Transient defect detection using a detected image

Abstract

An improved method and system for transient defect inspection using inspection images are disclosed. The method includes acquiring a plurality of inspection images, generating an average image of the plurality of inspection images, detecting first type defects in the average image, determining a mask area corresponding to the first type defects, and determining whether the plurality of inspection images have second type defects in the non-mask area.

Description

Technical Field

[0001] Cross - Reference to Related Applications

[0001] This application claims the priority of U.S. Application No. 63 / 368,601, filed on July 15, 2022, which is hereby incorporated by reference in its entirety.

[0002]

[0002] The embodiments provided herein relate to transient defect inspection techniques, and more particularly, to transient defect inspection techniques using inspection images.

Background Art

[0003]

[0003] In the manufacturing process of integrated circuits (ICs), unfinished or finished circuit components are inspected to ensure that they are manufactured according to the design and are free of defects. Inspection systems using optical microscopes or charged particle (e.g., electron) beam microscopes such as scanning electron microscopes (SEM) can be used. As the physical size of IC components continues to decrease, the accuracy of defect detection becomes more important. By using inspection images such as SEM images, defects (if any) in the manufactured IC can be identified or classified. To improve the performance of defect detection, methods and systems that can more accurately detect transient defects and hard defects are desired.

Summary of the Invention

[0004]

[0004] The embodiments provided herein disclose an inspection apparatus, and more particularly, disclose a transient defect inspection technique using inspection images obtained by the inspection apparatus.

[0005]

[0005] Some embodiments provide an apparatus for transient defect inspection using inspection images. The apparatus includes a memory storing a set of instructions, and at least one processor configured to execute the set of instructions to cause the apparatus to obtain a plurality of inspection images, generate an average image of the plurality of inspection images, detect first-type defects in the average image, determine a mask area corresponding to the first-type defects, and determine whether the plurality of inspection images have second-type defects within the non-mask area.

[0006]

[0006] Some embodiments provide an apparatus for transient defect inspection using inspection images. The apparatus includes a memory storing a set of instructions, and at least one processor configured to execute the set of instructions to cause the apparatus to obtain a plurality of inspection images, generate an average image of the plurality of inspection images, detect first-type defects in the average image, determine a mask area corresponding to the first-type defects, compare an individual inspection image among the plurality of inspection images with the average image for the non-mask area, and determine whether the plurality of inspection images have second-type defects within the non-mask area based on the comparison.

[0007]

[0007] Other advantages of the embodiments of the present disclosure will become apparent from the following description of specific embodiments of the invention for illustrative purposes with reference to the accompanying drawings.

Brief Description of the Drawings

[0008]

[0008] The above and other aspects of the present disclosure will become more apparent from the description of exemplary embodiments with reference to the accompanying drawings.

[0009]

Figure 1

[0009] FIG. is a schematic diagram showing an exemplary charged particle beam inspection system consistent with embodiments of the present disclosure.

Figure 2

[0010] Schematic diagram showing an exemplary multi-beam tool that may be part of an exemplary charged particle beam inspection system in accordance with embodiments of the present disclosure, corresponding to FIG. 1.

Figure 3

[0011] Exemplary diagram showing characteristics of transient defects, corresponding to embodiments of the present disclosure.

Figure 4

[0012] Block diagram of an exemplary transient defect detection system, corresponding to embodiments of the present disclosure.

Figure 5A

[0013] Showing a plurality of inspection images, corresponding to embodiments of the present disclosure.

Figure 5B

[0014] Showing the average image of a plurality of inspection images in FIG. 5A and the type-1 defects detected from this average image, corresponding to embodiments of the present disclosure.

Figure 5C

[0015] Showing a mask generated based on the detected type-1 defects, corresponding to embodiments of the present disclosure.

Figure 6A

[0016] Showing a first algorithm for detecting type-2 defects, corresponding to embodiments of the present disclosure.

Figure 6B

[0017] Showing a second algorithm for detecting type-2 defects, corresponding to embodiments of the present disclosure.

Figure 7A

[0018] Showing the detected type-2 defects, corresponding to embodiments of the present disclosure.

Figure 7B

[0019] Showing the combined type-1 defects and type-2 defects, corresponding to embodiments of the present disclosure.

