gradients.

Figure 10B

[0022] A graphic representation of the distribution of two additional exemplary initial data sets, including a corresponding model of the distribution of interference signals having various gradients.

Figure 11

[0023] A graphic representation of the distribution of a further exemplary initial data set, including a corresponding model of the distribution of interference signals where the interference range and the defect range overlap.

Figure 12A

[0024] A schematic diagram depicting images of samples, each having a plurality of features and defects.

Figure 12B

[0024] A schematic diagram depicting images of samples, each having a plurality of features and defects.

Figure 12C

[0024] A schematic diagram depicting images of samples, each having a plurality of features and defects.

Figure 12D

[0024] A schematic diagram depicting images of samples, each having a plurality of features and defects.

Mode for Carrying Out the Invention

[0014]

[0025] These schematic diagrams show the components described below. However, the components depicted in the accompanying drawings are not to scale.

[0015]

[0026] Here, reference is made in detail to exemplary embodiments, the examples of which are shown in the accompanying drawings. The following description refers to the accompanying drawings, and unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementation forms described in the following description of the exemplary embodiments do not represent all implementation forms that are consistent with the present invention. Instead, those implementation forms are merely examples of devices and methods that are consistent with aspects related to the present invention, as described in the appended claims.

[0016]

[0027] The improvement of the computing power of electronic devices to reduce the physical size of the devices can be achieved by significantly increasing the implementation density of circuit components such as transistors, capacitors, and diodes on the IC chip. This has been made possible by the improvement of the resolution that enables the fabrication of smaller structures. For example, the IC chip of a smartphone that is the size of a fingernail could contain more than 2 billion transistors before 2019, and the size of each transistor is less than 1 / 1000 of a human hair. Therefore, semiconductor IC manufacturing is a complex and time-consuming process with many individual steps. An error in one of these steps can significantly affect the function of the final product. The goal of the manufacturing process is to improve the overall yield of the process. For example, for a process with 50 steps (where the steps can represent the number of layers formed on the wafer), to achieve a yield of 75%, each individual step must have a yield exceeding 99.4%. If each individual step has a yield of 95%, the overall process yield is as low as 7%.

[0017]

[0028] In IC chip manufacturing equipment, while a high process yield is desirable, it is also essential to maintain a high substrate (i.e., wafer) throughput defined as the number of substrates processed per hour. High process yield and high substrate throughput can be affected by the presence of defects. This is especially true when operator intervention is required to investigate the defects. Therefore, high-throughput detection and identification of microscale and nanoscale defects by inspection tools such as scanning electron microscopes ("SEM") are essential to maintain high yield and low cost.

[0018]

[0029] The SEM includes a scanning device and a detector device. The scanning device includes an illumination device including an electron source for generating primary electrons, and a projection device for scanning a sample, such as a substrate, with one or a plurality of focused beams of primary electrons. Together, at least the illumination device or illumination system and the projection device or projection system may be collectively referred to as an electron optical system or device. The primary electrons interact with the sample to generate secondary electrons. The detection device captures secondary electrons from the sample when the sample is scanned so that the SEM can generate an image of the scanned area of the sample. Such an inspection device may utilize a single primary electron beam incident on the sample. For high-throughput inspection, some inspection devices use a plurality of focused beams of primary electrons, i.e., a multi-beam. The component beams of the multi-beam may be referred to as sub-beams or beamlets. The sub-beams may be arranged relative to each other within the multi-beam in a multi-beam arrangement. The multi-beam can scan various parts of the sample simultaneously. Thus, a multi-beam inspection device can inspect a sample much faster than a single-beam inspection device.

[0019]

[0030] Embodiments of known multi-beam inspection devices will be described below.

[0020]

[0031] The drawings are schematic. Accordingly, the relative dimensions of the parts in the drawings are exaggerated for clarity. In the following description of the drawings, the same or similar reference numerals refer to the same or similar parts or entities, and thus only the differences with respect to the individual embodiments are described. It is understood that the description and the drawings are directed to electron optics, but the embodiments are not used to limit the present disclosure to specific charged particles. Thus, throughout this specification, references to electrons may be considered to be more generally references to charged particles, and the charged particles are not necessarily electrons.

[0021]

[0032] Refer to FIG. 1, which is a schematic diagram showing an exemplary charged particle beam inspection apparatus 100. The charged particle beam inspection apparatus 100 of FIG. 1 includes a main chamber 10, a load lock chamber 20, a charged particle evaluation system 40 (which may also be referred to as an electron beam system or a tool), an equipment front end module (EFEM) 30, and a controller 50. The charged particle evaluation system 40 is located within the main chamber 10.

[0022]

[0033] The EFEM 30 includes a first loading port 30a and a second loading port 30b. The EFEM 30 may include one or more additional loading ports. The first loading port 30a and the second loading port 30b can receive, for example, a substrate front opening unified pod (FOUP) that houses a substrate (e.g., a semiconductor substrate or a substrate made of other materials) or a sample to be inspected (hereinafter, substrates, wafers, and samples are collectively referred to as "samples"). One or more robot arms (not shown) within the EFEM 30 carry the sample to the load lock chamber 20.

[0023]

[0034] The loading lock chamber 20 is used to remove the gas around the sample. This creates a vacuum which is a local gas pressure lower than the pressure of the ambient environment. The loading lock chamber 20 can be connected to a loading lock vacuum pump system (not shown), and the loading lock vacuum pump system removes the gas particles within the loading lock chamber 20. By the operation of the loading lock vacuum pump system, the loading lock chamber can reach a first pressure below atmospheric pressure. After reaching the first pressure, one or more robotic arms (not shown) carry the sample from the loading lock chamber 20 to the main chamber 10. The main chamber 10 is connected to a main chamber vacuum pump system (not shown). The main chamber vacuum pump system removes the gas particles within the main chamber 10 such that the pressure around the sample reaches a second pressure below the first pressure. After reaching the second pressure, the sample can be carried to and inspected by the charged particle evaluation system 40. The charged particle evaluation system 40 includes an electron optical system 41. The term "electron optical device" can be synonymous with the electron optical system 41. The electron optical system 41 can be a multi-beam electron optical system 41 configured to project a multi-beam towards the sample, for example, the sub-beams are arranged relative to each other in a multi-beam arrangement. Alternatively, the electron optical system 41 can be a single-beam electron optical system 41 configured to project a single beam towards the sample.

[0024]

[0035] Controller 50 is electronically connected to the charged particle evaluation system 40. The controller 50 can be a processor (such as a computer) configured to control the charged particle beam inspection apparatus 100. The controller 50 can also include a processing circuit configured to execute various signal and image processing functions. In FIG. 1, the controller 50 is shown as being located outside the structure including the main chamber 10, the load lock chamber 20, and the EFEM 30, but it is understood that the controller 50 can be a part of this structure. The controller 50 can be located inside one of the components of the charged particle beam inspection apparatus, or the controller 50 can be distributed across at least two of the components. Although the present disclosure provides an example of the main chamber 10 that houses the electron beam inspection tool, it should be noted that the aspects of the present disclosure in the broadest sense are not limited to the chamber that houses the electron beam inspection tool. Rather, it is understood that the foregoing principles can also be applied to other tools and other configurations of devices operating under a second pressure.

[0025]

[0036] Next, refer to FIG. 2, which is a schematic diagram showing an exemplary charged particle evaluation system 40 including a multi-beam electron optical system 41 that is a part of the exemplary charged particle beam inspection apparatus 100 of FIG. 1. The multi-beam electron optical system 41 includes an electron source 201 and a projection device 230. The charged particle evaluation system 40 further includes an electric stage 209 and a sample holder 207. The sample holder can have a holding surface (not depicted) for supporting and holding the sample. Thus, the sample holder can be configured to support the sample. Such a holding surface can be an electrostatic clamp operable to hold the sample during the operation of the electron optical system 41 (such as the evaluation or inspection of the sample). The holding surface (such as the surface of the sample holder oriented to face the electron optical system 41) can be recessed into the sample holder. The electron source 201 and the projection device 230 can be collectively referred to as the electron optical system 41. The sample holder 207 is supported by the electric stage 209 to hold a sample 208 (such as a substrate or a mask) for inspection. The multi-beam electron optical system 41 further includes a detector 240 (such as an electron detection device).

[0026]

[0037] The electron source 201 may include a cathode (not shown) and an extractor or anode (not shown). During operation, the electron source 201 is configured to emit electrons from the cathode as primary electrons. The primary electrons are extracted or accelerated by the extractor and / or anode to form a primary electron beam 202.

[0027]

[0038] The projection device 230 is configured to convert the primary electron beam 202 into a plurality of sub-beams 211, 212, 213 and direct each sub-beam onto the sample 208. For the sake of brevity, three sub-beams are shown, but there may be dozens, hundreds, or thousands of sub-beams. The sub-beams may be referred to as beamlets.

[0028]

[0039] The controller 50 may be connected to various parts of the charged particle beam inspection device 100 of FIG. 1, such as the electron source 201, the detector 240, the projection device 230, and the motorized stage 209. The controller 50 may perform various image and signal processing functions. The controller 50 may also generate various control signals for controlling the operation of the charged particle beam inspection device including the charged particle multi-beam device.

