Method of processing data obtained from a sample

Abstract

A method of processing data obtained from a sample includes processing an initial data set of elements obtained by detection by a detector for calibration, the data set including elements representative of an interference signal and a detection signal. Processing of the initial data set includes fitting a distribution model to the initial data set to create an interference distribution model; setting a signal intensity value, and selecting elements with amplitudes greater than the signal intensity value in the initial data set as a defect candidate set; fitting the distribution model to the defect candidate set to create a defect distribution model of the detection signal; and determining a signal strength threshold from at least the defect distribution model. The determining includes correcting the defect distribution model.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to EP application 22176199.2 filed on May 30, 2022 and EP application 22181883.4 filed on June 29, 2022, the entire contents of which are incorporated herein by reference. Technical Field

[0003] Embodiments provided herein generally relate to methods of processing data obtained from a sample, methods of identifying defect candidates, and evaluation systems. Background Art

[0004] When manufacturing semiconductor integrated circuit (IC) chips, undesirable pattern defects will inevitably appear on the substrate (i.e., wafer) or mask during the manufacturing process due to optical effects and accidental particles, thereby reducing the yield. Therefore, monitoring the degree of undesirable pattern defects is an important process in IC chip manufacturing. More generally, inspecting and / or measuring the surface of a substrate or other object / material during and / or after its manufacture is an important process.

[0005] Pattern inspection tools with charged particle beams have been used to inspect objects, such as detecting pattern defects. These tools typically use electron microscopy techniques, using electron optical systems, such as scanning electron microscopes (SEMs). In exemplary electron optical systems (such as SEMs), electron beams with relatively high energy are targeted at the final deceleration step so as to land on the sample with relatively low landing energy. The electron beam is focused as a probe point on the sample. The interaction between the material structure at the probe point and the landing electrons from the electron beam causes electrons, such as secondary electrons, backscattered electrons, or Auger electrons, to be emitted from the surface. The secondary electrons generated can be emitted from the material structure of the sample. By scanning the sample surface with the primary electron beam as a probe point, secondary electrons can be emitted across the sample surface. By collecting these emitted secondary electrons from the sample surface, the pattern inspection tool can obtain an image representing the material structure features of the sample surface. The intensity of the electron beam including backscattered electrons and secondary electrons can vary according to the characteristics of the internal and external structures of the sample, thereby indicating whether the sample has defects.

[0006] In order to identify true defects on the inspected object, it is best to first discard interfering signals, such as those caused by noise. In this way, it is possible to avoid spending time and resources on a more detailed analysis of a potentially large number of interfering signals. Interfering signals are usually identified as signals with a signal strength below a certain threshold. Signals with a signal strength equal to or greater than the threshold are considered possible defect signals, which 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 empirically through trial and error. This trial and error process can be time consuming and it is difficult to verify that the selected value is appropriate. If the threshold is set too high, there is a risk that true defects associated with signal strengths below the threshold may be missed. If the threshold is set too low, a large number of interfering signals will be included in the data set for further analysis. This can make further analysis time consuming and inefficient. Summary of the invention

[0007] The object of the present disclosure is to provide embodiments of a method of processing data obtained from a sample, a method of identifying defect candidates, and an evaluation system.

[0008] According to a first aspect of the present invention, there is provided a method for processing data obtained from a sample, comprising processing an initial data set of elements obtained by detection of a detector for calibration, the data set comprising elements representing interference signals and detection signals. Processing of the initial data set comprises: fitting a distribution model to the initial data set to create an interference distribution model; setting a signal strength value, and selecting elements in the initial data set whose amplitude is greater than the signal strength value as a defect candidate set; fitting the distribution model to the defect candidate set to create a defect distribution model of the detection signal; and determining a signal strength threshold based at least on the defect distribution model. Determination comprises correcting the defect distribution model. Ideally, the correction is suitable for correcting for overlap in amplitude between elements representing interference signals and detection signals.

[0009] According to a second aspect of the present invention, there is provided a method for processing data obtained from a sample, comprising processing an initial data set of elements obtained by detection of a detector for calibration, the data set comprising elements representing interference signals and detection signals. Processing of the initial data set comprises: fitting a distribution model to the initial data set to create an interference distribution model; setting a signal strength value and selecting elements in the initial data set whose amplitude is greater than the signal strength value as a defect candidate set; fitting the distribution model to the defect candidate set to create a defect distribution model of the detection signal; determining a signal strength threshold at least based on the defect distribution model; and determining a relationship between a capture rate and a signal strength threshold.

[0010] According to a third aspect of the present invention, there is provided a method for processing data obtained from a sample, comprising processing an initial data set of elements obtained by detection by a detector. The data set comprises elements representing interference signals and defect signals. The interference distribution comprises elements representing interference signals whose amplitudes are within an interference range. The defect distribution comprises elements representing defect signals whose amplitudes are within a defect range. The interference range overlaps with the defect range. The interference range overlaps with the defect range in the overlap. The amplitude of at least one element in the defect range exceeds the upper limit of the amplitude within the interference range.

[0011] According to a fourth aspect of the present invention, there is provided a method for identifying defect candidates, comprising processing an element data set obtained by detection by a detector, the data set comprising elements representing interference signals and detection signals. Using an initial data set, a capture threshold relationship between a capture rate and a signal strength threshold is calibrated. The processing comprises: setting a signal strength threshold by selecting a capture rate and based on the capture threshold relationship; and processing the data set using the signal strength threshold to select elements representing the detection signal.

[0012] According to a fifth aspect of the present invention, an evaluation system is provided for identifying defect candidates in inspection data obtained from a sample. The evaluation system comprises: a detector and a processor. The detector is configured to generate a detection signal representing one or more features of the sample. The processor is configured to: process a data set of elements obtained by detection by the detector, the data set comprising elements representing interference signals and detection signals; set a signal strength threshold by selecting a capture rate and based on a capture threshold relationship between the capture rate and a signal strength threshold, the capture relationship calibration is pre-calibrated using an initial data set; and process the data set using the signal strength threshold to select elements representing the detection signal. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The above and other aspects of the present disclosure will become more apparent through the description of exemplary embodiments with reference to the attached drawings.

[0014] Figure 1 is a schematic diagram illustrating an exemplary electron beam inspection apparatus.

[0015] Figure 2 is shown as Figure 1 Schematic diagram of an exemplary multi-beam charged particle evaluation system of a portion of an exemplary electron beam inspection apparatus.

[0016] Figure 3 is a schematic diagram of an exemplary multi-beam charged particle evaluation system according to one embodiment.

[0017] Figure 4 is a schematic diagram of an exemplary multi-beam charged particle evaluation system according to one embodiment.

[0018] Figure 5 is an exemplary histogram of signal intensity data derived from a sample.

[0019] Fig. 6A is an exemplary graphical representation of the sum distribution model, the actual distribution of the initial data set, the defect distribution model, and the interference distribution model, and Figure 6B yes Fig. 6A Magnified view of the area.

[0020] Fig. 7A is an exemplary graph of jamming rate versus capture rate; Figure 7B is an example graph of capture rate versus signal strength threshold.

[0021] Fig. 8A and Figure 8B are other examples of comparing the calibrated distribution model with the corresponding actual data.

[0022] Fig. 9 is a graphical representation of the distribution of an exemplary initial data set, including a distribution model of the interfering signal.

[0023] Fig. 10A and Fig. 10B are graphical representations of the distributions of two further exemplary initial data sets, including corresponding models of the distributions of interfering signals with different gradients.

[0024] Fig.11 is a graphical representation of the distribution of another exemplary initial data set, including a corresponding model of the distribution of the interference signal, where the interference range and the defect range overlap.

[0025] Figures 12A-12D is a schematic diagram showing an image of a sample having multiple features and defects.

[0026] The schematic diagrams and views show the components described below. However, the components depicted in the figures are not drawn to scale. DETAILED DESCRIPTION

[0027] Reference will now be made in detail to exemplary embodiments, examples of which are shown in the accompanying drawings. The following description refers to the accompanying drawings, wherein the same numbers in different figures represent the same or similar elements unless otherwise specified. The implementations set forth in the following description of exemplary embodiments do not represent all implementations consistent with the present invention. Instead, they are merely examples of devices and methods consistent with aspects related to the present invention as described in the appended claims.

[0028] The increase in computing power of electronic devices has led to a reduction in the physical size of the devices, which can be achieved by significantly increasing the packing density of circuit components such as transistors, capacitors, diodes, etc. on IC chips. This is made possible by the increased resolution that allows the manufacture of smaller structures. For example, a thumbnail-sized IC chip for a smartphone, which will be launched in 2019 or earlier, may contain more than 2 billion transistors, each less than one thousandth the size of a human hair. Semiconductor IC manufacturing is therefore a complex and time-consuming process involving many individual steps. An error in one of these steps can seriously affect the functionality of the final product. The goal of the manufacturing process is to increase the yield of the entire process. For example, to achieve a 75% yield for a 50-step process (where the step indicates the number of layers formed on the wafer), the yield of each individual step must be greater than 99.4%. If the yield of each individual step is 95%, the yield of the entire process would be as low as 7%.