Figure 8

[0020] Process flowchart representing an exemplary transient defect detection method, corresponding to embodiments of the present disclosure.

DETAILED DESCRIPTION OF THE INVENTION

[0010]

[0021] Hereinafter, exemplary embodiments shown in the accompanying drawings will be described in detail. The following description refers to the accompanying drawings, in which, unless otherwise specified, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following description of the exemplary embodiments do not represent all embodiments. Rather, they are merely examples of apparatuses and methods that conform to aspects related to the disclosed embodiments described in the claims. For example, some embodiments are described in the context of using an electron beam, but the present disclosure is not limited thereto. Other types of charged particle beams can be similarly applied. Also, other imaging systems such as optical imaging, photon detection, X-ray detection, etc. can be used.

[0011]

[0022] An electronic device is constructed of circuits formed on a piece of semiconductor material called a substrate. Examples of semiconductor materials include silicon, gallium arsenide, indium phosphide, silicon germanium, and the like. Multiple circuits may be formed on the same silicon piece, which is called an integrated circuit or IC. These circuits are extremely miniaturized, enabling more circuits to be accommodated on the substrate. For example, an IC chip of a smartphone can incorporate over 2 billion transistors while being as small as a fingernail, and the size of each transistor is smaller than 1 / 1000 of a human hair.

[0012]

[0023] The manufacture of these ICs with extremely small structures or components is a complex, time-consuming, and costly process, often involving hundreds of individual steps. If an error occurs even in one step, the resulting IC may have defects and become unusable. Therefore, one of the goals of the manufacturing process is to avoid such defects and maximize the number of functional ICs created in the process, that is, to improve the overall yield of the process.

[0013]

[0024] As one element to improve the yield, it may be necessary to monitor the chip manufacturing process to confirm that a sufficient number of functional integrated circuits are being produced. One way to monitor the process is to inspect the circuit structure of the chip at various stages of its formation. The inspection can be performed using a scanning charged particle microscope (“SCPM”). For example, the SCPM may be a scanning electron microscope (SEM). By using the SCPM, it is possible to image these extremely small structures, substantially taking a “photo” of the structure of the wafer. This image can be used to determine whether the structure is properly formed in the appropriate position. If there are defects in the structure, the process can be adjusted, thus reducing the likelihood of such defects recurring.

[0014]

[0025] As the physical size of IC components continues to decrease, the accuracy of defect detection and the yield become more important. By using inspection images such as SCPM images, defects (if any) in the manufactured IC can be identified or classified. The SCPM image contains not only the structure information of the wafer but also noise. Due to the noise in the SCPM image, it becomes difficult to detect or identify defects from the SCPM image. Therefore, frame averaging can be used to obtain a cleaner SCPM image with less noise, and defects can be detected from this SCPM image. In frame averaging, multiple SCPM images of a single imaging area of a wafer (e.g., a single die) are taken over time, and the average image of the multiple SCPM images is used for defect detection. Since the noise in the SCPM image is random, the noise level can be reduced by averaging multiple SCPM images. Therefore, the accuracy of defect detection can be improved. However, hard defects that repeatedly appear on multiple SCPM images can be efficiently detected from the average image with less noise, but transient defects that do not always appear on the SCPM image may not be detected from the average image. Therefore, there is a need for a method and system that can more accurately detect transient defects.

[0015]

[0026] Embodiments of the present disclosure can provide improved transient defect inspection techniques. According to some embodiments of the present disclosure, a given technique can detect both transient defects and hard defects. According to some embodiments of the present disclosure, by excluding regions containing defects already identified in transient defect inspection, defect detection performance and the overall efficiency of the system can be improved. According to some embodiments of the present disclosure, by using an average image as a reference image, the nuisance rate can be reduced when detecting transient defects.

[0016]

[0027] The relative dimensions of components in the drawings may be exaggerated for clarity. In the following description of the drawings, the same or similar reference numerals refer to the same or similar components or entities, and only the differences with respect to individual embodiments are described. As used herein, unless otherwise specified, the term "or" includes all possible combinations except when the combination is infeasible. For example, if it is described that a component may include A or B, unless otherwise specified or infeasible, the component may include A, or B, or A and B. As a second example, if it is described that a component may include A, B, or C, unless otherwise specified or infeasible, the component may include A, or B, or C, or A and B, or A and C, or B and C, or A and B and C.