[0029]

[0040] The projection device 230 can be configured to focus the sub-beams 211, 212, and 213 onto the sample 208 for inspection, and can form three probe spots 221, 222, and 223 on the surface of the sample 208. The projection device 230 can be configured to deflect the primary sub-beams 211, 212, and 213 to scan the probe spots 221, 222, and 223 across individual scanning areas within a section of the surface of the sample 208. In response to the incidence of the primary sub-beams 211, 212, and 213 onto the probe spots 221, 222, and 223 on the sample 208, electrons including secondary electrons and backscattered electrons are generated from the sample 208. Secondary electrons typically have an electron energy of 50 eV or less. The actual secondary electrons can have an energy of less than 5 eV, but any energy less than 50 eV is typically considered for secondary electrons. Backscattered electrons typically have an electron energy between 0 eV and the landing energy of the primary sub-beams 211, 212, 213. Since detected electrons having an energy of less than 50 eV are typically considered as secondary electrons, a certain proportion of the actual backscattered electrons are counted as secondary electrons.

[0030]

[0041] The detector 240 is configured to detect signal particles such as secondary electrons and / or backscattered electrons and generate corresponding signals, and these signals are sent to the signal processing system 280, for example, to construct an image of the corresponding scanning area of the sample 208. The detector 240 can be incorporated into the projection device 230.

[0031]

[0042] The signal processing system 280 may include a circuit (not shown) configured to process signals from the detector 240 to form an image. The signal processing system 280 may alternatively be referred to as an image processing system. The signal processing system may be incorporated into a component (such as the detector 240) of the multi-beam charged particle evaluation system 40 (as shown in FIG. 2). However, the signal processing system 280 may be incorporated into any component of the inspection apparatus 100 or the multi-beam charged particle evaluation system 40 (for example, as part of the projection device 230 or the controller 50). The signal processing system 280 may be physically separated from the projection device 230 and the controller 50 (for example, in different rooms). The signal processing system 280 may include an image acquirer (not shown) and a storage device (not shown). For example, the signal processing system may include a processor, a computer, a server, a mainframe host, a terminal, a personal computer, any type of mobile computing device, etc., or a combination thereof. The image acquirer may include at least a portion of the processing functions of the controller. Thus, the image acquirer may include at least one or a plurality of processors. The image acquirer may be communicatively coupled to the detector 240 that enables signal communication, such as, among others, a conductor, an optical fiber cable, a portable storage medium, IR, Bluetooth, the Internet, a wireless network, a wireless radio, or a combination thereof. The image acquirer can receive signals from the detector 240, process the data contained in the signals, and construct an image therefrom. Thus, the image acquirer can acquire an image of the sample 208. The image acquirer can also perform various post-processing functions, such as contour generation and superimposition of indicators on the acquired image. The image acquirer may be configured to perform adjustments such as brightness and contrast of the acquired image. The storage may be a storage medium such as a hard disk, a flash drive, cloud storage, random access memory (RAM), or other types of computer-readable memory. The storage may be coupled to the image acquirer and used to store the scanned raw image data as the original image or to store the post-processed image.

[0032]

[0043] The signal processing system 280 may include a measurement circuit (e.g., an analog-to-digital converter) to obtain the distribution of the detected secondary electrons. The electron distribution data collected during the detection time window can be combined with the corresponding scanning path data of each of the primary sub-beams 211, 212, and 213 incident on the sample surface and used to reconstruct an image of the sample structure being inspected. The reconstructed image can be used to reveal various features of the internal or external structure of the sample 208. Thus, the reconstructed image can be used to reveal any defects that may be present in the sample.

[0033]

[0044] The controller 50 can control the electric stage 209 to move the sample 208 during the inspection of the sample 208. The controller 50 can enable the electric stage 209 to move the sample 208 in a direction, at least during the inspection of the sample, preferably continuously, for example, at a constant speed. The controller 50 can control the movement of the electric stage 209 so that the electric stage 209 changes the speed of the movement of the sample 208 depending on various parameters. For example, the controller 50 can control the stage speed (including its direction) according to the characteristics of the inspection steps of the scanning process.

[0034]

[0045] Known multi-beam systems such as the charged particle evaluation system 40 and the charged particle beam inspection apparatus 100 described above (and elsewhere in this specification) are disclosed in U.S. Patent Application Publication No. 2020 / 118784, U.S. Patent Application Publication No. 2020 / 0203116, U.S. Patent Application Publication No. 2019 / 0259570, and U.S. Patent Application Publication No. 2019 / 0259564, which are incorporated herein by reference.

[0035]

[0046] As shown in FIG. 2, in one embodiment, the charged particle evaluation system 40 includes a projection assembly 60. The projection assembly 60 can be a module and can be called an ACC module. The projection assembly 60 is arranged to direct the light beam 62 so that the light beam 62 is incident between the electron optical system 41 and the sample 208.

[0036]

[0047] When the electron beam scans the sample 208, charge can accumulate on the sample 208 due to the large beam current, which can affect the quality of the image. To regulate the accumulation of charge on the sample, the projection assembly 60 can be employed to irradiate the sample 208 with the light beam 62 so as to control the accumulated charge resulting from effects such as the photoconductive effect, the photoelectric effect, or the thermal effect.

[0037]

[0048] The components of the charged particle evaluation system 40 that can be used in the present invention will be described below in relation to FIG. 3, which is a schematic diagram of the charged particle evaluation system 40. The charged particle evaluation system 40 in FIG. 3 can correspond to the above-described charged particle evaluation system 40 (which can also be called a charged particle evaluation apparatus or tool).

[0038]

[0049] The electron source 201 directs electrons towards an array of condenser lenses 231 (otherwise referred to as a condenser lens array). The electron source 201 is preferably a high-brightness thermionic field emitter arranged to operate within an optimized electron optical performance range which is a compromise (such a compromise may be considered a "good" compromise) between brightness and total emission current. There may be dozens, hundreds or thousands of condenser lenses 231. The condenser lenses 231 include multi-electrode lenses and may have a structure based on European Patent Application Publication No. 1602121A1, which patent document is hereby incorporated by reference herein with respect to the disclosure of a lens array (which array provides one lens per sub-beam) for splitting an electron beam into a plurality of sub-beams in particular. The array of condenser lenses 231 may take the form of at least two plates that act as electrodes, with the apertures of each plate being aligned with each other and corresponding to the locations of the respective sub-beams. At least two plates are maintained at different potentials during operation to achieve the desired lens effect.

[0039]

[0050] Each condenser lens 231 of the array guides electrons to the respective sub-beams 211, 212, 213, and the respective sub-beams 211, 212, 213 are focused at respective intermediate foci on the down-beam side of the condenser lens array. The sub-beams diverge from each other. In one embodiment, the deflector 235 is provided at the intermediate focus. The deflector 235 is positioned at the position of the corresponding intermediate focus in the sub-beam path or at least in its vicinity. The deflector 235 is positioned at or near the intermediate image plane of the associated sub-beam in the sub-beam path. The deflector 235 is configured to manipulate the respective sub-beams 211, 212, 213. The deflector 235 is configured to bend each of the respective sub-beams 211, 212, 213 by an effective amount to ensure that the principal ray (sometimes referred to as the beam axis) is incident on the sample 208 substantially perpendicularly (i.e., at substantially 90° with respect to the nominal surface of the sample). The deflector 235 is also sometimes referred to as a collimator or a collimator deflector. The deflector 235 effectively collimates the path of the sub-beams. Before the deflector, the paths of the sub-beams diverge from each other. On the down-beam side of the deflector, the sub-beam paths are substantially parallel to each other (i.e., substantially collimated). A suitable collimator is the deflector disclosed in European Patent No. 20156253.5 filed on February 7, 2020, which is incorporated herein by reference with respect to the application of the deflector to a multi-beam array. The collimator may include a macro-collimator 270 (such as shown in FIG. 4, for example) instead of or in addition to the deflector 235. Accordingly, the macro-collimator 270 described below in connection with FIG. 4 may be provided with the features of FIG. 3. This is generally less preferred than providing a collimator array as the deflector 235.

[0040]

[0051] Below the deflector 235 (i.e., on the downbeam side or farther from the electron source 201), there is a control lens array 250. The sub-beams 211, 212, 213 that have passed through the deflector 235 become substantially parallel when they enter the control lens array 250. The control lenses pre-focus the sub-beams (e.g., apply a focusing operation to the sub-beams before they reach the objective lens array 241). By pre-focusing, the divergence of the sub-beams can be reduced or the convergence rate of the sub-beams can be increased. The control lens array 250 and the objective lens array 241 cooperate to provide a composite focal length. By combining operations excluding the intermediate focus, the risk of aberration can be reduced.

[0041]

[0052] The control lens array 250 includes a number of control lenses. Each control lens includes at least two electrodes (e.g., two or three electrodes) connected to respective potential sources. The control lens array 250 may include two or a plurality of (e.g., three) plate electrode arrays connected to respective potential sources. The control lens array 250 is associated with the objective lens array 241 (e.g., the two arrays are positioned close to each other, mechanically connected to each other, and / or controlled together as one unit). Each control lens can be associated with a respective objective lens. The control lens array 250 is positioned on the upbeam side of the objective lens array 241.