[0029] While high process yield is desirable in IC chip fabrication plants, it is also essential to maintain high substrate (i.e., wafer) throughput (defined as the number of substrates processed per hour). High process yield and high substrate throughput can be compromised by the presence of defects. This is especially true if operator intervention is required to detect the defect. Therefore, high throughput detection and identification of micron and nanometer-scale defects by inspection tools such as scanning electron microscopes (SEMs) is critical to maintaining high yields and low costs.

[0030] The SEM includes a scanning device and a detector device. The scanning device includes an irradiation device, which includes an electron source for generating primary electrons, and a projection device for scanning a sample (e.g., a substrate) with one or more focused primary electron beams. The irradiation device or irradiation system and the projection device or projection system can at least be collectively referred to as an electron optical system or device. The primary electrons interact with the sample and generate secondary electrons. The detection device captures secondary electrons from the sample as the sample is scanned so that the SEM can create an image of the scanned area of ​​the sample. Such an inspection device can utilize a single primary electron beam incident on the sample. For high-throughput inspection, some inspection devices use multiple focused beams of primary electrons, i.e., multi-beams. The component beams of the multi-beams can be referred to as sub-beams or sub-beams. The sub-beams can be arranged in the multi-beam arrangement relative to each other. The multi-beam can scan different parts of the sample at the same time. Therefore, a multi-beam inspection device can inspect the sample at a much higher speed than a single-beam inspection device.

[0031] The following describes a known implementation of a multi-beam inspection device.

[0032] The drawings are schematic. Therefore, for the sake of clarity, the relative sizes of the components in the drawings are exaggerated. In the following description of the drawings, the same or similar reference numbers refer to the same or similar components or entities, and only the differences with respect to the various embodiments are described. Although the description and drawings are directed to electron optical systems, it should be understood that the embodiments are not intended to limit the present disclosure to specific charged particles. Therefore, references to electrons in this document can be considered more generally as references to charged particles, where the charged particles are not necessarily electrons.

[0033] Reference now Figure 1 , which is a schematic diagram illustrating an exemplary charged particle beam inspection apparatus 100 . Figure 1 The charged particle beam inspection apparatus 100 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 tool), an equipment front end module (EFEM) 30, and a controller 50. The charged particle evaluation system 40 is located in the main chamber 10.

[0034] The EFEM 30 includes a first loading port 30a and a second loading port 30b. The EFEM 30 may include additional loading ports. The first loading port 30a and the second loading port 30b may, for example, receive a substrate front opening pod (FOUP) containing a substrate (e.g., a semiconductor substrate or a substrate made of other materials) or a sample to be inspected (substrate, wafer, and sample are collectively referred to as "sample" hereinafter). One or more robotic arms (not shown) in the EFEM 30 transport the sample to the load lock chamber 20.

[0035] The load lock chamber 20 is used to remove the gas around the sample. This will produce a vacuum, that is, the local gas pressure is lower than the pressure of the surrounding environment. The load lock chamber 20 can be connected to a load lock vacuum pump system (not shown), which removes gas particles in the load lock chamber 20. The operation of the load lock vacuum pump system enables the load lock chamber to reach a first pressure lower than atmospheric pressure. After reaching the first pressure, one or more robotic arms (not shown) transport the sample from the load lock chamber 20 to the main chamber 10. The main chamber 10 is connected to the main chamber vacuum pump system (not shown). The main chamber vacuum pump system removes gas particles in the main chamber 10 so that the pressure around the sample reaches a second pressure lower than the first pressure. After reaching the second pressure, the sample is transported to the charged particle evaluation system 40, through which it can be inspected. 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 may be a multi-beam electron optical system 41 configured to project multiple beams to the sample, eg the sub-beams are arranged relative to each other in a multi-beam arrangement. Alternatively, the electron optical system 41 may be a single-beam electron optical system 41 configured to project a single beam to the sample.

[0036] The controller 50 is electrically connected to the charged particle evaluation system 40. The controller 50 may be a processor (such as a computer) configured to control the charged particle beam inspection apparatus 100. The controller 50 may also include processing circuitry configured to perform various signal and image processing functions. Figure 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 part of the structure. The controller 50 can be located in one of the constituent elements of the charged particle beam inspection device, or can be distributed on at least two constituent elements. Although the present disclosure provides an example of a main chamber 10 that accommodates an electron beam inspection tool, it should be noted that the aspects of the present disclosure in its broadest sense are not limited to a chamber that accommodates an electron beam inspection tool. On the contrary, it is understood that the aforementioned principles can also be applied to the arrangement of other tools and other devices operating at a second pressure.

[0037] Reference now Figure 2 , which is a schematic diagram showing an exemplary charged particle evaluation system 40, which includes a multi-beam electron optical system 41, the multi-beam electron optical system 41 is Figure 1 A portion of an exemplary charged particle beam inspection device 100 of the present invention. The multi-beam electron optical system 41 includes an electron source 201 and a projection device 230. The charged particle evaluation system 40 also includes an actuation stage 209 and a sample holder 207. The sample holder may have a holding surface (not depicted) for supporting and holding a sample. Therefore, the sample holder may be configured to support a sample. Such a holding surface may be an electrostatic clamp that is operable to hold a sample during operation of the electron optical system 41 (e.g., to evaluate or inspect the sample). The holding surface may be recessed in the sample holder, for example, the surface of the sample holder is oriented to face the electron optical system 41. The electron source 201 and the projection device 230 may be collectively referred to as the electron optical system 41. The sample holder 207 is supported by the actuation stage 209 so as to hold a sample 208 (e.g., a substrate or mask) for inspection. The multi-beam electron optical system 41 also includes a detector 240 (e.g., an electronic detection device).

[0038] 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 extractor and / or anode extract or accelerate the primary electrons to form a primary electron beam 202.

[0039] 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. Although only three sub-beams are shown for simplicity, there may be tens, hundreds or thousands of sub-beams. A sub-beam may be referred to as a beamlet.

[0040] The controller 50 may be connected to Figure 1 The controller 50 may be used to control various parts of the charged particle beam detection device 100, such as the electron source 201, the detector 240, the projection device 230 and the actuator stage 209. The controller 50 may perform various image and signal processing functions. The controller 50 may also generate various control signals to control the operation of the charged particle beam inspection device (including the charged particle multi-beam device).

[0041] 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 detection points 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 detection points 221, 222 and 223 across the respective scanning areas in the surface portion of the sample 208. In response to the primary sub-beams 211, 212 and 213 being incident on the detection points 221, 222 and 223 on the sample 208, electrons are generated from the sample 208, which include secondary electrons and backscattered electrons. The electron energy of the secondary electrons is typically ≤50eV. The energy of the actual secondary electrons can be less than 5eV, but any electrons below 50eV are generally regarded as secondary electrons. The electron energy of the backscattered electrons is typically between 0eV and the landing energy of the primary sub-beams 211, 212 and 213. Since the detected electrons with energy less than 50 eV are generally regarded as secondary electrons, a portion of the actual backscattered electrons will be regarded as secondary electrons.

[0042] The detector 240 is configured to detect signal particles (such as secondary electrons and / or backscattered electrons) and generate corresponding signals, which are sent to the signal processing system 280, for example, to construct an image of the corresponding scanned area of ​​the sample 208. The detector 240 can be incorporated into the projection device 230.

[0043] The signal processing system 280 may include circuitry (not shown) configured to process signals from the detector 240 to form an image. The signal processing system 280 may also be referred to as an image processing system. The signal processing system may be incorporated into a component of the multi-beam charged particle evaluation system 40, such as the detector 240 (e.g., Figure 2). However, the signal processing system 280 can be incorporated into any component of the inspection device 100 or the multi-beam charged particle evaluation system 40, such as as part of the projection device 230 or the controller 50. The signal processing system 280 can be physically separated from the projection device 230 and the controller 50, such as 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 part of the processing functionality of the controller. Therefore, the image acquirer may include at least one or more processors. The image acquirer may be communicatively coupled to the detector 240 to allow signal communication, such as an electrical conductor, a fiber optic cable, a portable storage medium, IR, Bluetooth, the Internet, a wireless network, radio, etc., or a combination thereof. The image acquirer may receive a signal from the detector 240, may process the data contained in the signal, and may construct an image therefrom. Therefore, the image acquirer may acquire an image of the sample 208. The image acquirer may also perform various post-processing functions, such as generating contours, superimposing indicators on the acquired image, etc. The image acquirer may be configured to perform adjustments to the brightness and contrast of the acquired image, etc. The memory may be a storage medium such as a hard disk, a flash memory, a cloud storage, a random access memory (RAM), other types of computer-readable memory, etc. The memory is coupled to the image acquirer for saving the scanned raw image data as raw images and post-processed images.

[0044] The signal processing system 280 may include measurement circuitry (e.g., an analog-to-digital converter) to obtain a distribution of the detected secondary electrons. The electron distribution data collected during the detection time window may be used in conjunction with the corresponding scan path data of each primary sub-beam 211, 212, and 213 incident on the sample surface to reconstruct an image of the inspected sample structure. The reconstructed image may be used to reveal various features of the internal or external structure of the sample 208. Thus, the reconstructed image may be used to reveal any defects that may be present in the sample.