[0017]

[0028] Figure 1 shows an exemplary electron beam inspection (EBI) system 100 that conforms to an embodiment of the present disclosure. The EBI system 100 can be used for imaging. As shown in Figure 1, the EBI system 100 includes a main chamber 101, a load lock chamber 102, a beam tool 104, and an equipment front end module (EFEM) 106. The beam tool 104 is disposed within the main chamber 101. The EFEM 106 includes a first load port 106a and a second load port 106b. The EFEM 106 may include additional load port(s). The first load port 106a and the second load port 106b receive a wafer front opening unified pod (FOUP) that includes a wafer to be inspected (e.g., a semiconductor wafer or a wafer made of other material(s)) or a sample (the wafer and the sample can be used interchangeably). A "lot" is a plurality of wafers that can be filled for processing as a batch.

[0018]

[0029] The wafer can be transported to the load lock chamber 102 by one or more robot arms (not shown) within the EFEM 106. The load lock chamber 102 is connected to a load lock vacuum pump system (not shown), and this pump system removes gas molecules within the load lock chamber 102 to reach a first pressure lower than atmospheric pressure. After reaching the first pressure, the wafer can be transported from the load lock chamber 102 to the main chamber 101 by one or more robot arms (not shown). The main chamber 101 is connected to a main chamber vacuum pump system (not shown), and this pump system removes gas molecules within the main chamber 101 to reach a second pressure lower than the first pressure. After reaching the second pressure, the wafer is subjected to inspection by the beam tool 104. The beam tool 104 may be a single beam system or a multi-beam system.

[0019]

[0030] Controller 109 is electronically connected to beam tool 104. Controller 109 can be a computer configured to perform various controls of EBI system 100. In FIG. 1, controller 109 is described as being outside the structure including main chamber 101, load lock chamber 102, and EFEM 106, but it will be understood that controller 109 may be part of this structure.

[0020]

[0031] In some embodiments, controller 109 may include one or more processors (not shown). A processor can be a general-purpose or specific electronic device capable of manipulating or processing information. For example, such processors include a central processing unit (or "CPU"), a graphics processing unit (or "GPU"), an optical processor, a programmable logic controller, a microcontroller, a microprocessor, a digital signal processor, an IP (intellectual property) core, a programmable logic array (PLA), a programmable array logic (PAL), a generic array logic (GAL), a complex programmable logic device (CPLD), a field programmable gate array (FPGA), a system on chip (SoC), an application specific integrated circuit (ASIC), and any number of any combination of any type of circuit capable of performing data processing. The processor may be a virtual processor including one or more processors distributed among a plurality of machines or devices coupled via a network.

[0021]

[0032] In some embodiments, the controller 109 may further include one or more memories (not shown). The memory may be a general-purpose or specific electronic device capable of storing code and data accessible by the processor (e.g., via a bus). For example, such memories may include random access memory (RAM), read-only memory (ROM), optical disks, magnetic disks, hard drives, solid-state drives, flash drives, secure digital (SD) cards, memory sticks, compact flash (CF) cards, or any number of any combination of any type of storage device. The code and data may include an operating system (OS) and one or more application programs (or "apps") for specific tasks. The memory may be a virtual memory including one or more memories distributed among a plurality of machines or devices coupled via a network.

[0022]

[0033] FIG. 2 is a schematic diagram of an exemplary multi-beam tool 104 (also referred to herein as apparatus 104) and an image processing system 290 that may be configured for use in an EBI system 100 (FIG. 1) in accordance with embodiments of the present disclosure.

[0023]

[0034] The beam tool 104 includes a charged particle source 202, a gun aperture 204, a condenser lens 206, a primary charged particle beam 210 emitted from the charged particle source 202, a radiation source conversion unit 212, a plurality of beamlets 214, 216, and 218 of the primary charged particle beam 210, a primary projection optical system 220, a motorized wafer stage 280, a wafer holder 282, a plurality of secondary charged particle beams 236, 238, and 240, a secondary optical system 242, and a charged particle detection device 244. The primary projection optical system 220 may include a beam separator 222, a deflection scanning unit 226, and an objective lens 228. The charged particle detection device 244 may include detection sub-regions 246, 248, and 250.