[0042]

[0053] The control lens array 250 includes control lenses for each of the sub-beams 211, 212, 213. The function of the control lens array 250 is to optimize the opening angle of the beam with respect to the beam reduction ratio and / or to control the beam energy delivered to the objective lens array 241 that guides the sub-beams 211, 212, 213 to the sample 208. The objective lens array 241 can be positioned at or near the base of the electron optical system 41. The control lens array 250 is preferably, although optionally, for optimizing the sub-beams on the upbeam side of the objective lens array.

[0043]

[0054] The control lens array 250 can be considered to provide, for example, electrodes added to the electrodes of the objective lens array 241. The objective lens array 241 can have any number (e.g., 5, 7, 10, or 15) of additional electrodes associated with and proximate to the objective lens array 241. Additional electrodes such as the control lens array 250 allow for additional degrees of freedom for controlling the electron optical parameters of the sub-beams. Such additional associated electrodes can be considered to be additional electrodes of the objective lens array 241 that enable additional functionality for each objective lens of the objective lens array 241. In one configuration, such electrodes can be considered to be part of the objective lens array 241 that provides the additional functionality to the objective lenses of the objective lens array 241. Thus, even if the control lens is only referred to as being part of the objective lens, the control lens is considered to be part of the corresponding objective lens.

[0044]

[0055] For simplicity of explanation, in this specification, the lens array is schematically depicted by an elliptical array (such as that shown in FIG. 3). Each elliptical shape represents one of the plurality of lenses of the lens array. Elliptical shapes are conventionally used to represent lenses by analogy to the biconvex shape often employed in optical lenses. However, in the context of charged particle configurations such as those discussed herein, it will be understood that since the lens array typically operates electrostatically, a physical element adopting a biconvex shape may not be necessary. Instead, the lens array can include a plurality of plates having apertures.

[0045]

[0056] Optionally, an array of scanning deflectors 260 is provided between the control lens array 250 and the array of objective lenses 234. The array of scanning deflectors 260 includes one scanning deflector for each of the sub-beams 211, 212, 213. Each scanning deflector is configured to deflect the sub-beam in one or two directions so as to scan the respective sub-beams 211, 212, 213 in one or two directions across the entire sample 208.

[0046]

[0057] Any of the objective lens array assemblies described herein may further include a detector 240. The detector detects electrons emitted from the sample 208. The detected electrons may include any of the electrons detected by the SEM (including secondary electrons and / or backscattered electrons emitted from the sample 208). An exemplary structure of the detector 240 is shown in FIG. 3.

[0047]

[0058] FIG. 4 schematically depicts a charged particle evaluation system 40 according to one embodiment. Features that are the same as those described above are given the same reference numerals. For the sake of brevity, such features are not described in detail with reference to FIG. 4. For example, the electron source 201, the condenser lens 231, the objective lens array 241, and the sample 208 may be as described above.

[0048]

[0059] In the example shown, a collimator is provided on the up-beam side of the objective lens array assembly. The collimator may include a macro collimator 270. The macro collimator 270 acts on the beam from the electron source 201 before the beam is split into a multi-beam. The macro collimator 270 bends each portion of the beam by an effective amount to ensure that the beam axis of each sub-beam derived from the beam is incident on the sample 208 substantially perpendicularly (i.e., at approximately 90° with respect to the nominal surface of the sample 208). The macro collimator 270 applies macro collimation to the beam. Thus, the macro collimator 270 acts on all the beams rather than including an array of collimator elements configured to act on different individual portions of the beam. The macro collimator 270 may include a magnetic lens or a magnetic lens configuration including a number of magnetic lens sub-units (e.g., a number of electromagnets forming a multipole configuration). As an alternative or in addition thereto, the macro collimator can be implemented at least partially electrostatically. The macro collimator may include an electrostatic lens or an electrostatic lens configuration including a number of electrostatic lens sub-units. The macro collimator 270 can use a combination of a magnetic lens and an electrostatic lens.

[0049]

[0060] As described above, in one embodiment, the detector 240 is between the objective lens array 241 and the sample 208. The detector 240 can face the sample 208. Alternatively, as shown in FIG. 4, in one embodiment, the objective lens array 241 including a plurality of objective lenses is between the detector 240 and the sample 208.

[0050]

[0061] In one embodiment, the deflector array 95 is between the detector 240 and the objective lens array 241. In one embodiment, since the deflector array 95 includes a Wien filter (or even a Wien filter array), it can be called a beam separator. The deflector array 95 is configured to provide a magnetic field for separating charged particles projected onto the sample 208 and secondary electrons from the sample 208.

[0051]

[0062] In one embodiment, the detector 240 is configured to detect signal particles by referring to the energy of charged particles, i.e., depending on the bandgap. Such a detector 240 can be called an indirect current detector. The secondary electrons emitted from the sample 208 obtain energy from the electric field between the electrodes. When the secondary electrodes reach the detector 240, they have sufficient energy.

[0052]

[0063] The present invention can be applied to a variety of tool architectures whose arrangement, depicted and described with reference to FIGS. 3 and 4, is an exemplary multi-beam arrangement. For example, the charged particle evaluation system 40 can be a single-beam tool (e.g., U.S. Patent Application Publication No. 20210319977A1), or can include a plurality of single-beam columns (or devices), or can include a plurality of multi-beam columns. The column can include the electron optical system 41 described in any of the above embodiments or aspects. A plurality of columns (or multi-column tools), for example, columns (or multi-beam columns) that project a plurality of beams depicted and described with reference to FIGS. 3 and 4 towards a sample, some devices can be arranged in an array having 2 to 100 or more columns. The charged particle evaluation system 40 can take the form of the embodiment described with respect to FIG. 3 and depicted in FIG. 3, but preferably has an electrostatic scanning deflector array and an electrostatic collimator array. The charged particle column can optionally include a light source.

[0053]

[0064] As shown in FIG. 2 (read in the context of the electron optical device 41 depicted and described with respect to FIGS. 3-4), in one embodiment, the projection assembly 60 includes an optical system 63. In one embodiment, the projection system 60 includes a light source 61. The light source 61 is configured to emit a light beam 62. In one embodiment, the light source 61 is a laser light source. The laser light provides a coherent light beam 62. However, other types of light sources can alternatively be used. As described above, the projection assembly 60 is used to irradiate the light beam 62 onto the sample 208 so as to control the accumulated charge resulting from effects such as the photoconductive effect, the photoelectric effect, or the thermal effect, and thus regulate the accumulated charge on the sample.

[0054]

[0065] In one embodiment, the optical system 63 includes a lens (e.g., cylindrical lens 64). The cylindrical lens 64 is configured to focus the light beam 62 in one direction rather than in the orthogonal directions. The cylindrical lens increases the design freedom of the light source 61. In one embodiment, the light source 61 is configured to emit a light beam 62 having a circular cross-section. The cylindrical lens 64 is configured to focus the light beam 62 such that the light beam has an elliptical cross-section. Even if lenses other than the cylindrical lens are used, the lens is positioned and designed to ensure that "the light beam reaches a portion of the sample that needs to be irradiated despite the small dimension between the sample and the most downbeam surface of the electron optical device 41 and the large dimension of the downbeam surface of the electron optical device orthogonal to the orientation of the beam path". In order for the light beam to reach the sample surface, the light beam can be reflected by one or more reflecting surfaces 65 and 66 such as mirrors. The use of the reflecting surfaces 65, 66 can improve the reach of the light beam 62 between the most downbeam surface of the electron optical device and the sample.

[0055]

[0066] As described above, in one embodiment, the charged particle evaluation system 40 includes a detector 240 configured to detect signal particles emitted by the sample 208. As shown in FIG. 3, in one embodiment, the detector 240 forms the most down-beam side of the electron optical device 41 with respect to the electron beams 211, 212, 213. In other arrangements, as described herein, the detector 240 can be associated with and even include part of the objective lens arrangement. For example, the detector 240 can be associated with the objective lens array but can be associated at different positions along the primary beam path (e.g., can be associated with the electrodes of the objective lens array), just on the up-beam side of the objective lens array, can be dispersed at various positions along the beam path within and adjacent to the objective lens array, or can be positioned adjacent to the objective lens array. In another arrangement, the detector is located within a secondary column adjacent to or connected to the charged particle column including the electron optical device 41. In all of these arrangements, there is a most down-beam side element (such as the detector 240) of the electron optical system closest to the sample. The most down-beam side surface of the most down-beam side element can face the sample. The most down-beam side surface can be referred to as the facing surface.

[0056]

[0067] To detect defects on a sample, data derived from the sample is processed. This data can be derived, for example, from an optical inspection of the sample. This data can be derived from an inspection of the sample by a charged particle evaluation system 40 shown in FIGS. 1-4. For example, data detected by a detector (such as detector 240) can be compared with expected data for a sample without any defects, and the difference between the detected data and the expected data can represent a combination of real defects and interference signals (e.g., caused by noise). Interference signals are often identified as signals having a signal intensity below a certain threshold. Signals having a signal intensity above the threshold are likely to be defect signals and can then undergo further analysis to determine whether a defect is present and, if so, to classify the nature of the defect. It is desirable to have a method of identifying a suitable threshold such that the number of real defects missed is low without unnecessarily post-processing a large number of interference signals.