[0045] The controller 50 may control the actuation stage 209 to move the sample 208 during the inspection of the sample 208. The controller 50 may cause the actuation stage 209 to move the sample 208 preferably continuously (e.g., at a constant speed) in a certain direction at least during the inspection of the sample. The controller 50 may control the movement of the actuation stage 209 so that it changes the movement speed of the sample 208 according to various parameters. For example, the controller 50 may control the stage speed (including its direction) according to the characteristics of the inspection step of the scanning process.

[0046] Known multi-beam systems, such as the charged particle evaluation system 40 and the charged particle beam inspection device 100 described above (and described elsewhere herein), are disclosed in US2020118784, US20200203116, US2019 / 0259570, and US2019 / 0259564, which are incorporated herein by reference.

[0047] like Figure 2 As shown, 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 referred to as an ACC module. The projection assembly 60 is arranged to direct a light beam 62 so that the light beam 62 enters between the electron optical system 41 and the sample 208.

[0048] When the electron beam scans the sample 208, due to the large beam current, charges may accumulate on the sample 208, affecting the imaging quality. In order to adjust the charges accumulated on the sample, a projection assembly 60 may be used to irradiate a light beam 62 onto the sample 208, so as to control the charges accumulated due to effects such as photoconductivity, photoelectric or thermal effects.

[0049] Combine the following Figure 3 Components of a charged particle evaluation system 40 that may be used with the present invention are described. Figure 3 is a schematic diagram of a charged particle evaluation system 40, Figure 3 The charged particle evaluation system 40 may correspond to the charged particle evaluation system 40 mentioned above (which may also be referred to as an apparatus or a tool).

[0050] The electron source 201 directs the electrodes to the converging lens array 231 (also referred to as the converging lens array). The electron source 201 is expected to be a high brightness thermal field emitter, which is arranged to work within an optimized electron optical performance range, which is a compromise between brightness and total emission current (this compromise can be considered a "good" compromise). There may be dozens, hundreds or thousands of converging lenses 231. The converging lens 231 may include a multi-electrode lens and have a construction based on EP1602121A1, which is incorporated herein by specifically citing a lens array for dividing an electron beam into a plurality of sub-beams, wherein the array provides a lens for each sub-beam. The converging lens array 231 can take the form of at least two plates, used as electrodes, wherein the apertures in each plate are aligned with each other and correspond to the position of the sub-beams. At least two plates are maintained at different potentials during operation to achieve the desired lens effect.

[0051] Each converging lens 231 in the array directs electrons into corresponding sub-beams 211, 212, 213, which are focused at corresponding intermediate focal points downstream of the converging lens array. The sub-beams diverge from each other. In one embodiment, a deflector 235 is provided at the intermediate focal point. The deflector 235 is positioned at or at least around the position of the corresponding intermediate focal point in the sub-beam path. The deflector 235 is positioned in or near the sub-beam path at the intermediate image plane of the associated sub-beam. The deflector 235 is configured to operate on the corresponding sub-beams 211, 212, 213. The deflector 235 is configured to bend the corresponding sub-beams 211, 212, 213 by a certain amount to ensure that the main ray (which may also be referred to as the beam axis) is incident on the sample 208 substantially vertically (i.e., substantially 90° to the nominal surface of the sample). The deflector 235 may also be referred to as a collimator or a collimator deflector. The deflector 235 actually collimates the paths of the sub-beams so that before the deflector, the sub-beam paths are divergent relative to each other. Downstream of the deflector, the sub-beam paths are substantially parallel relative to each other, i.e., substantially collimated. A suitable collimator is the deflector disclosed in EP application 20156253.5 filed on February 7, 2020, which is incorporated herein by reference with respect to the application of deflectors in multi-beam arrays. The collimator may include a macro-collimator 270 (e.g., such as Figure 4 ), replacing or supplementing the deflector 235. Therefore, the following Figure 4 The macro collimator 270 described may have Figure 3 This is generally less desirable than providing a collimator array as deflector 235.

[0052] Below the deflector 235 (i.e., below the beam or away from the source 201) is a control lens array 250. The sub-beams 211, 212, 213 passing through the deflector 235 are substantially parallel when entering the control lens array 250. The control lenses pre-focus the sub-beams (e.g., apply a focusing action to the sub-beams before they reach the objective lens array 241). Pre-focusing can reduce the divergence of the sub-beams or increase the rate of convergence of the sub-beams. The control lens array 250 and the objective lens array 241 operate together to provide a combined focal length. The combined operation without an intermediate focus can reduce the risk of aberrations.

[0053] The control lens array 250 includes a plurality of control lenses. Each control lens includes at least two electrodes (e.g., two or three electrodes) connected to a corresponding potential source. The control lens array 250 may include two or more (e.g., three) plate electrode arrays connected to a corresponding potential source. The control lens array 250 is associated with the objective lens array 241 (e.g., the two arrays are close to each other and / or mechanically connected to each other and / or controlled together as a unit). Each control lens may be associated with a corresponding objective lens. The control lens array 250 is positioned upstream of the objective lens array 241.

[0054] The control lens array 250 includes a control lens for each sub-beam 211, 212, 213. The function of the control lens array 250 is to optimize the beam opening angle relative to the beam reduction rate and / or control the beam energy delivered to the objective lens array 241, which directs the sub-beams 211, 212, 213 onto the sample 208. The objective lens array 241 can be positioned at the bottom or near the electron optical system 41. The control lens array 250 is optional, but is preferably used to optimize the sub-beams upstream of the objective lens array.

[0055] For example, the control lens array 250 may be considered as providing an electrode in addition to the electrodes of the objective lens array 241. The objective lens array 241 may have any number of additional electrodes associated with and near the objective lens array 241, such as five, seven, ten or fifteen. Additional electrodes such as the control lens array 250 allow other degrees of freedom to control the electron optical parameters of the sub-beams. Such additional associated electrodes may be considered as additional electrodes of the objective lens array 241, thereby realizing additional functions of the corresponding objective lenses of the objective lens array 241. In the arrangement, such electrodes may be considered as part of the objective lens array 241, thereby providing additional functions for the objective lenses of the objective lens array 241. Therefore, the control lens is considered as a part of the corresponding objective lens, and even to some extent the control lens is only referred to as a part of the objective lens.

[0056] For ease of explanation, this article describes the lens array as an elliptical array (such as Figure 3 ). Each ellipse represents a lens in the lens array. By convention, ellipses are used to represent lenses, similar to the biconvex shapes often used in optical lenses. However, in the context of charged particle arrangements (such as the arrangements discussed herein), it should be understood that lens arrays typically operate electrostatically and therefore may not require any physical elements that use biconvex shapes. The lens array may alternatively include multiple plates with apertures.

[0057] Optionally, a scanning deflector array 260 is provided between the control lens array 250 and the objective lens array 234. The scanning deflector array 260 includes a scanning deflector for each sub-beam 211, 212, 213. Each scanning deflector is configured to deflect the corresponding sub-beam 211, 212, 213 in one or two directions, thereby scanning the sub-beam across the sample 208 in one or two directions.

[0058] Any objective lens array assembly described herein may also include a detector 240. The detector detects electrons emitted from the sample 208. The detected electrons may include any electrons detected by the SEM, including secondary electrons and / or backscattered electrons emitted from the sample 208. Figure 3 An exemplary configuration of detector 240 is shown.

[0059] Figure 4 A charged particle evaluation system 40 according to one embodiment is schematically shown. Features identical to those described above are given the same reference numerals. For the sake of brevity, these features are no longer referred to. Figure 4 Detailed Description For example, source 201, focusing lens 231, objective lens array 241, and sample 208 may be as described above.

[0060] In the example shown, a collimator is provided upstream of the objective lens array assembly. The collimator may include a macro collimator 270. The macro collimator 270 acts on the beam from the source 201 before the beam is divided into multiple beams. The macro collimator 270 bends the corresponding portion of the beam to an effective amount to ensure that the beam axis of each sub-beam obtained from the beam is substantially vertically incident on the sample 208 (i.e., substantially 90° with the nominal surface of the sample 208). The macro collimator 270 applies macroscopic collimation to the beam. Therefore, the macro collimator 270 can act on the entire beam, rather than including an array of collimator elements, each of which is configured to act on different individual portions of the beam. The macro collimator 270 may include a magnetic lens or a magnetic lens arrangement, which includes a plurality of magnetic lens subunits (e.g., a plurality of electromagnets forming a multipole arrangement). Alternatively or additionally, the macro collimator may be implemented at least partially in an electrostatic manner. The macro-collimator may include an electrostatic lens or an electrostatic lens arrangement including a plurality of electrostatic lens sub-units. The macro-collimator 270 may use a combination of magnetic lenses and electrostatic lenses.

[0061] As described above, in one embodiment, the detector 240 is located between the objective lens array 241 and the sample 208. The detector 240 may face the sample 208. Alternatively, Figure 4 As shown, in one embodiment, an objective lens array 241 including a plurality of objective lenses is located between the detector 240 and the sample 208 .

[0062] In one embodiment, the deflector array 95 is located between the detector 240 and the objective lens array 241. In one embodiment, the deflector array 95 includes a Wien filter (or even an array of Wien filters), so the deflector array can be referred to as a beam splitter. The deflector array 95 is configured to provide a magnetic field to separate the charged particles projected onto the sample 208 from the secondary electrons from the sample 208.