[0024]

[0035] The charged particle source 202, the gun aperture 204, the condenser lens 206, the radiation source conversion unit 212, the beam separator 222, the deflection scanning unit 226, and the objective lens 228 can be aligned with the primary optical axis 260 of the apparatus 104. The secondary optical system 242 and the charged particle detection device 244 can be aligned with the secondary optical axis 252 of the apparatus 104.

[0025]

[0036] The charged particle source 202 can emit one or more charged particles such as electrons, protons, ions, muons, or any other particles that carry a charge. In some embodiments, the charged particle source 202 may be an electron emission source. For example, the charged particle source 202 may include a cathode, an extractor, or an anode, and the primary electrons are emitted from the cathode and can be extracted or accelerated to form a primary charged particle beam 210 (in this case, a primary electron beam) together with a (virtual or real) crossover 208. For the sake of easy explanation without causing ambiguity, electrons are used as an example in some parts of the description herein. However, it should be noted that any charged particles can be used in any embodiment of the present disclosure, not limited to electrons. The primary charged particle beam 210 can be visualized as being emitted from the crossover 208. The gun aperture 204 can block the charged particles around the primary charged particle beam 210 to reduce the Coulomb effect. The Coulomb effect can cause an increase in the size of the probe spot.

[0026]

[0037] The radiation source conversion unit 212 may include an array of image forming elements and an array of beam limiting apertures. The array of image forming elements may include an array of micro deflectors or microlenses. The array of image forming elements may form a plurality of (virtual or real) parallel images of the crossover 208 together with a plurality of beamlets 214, 216, and 218 of the primary charged particle beam 210. The array of beam limiting apertures may limit the plurality of beamlets 214, 216, and 218. Although three beamlets 214, 216, and 218 are shown in FIG. 2, embodiments of the present disclosure are not limited thereto. For example, in some embodiments, the apparatus 104 may be configured to generate a first number of beamlets. In some embodiments, the first number of beamlets may be in the range from 1 to 1000. In some embodiments, the first number of beamlets may be in the range of 200 to 500. In an exemplary embodiment, the apparatus 104 may generate 400 beamlets.

[0027]

[0038] The condenser lens 206 may focus the primary charged particle beam 210. The currents of the beamlets 214, 216, and 218 downstream of the radiation source conversion unit 212 may vary by adjusting the focusing power of the condenser lens 206 or by changing the size of the radius of the corresponding beam limiting aperture within the array of beam limiting apertures. The objective lens 228 may focus the beamlets 214, 216, and 218 onto the imaging wafer 230 and may form a plurality of probe spots 270, 272, and 274 on the surface of the wafer 230.

[0028]

[0039] The beam separator 222 can be a Wien filter type beam separator that generates an electrostatic dipole field and a magnetic dipole field. In some embodiments, when these are applied, the force exerted by the electrostatic dipole field on the charged particles (e.g., electrons) of the beamlets 214, 216, and 218 can be substantially equal in magnitude and opposite in direction to the force exerted by the magnetic dipole field on the charged particles. Thus, the beamlets 214, 216, and 218 can pass straight through the beam separator 222 with a zero deflection angle. However, the total dispersion of the beamlets 214, 216, and 218 generated by the beam separator 222 may not be zero. The beam separator 222 can separate the secondary charged particle beams 236, 238, and 240 from the beamlets 214, 216, and 218 and direct the secondary charged particle beams 236, 238, and 240 toward the secondary optical system 242.

[0029]

[0040] The deflection scanning unit 226 can deflect the beamlets 214, 216, and 218 to scan the probe spots 270, 272, and 274 on the surface area of the wafer 230. In response to the incidence of the beamlets 214, 216, and 218 at the probe spots 270, 272, and 274, the secondary charged particle beams 236, 238, and 240 can be emitted from the wafer 230. The secondary charged particle beams 236, 238, and 240 can include charged particles (e.g., electrons) having an energy distribution. For example, the secondary charged particle beams 236, 238, and 240 can be a secondary electron beam including secondary electrons (energy ≤ 50 eV) and backscattered electrons (energy between 50 eV and the incident energy of the beamlets 214, 216, and 218). The secondary optical system 242 can focus the secondary charged particle beams 236, 238, and 240 onto the detection sub-regions 246, 248, and 250 of the charged particle detection device 244. The detection sub-regions 246, 248, and 250 can be configured to detect the corresponding secondary charged particle beams 236, 238, and 240 and generate corresponding signals (e.g., voltage, current, etc.) for reconstructing an inspection image of the structure on or under the surface area of the wafer 230.