[0057]

[0068] Figure 5 shows a histogram of an exemplary initial data set that includes elements representing interference signal 53 and defect signal 52. Such elements may be referred to as data elements. Thus, the initial data set has a distribution of elements that consists of the distribution of interference signal 53 and the distribution of defect signal 52. The distribution of defect signal 52 represents actual defects. The X-axis of FIG. 5 represents signal magnitude, and the Y-axis of FIG. 5 represents the number of occurrences of individual signals (e.g., defect signals or interference signals) at each signal magnitude. FIG. 5 also shows an exemplary threshold 51. The threshold is the magnitude of a signal that can be used to distinguish between signals that are interference signals and defect signals. When applying the threshold, signals having a magnitude greater than the threshold are considered detection signals (and are considered likely to be actual defect signals), and signals having a magnitude less than the threshold are considered interference signals. With respect to the data collected during sample inspection within the data set, it is not known in advance "which elements of the data set represent interference signals and which elements represent actual defects". Thus, an appropriate threshold needs to be determined and applied to select the best-fit defect candidate data for further analysis. FIG. 5 provides an inset of an enlarged view 54 of the most interesting region. Within the region of this histogram, there is an overlap between the distribution of interference signal 53 and the distribution of defect signal 52. The overlap has interference signal 53 and defect signal 52 having the same signal strength value. Within the overlap, the initial data set includes the contribution of interference signal 53 and the contribution of defect signal 52. The threshold crosses the overlap and separates the overlap into two parts. Each part of the two parts of the overlap includes elements of the initial data set (which is actually a mixture of interference signals and actual defect signals), but elements that exceed the threshold are classified as detection signals (those signals that are likely considered defect signals). Elements of the overlap that are below the threshold are classified as interference signals. Thus, some of the actual defect signals may be classified as interference signals. Some of the interference signals may be classified as defects (i.e., misidentified as defects).

[0058]

[0069] When using a threshold value to distinguish between defect signals and interference signals, as the threshold value increases, the likelihood of defects below the threshold value increases. As the threshold value decreases, the number of occurrences of interference signals increases. As a result, if the threshold value is set too low, a large number of signals that do not represent actual defects (signals misidentified as defects) will undergo further analysis (i.e., as if they were defects), which is inefficient. Signals misidentified as defects will provide inaccurate evaluation information (e.g., evaluation data (such as inspection data)). On the other hand, if the threshold value is set too high, the capture rate of defects may be low. Here, the capture rate is a measure of the ratio or percentage of elements in a dataset representing actual defects that are above the threshold value. The capture rate can be defined as the percentage of data elements representing actual defects identified as defect candidates.

[0059]

[0070] The present disclosure provides a method for processing data derived from a sample, the method including processing an initial dataset of elements. The initial dataset of elements is derived from detections by a detector for calibration. The sample can be sample 207 described above with reference to FIG. 2, and the detector can be detector 240 described above with reference to FIGS. 2-4 or any of the other evaluation systems disclosed herein, such as a single-beam system or a multi-column system. The processing of the data can be performed by using signal processing system 280 (described above with reference to FIG. 2). The processing can be performed by an inspection device (such as within a column (or device)), or at a remote location such as a processing rack far from the location of the column, or the processing can be distributed, for example, such that a portion of the processing within the device is remote (at the processing rack). The data can be stored and processed later, or the processing can be simultaneous with the on-the-fly detection of the sample data. A computer program (which can take the form of a group of programs distributed across multiple processors) can provide instructions configured to control the processor to perform the processing of the data.

[0060]

[0071] For example, as shown in FIG. 5, the initial data set includes elements representing interference signals 53 and defect signals 52. Processing of the initial data set includes adapting a distribution model to the initial data set to generate an interference distribution model. The interference distribution model desirably represents the distribution of the initial data set. Due to mainly including interference signals representing noise, the initial data set may have approximately a normal distribution. The interference distribution model may include, for example, a Gaussian function. The number of data elements in the initial data set representing actual defects is expected to be significantly lower than the number of elements in the initial data set representing interference signals. As a result, a preliminary model of the distribution of interference signals that is reasonably accurate can be determined by adapting the model to the entire initial data set. The preliminary model is called "reasonably accurate" because the initial data set includes not only signals representing actual defects but also interference signals. Since the interference distribution model is based on initial data including signals representing actual defects, there are errors in the interference distribution model. However, the population of actual defects in the initial data set can be considered negligible because it is much smaller than the population of interference signals (as will be described later in this specification).

[0061]

[0072] Processing of the initial data set further includes setting a signal strength value and selecting elements in the initial data set having a magnitude greater than the signal strength value as a set of defect candidates. The signal strength value acts similarly to the threshold value 51 in FIG. 5. Data elements having a signal strength magnitude exceeding the selected signal strength value are those elements that are considered most likely to represent actual defects. The distribution model is adapted to a set of defect candidates to generate a defect distribution model of the detected signals. The detected signals are expected to include the majority of the defect signals. However, it is possible that some interference signals representing noise may also be included within the set of defect candidates, or that some signals representing actual defects may be omitted from the set of defect candidates due to having a signal strength magnitude less than the signal strength value. Nevertheless, the defect distribution model of the detected signals is intended as a preliminary model representing the distribution of signals representing defects in the sample. The set of defect candidates may have approximately a normal distribution. The defect distribution model may include, for example, a Gaussian function.

[0062]

[0073] Processing of the initial data set further includes determining at least a signal strength threshold that depends on the defect distribution model. In other words, the defect distribution model can be used to determine an appropriate signal strength threshold, and it is expected that beyond this signal strength threshold, an appropriate proportion of the signals representing defects will be captured.

[0063]

[0074] Determining the signal strength threshold includes correcting the defect distribution model. It is desirable that this correction is suitable for correcting the magnitude overlap between the elements representing the interference signal and the elements representing the defect signal. This is advantageous in scenarios where the initial data set has no distinct dips or minima. Below the minimum, the data mainly or completely contains interference signals. Beyond the minimum, the data mainly or completely contains data representing actual defects. During the inspection of some samples, it was found that there is an overlap between the interference signal 53 and the defect signal 52 as shown in the enlarged view 54 of FIG. 5. Due to this overlap, there may be no minimum in the number of occurrences of the initial data set to indicate a suitable value of the signal strength threshold. For example, when the initial data set is generally considered to be interference signals and detection signals (candidates that may be defect signals) before the data is categorized.

[0064]

[0075] Further, in these scenarios, the initially generated defect distribution model may not be an accurate representation of the distribution of data representing actual defects due to, for example, being set such that the signal strength values include too many interference signals or too few defect signals, and / or due to a large overlap between the interference signals and the defect signals. A large overlap between the interference signals and the defect signals may mean that "it is not possible to effectively separate the two sets of data by using a simple cut-off value such as a signal strength value". Therefore, it is desirable to correct the overlap to obtain a more representative corrected defect distribution model for use in setting the signal strength threshold used to filter out data for further processing and omit data considered unnecessary.

[0065]

[0076] Correcting the overlap may include correcting to a corrected defect distribution model of the detection signal. Correcting the overlap preferably includes generating a total distribution model of the initial data set by using the interference distribution model and the defect distribution model. Generating the total distribution model may include summing the interference distribution model and the defect distribution model. Accordingly, the total distribution model is a model representing the entire initial data set by combining the interference distribution model and the defect distribution model. This is because, for example, as shown in FIG. 5, it is expected that the initial data set includes both the interference signal 53 and the defect signal 52.

[0066]

[0077] Once the total distribution model is generated, the total distribution model can be improved by fitting the total distribution model to the actual distribution of the initial data set. Fitting the total distribution model means updating the parameter values of the model until it more closely matches the distribution of the initial data set. The updated total distribution model may be referred to as the corrected total distribution model. This model is regarded as "corrected" because "a fitted model is likely to match the data being modeled more closely than an unfitted model".

[0067]

[0078] For example, FIG. 6A shows a graph representing a defect distribution model 71 (thick continuous line), an interference distribution model 72 (thick dashed line), a total distribution model 73 (or original total distribution model) (thick chain line), a corrected total distribution model 74 (thin dashed line), and the distribution 75 of the initial data set (thin continuous line), where the X-axis represents the signal intensity magnitude and the Y-axis represents, for example, the number of occurrences of signals having the intrinsic signal intensity magnitude. Since the details of FIG. 6A may be difficult to identify, FIG. 6B provides an enlarged view of the most interesting region from FIG. 6A. As can be seen from FIG. 6B, the corrected total distribution model 74 provides a closer approximation of the distribution 75 of the initial data set than that shown by the original total distribution model 73.