[0063] In one embodiment, the detector 240 is configured to detect signal particles by reference to the energy of the charged particles (i.e., according to the band gap). Such a detector 240 may be referred to as an indirect current detector. The secondary electrons emitted from the sample 208 gain energy from the field between the electrodes. The secondary electrodes have sufficient energy once they reach the detector 240.

[0064] The present invention can be applied to various tool architectures, wherein reference Figure 3 and Figure 4 The arrangement shown and described is an exemplary multi-beam arrangement. For example, the charged particle evaluation system 40 can be a single beam tool, such as US20210319977A1, or can include multiple single beam device columns (or devices), or can include multiple multi-beam device columns. These device columns can include the electron optical system 41 described in any of the above embodiments or aspects. As multiple device columns (or multi-device column tools), for example, a device column (or multi-beam device column) that projects multiple beams toward a sample, such as Figure 3 and Figure 4 As shown and described, the devices may be arranged in an array which may number from two to one hundred or more device columns. The charged particle evaluation system 40 may employ reference Figure 3 The form of the embodiment described and illustrated, but preferably with an electrostatic scanning deflector array and an electrostatic collimator array.The charged particle device array may optionally include a source.

[0065] like Figure 2 As shown (when combined with Figure 3 to Figure 4 In one embodiment, the projection assembly 60 includes an optical system 63 when read in the context of the electron optical device 41 shown and described. 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 provides a coherent light beam 62. However, other types of light sources may alternatively be used. As described above, the projection assembly 60 is used to irradiate the light beam 62 onto the sample 208 to control the charge accumulated due to effects such as photoconductivity, photoelectric or thermal effects; thereby regulating the charge accumulated on the sample.

[0066] In one embodiment, the optical system 63 includes a lens, such as a cylindrical lens 64. The cylindrical lens 64 is configured to focus the light beam 62 more in one direction rather than in an orthogonal direction. 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 so that the light beam has an elliptical cross-section. Even if a lens other than a cylindrical lens is used, the lens is positioned and designed to ensure that the light beam reaches the portion of the sample that needs to be irradiated, although the size between the sample and the most downstream surface of the electron-optical device 41 is small, and the size of the downstream surface of the electron-optical device that is orthogonal to the orientation of the beam path is large. In order to make the light beam reach the sample surface, the beam can be reflected from one or more reflective surfaces 65, 66 (such as mirrors). The use of reflective surfaces 65, 66 can improve the reach of the light beam 62 between the most downstream surface of the electron-optical device and the sample.

[0067] 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. Figure 3 As shown, in one embodiment, the detector 240 is the most downstream of the surface of the electron optical device 41 relative to the electron beams 211, 212, 213. In other arrangements mentioned herein, the detector 240 can be associated with the objective lens arrangement, or even include a part of the objective lens arrangement. For example, the detector 240 can be associated with the objective lens array, but located at a different position along the primary beam path, such as being associated with an electrode of the objective lens array, just upstream of the objective lens array, near and inside the objective lens array, or positioned at the proximal end of the objective lens array distributed at different positions along the beam path. In another arrangement, the detector is located in a secondary device column adjacent to or connected to a column of charged particle devices including the electron optical device 41. In all of these arrangements, the most downstream element of the electron optical system (such as the detector 240) is closest to the sample. The most downstream surface of the most downstream element can face the sample. The most downstream surface can be referred to as a facing surface.

[0068] In order to detect defects on the sample, data obtained from the sample is processed. For example, data can be obtained from optical inspection of the sample. Data can be obtained from inspection of the sample by the charged particle evaluation system 40, such as Figure 1-Figure 4As shown. For example, data detected by a detector (such as detector 240) can be compared with corresponding expected data for a sample without any defects, and the difference between the detected data and the expected data may represent a combination of real defects and interference signals (such as those caused by noise). Interference signals are generally identified as signals with signal strengths below a certain threshold. Signals with signal strengths equal to or greater than the threshold are considered possible defect signals, which can then be further analyzed to determine whether a defect exists and, if so, to classify the nature of the defect. It is desirable to have a method to identify a suitable threshold so that the number of real defects that are ignored can be low without unnecessarily post-processing a large number of interference signals.

[0069] Figure 5 A histogram of an exemplary initial data set is shown, which includes elements representing interference signals 53 and defect signals 52. These elements may be referred to as data elements. Thus, the initial data set has an element distribution including a distribution of interference signals 53 and a distribution of defect signals 52. The distribution of defect signals 52 represents actual defects. Figure 5 The X-axis represents the signal amplitude, Figure 5 The Y-axis of represents the number of occurrences of a single signal (such as a defect or interference) at each signal amplitude. Figure 5 An exemplary threshold 51 is also shown. The threshold is a signal amplitude that can be used to distinguish between interference signals and defect signals. When the threshold is applied, signals with amplitudes greater than the threshold are considered detection signals (considered to be possible actual defect signals); signals with amplitudes less than the threshold are considered interference signals. For data collected in a data set during sample inspection, it is not possible to know in advance which elements in the data set represent interference signals and which elements represent actual defects. Therefore, it is necessary to determine and apply an appropriate threshold in order to select the most appropriate defect candidate data for further analysis. Figure 5 An illustration of a magnified view 54 of the area of ​​greatest interest is provided. In this area of ​​the histogram, there is an overlap between the distribution of the interference signal 53 and the distribution of the defect signal 52. The interference signal 53 and the defect signal 52 in the overlap have the same signal strength values. In the overlap, the initial data set includes the contribution of the interference signal 53 and the contribution of the defect signal 52. A threshold passes through the overlap, dividing the overlap into two parts. Each of the two parts of the overlap includes elements of the initial data set that are a mixture of actual interference signals and actual defect signals; however, elements above the threshold are classified as detection signals (i.e., those signals that are considered possible defect signals). Overlapping elements below the threshold are classified as interference signals. Therefore, some actual defect signals may be classified as interference. Some interference may be classified as defects, i.e., mistakenly identified as defects.

[0070] When using a threshold to distinguish defect signals from interference signals, as the threshold increases, the probability that the defect is below the threshold increases. As the threshold decreases, the number of occurrences of interference signals increases. Therefore, if the threshold is set too low, a large number of signals that do not represent actual defects (these signals are mistakenly identified as defects) will be further analyzed, that is, they will be treated as defects, which is inefficient. Signals that are mistakenly identified as defects will provide inaccurate evaluation information, for example, evaluation data (such as inspection data). And if the threshold is set too high, the capture rate of defects may be very low. Here, the capture rate is a measure of the proportion or percentage of elements in a data set representing actual defects that are equal to or greater than the threshold. The capture rate can be defined as the percentage of data elements representing actual defects that are identified as defect candidates.

[0071] The present disclosure provides a method for processing data obtained from a sample, including processing an initial data set of elements. The initial data set of elements is obtained from detection of a detector for calibration. The sample can be sample 207, as described above with reference to Figure 2 The detector may be detector 240, as described above with reference to Figure 2-Figure 4 Any other evaluation system described or disclosed herein, such as a single beam system or a multi-device array system. Data processing may be performed using a signal processing system 280, as described above with reference to Figure 2 The processing can be performed with an inspection device, such as in a device array (or device), or can be performed at a remote location, such as a processing rack away from the device array location, or the processing can be distributed, such as so that part of the processing is in the device and part is at a remote location, such as at a processing rack. The data can be stored and processed later, or the processing can be performed simultaneously with the continuous detection of sample data. A computer program (which can take the form of a set of programs distributed on multiple processors) can provide instructions configured to control a processor to perform data processing.

[0072] For example, Figure 5As shown, the initial data set includes elements representing interference signals 53 and defect signals 52. The processing of the initial data set includes fitting a distribution model to the initial data set to create an interference distribution model. The interference distribution model is expected to be a model representing the distribution of the initial data set. Since the initial data set mainly includes interference signals representing noise, the initial data set may have a substantially normal distribution. The interference distribution model may include a Gaussian function, for example. The number of data elements of the initial data set representing actual defects is expected to be significantly lower than the number of data elements of the initial data set representing interference signals. Therefore, a preliminary model of reasonably accurate interference signal distribution can be determined by fitting the model to the entire initial data set. The preliminary model is called "reasonably accurate" because the initial data set includes signals representing actual defects as well as interference signals. Since the interference distribution model is based on the initial data including signals representing actual defects, there are errors in the interference distribution model. However, the actual number of defects in the initial data set is much less than the number of interference signals (mentioned later in this article), so it can be ignored.

[0073] The processing of the initial data set also includes setting a signal strength value and selecting the elements in the initial data set whose amplitude is greater than the signal strength value as a set of defect candidates. The role of the signal strength value is similar to Figure 5 A threshold of 51 is set. Data elements with a single intensity amplitude above the selected signal intensity value are the elements that are most likely to represent actual defects. The distribution model is fit to the defect candidate set to create a defect distribution model of the detection signal. The detection signal is expected to include most defect signals. However, some interference signals (representing noise) may also be included in the defect candidate set, or some signals representing actual defects may be omitted from the defect candidate set due to signal intensity amplitudes below the signal intensity value. Nevertheless, the defect distribution model of the detection signal is intended to serve as a preliminary model representing the distribution of signals representing defects in the sample. The defect candidate set can have a substantially normal distribution. The defect distribution model may, for example, include a Gaussian function.