[0030]

[0041] The generated signals may represent the intensities of the secondary charged particle beams 236, 238, and 240 and may be provided to an image processing system 290 that communicates with the charged particle detection device 244, the primary projection optical system 220, and the motorized wafer stage 280. The moving speed of the motorized wafer stage 280 can be synchronized and adjusted with the deflection of the beam controlled by the deflection scanning unit 226 so that the movement of the scanning probe spots (e.g., scanning probe spots 270, 272, and 274) can systematically target and include the regions of interest on the wafer 230. Such synchronization and adjustment parameters can be adjusted to adapt to different materials of the wafer 230. For example, different materials of the wafer 230 may have different resistance-capacitance characteristics that can cause different signal sensitivities to the movement of the scanning probe spots.

[0031]

[0042] The intensities of the secondary charged particle beams 236, 238, and 240 may change according to the external or internal structure of the wafer 23 and thus may indicate whether the wafer 230 contains defects. Further, as described above, the beamlets 214, 216, and 218 may be projected onto different positions on the upper surface of the wafer 230 or onto different sides of the local structure of the wafer 230, whereby secondary charged particle beams 236, 238, and 240 having different intensities may be generated. Therefore, by mapping the intensities of the secondary charged particle beams 236, 238, and 240 to the area of the wafer 230, the image processing system 290 can reconstruct an image reflecting the characteristics of the internal or external structure of the wafer 230.

[0032]

[0043] In some embodiments, the image processing system 290 may include an image acquirer 292, a storage 294, and a controller 296. The image acquirer 292 may include one or more processors. For example, the image acquirer 292 may include a computer, a server, a mainframe host, a terminal, a personal computer, any type of portable computer device, etc., or a combination thereof. The image acquirer 292 may be communicatively coupled to the charged particle detection device 244 of the beam tool 104 via a medium such as a conductor, an optical fiber cable, a portable storage medium, IR, Bluetooth, the Internet, a wireless network, wireless communication, or a combination thereof. In some embodiments, the image acquirer 292 can receive a signal from the charged particle detection device 244 and construct an image. Thus, the image acquirer 292 can acquire an inspection image of the wafer 230. The image acquirer 292 may also perform various post-processing functions such as generating a contour line and superimposing an indicator on the acquired image. The image acquirer 292 may be configured to perform adjustment of the brightness and contrast of the acquired image. In some embodiments, the storage 294 may be a storage medium such as a hard disk, a flash drive, cloud storage, random access memory (RAM), or other types of computer-readable memory. The storage 294 is coupled to the image acquirer 292 and can be used to store the scanned raw image data as the original image and to store the post-processed image. The image acquirer 292 and the storage 294 may be connected to the controller 296. In some embodiments, the image acquirer 292, the storage 294, and the controller 296 may be integrated as one control unit.

[0033]

[0044] In some embodiments, the image acquirer 292 can acquire one or more inspection images of the wafer based on the imaging signals received from the charged particle detection device 244. The imaging signals may correspond to a scanning operation for performing charged particle imaging. The acquired image may be a single image including a plurality of imaging areas. This single image can be stored in the storage 294. This single image may be an original image that can be divided into a plurality of regions. Each of these regions may include one imaging area including features of the wafer 230. The acquired image may include a plurality of images of a single imaging area of the wafer 230 sampled multiple times over time. These plurality of images can be stored in the storage 294. In some embodiments, the image processing system 290 may be configured to perform image processing steps using a plurality of images of the same location of the wafer 230.

[0034]

[0045] In some embodiments, the image processing system 290 may include a measurement circuit (e.g., an analog / digital converter) to acquire the distribution of detected secondary charged particles (e.g., secondary electrons). The data of the charged particle distribution collected during the detection time window can be combined with the scanning path data corresponding to the beamlets 214, 216, and 218 incident on the wafer surface to reconstruct an image of the wafer structure being inspected. The reconstructed image can be used to reveal various features of the internal or external structure of the wafer 230, thereby using the reconstructed image to reveal defects that may be present in the wafer.