[0068]

[0079] Typically, there are more interference signals than defect signals by several orders of magnitude. For example, the distribution of interference signals can have about 10 10 elements, and the distribution of defect signals can have about 10 2 elements. This can make it difficult to consider the relative importance of the interference distribution model and the defect distribution model when fitting the total distribution model. The total distribution model and the actual distribution can each be the inverse logarithm of their respective cumulative distributions. Desirably, the corrected total distribution model is a function (in particular, the inverse logarithm) of the respective cumulative distributions. Application of this function can reduce the differences in magnitude order, for example, reducing the signal strength magnitudes (and thus the lengths of the Y-axis) required for the graphs of the distributions shown in FIGS. 6A and 6B.

[0069]

[0080] Correcting the overlap can include generating a corrected defect distribution model by adjusting the parameter values of the defect distribution model based on the parameter values of the corrected total distribution model. Alternatively, correcting the overlap can include generating a corrected defect distribution model based on the parameter values of the corrected total distribution model associated with the defect distribution model. Note that the corrected total distribution is associated with the interference distribution model; that is, in fact, by definition, "the corrected total distribution is related to both the defect distribution model and the interference distribution model". Thus, the corrected interference distribution model can be generated, for example, based on the parameter values of the corrected total distribution model associated with the interference distribution model.

[0070]

[0081] In either case of correcting the overlap, the corrected defect distribution model is expected to be in better agreement with the actual distribution of the signals representing the defects in the initial data. This is because the parameter values used in the corrected defect distribution model are based on the parameter values in the corrected total distribution model. The corrected total distribution model can be considered to be in better agreement because it is correlated to the distribution of the initial data set.

[0071]

[0082] It is preferable that setting the signal strength threshold is based on the parameter values of the corrected defect distribution model. This is because the corrected defect distribution model can be used to establish what signal strength threshold is required to capture a sufficient amount of data expected to represent defects. The sufficient amount can be determined, for example, by the user or by a use case selected in advance. For example, the sufficient amount is at least 90 percent (90%) (e.g., between 90% and almost 100%).

[0072]

[0083] Furthermore, it is desirable to determine the relationship between the capture rate and the signal strength threshold. In particular, it is desirable to determine the capture rate as a function of the signal strength threshold. This can be achieved, for example, by using the corrected defect distribution model.

[0073]

[0084] Alternatively or additionally, a method of processing data derived from a sample may include, for calibration, processing an initial dataset of elements derived from detection by a detector. The dataset includes elements representing interference signals and detection signals as described above (and as described elsewhere herein). Processing the initial dataset includes fitting a distribution model, setting signal strength values, selecting elements within the initial dataset, fitting the distribution model to a set of selected elements, and determining a signal strength threshold. Fitting the distribution model is performed on the initial dataset to generate an interference distribution model. Selecting elements within the initial dataset selects elements having a magnitude greater than the signal strength value. The selected elements form a set of defect candidates. Fitting the distribution model to a set of defect candidates is to generate a defect distribution model of the detection signal. Determining the signal strength threshold depends at least on the defect distribution model, and it is desirable that the defect distribution model is corrected to be a corrected distribution model as described above (and as described elsewhere herein).

[0074]

[0085] The processing of the initial dataset includes determining the relationship between the capture rate and the signal strength threshold. Determining the relationship between the capture rate and the signal strength threshold includes determining the capture rate as a function of the signal strength threshold.

[0075]

[0086] Determining the signal strength threshold includes correcting the magnitude overlap between the element representing the interference signal and the element representing the defect signal, as described above (and as described elsewhere in this specification). Desirably, correcting the overlap includes correcting to a corrected defect distribution model. The corrected total distribution model can be generated by summing the interference distribution model and the defect distribution model and fitting to the actual distribution of the initial dataset. The corrected defect distribution model can be based on the parameter values of the corrected total distribution model. It is desirable that determining the capture rate as a function of the signal strength threshold and / or determining the capture rate as a function of the signal strength threshold is based on the parameter values of the corrected total distribution model.

[0076]

[0087] FIG. 7A shows a graph of the capture rate on the Y-axis versus the interference rate on the X-axis. The actual data 91 is plotted in the same manner as the modeled data 92 (the modeled data 92 is based on the interference distribution contribution and the defect distribution contribution of the corrected total distribution model). The signal strength threshold can be automatically set based on the corrected defect distribution model. Alternatively or additionally, the user can adjust the signal strength threshold to achieve a balance between the defect capture rate and the interference rate. In other words, it may be desirable to select a signal strength threshold that has a sufficiently high capture rate such that a sufficient percentage of the defects are captured, where the percentage of interference signals captured is low enough so that further processing of the data is not overly inefficient (e.g., not unreasonably inefficient). Note that any percentage of interference signals in the data for further processing will slow down the processing. Thus, in this arrangement, the percentage of interference signals in the data for further processing (including post-processing of the resulting data set and image) and a certain resulting inefficiency are acceptable. However, this is only somewhat true. If the percentage of interference signals in the data for further processing is too high, further processing including post-processing will be inefficient (perhaps to the point where further processing becomes meaningless).

[0077]

[0088] The signal strength threshold can be set based on the determined capture rate. For example, it may be desirable for the defect capture rate to be at least 85%. The corrected defect distribution model can be used to determine at what signal strength 85% of the defects occur above that signal strength. FIG. 7B shows a graph of the capture rate on the Y-axis versus the signal strength threshold on the X-axis. The modeled data 94 is plotted in the same manner as the actual data 93 is plotted (based on the corrected defect distribution model).

[0078]

[0089] FIGS. 8A and 8B provide another example of comparing a corrected distribution model with corresponding actual data. Since the initial data set used to generate FIGS. 8A and 8B is based on images taken from samples with pre-programmed defects, the exact amount (or number) and locations of the actual defects are known. Accordingly, a comparison between the corrected distribution model and the corresponding actual data can be made. The upper graphs of FIGS. 8A and 8B show graphs representing the defect distribution model 71, the interference distribution model 72, the corrected total distribution model 74, and the distribution 75 of the initial data set, where the X-axis represents the signal strength magnitude and the Y-axis represents the number of occurrences. That is, the upper graphs of FIGS. 8A and 8B represent similar (to those depicted and described with reference to FIG. 6A) parameters of various data as the initial data set. The lower graphs of FIGS. 8A and 8B show the capture rate on the Y-axis with respect to the signal strength threshold on the X-axis. The actual data 93 is plotted in the same manner as the modeled data 94. (The modeled data 94 is based on the corrected defect distribution model.) That is, with respect to the same data depicted in the upper graphs of FIGS. 8A and 8B, the lower graphs represent the parameters depicted and described with reference to FIG. 7B. From these figures, it can be seen that the model provides a means of quantifying the percentage of defects likely to be captured for a given signal strength threshold. This may enable a user to make more efficient and information-based selections of signal strength thresholds by using these models. Alternatively or additionally, the signal strength threshold (parameters such as boundary conditions, etc.) can be automatically set for data processing based on the corrected defect distribution model (e.g., by using a computer).

[0079]

[0090] As described above (and as described elsewhere herein), the processing of the initial data set includes setting signal strength values and selecting elements within the initial data set. By selecting elements within the initial data set, elements having a magnitude greater than the signal strength value are selected. The selected elements of the initial data set are selected as a set of defect candidates. The signal strength value can be set based on the interference distribution model.

[0080]

[0091] For example, setting the signal strength value may include determining an interference threshold based on an interference distribution model. The interference threshold represents the signal strength magnitude, which is the number of elements representing interference signals exceeding this threshold, and the interference threshold is usually low. A predetermined interference threshold is set to represent the number of elements representing interference signals having a magnitude greater than the interference threshold. Next, the interference threshold can be determined based on the predetermined interference threshold and the interference distribution model. According to the interference distribution model, the number of elements representing interference signals having a magnitude greater than the interference threshold is equal to or less than the predetermined interference threshold. The predetermined interference threshold can be ten (10), preferably one (1), more preferably almost negligible.

[0081]

[0092] The signal strength value can be selected based on the interference threshold. It is desirable that the signal strength value be set equal to the interference threshold. In this way, the cut-off signal strength (above which elements are selected for inclusion within a set of defect candidates) is based on the interference distribution model. The interference distribution model may indicate that there are few occurrences of interference signals exceeding a particular signal strength. The signal strength value can be set equal to the particular signal strength. Here, a set of defect candidates represents a detection signal (expected to be a defect signal).

[0082]

[0093] The interference distribution model can be based on the following model: ln(y)=a + c * x 2 (1) Here, y is the number of occurrences (e.g., the number of elements within a dataset having a particular signal strength magnitude), x is the signal strength, and a and c are parameter values. The parameter values "a" and "c" are determined by fitting to the distribution of the initial dataset.

[0083]

[0094] The distribution of an exemplary initial dataset 81 is shown in FIG. 9, where the X-axis is the squared signal strength (x 2and the Y-axis is the natural logarithm of the number of occurrences. In this figure, a first-degree polynomial 83 (i.e., a straight line) is fitted to the initial data 83, and this distribution exhibits linear behavior. The slope of the first-degree polynomial is the parameter "c" in Equation (1). The Y-axis intercept of the first-degree polynomial 83 is the parameter "a" in Equation (1). The X-axis intercept of the first-degree polynomial 83 can be used as the signal strength value for selecting elements of the initial data set for inclusion within a set of defect candidates. Elements of the initial data set that are found to represent actual defects are represented by the actual defect line 82. Note that the distribution of actual defects indicated by the actual defect line 82 exists on both sides (i.e., above and below) of the X-axis intercept of the first-degree polynomial 83. Thus, some of the actual defects to the left of the X-axis intercept were not included in the set of defect candidates to the right of the X-axis intercept.