[0074] Processing the initial data set also includes determining a signal strength threshold based at least on the defect distribution model. In other words, the defect distribution model can be used to determine an appropriate signal strength threshold above which an appropriate proportion of signals representing defects are expected to be captured.

[0075] Determining the signal strength threshold includes correcting the defect distribution model. Desirably, the correction is applied to correct for overlap in amplitude between elements representing the interference signal and the defect signal. This is advantageous in cases where the initial data set does not have a clear dip or local minimum. Below the local minimum, the data consists primarily or entirely of interference signals. Above the local minimum, the data consists primarily or entirely of data representing actual defects. During inspection of some samples, it was found that there was overlap between the interference signal 53 and the defect signal 52, such as Figure 554. Due to overlap, there may be no local minimum in the number of occurrences of the initial data set to indicate a suitable value for the signal strength threshold. For example, when the initial data set is considered as a whole before classifying the data into: interference signals; and detection signals (which are candidates to be considered as possible defect signals).

[0076] Furthermore, in these cases, the defect distribution model initially created may not be an accurate representation of the data distribution that represents the actual defects: for example, because the signal strength values ​​are set to include too many interference signals or too few defect signals; and / or because the interference signals and defect signals overlap significantly. A large overlap between interference signals and defect signals may mean that a simple cutoff value (such as a signal strength value) cannot be used to effectively separate the two sets of data. Therefore, it is best to correct the overlap to obtain a more representative corrected defect distribution model for setting a signal strength threshold that is used to filter the data for further processing and ignore data that is considered unnecessary.

[0077] Correcting the overlap may include correcting the defect distribution model of the detection signal. Correcting for overlap preferably includes using the interference distribution model and the defect distribution model to create a sum distribution model of the initial data set. Creating the sum distribution model may include adding the interference distribution model and the defect distribution model. Thus, the sum distribution model is a model that represents the entire initial data set by combining the interference distribution model and the defect distribution model. This is because, for example, Figure 5 As shown, the initial data set is expected to include interference signals 53 and defect signals 52 .

[0078] Once the sum distribution model has been created, it can be improved by fitting the sum distribution model to the actual distribution of the initial data set. Fitting the sum distribution model means updating the parameter values ​​of the model until it more closely matches the distribution of the initial data set. The updated sum distribution model can be referred to as a corrected sum distribution model. The model is considered "corrected" because a fitted model is more likely to more closely match the data being modeled than an unfitted model.

[0079] For example, Fig. 6A A graph showing a defect distribution model 71 (thick solid line), an interference distribution model 72 (thick dashed line), a sum distribution model 73 (or an original sum distribution model) (thick dashed line), a corrected sum distribution model 74 (thin dashed line), and a distribution of an initial data set 75 (thin solid line) is shown, wherein the X-axis represents the signal strength amplitude and the Y-axis represents, for example, the number of occurrences of a signal having a specific signal strength amplitude. Fig. 6A The details may be difficult to discern. Figure 6B Provided Fig. 6A A magnified view of the area of ​​most interest in the image. Figure 6BIt can be seen that the corrected sum distribution model 74 provides a distribution that is closer to the initial data set 75 than the distribution shown by the original sum distribution model 73.

[0080] Typically, interference signals are several orders of magnitude more numerous than defect signals. For example, the distribution of interference signals may be about 10 10 elements, and the distribution of defect signals may be 10 2 elements. This may make it difficult to take into account the relative importance of the interference and defect distribution models when fitting the sum distribution model. The sum distribution model and the actual distribution may be the logarithm of the inverse of the corresponding cumulative distribution, respectively. Desirably, the corrected sum distribution model is a function, specifically the logarithm of the inverse of the corresponding cumulative distribution. Applying this function can reduce the difference in magnitude, for example, reducing Fig. 6A and Figure 6B The signal intensity required for the distribution diagram shown in is reduced, thereby reducing the length of the Y axis.

[0081] Correction for overlap may include creating a corrected defect distribution model by adjusting parameter values ​​of the defect distribution model based on parameter values ​​of the corrected sum distribution model. Alternatively, correction for overlap includes creating a corrected defect distribution model based on parameter values ​​of the corrected sum distribution model associated with the defect distribution model. Note that the corrected sum distribution is associated with the interference distribution model; indeed, by definition, the corrected sum distribution is associated with both the defect distribution model and the interference distribution model. Thus, a corrected interference distribution model may be created, for example, based on parameter values ​​of the corrected sum distribution model associated with the interference distribution model.

[0082] In either case of correcting overlap, the corrected defect distribution model is expected to better match the actual distribution of the signals representing 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 sum distribution model. The corrected sum distribution model can be considered a better match because it has been related to the distribution of the initial data set.

[0083] The signal strength threshold is preferably set based on the parameter value of the calibrated defect distribution model. This is because the calibrated defect distribution model can be used to determine what signal strength threshold is needed to capture a sufficient amount of data expected to represent defects. The sufficient amount can be determined by, for example, a user or a pre-selected use case. For example, the sufficient amount is at least ninety percent (90%), such as between 90% and substantially 100%.

[0084] In addition, it is desirable to determine the relationship between the capture rate and the signal strength threshold. Specifically, it is desirable to determine the capture rate as a function of the signal strength threshold. For example, this can be achieved using a corrected defect distribution model.

[0085] Alternatively or additionally, the method of processing data obtained from a sample may include processing an initial data set of elements obtained by detection by a detector for calibration. The data set includes elements representing interference signals and detection signals, as described above (and elsewhere herein). Processing of the initial data set includes: fitting a distribution model; setting a signal intensity value; selecting elements in the initial data set; fitting the distribution model to the selected set of elements; and determining a signal intensity threshold. Fit the distribution model to the initial data set to create an interference distribution model. Selecting elements in the initial data set selects elements whose amplitude is greater than the signal intensity value. The selected elements form a set of defect candidates. Fit the distribution model to the set of defect candidates to create a defect distribution model for the detection signal. The signal intensity threshold is determined at least based on the defect distribution model, and desirably, the defect distribution model has been corrected to a corrected distribution model, as described above (and elsewhere herein).

[0086] Processing the initial data set includes determining a 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.

[0087] Determining the signal strength threshold may include correcting for overlap in amplitude between elements representing interference signals and defect signals, as described above (and elsewhere herein). Desirably, the correction for overlap includes correcting to a corrected defect distribution model. The corrected sum distribution model may be created by adding the interference distribution model and the defect distribution model and fitting to the actual distribution of the initial data set. The corrected defect distribution model may be based on parameter values ​​of the corrected sum distribution model. 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 desirably based on parameter values ​​of the corrected sum distribution model.

[0088] Fig. 7AA graph of capture rate on the Y axis versus interference rate on the X axis is shown. Actual data 91 is plotted as is model data 92. (Modeled data 92 is based on interference and defect distribution contributions to the corrected sum distribution model). The signal strength threshold can be automatically set based on the corrected defect distribution model. Alternatively, or in addition, the user can adjust the signal strength threshold to achieve a balance between defect capture rate and interference rate. In other words, it may be desirable to select a signal strength threshold that has a capture rate that is high enough to capture a sufficient proportion of defects, but a proportion of captured interference signals that is low enough so that further processing of the data is not too inefficient; for example, further processing of the data is not unreasonably inefficient. It is worth noting that any proportion of interference signals in the data used for further processing will slow down processing. Therefore, under this arrangement, a certain proportion of interference signals in the data requires further processing, including post-processing of the resulting data sets and images, and some of the resulting inefficiencies are acceptable. However, this is true to a certain extent. If the proportion of interference signals in the data used for further processing is too high, further processing, including post-processing, is inefficient, and may even reach a point where further processing becomes meaningless.

[0089] The signal strength threshold may be set based on the determined capture rate. For example, it may be desirable to have a capture rate of at least 85% of defects. A corrected defect distribution model may be used to determine at which signal strength 85% of defects occur above the signal strength. Figure 7B A graph is shown with capture rate on the Y-axis versus signal intensity threshold on the X-axis. Actual data 93 is plotted as is modeled data 94 (based on a corrected defect distribution model).

[0090] Fig. 8A and Figure 8B Additional examples of comparing a corrected distribution model with corresponding actual data are provided. Fig. 8A and Figure 8B The initial data set of is based on images acquired from samples with pre-programmed defects, so the exact amount (or number) of actual defects and their locations are known. Therefore, a comparison can be made between the corrected distribution model and the corresponding actual data. Fig. 8A and Figure 8B The upper figure of shows a diagram representing the distribution of the defect distribution model 71, the interference distribution model 72, the corrected sum distribution model 74 and the initial data set 75, where the X-axis represents the signal strength amplitude and the Y-axis represents the number of occurrences. That is, Fig. 8A and Figure 8B The upper plot of shows similar parameters for data different from the initial dataset, such as Fig. 6A Shown and described. Fig. 8A and Figure 8BThe lower graph of shows the capture rate on the Y axis versus the signal strength threshold on the X axis. Actual data 93 is plotted as is model data 94. (Model data 94 is based on a corrected defect distribution model.) That is, for Fig. 8A and Figure 8B The same data shown in the upper graph, the lower graph shows Figure 7B Parameters shown and described in . As can be seen from these figures, the models provide a way to quantify the percentage of defects that may be captured for a given signal strength threshold. This can enable users to more efficiently and intelligently select signal strength thresholds by using these models. Alternatively or additionally, the signal strength threshold can be automatically set based on the corrected defect distribution model, such as by setting such parameters (e.g., boundary conditions) through the computer used to perform data processing.