[0035]

[0046] In some embodiments, the charged particles can be electrons. When the electrons of the primary charged particle beam 210 are projected onto the surface of the wafer 230 (e.g., probe spots 270, 272, and 274), the electrons of the primary charged particle beam 210 interact with the particles of the wafer 230 and can penetrate the surface of the wafer 230 to a certain depth. Some electrons of the primary charged particle beam 210 can interact elastically with the material of the wafer 230 (e.g., in the form of elastic scattering or collision) and can be reflected or bounced back from the surface of the wafer 230. Elastic interaction conserves the total kinetic energy of the interacting objects (e.g., the electrons of the primary charged particle beam 210), and the kinetic energy of the interacting objects is not converted into other forms of energy (e.g., heat, electromagnetic energy, etc.). The reflected electrons generated from such elastic interactions are sometimes called backscattered electrons (BSE). Some electrons of the primary charged particle beam 210 can interact inelastically with the material of the wafer 230 (e.g., in the form of inelastic scattering or collision). Inelastic interaction does not conserve the total kinetic energy of the interacting objects, and part or all of the kinetic energy of the interacting objects is converted into other forms of energy. For example, due to inelastic interaction, the kinetic energy of some electrons of the primary charged particle beam 210 may cause electron excitation and transition of the atoms of the material. Such inelastic interaction may also generate electrons that exit from the surface of the wafer 230, sometimes called secondary electrons (SE). The yield or emission rate of BSE and SE depends on, for example, among other things, the material being inspected and the incident energy of the electrons of the primary charged particle beam 210 incident on the surface of the material. The energy of the electrons of the primary charged particle beam 210 can be partially imparted by its acceleration voltage (e.g., the acceleration voltage between the anode and the cathode of the charged particle source 202 in FIG. 2). The amount of BSE and SE may be more or less (or the same) than the injected electrons of the primary charged particle beam 210.

[0036]

[0047] The image generated by SCPM can be used for defect inspection. For example, the generated image capturing the test device area of a wafer can be compared with a reference image capturing the same test device area. The reference image may be predetermined (e.g., by simulation) and may not contain known defects. When the difference between the generated image and the reference image exceeds the tolerance level, potential defects can be identified. As another example, SCPM can scan multiple regions of a wafer, where each region contains a test device area designed in the same way, and generate multiple images capturing those test device areas during manufacturing. The multiple images can be compared with each other. When the difference between the multiple images exceeds the tolerance level, potential defects can be identified.

[0037]

[0048] The SCPM image includes not only the structural information of the test device area of the wafer but also noise. Due to the noise in the SCPM image, it becomes difficult to detect or identify defects from the SCPM image. Therefore, frame averaging is used to obtain a cleaner SCPM image with less noise, and defects can be detected from this SCPM image. In frame averaging, a plurality of SCPM images of a single imaging area of a wafer (for example, a single die) are taken over time, and the average image of the plurality of SCPM images is used for defect detection. Since the noise in the SCPM image is random, the noise level can be reduced by averaging a plurality of SCPM images. Therefore, the accuracy of defect detection can be improved. However, defects that repeatedly appear on a plurality of SCPM images can be efficiently detected from the average image with less noise, but some defects that do not always appear on the SCPM image may not be detected from the average image. In the present disclosure, such defects that intermittently appear on the SCPM image are called transient defects, and defects that are repeated on the SCPM image are called hard defects. In some cases, transient defects may occur because the electrical characteristics of the defect structure, especially the thin device structure having the defect, can change over time. For example, even if a defect actually exists on the wafer during inspection, due to such time-dependent behavior of the thin device structure, this defect may not be captured by a certain SCPM image but may be captured by another SCPM image.