[0084]

[0095] FIGS. 10A and 10B show a distribution similar to that of FIG. 9. The distributions of FIGS. 10A and 10B were generated by applying the model defined by Equation (1) to two further exemplary initial data sets. These exemplary initial data sets have known distributions of potential interfering defects and actual defects. Since these initial data sets are based on images taken from samples having pre-programmed (or predetermined or known) defects, the exact amount (or number) of actual defects and their locations are known. Thus, a comparison can be made, for example, between the model represented by line 83 (i.e., the first-degree polynomial) and the actual data (e.g., the initial data set) 81. FIGS. 10A and 10B demonstrate that "the distribution of interfering signals can be modeled for various initial data sets having various distributions of interfering signals, resulting in various slopes (i.e., first-degree polynomials 83) of the interference model." In this way, the signal strength value can be determined for each of the initial data sets such that a set of defect candidates is likely to capture most of the signals representing actual defects.

[0085]

[0096] Alternatively or additionally, a method of processing data derived from a sample may include processing an initial data set of elements derived from detection by a detector. Such a data set includes elements representing interference signals and defect signals as described above with reference to FIG. 5 (and described elsewhere herein). The interference distribution includes elements representing interference signals having a magnitude interference range. The defect distribution includes elements representing detection signals having a magnitude defect range. The interference range overlaps the defect range. The location where the interference range overlaps the defect range is the overlap. For example, a sufficient number of elements within the defect range have a magnitude that exceeds the upper limit of the interference range for a good prediction. For a good prediction, the upper limit of the defect range exceeds the upper limit of the interference range. That is, the magnitude of the upper limit of the defect range (i.e., of the signal strength) exceeds (e.g., extends above) the upper limit of the interference range (i.e., of the magnitude). The location where the lower limit of the defect range is less than the upper limit of the interference range is the overlap. That is, the lower limit of the overlap is the lower limit of the magnitude defect range (extending below and thereby being exceeded by the upper limit of the interference range). A sufficient number is the number of elements of the defect range sufficient to estimate the distribution of the defect signal given other parameters of the distribution of the initial data set. By a sufficient number of elements of the defect range, the distribution of the defect signal can be sufficiently distinguishable for the estimation of the distribution of the defect signal identified from the interference distribution. A sufficient number of elements of the defect range can be a threshold number of elements of the defect range having a magnitude greater than the upper limit of the interference range. If the number of elements of the defect range matches or exceeds the threshold number, the defect distribution is distinguishable and can be distinguishable from the interference distribution. FIG. 11 shows that 1% of the actual defects included within a set of defect candidates is insufficient, while in FIG. 10B approximately 50% of the actual defects are included in a set of defect candidates and in FIG. 10A much more than 50% of the actual defects are included in a set of defect candidates. Thus, the threshold for the percentage of elements of the defect distribution that are separable and distinguishable to enable a good prediction is between the results depicted in FIG. 10B and the results depicted in FIG. 11.That is, the threshold of the ratio of the elements of the defect distribution occurring within the interference range is greater than 1 / 2 and minimally greater (thus not all of the defect distribution), or is 1 to 50%, or (alternatively stated) the ratio of the defect distribution having a signal intensity magnitude exceeding the signal intensity value is less than 1 / 2 (50%) (e.g., 1 to 50%). Thus, in principle, at least one defect element of the defect distribution having a signal intensity magnitude exceeding the upper limit of the interference distribution may be a sufficient number of defects. However, it is likely that the number of defect elements of the defect distribution where a sufficient number of defects exceed the magnitude of the upper limit of the interference distribution is two or more.

[0086]

[0097] The defect distribution can be separated / distinguished from the interference distribution. At least one element represents a detection signal including a subset of the elements representing the detection signal. Desirably, the subset of the elements representing the detection signal indicates a defect distribution separable / distinguishable from the interference distribution. The initial data set depicted and described with reference to FIGS. 9, 10A, and 10B has such a defect distribution.

[0087]

[0098] FIG. 11 shows a graph similar to FIG. 9 generated by applying the model defined by Equation (1) to another exemplary initial data set. In this data set, the interference range completely overlaps the defect range (desirably over the entire defect distribution). The defect range is either completely within the interference range or, otherwise, has an insufficient number of elements separable and distinguishable from the interference range for a good prediction of the distribution of the defect signal (e.g., for the estimation of the defect distribution identified from the interference distribution). It can be seen that the defect signal represented by the actual defect line 82 occurs within a range very close to the upper end of the interference data and the interference model range (i.e., the first-degree polynomial 83). In other words, the distribution of the defect signal completely or at least substantially (almost completely) represented by the actual defect line 82 overlaps the distribution of the interference signal (i.e., the first-degree polynomial 83).

[0088]

[0099] The defect distribution depicted in FIG. 11 by the actual defect line 82 may be indistinguishable or inseparable from the interference distribution represented, for example, by the line (i.e., first-degree polynomial) 83. Unlike the arrangements depicted in FIGS. 9, 10A, and 10B (which show a distribution of defect signals distinguishable from the distribution of interference signals), the elements of the initial data set that are defect candidates are close to the empty set. That is, there are no defect signals that are separable and distinguishable from the interference signals of the interference distribution, or there are not enough defect signals that are separable and distinguishable from the interference signals of the interference distribution. There are no defect signals having a defect range with a magnitude exceeding the upper limit of the interference range at elements having a corresponding signal strength, or at most an insufficient number of defect signals having a defect range with a magnitude exceeding the upper limit of the interference range at elements having a corresponding signal strength magnitude. That is, the number of elements of the defect range having a magnitude exceeding the upper limit of the interference range does not reach the threshold of the elements of the defect having a magnitude greater than the upper limit interference range. Accordingly, there exists a condition under which defect signals within the initial data set can be identified based on the relative signal strength of the defect distribution compared to the interference distribution.

[0089]

[0100] The initial data set can be identified from the initial signal (or inspection signal or evaluation signal) from the detector. The initial data set can include all elements of the initial signal detected by the detector. However, this approach has the drawback that a large amount of data, most of which is interference signal, is processed. Alternatively, the initial data set can be identified by extracting elements from the initial signal and selecting elements having a magnitude greater than a predetermined signal strength value. It is desirable that the processing of the initial data set be performed by using the selected elements. In this way, elements having a magnitude low enough to indicate an interference signal can be filtered out at an early stage before the initial data set is processed as described above (and as described elsewhere herein). Thus, this processing can be more efficient. Desirably, the predetermined signal strength value is lower than this signal strength value. In this way, any element having a magnitude high enough to indicate a possible defect is less likely to be ignored, but instead will be included in the initial data set. This advantageously provides an initial data set with a small amount of interference data having a low risk of inadvertently omitting data representing actual defects. The predetermined signal strength value can be set based on information from a previous equivalent data set or based on a model.

[0090]

[0101] For example, when a signal strength threshold is set by using the method described above (and described elsewhere herein), defects on the sample can be detected. The defects can be detected by evaluating a subset of defect candidates having a magnitude greater than the signal strength threshold. In other words, the subset of defect candidates can be evaluated to determine which of the subset of defect candidates corresponds to an actual defect. Once the actual defect is determined, the corresponding signal can be further evaluated to determine the type of defect. The evaluation of the signal for identifying and classifying the defect can require a significant amount of time and computer effort. Thus, it is desirable to use the method described above (and described elsewhere in this specification) to set an appropriate signal strength threshold so that a detailed evaluation is not performed mainly on a large amount of signals including interference signals.

[0091]

[0102] The signal strength threshold can be set by evaluating a subset of defect candidates based on an initial data set and can be used to determine defects. Alternatively or additionally, the initial data set can be used to determine a signal strength threshold that can be applied to further and later processed and / or collected signal data. For example, an initial data set based on data from a single sample or data from a portion of a single sample can be used to determine the signal strength threshold. Following this determination of the signal strength threshold, further initial signals can be received and / or processed. The further initial signals can be derived from the inspection of another sample from the same batch as the single initial sample. The further initial signals can be derived from the inspection of the remainder of the single sample if only data from a portion of the single sample was used to determine the signal strength threshold. Further elements of the further initial signals having a magnitude greater than the signal strength threshold can be extracted as further detection signals. The further detection signals can be evaluated to determine which of the further detection signals correspond to actual defects.

[0092]

[0103] Optionally, the further detection signals can be placed within a subset of defect candidates along with the subset of defect candidates identified from the initial data set. In this way, actual defects within both the initial data set and the further detection signals can be evaluated to determine which of the further detection signals correspond to actual defects.