[0091] As described above (and elsewhere herein), processing of the initial data set includes setting a signal strength value and selecting elements in the initial data set. Selecting elements in the initial data set selects elements whose magnitude is greater than the signal strength value. 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 an interference distribution model.

[0092] 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 amplitude, and elements above this amplitude represent interference signals; the interference threshold is generally low. A predetermined interference threshold is set to represent the number of elements representing interference signals whose amplitudes are greater than the interference threshold. The interference threshold can then 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 whose amplitudes are greater than the interference threshold is less than or equal to the predetermined interference threshold. The predetermined interference threshold can be ten (10), desirably one (1), and more desirably substantially negligible.

[0093] The signal strength value may be selected based on an interference threshold. The signal strength value is desirably set equal to the interference threshold. Thus, the cutoff signal strength above which elements are selected for inclusion in the defect candidate set is based on an interference distribution model. The interference distribution model may indicate that the number of occurrences of interference signals above a certain signal strength is low. The signal strength value may be set equal to a certain signal strength. Here, the defect candidate set represents a detection signal (expected to be a defect signal).

[0094] The interference distribution model can be based on the following models:

[0095] ln(y)=a+c*x2 (1)

[0096] Where y is the number of occurrences (e.g., the number of elements in the data set with a certain 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 the distribution of the initial data set.

[0097] Fig. 9 The distribution of an exemplary initial data set 81 is shown, where the X-axis is the square of the signal intensity (x 2 ), the Y-axis is the natural logarithm of the number of occurrences. In this figure, a first-order polynomial 83 (or straight line) is fitted to the initial data 83, and its distribution exhibits linear behavior. The gradient of the first-order polynomial is the parameter "c" in equation (1). The Y-axis intercept of the first-order polynomial 83 is the parameter "a" in equation (1). The X-axis intercept of the first-order polynomial 83 can be used as a signal strength value, which is used to select elements of the initial data set to be included in the defect candidate set. It is found that the elements of the initial data set that represent actual defects are represented by the actual defect line 82. Note that the actual defect distribution shown by the actual defect line 82 exists on both sides of the X-axis intercept of the first-order polynomial 83, that is, above and below. Therefore, some actual defects on the left side of the X-axis intercept have not been included in the defect candidate set on the right side of the X-axis intercept.

[0098] Fig. 10A and Fig. 10B Shows the Fig. 9 Similar distribution. Fig. 10A and Fig. 10B The distribution of is generated by applying the model defined by equation (1) to two other example initial data sets. These example initial data sets have known perturbations and underlying distributions of actual defects. These initial data sets are based on images acquired from samples with pre-programmed (or predetermined or known) defects, so the exact number (or number) of actual defects and their locations are known. Therefore, the model (e.g., represented by the line (or first order polynomial) 83) and the actual data (e.g., the initial data set) 81 can be compared. Fig. 10A and Fig. 10B It is shown that the distribution of interference signals can be modeled for different initial data sets, for example, which have different distributions of interference signals, resulting in different gradients of the interference model, i.e., first-order polynomials 83. In this way, a signal strength value can be determined for each initial data set so that the defect candidate set is likely to capture most of the signals representing actual defects.

[0099] Alternatively or additionally, the method of processing data obtained from a sample may include processing an initial data set of elements obtained from detection by a detector. Such a data set includes elements representing interference signals and defect signals, as described above with reference to Figure 5As described (and elsewhere herein). The interference distribution includes elements representing interference signals with amplitudes within the interference range. The defect distribution includes elements representing detection signals with amplitudes within the defect range. The interference range overlaps the defect range. When the interference ranges overlap, the defect ranges overlap. For example, a sufficient number of elements in the defect range have amplitudes exceeding the upper limit of the interference range in order to make a good prediction. For a good prediction, the upper limit of the defect range is higher than the upper limit of the interference range. That is, the amplitude (i.e., signal strength) of the defect range exceeds the upper limit (i.e., amplitude) of the interference range, for example, extending above the upper limit of the interference range. When the lower limit of the defect range is lower than the upper limit of the interference range, it is an overlap. That is, the lower limit of the overlap is the lower limit of the defect range, whose amplitude is lower than the upper limit of the interference range or is 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. In the case of a sufficient number of elements of the defect range, the distribution of the defect signal can be sufficiently obvious to distinguish the estimation of the distribution of the defect signal from the interference distribution. A sufficient number of elements of the defect range may be a threshold number of elements of the defect range whose magnitude is greater than an upper limit of the interference range. When the number of elements of the defect range equals or exceeds the threshold number, the defect distribution is different and can be distinguished from the interference distribution. Fig.11 It shows that including 1% of actual defects in the defect candidate set is not enough; Fig. 10B In the defect candidate set, about 50% of the actual defects are included. Fig. 10A In the defect candidate set, the actual defects included in it are much more than 50%. Therefore, the threshold of the proportion of independent and different elements in the defect distribution that can achieve good prediction is between Fig. 10B and Fig.11 The threshold value for the proportion of elements in the defect distribution that appear in the interference range is: more than half to more than the minimum amount (thus not all defect distributions), or between 1% and 50%, or (in other words) the proportion of defect distributions with signal intensity amplitudes above the signal intensity value is less than half (50%), for example between 1 and 50%. Therefore, in principle, the number of at least one defect element in the defect distribution with a signal intensity amplitude exceeding the upper limit of the interference distribution may be a sufficient number of defects. However, for a sufficient number of defects, the number of defect elements in the defect distribution with an amplitude exceeding the upper limit of the interference distribution may be more than one.

[0100] The defect distribution may be separate / different from the interference distribution. At least one element represents a detection signal, which includes a subset of elements representing the detection signal. Desirably, the subset of elements representing the detection signal indicates that the defect distribution is separate / different from the interference distribution. Reference Fig. 9 , Fig. 10A and Fig. 10BThe initial dataset shown and described in has such a defect distribution.

[0101] Fig.11 Shown with Fig. 9 A similar graph is generated by applying the model defined by equation (1) to another example initial data set. In this data set, the interference range completely overlaps with the defect range, and it is expected that it spans the defect distribution. The defect range is completely within the interference range, or has an insufficient number of elements that are separate and different from the interference range, and the distribution of the defect signal cannot be well predicted (for example, the estimated value of the defect distribution cannot be distinguished from the interference distribution). It can be seen that the defect signal represented by the actual defect line 82 appears in the range near the upper end of the interference data range and the interference model, i.e., the first-order polynomial 83. In other words, the distribution of the defect signal represented by the actual defect line 82 completely or at least substantially (almost completely) overlaps with the distribution of the interference signal, i.e., the first-order polynomial 83.

[0102] Fig.11 The defect distribution shown by the actual defect line 82 in FIG. 8 may be indistinguishable or inseparable (eg, cannot be separated) from the interference distribution represented by, for example, the line (or first-order polynomial) 83. Fig. 9 , Fig. 10A and Fig. 10B Unlike the arrangements shown, which distinguish the distribution of defect signals from the distribution of interference signals, the elements in the initial data set that are defect candidates are close to the empty set. That is, there are no or insufficient defect signals that are separate and different from the interference signals of the interference distribution. At the elements with corresponding signal strength amplitudes, there are no or at most an insufficient number of defect signals with defect ranges whose amplitudes exceed the upper limit of the interference range. That is, the number of elements in the defect range with amplitudes exceeding the upper limit of the interference range does not reach the threshold of elements in the defect range with amplitudes greater than the upper interference range. Therefore, there is a situation where defect signals in the initial data set can be identified based on the relative signal strength of the defect distribution compared to the interference distribution.

[0103] An initial data set can be identified from an initial signal (or inspection signal or evaluation signal) from a detector. The initial data set may include all elements of the initial signal detected by the detector. However, a disadvantage of this approach is that a large amount of data needs to be processed, most of which are interference signals. Alternatively, the initial data set can be identified by extracting elements from the initial signal; and selecting elements whose amplitude is greater than a predetermined signal strength value. The processing of the initial data set is expected to be performed using the selected elements. In this way, elements with amplitudes low enough to indicate interference signals can be filtered out at an early stage, and then the initial data set is processed as described above (and elsewhere in this article). Therefore, the processing may be more efficient. Desirably, the predetermined signal strength value is lower than the signal strength value. In this way, any element with an amplitude high enough to indicate a possible defect is less likely to be discarded, but will be included in the initial data set. This is conducive to providing an initial data set with a small amount of interference data, and the risk of inadvertently missing data representing actual defects is low. The predetermined signal strength value can be set based on information from a previous comparable data set or based on a model.

[0104] Once the signal strength threshold is set, for example by using the above method (and methods described elsewhere herein), defects on the sample can be detected. Defects can be detected by evaluating a subset of defect candidates whose amplitudes are greater than the signal strength threshold. In other words, the defect candidate subsets can be evaluated to determine which subset of the defect candidate subsets corresponds to the actual defect. Once the actual defect is determined, the corresponding signal can be further evaluated to determine the type of defect. Evaluating the signal to identify and classify defects can require a lot of time and computational effort. Therefore, it is best to use the above method (and methods described elsewhere herein) to set an appropriate signal strength threshold so that a large number of signals, including most of the interference signals, are not evaluated in detail.

[0105] A signal strength threshold may be set and defects may be determined by evaluating a subset of defect candidates based on an initial data set. Alternatively or additionally, a signal strength threshold may be determined using the initial data set, which may be applied to additional, subsequently processed and / or collected signal data. For example, a signal strength threshold may be determined using an initial data set based on data of a single sample or a portion of a single sample. After determining the signal strength threshold, additional initial signals may be received and / or processed. Additional initial signals may be obtained from an inspection of another sample from the same batch as the single initial sample. If only data of a portion of a single initial sample is used to determine the signal strength threshold, additional initial signals may be obtained from an inspection of the remainder of the single sample. Additional elements of additional initial signals having an amplitude greater than the signal strength threshold may be extracted as additional detection signals. Additional detection signals may be evaluated to determine which additional detection signals correspond to actual defects.

[0106] Optionally, the additional detection signals may be placed into the defect candidate subset along with the defect candidate subset identified from the initial data set. In this way, actual defects in the initial data set and the additional detection signals may be evaluated to determine which additional detection signals correspond to actual defects.

[0107] Figures 12A-12D A sample is shown having a plurality of features 110, wherein one or more features 110 are not formed correctly and are therefore considered defects. Data for such defects depicted and described herein may include, for example, defects that fill a defect distribution, such as the defect distribution of an initial data set (as actual defects). Fig. 12A An example is shown where the defect is a missing feature, such as a hole on the sample in the center of the depicted area. The image can be analyzed to classify the defect as being in the form of a missing hole 111. The type of defect can be classified by determining that the unmatched signal does not match the other signals by comparing the unmatched signal to other signals, where the other signals are similar and match each other. Alternatively, the type of defect can be classified by comparing the unmatched signal to a known expected signal pattern for that area of ​​the sample. The expected signal pattern can be in the form of a data file to which the unmatched signal is compared.

[0108] Fig. 12B An example of a defect being a bridging feature 112 is shown. Specifically, Fig. 12B A sample is shown that includes a series of circular features on its surface. The bridging features 112 are elongated, for example forming an oval, rectangular or irregular shape, rather than a circle. Fig. 12C and Fig.12D An example of a defect being a feature of the wrong size is shown. Specifically, Fig. 12C A sample including oversized features 113 is shown, while Fig.12D A sample including undersized features 114 is shown.

[0109] The following terms are provided:

[0110] Item 1: A method for processing data obtained from a sample, comprising processing an initial data set of elements obtained by detection of a detector for calibration, the data set comprising elements representing interference signals and detection signals, the processing of the initial data set comprising: fitting a distribution model to the initial data set to create an interference distribution model; setting a signal intensity value, and selecting elements in the initial data set whose amplitudes are greater than the signal intensity value as a defect candidate set; fitting a distribution model to the defect candidate set to create a defect distribution model of the detection signal; and determining a signal intensity threshold based at least on the defect distribution model, the determination comprising correcting the defect distribution model, and it is expected that the correction is applicable to correction for overlap in amplitudes between elements representing interference signals and detection signals.

[0111] Clause 2: The method of clause 1, wherein the correction for overlap comprises correcting a corrected defect distribution model of the detection signal.

[0112] Clause 3: The method of clause 2, wherein the correction for overlap comprises creating a summed distribution model of the initial data set using the interference distribution model and the defect distribution model.

[0113] Clause 4: The method of clause 3, wherein creating the summed distribution model comprises adding the interference distribution model and the defect distribution model.

[0114] Clause 5: The method of any one of clauses 3 or 4, further comprising fitting the sum distribution model to an actual distribution of the initial data set to create a corrected sum distribution model.

[0115] Clause 6: The method of clause 5, wherein the correction for overlap comprises creating a corrected defect distribution model by adjusting parameter values ​​of the defect distribution model based on parameter values ​​of the corrected sum distribution model.

[0116] Clause 7: The method of clause 5, wherein the correction for the overlap comprises creating a corrected defect distribution model based on parameter values ​​of the corrected sum distribution model associated with the defect distribution model.

[0117] Clause 8: The method of any one of clauses 2 to 7, wherein setting the signal strength threshold is based on a corrected parameter value of the defect distribution model.

[0118] Clause 9: A method according to any preceding clause, further comprising determining a relationship between a capture rate and the signal strength threshold, desirably determining the capture rate as a function of the signal strength threshold.

[0119] Item 10: A method for processing data obtained from a sample, comprising processing an initial data set of elements obtained by detection by a detector for calibration, the data set including elements representing interference signals and detection signals, the processing of the initial data set comprising: fitting a distribution model to the initial data set to create an interference distribution model; setting a signal strength value and selecting elements in the initial data set whose amplitudes are greater than the signal strength value as a defect candidate set; fitting a distribution model to the defect candidate set to create a defect distribution model for the detection signal; determining a signal strength threshold based at least on the defect distribution model; and determining the relationship between the capture rate and the signal strength threshold.

[0120] Clause 11: A method according to clause 10, wherein determining the signal strength threshold includes correcting for overlap in amplitude between elements representing an interference signal and a detection signal, and desirably, the correction for overlap includes correcting to a corrected defect distribution model, desirably using a corrected sum distribution model obtained by adding the interference distribution model to the defect distribution model and fitting to the actual distribution of the initial data set.

[0121] Clause 12: The method of any of clauses 9 to 11, wherein determining the relationship between capture rate and a signal strength threshold comprises determining the capture rate as a function of the signal strength threshold.

[0122] Clause 13: The method of clause 12, wherein determining the capture rate as a function of a signal strength threshold is based on corrected parameter values ​​of the sum distribution model.

[0123] Clause 14: The method of clause 13, comprising determining a capture rate as a function of a signal strength threshold based on the corrected defect distribution model.

[0124] Clause 15: The method of any of clauses 13 or 14, further comprising setting a signal strength threshold based on the determined capture rate.

[0125] Clause 16: The method of any one of clauses 1 to 15, wherein the interference distribution model comprises a Gaussian function.

[0126] Clause 17: The method of any one of clauses 1 to 16, wherein the defect distribution model comprises a Gaussian function.

[0127] Clause 18: A method according to any one of clauses 5 to 9 and 11 to 17, wherein the sum distribution model and the actual distribution are both logarithms of the inverse of the corresponding cumulative distributions, desirably, the corrected sum distribution model is the logarithm of the inverse of the corresponding cumulative distribution.

[0128] Clause 19: A method as described in any of clauses 1 to 18, wherein the signal strength value is set based on the interference distribution model.

[0129] Clause 20: A method according to Clause 19, wherein setting the signal strength value comprises: determining an interference threshold based on the interference distribution model, wherein, according to the interference distribution model, the number of elements representing interference signals with amplitudes greater than the interference threshold is less than or equal to a predetermined interference threshold; and selecting the signal strength value based on the interference threshold.

[0130] Clause 21: The method of clause 20, wherein the signal strength value is set equal to the interference threshold.

[0131] Clause 22: A method according to any one of clauses 20 or 21, wherein the predetermined interference threshold is 1.

[0132] Clause 23: A method according to any one of clauses 1 to 22, wherein the interference distribution model is based on the following model: ln(y) = a + c*x^2, wherein 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 data set.

[0133] Clause 24: The method of any one of clauses 1 to 23, further comprising receiving a detection signal from a detector; and representing the initial data set from the detection signal.

[0134] Clause 25: The method according to any one of clauses 1 to 24 further includes identifying the initial data set by: extracting elements from the detection signal; and selecting the elements whose amplitudes are greater than a predetermined signal strength value, wherein the predetermined signal strength value is lower than the signal strength value; wherein the processing of the initial data set is performed using the selected elements.

[0135] Clause 26: The method of any one of clauses 1 to 25, wherein processing the initial data set further comprises identifying a subset of defect candidates having amplitudes greater than the signal strength threshold.

[0136] Clause 27: The method according to any one of clauses 1 to 26 further includes receiving an additional initial signal; and extracting an additional element having an amplitude greater than the signal strength threshold from the additional initial signal; desirably including the additional element in a defect candidate set, desirably the additional element can be referred to as a defect candidate subset.

[0137] Clause 28: The method of any one of clauses 26 and 27, wherein the processing of the initial data set further comprises detecting defects on the sample by evaluating the defect candidate subset.

[0138] Clause 29: The method of any one of clauses 1 to 28, wherein the processing data obtained from the sample further comprises using a processor included in a charged particle optics device.

[0139] Clause 30: The method according to any one of clauses 1 to 29 further includes projecting at least a beam of charged particles toward a sample using a charged particle optical device including a detector, wherein the detector detects the detection signal in response to signal particles received from the sample, wherein the signal particles are received in response to the impact of the beam with the sample.