[0093]

[0104] Figures 12A - 12D show samples having a plurality of features 110, one or more of which are not formed correctly and are thus considered defects. Data on such defects described and explained herein may include defects in the defect distribution (as actual defects) of the initial data set (e.g., captured). Figure 12A shows an example where the defect is a missing feature (e.g., a hole) on a sample at the center of the depicted area. This image may be analyzed to classify that the defect takes the form of a missing hole 111. The type of defect may be classified by comparing the mismatch signal with other signals to determine that the mismatch signal mismatches with other signals, where the other signals are similar and thus match each other. Alternatively, the type of defect may be classified by comparing the mismatch signal with the known expected signal pattern of the relevant area of the sample. The expected signal pattern may take the form of a data file to be compared with the mismatch signal.

[0094]

[0105] Figure 12B shows an example where the defect is a bridge feature 112. In particular, Figure 12B depicts a sample including a series of circular features on its surface. The bridge feature 112 is stretched by forming, for example, an ellipse, a rectangle, or an irregular shape rather than a circular shape. Figures 12C and 12D show examples where the defect is a feature of incorrect size. In particular, Figure 12C depicts a sample including an over - sized feature 113, and Figure 12D depicts a sample including an under - sized feature 114.

[0095]

[0106] The following clauses are provided:

[0096]

[0107] Clause 1: A method for processing data derived from a sample, the method comprising, for calibration, processing an initial data set of elements derived from detection by a detector, the data set including elements representing interference signals and detection signals, the processing of the initial data set including adapting a distribution model to the initial data set to generate an interference distribution model, setting a signal strength value, selecting elements within the initial data set having a magnitude greater than the signal strength value as a set of defect candidates, adapting a distribution model to the set of defect candidates to generate a defect distribution model for the detection signal, and determining at least a signal strength threshold that depends on the defect distribution model, the determination including correcting the defect distribution model, the correction being preferably suitable for correcting the magnitude overlap between elements representing interference signals and elements representing detection signals.

[0097]

[0108] Clause 2: The method according to clause 1, wherein correcting the overlap includes correcting the corrected defect distribution model of the detection signal.

[0098]

[0109] Clause 3: The method according to clause 2, wherein correcting the overlap includes generating a total distribution model of the initial data set using the interference distribution model and the defect distribution model.

[0099]

[0110] Clause 4: The method according to clause 3, wherein generating the total distribution model includes summing the interference distribution model and the defect distribution model.

[0100]

[0111] Clause 5: The method according to any one of clauses 3 and 4, further including adapting the total distribution model to the actual distribution of the initial data set to generate a corrected total distribution model.

[0101]

[0112] Clause 6: The method according to clause 5, wherein correcting the overlap includes generating a corrected defect distribution model by adjusting the parameter values of the defect distribution model based on the parameter values of the corrected total distribution model.

[0102]

[0113] Clause 7: Correcting the overlap includes generating a corrected defect distribution model based on the parameter values of the corrected total distribution model associated with the defect distribution model, the method described in Clause 5.

[0103]

[0114] Clause 8: Setting the signal strength threshold is the method described in any one of Clauses 2 to 7 based on the parameter values of the corrected defect distribution model.

[0104]

[0115] Clause 9: The method described in any one of the preceding clauses, further including determining the relationship between the capture rate and the signal strength threshold (preferably, determining the capture rate as a function of the signal strength threshold).

[0105]

[0116] Clause 10: A method for processing data derived from a sample, the method including, for calibration, processing an initial data set of elements derived from detection by a detector, the data set including elements representing interference signals and detection signals, and processing the initial data set includes fitting a distribution model to the initial data set to generate an interference distribution model, setting a signal strength value, selecting elements within the initial data set having a magnitude greater than the signal strength value as a set of defect candidates, fitting a distribution model to the set of defect candidates to generate a defect distribution model of the detection signal, determining at least a signal strength threshold that depends on the defect distribution model, and determining the relationship between the capture rate and the signal strength threshold.

[0106]

[0117] Clause 11: Determining the signal strength threshold includes correcting the magnitude overlap between the elements representing the interference signal and the elements representing the detection signal, and preferably, correcting the overlap preferably includes correcting the corrected defect distribution model using the corrected total distribution model by summing the interference distribution model and the defect distribution model and fitting it to the actual distribution of the initial data set, the method described in Clause 10.

[0107]

[0118] Clause 12: The method according to any one of Clauses 9 to 11, wherein determining the relationship between the capture rate and the signal strength threshold includes determining the capture rate as a function of the signal strength threshold.

[0108]

[0119] Clause 13: The method according to Clause 12, wherein determining the capture rate as a function of the signal strength threshold is based on the parameter values of the corrected total distribution model.

[0109]

[0120] Clause 14: The method according to Clause 13, including determining the capture rate as a function of the signal strength threshold based on the corrected defect distribution model.

[0110]

[0121] Clause 15: The method according to any one of Clauses 13 or 14, further including setting the signal strength threshold based on the determined capture rate.

[0111]

[0122] Clause 16: The method according to any one of Clauses 1 to 15, wherein the interference distribution model includes a Gaussian function.

[0112]

[0123] Clause 17: The method according to any one of Clauses 1 to 16, wherein the defect distribution model includes a Gaussian function.

[0113]

[0124] Clause 18: The method according to any one of Clauses 5 to 9 and Clauses 11 to 17, wherein the total distribution model and the actual distribution are each the negative logarithm of the inverse of their respective cumulative distributions, and preferably the corrected total distribution model is the negative logarithm of the inverse of its respective cumulative distribution.

[0114]

[0125] Clause 19: The method according to any one of Clauses 1 to 18, wherein the signal strength value is set based on the interference distribution model.

[0115]

[0126] Clause 20: Setting the signal strength value includes determining an interference threshold based on the interference distribution model, wherein according to the interference distribution model, the number of elements representing interference signals having a magnitude greater than the interference threshold is equal to or less than a predetermined interference threshold, and selecting the signal strength value based on the interference threshold. The method according to Clause 19.

[0116] Article 21: The method according to Article 20, wherein the signal strength value is set equal to the interference threshold value.

[0117]

[0128] Article 22: The method according to any one of Articles 20 to 21, wherein the predetermined interference threshold value is 1.

[0118]

[0129] Article 23: The interference distribution model is based on the following model: ln(y)=a + c * x 2 , where y is the number of occurrences, x is the signal strength, and "a" and "c" are parameter values determined by fitting to the distribution of the initial dataset, the method according to any one of Articles 1 to 22.

[0119]

[0130] Article 24: The method according to any one of Articles 1 to 23, further comprising receiving a detection signal from a detector and identifying an initial dataset from the detection signal.

[0120]

[0131] Article 25: The method according to any one of Articles 1 to 24, further comprising identifying the initial dataset by extracting elements from the detection signal and selecting shaping elements having a magnitude greater than a predetermined signal strength value, wherein the predetermined signal strength value is lower than the signal strength value, and the processing of the initial dataset is performed using the selected elements.

[0121]

[0132] Article 26: The method according to any one of Articles 1 to 25, wherein the processing of the initial dataset further includes identifying a subset of defect candidates having a magnitude greater than the signal strength threshold.

[0122]

[0133] Clause 27: The method according to any one of Clauses 1 to 26, further comprising receiving a further initial signal and extracting a further element having a magnitude greater than a signal strength threshold from the further initial signal (preferably including a further element within a set of defect candidates), wherein preferably the further element can be referred to as a subset of the defect candidates.

[0123]

[0134] Clause 28: The method according to any one of Clauses 26 to 27, wherein the processing of the initial data set further comprises detecting a defect on the sample by evaluating a subset of the defect candidates.

[0124]

[0135] Clause 29: The method according to any one of Clauses 1 to 28, wherein processing the data derived from the sample further comprises using a processor included within a charged particle optical device.

[0125]

[0136] Clause 30: The method according to any one of Clauses 1 to 29, further comprising projecting at least a beam of charged particles towards a sample using a charged particle optical device including a detector that detects a detection signal in response to signal particles received from the sample in response to an impact of the beam with the sample.

[0126]

[0137] Clause 31: A method for processing data derived from a sample, the method including processing an initial dataset of elements derived from detection by a detector, the initial dataset including elements representing interference signals and defect signals, the interference distribution including elements representing interference signals having an interference range of magnitudes, the defect distribution including elements representing detection signals having a defect range of magnitudes, the interference range overlapping the defect range (desirably within the overlap), at least one element of the defect range having a magnitude that exceeds (desirably, the upper limit of the defect range extends above the upper limit of the interference range) the upper limit of the interference range (desirably of magnitudes), and desirably, at least one element of the defect range being a sufficient number of elements of the defect range such that the defect distribution is distinguishable from the interference distribution, the sufficient number being desirably a threshold number of elements of the defect range having a magnitude greater than the upper limit of the interference range such that the defect distribution is distinguishable from the interference distribution, or greater than or equal to it, the method.

[0127]

[0138] Clause 32: The method according to Clause 31, wherein the defect distribution is separable / distinguishable from the interference distribution.

[0128]

[0139] Clause 33: The method according to any one of Clauses 31 to 32, wherein at least one element represents a detection signal including a subset of elements representing detection signals, and desirably, the subset of elements representing the detection signal indicates a defect distribution separable / distinguishable from the interference distribution.