[0140] Item 31: A method for processing data obtained from a sample, comprising processing an initial data set of elements obtained by detection by a detector, the data set comprising elements representing interference signals and defect signals, the interference distribution comprising elements representing interference signals having amplitudes of an interference range, and the defect distribution comprising elements representing detection signals having amplitudes of a defect range, wherein the interference range overlaps with the defect range, desirably overlaps, and the amplitude of at least one element of the defect range exceeds an upper limit of the interference range, desirably exceeds in amplitude, desirably exceeds above the upper limit of the interference range, desirably the at least one element of the defect range is a sufficient number of elements of the defect range to make the defect distribution different from the interference distribution, the sufficient number being able to be or being able to exceed a threshold number of elements in the defect range having an amplitude greater than the upper limit of the interference range, desirably making the defect distribution different from the interference distribution.

[0141] Clause 32: The method of clause 31, wherein the defect distribution is separate / different from the disturbance distribution.

[0142] Clause 33: A method according to any one of clauses 31 and 32, wherein the at least one element represents a detection signal, the detection signal comprises a subset of elements representing the detection signal, desirably, the subset of elements representing the detection signal indicates that the defect distribution is separate / different from the interference distribution.

[0143] Clause 34: A method of processing data obtained from a sample according to any one of clauses 31 to 33, comprising the method of any one of clauses 1 to 30.

[0144] Clause 35: A method of evaluating a sample comprising the method of any one of clauses 1 to 34.

[0145] Item 36: A method for identifying defect candidates, comprising processing a data set of elements detected by a detector, the data set including elements representing interference signals and detection signals, a capture threshold relationship between a capture rate and a signal strength threshold being calibrated using an initial data set, the processing comprising: setting a signal strength threshold by selecting a capture rate and based on the capture threshold relationship; and processing the data set using the signal strength threshold to select elements representing the detection signal.

[0146] Clause 37: The method of clause 36, wherein the processing comprises selecting elements representing the detection signal by identifying a subset of defect candidates having magnitudes greater than the signal strength threshold.

[0147] Clause 38: The method of any of clauses 37, wherein the processing further comprises detecting defects on the sample by evaluating a subset of the defect candidates.

[0148] Clause 39: The method according to any one of clauses 36 to 38, further comprising: receiving a detection signal comprising said data set; and during said processing, extracting said element representing the detection signal.

[0149] Clause 40: A method for identifying defect candidates according to clauses 36 to 39, wherein the method for processing data described in any one of clauses 9 to 30 is used to determine the capture threshold relationship in calibration using the initial data set, desirably based on the relationship between capture rate and the signal strength threshold.

[0150] Clause 41: A processing device comprising: a processor configured to perform the processing according to any one of clauses 1 to 40.

[0151] Clause 42: A computer program comprising instructions configured to control a processor to perform the method of any one of clauses 1 to 40.

[0152] Item 43: An evaluation system for identifying defect candidates in evaluation data obtained from a sample, the evaluation system comprising: a detector configured to generate a detection signal representing one or more features of the sample; a processor configured to: process a data set of elements obtained by 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 a signal strength threshold, the capture relationship calibration being pre-calibrated by an initial data set; and process the data set using the signal strength threshold to select elements representing the detection signal.

[0153] Reference to a component or system or element of a component that can be controlled to manipulate a charged particle beam in a certain manner includes configuring a controller or control system or control unit to control the component to manipulate the charged particle beam in the manner described, and optionally using other controllers or devices (e.g., voltage sources and / or current sources) to control the component to manipulate the charged particle beam in this manner. For example, a voltage source can be electrically connected to one or more components to apply a potential to the component under the control of a controller or control system or control unit, such as the control lens array 250, the objective lens array 241, the converging lens 231, the corrector, the collimator element array, and the scanning deflector array 260 in a non-limiting list. Actuable components (such as stages) can be controllable to actuate and thus use one or more controllers, control systems, or control units to control the actuation of the component to move relative to another component (such as a beam path).

[0154] The functions provided by a controller or control system or control unit can be implemented by a computer. Any suitable combination of components can be used to provide the desired functions, including, for example, CPU, RAM, SSD, mainboard, network connection, firmware, software and / or other components known in the art that allow the required computing operations to be performed. The required computing operations can be defined by one or more computer programs. One or more computer programs can be provided in the form of a medium (optionally a non-transient medium) storing computer-readable instructions. When a computer reads a computer-readable instruction, the computer executes the required method steps. The computer can be composed of an independent unit or a distributed computing system, which has a plurality of different computers interconnected by a network.

[0155] The computer program may include instructions to instruct the controller 50 to perform the following steps. The controller 50 controls the charged particle beam device to project the charged particle beam toward 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 scanning deflectors 260) to operate the charged particle beam 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., a detector 240) to operate the charged particle beam emitted from the sample 208 in response to the charged particle beam.

[0156] An evaluation system according to an embodiment of the present disclosure may be a tool for performing a qualitative evaluation of a sample (e.g., pass / fail), a tool for performing a quantitative measurement of a sample (e.g., the size of a feature), or a tool for generating an image or 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 performing any combination of evaluation functions associated with inspection tools, review tools, or metrology tools (e.g., metrology-inspection tools). The electron-optical device array 40 may be a component of an evaluation system, such as an inspection tool or a metrology-inspection tool. Any reference to a tool herein is intended to cover an apparatus, device, or system comprising various components that may or may not be co-located, and may even be located in separate rooms, particularly, for example, for data processing elements.

[0157] References to upper and lower, upward and downward, above and below should be understood to refer to directions parallel to the upstream and downstream directions (typically but not always perpendicular) of the electron beam or beams impinging on the sample 208. Thus, references to upstream and downstream are intended to refer to the direction of the beam path independent of any existing gravitational field.

[0158] The terms "sub-beam" and "beamlet" are used interchangeably herein and are both understood to include any radiation beam derived from a parent radiation beam by dividing or splitting the parent radiation beam. The term "manipulator" is used to include any element that affects the path of a sub-beam or beamlets, such as a lens or deflector.

[0159] Reference to an element being aligned along a beam path or a beam sub-path is to be understood as referring to the respective element being positioned along the beam path or beam sub-path.

[0160] Although the present invention has been described in conjunction with various embodiments, other embodiments of the present invention will be apparent to those skilled in the art from the specification disclosed herein and the practice of the present invention. The specification and examples should be considered as exemplary only, and the true scope and spirit of the present invention are indicated by the following claims and clauses.

[0161] The above description is intended to be illustrative and not limiting. Therefore, those skilled in the art will appreciate that modifications may be made as described without departing from the scope of the claims and clauses described herein.

Claims

1. A method for processing data obtained from a sample, comprising processing an initial data set of elements for calibration obtained by detection by a detector, the data set comprising elements representing interference signals and detection signals, the processing of the initial data set comprising: fitting a distribution model to the initial data set to create an interference distribution model; Setting a signal strength value, and selecting elements in the initial data set whose amplitude is greater than the signal strength value as a defect candidate set; fitting a distribution model to the defect candidate set to create a defect distribution model of the detection signal; as well as A signal strength threshold is determined based at least on the defect distribution model, the determination comprising correcting the defect distribution model, desirably the correction being adapted to correct for overlap in amplitude between elements representing interference signals and detection signals. 2 . The method of claim 1 , wherein the correction for overlap comprises correcting a corrected defect distribution model to a detection signal. 3 . The method of claim 2 , wherein the correction for overlap comprises using the interference distribution model and the defect distribution model to create a summed distribution model of the initial data set. The method of claim 3 , wherein creating the summed distribution model comprises adding the interference distribution model and the defect distribution model.

5. The method according to any one of claims 3 or 4, further comprising fitting the sum distribution model to the actual distribution of the initial data set to create a corrected sum distribution model. 6 . The method of claim 5 , wherein the correction for overlap comprises creating a corrected defect distribution model based on parameter values ​​of the corrected sum distribution model associated with the defect distribution model. 7 . The method according to claim 2 , wherein setting the signal strength threshold is based on a corrected parameter value of the defect distribution model.

8. A method according to any preceding claim, further comprising determining a relationship between capture rate and the signal strength threshold, desirably determining the capture rate as a function of the signal strength threshold.

9. The method of any one of claims 8, wherein determining the relationship between capture rate and a signal strength threshold comprises determining the capture rate as a function of the signal strength threshold.

10. The method of claim 9, wherein determining the capture rate as a function of a signal strength threshold is based on corrected parameter values ​​of the sum distribution model.

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

12. The method according to any one of claims 5 to 11, wherein the sum distribution model and the actual distribution are both logarithms of the inverses of the corresponding cumulative distributions, desirably, the corrected sum distribution model is the logarithm of the inverses of the corresponding cumulative distributions.

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

14. The method of claim 13, wherein setting the signal strength value comprises: determining an interference threshold based on the interference distribution model, wherein according to the interference distribution model, the number of the elements representing interference signals having amplitudes greater than the interference threshold is less than or equal to a predetermined interference threshold; and The signal strength value is selected based on the interference threshold.

15. The method according to any one of claims 1 to 14, further comprising: receiving a detection signal from a detector; as well as The initial data set is identified from the detection signal.

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