[0129]

[0140] Clause 34: A method for processing data derived from a sample of the method according to any one of Clauses 31 to 33, the method including the method according to any one of Clauses 1 to 30.

[0130]

[0141] Clause 35: A method for evaluating a sample, including the method according to any one of Clauses 1 to 34.

[0131]

[0142] Clause 36: A method of identifying defect candidates, the method including processing a dataset of elements derived from detections by a detector, the dataset including elements representing interference signals and detection signals, wherein a capture threshold relationship between a capture rate and a signal intensity threshold is calibrated using an initial dataset, and processing includes selecting a capture rate and setting a signal intensity threshold based on the capture threshold relationship, and processing the dataset using the signal intensity threshold to select elements representing detection signals.

[0132]

[0143] Clause 37: The method according to clause 36, wherein processing includes selecting elements representing detection signals by identifying a subset of defect candidates having a magnitude greater than the signal intensity threshold.

[0133]

[0144] Clause 38: The method according to clause 37, wherein processing further includes detecting defects on a sample by evaluating a subset of defect candidates.

[0134]

[0145] Clause 39: The method according to any one of clauses 36 to 38, further including receiving a detection signal including a dataset, and extracting elements representing the detection signal during processing.

[0135]

[0146] Clause 40: A method of identifying defect candidates according to clauses 36 to 39, including determining a capture threshold relationship (preferably based on a relationship between a capture rate and a signal intensity threshold) when calibrating using an initial dataset using a method of processing data according to any one of clauses 9 to 30.

[0136]

[0147] Clause 41: A processing apparatus including a processor configured to perform the method according to any one of clauses 1 to 40.

[0137]

[0148] Clause 42: A computer program including instructions configured to control a processor to perform the method according to any one of clauses 1 to 40.

[0138]

[0149] Clause 43: An evaluation system for identifying defect candidates in evaluation data derived from a sample, the evaluation system comprising a detector configured to generate a detection signal representing one or more characteristics of the sample, a processor configured to process a data set of elements derived from the detection by the detector, the data set including elements representing interference signals and detection signals, by selecting a capture rate and setting a signal strength threshold based on a capture threshold relationship between the capture rate and the signal strength threshold (the capture relationship calibration being pre-calibrated by an initial data set), and a processor configured to process the data set using the signal strength threshold to select elements representing the detection signal.

[0139]

[0150] A reference to a component, or a system of components or elements, that can be controlled to manipulate a charged particle beam in a certain way includes configuring a controller, control system or control unit to control the component to manipulate the charged particle beam in the described way, and optionally using other controllers or devices (such as a voltage source and / or a current source) to control the component to manipulate the charged particle beam in the described way. For example, a voltage source can be electrically connected to one or more components in a non-limiting list such as a control lens array 250, an objective lens array 241, a condenser lens 231, a corrector, a collimator element array and a scanning deflector array 260 to apply a potential to those components under the control of a controller, control system or control unit. An operable component such as a stage can be controllable to move relative to and thus operate relative to another component such as a beam path using one or more controllers, control systems or control units to control the operation of the component.

[0140]

[0151] The functions provided by a controller or a control system or a control unit may be computer-implemented. Any suitable combination of elements may be used to provide the required functionality (including, for example, a CPU, RAM, SSD, motherboard, network connection, firmware, software, and / or other elements known in the art that enable the required computing operations to be performed). The required computing operations may be defined by one or more computer programs. The one or more computer programs may be provided in the form of a medium (optionally a non-transitory medium) storing computer-readable instructions. When the computer-readable instructions are read by a computer, the computer performs the required method steps. The computer may be a built-in unit or a distributed computing system composed of a plurality of different computers connected to each other via a network.

[0141]

[0152] The computer program may include instructions that direct the controller 50 to perform the following steps. The controller 50 controls the charged particle beam apparatus to project a charged particle beam in the direction of the sample 208. In one embodiment, the controller 50 controls at least one charged particle optical element (e.g., an array of multiple deflectors or a scanning deflector 260) to act on the charged particles in the charged particle beam path. Additionally or alternatively, in one embodiment, the controller 50 controls at least one charged particle optical element (e.g., the detector 240) to act on the charged particle beam emitted from the sample 208 in response to the charged particle beam.

[0142]

[0153] An evaluation system according to an embodiment of the present disclosure can be a tool for performing qualitative evaluation (e.g., pass / fail) of a sample, a tool for performing quantitative measurement (e.g., size of a feature) of a sample, or a tool for generating an image of a map of a sample. Examples of evaluation systems are inspection tools (e.g., for identifying defects), review tools (e.g., for classifying defects) and metrology tools, or tools capable of executing any combination of evaluation functionality associated with inspection tools, review tools or metrology tools (e.g., metrology / inspection tools). The electron optical column 40 can be a component of the evaluation system, e.g., an inspection tool or a metrology / inspection tool. As used herein, any reference to a tool is intended to encompass a device, apparatus or system, and the tools may or may not be located in the same place and may be located in separate rooms (in particular, e.g., for data processing elements) and include various components.

[0143]

[0154] References to upper and lower, top and bottom, above and below are to be understood as referring to directions parallel to the up-beam side and down-beam side of an electron beam or multi-beam impinging on the sample 208 (typically in a vertical direction, but not always). Thus, references to up-beam and down-beam are intended to refer to directions with respect to the beam path, independent of any existing gravitational field.

[0144]

[0155] The terms "sub-beam" and "beamlet" are used interchangeably herein and are understood to encompass any radiation beam derived from a parent radiation beam by splitting or separating the parent radiation beam. The term "manipulator" is used to encompass any element that affects the path of a sub-beam or beamlet, such as a lens or deflector.

[0145]

[0156] References to elements aligned along a beam path or sub-beam path are understood to mean that each element is positioned along the beam path or sub-beam path.

[0146]

[0157] Although the present invention has been described in connection with various embodiments, other embodiments of the present invention will become apparent to those skilled in the art upon consideration of the specification and implementation of the present invention disclosed herein. It is contemplated that the specification and examples are merely exemplary, and the true scope and spirit of the present invention are intended to be indicated by the following claims and clauses.

[0147]

[0158] The foregoing description is intended to be illustrative and not limiting. Accordingly, it will be apparent to those skilled in the art that modified embodiments can be made as described without departing from the scope of the claims and the clauses set forth herein.

Claims

Claim 1 A method for processing data derived from a sample, the method comprising: processing, for calibration, an initial data set of elements derived from detections by a detector, the data set including elements representing interference signals and detection signals, wherein processing the initial data set comprises: adapting a distribution model to the initial data set to generate an interference distribution model; setting a signal intensity value and selecting, as a set of defect candidates, elements in the initial data set having a magnitude greater than the signal intensity value; adapting a distribution model to the set of defect candidates to generate a defect distribution model for the detection signal; determining at least a signal intensity threshold that depends on the defect distribution model, the determining including correcting the defect distribution model, and preferably, the correcting is suitable for correcting the magnitude overlap between elements representing interference signals and elements representing detection signals. A method. Claim 2 The method according to claim 1, wherein correcting the overlap includes correcting to a corrected defect distribution model of the detection signal. Claim 3 The method according to claim 2, wherein correcting the overlap includes generating a total distribution model of the initial data set using the interference distribution model and the defect distribution model. Claim 4 The method according to claim 3, wherein generating the total distribution model includes summing the interference distribution model and the defect distribution model. Claim 5 The method according to claim 3 or 4, further comprising adapting the total distribution model to the actual distribution of the initial data set to generate a corrected total distribution model. Claim 6 The method according to claim 5, wherein correcting the overlap includes generating the corrected defect distribution model based on parameter values of the corrected total distribution model associated with the defect distribution model. Claim 7 The method according to any one of claims 2 to 6, wherein setting the signal intensity threshold is based on parameter values of the corrected defect distribution model. Claim 8 The method according to any one of claims 1 to 7, further comprising determining a relationship between a capture rate and the signal intensity threshold, and preferably determining the capture rate as a function of the signal intensity threshold. Claim 9 Determining the relationship between the capture rate and the signal strength threshold includes determining the capture rate as a function of the signal strength threshold, the method according to claim 8.

10. Determining the capture rate as a function of the signal strength threshold is based on the parameter values of the corrected total distribution model, the method according to claim 9.

11. The interference distribution model includes a Gaussian function and / or the defect distribution model includes a Gaussian function, the method according to any one of claims 1 to 10.

12. The total distribution model and the actual distribution are each the negative logarithm of the inverse of their respective cumulative distributions. Desirably, the corrected total distribution model is the negative logarithm of the inverse of each cumulative distribution, the method according to any one of claims 5 to 11.

13. The signal strength value is set based on the interference distribution model, the method according to any one of claims 1 to 12.

14. Setting the signal strength value is to determine an interference threshold based on the interference distribution model, according to the interference distribution model, the number of elements representing interference signals having a magnitude greater than the interference threshold is below a predetermined interference threshold, and selecting the signal strength value based on the interference threshold and includes, the method according to claim 13.

15. Receiving a detection signal from a detector, identifying the initial data set from the detection signal and further includes, the method according to any one of claims 1 to 14.

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