[0038]

[0049] FIG. 3 is an exemplary diagram showing the characteristics of transient defects that conform to an embodiment of the present disclosure. FIG. 3 shows a graph 300 of the gray levels of the SCPM image and the reference image at a specific position in each frame. In graph 300, the X-axis represents the frame number, and the Y-axis represents the gray level. In graph 300, line 310 represents the gray level of the SCPM image of each frame, and line 320 represents the gray level of the reference image of each frame. As shown in graph 300, in the 1st frame to the 3rd frame, the 5th frame, and the 8th frame, the gray level of the SCPM image is approximately equal to the corresponding gray level of the reference image, but in the 4th frame, the 6th frame, and the 7th frame, the gray level of the SCPM image is different from the corresponding gray level of the reference image. From graph 300, it can be seen that defects appear only in some of the eight SCPM images (for example, the SCPM images in the 4th, 6th, and 7th frames), and this defect can be called a transient defect.

[0039]

[0050] Figure 3 further shows, at the bottom of graph 300, the first to eighth SCPM images 311_1 to 311_8 corresponding to the first to eighth frames. Consistent with graph 300, defect 312 appears only in the fourth SCPM image 311_4, the sixth SCPM image 311_6, and the seventh SCPM image 311_7, and does not appear in the first to third SCPM images 311_1 to 311_3, the fifth SCPM image 311_5, and the eighth SCPM image 311_8. When generating an average image 330 by averaging the eight SCPM images 311_1 to 311_8 of the eight frames, since the gray level differences between the SCPM images of the fourth, sixth, and seventh frames and the reference image are averaged, the gray level difference may not be prominent in the average image 330. At the bottom of Figure 3, it is shown that the defect 332 corresponding to the defect 312 is not prominent on the average image 330. This is because the defect 312 on the fourth, sixth, and seventh SCPM images 311_4, 311_6, and 311_7 is averaged within the average image 330. As described above with reference to Figure 3, since transient defects cannot be effectively detected using frame averaging, a method and system that can more accurately detect transient defects are desired.

[0040]

[0051] Referring now to Figure 4, which is a block diagram of an exemplary transient defect detection system consistent with embodiments of the present disclosure. In some embodiments, the transient defect detection system 400 may include one or more processors and memory. It is understood that in various embodiments, the transient defect detection system 400 may be part of a charged particle beam inspection system (e.g., the EBI system 100 of Figure 1) or may be separate. In some embodiments, the transient defect detection system 400 may include one or more components (e.g., software modules) that can be implemented within the controller 109 as described herein. As shown in Figure 4, the transient defect detection system 400 may include an inspection image acquirer 410, a first type defect detector 420, a mask generator 430, a second type defect detector 440, and a defect combiner 450.

[0041]

[0052] According to some embodiments of the present disclosure, the inspection image acquirer 410 can acquire a plurality of inspection images. In some embodiments, the inspection image is an SCPM image of a sample or a wafer. In some embodiments, the inspection image can be, for example, an inspection image generated by the EBI system 100 of FIG. 1 or the electron beam tool 104 of FIG. 2. In some embodiments, the inspection image acquirer 410 can acquire the inspection image from a storage device or a storage system that stores the inspection image. FIG. 5A shows a plurality of inspection images 501_1 to 501_N of a region of a wafer (for example, a single image area or die of the wafer). In some embodiments, the plurality of inspection images 501_1 to 501_N are taken over a certain period of time. In some embodiments, the plurality of inspection images 501_1 to 501_N are taken over time series.

[0042]

[0053] Returning to FIG. 4, the first type defect detector 420 can detect the first type of defects from the plurality of inspection images 501_1 to 501_N acquired by the inspection image acquirer 410. In some embodiments of the present disclosure, the first type defect detector 420 can acquire an average image of the plurality of inspection images 501_1 to 501_N. FIG. 5B shows the average image 510 of the plurality of inspection images 501_1 to 501_N. In some embodiments, the average image 510 can be acquired by averaging the plurality of inspection images 501_1 to 501_N for each pixel. For example, the pixel value at a specific position of the average image 510 can be obtained by adding each pixel value (for example, gray level value) at the corresponding relative position on the plurality of inspection images 50_1 to 501_N and dividing the added value by the number of the plurality of inspection images 501_1 to 501_N (i.e., N).

[0043]

[0054] According to some embodiments of the present disclosure, the first type of defect detector 420 can detect the first type of defects from the average image 510. In some embodiments, the first type of defects can be detected by comparing the average image 510 with a reference image. For example, the average image 510 capturing the area of the wafer can be compared with the reference image capturing the same area. The reference image may be predetermined (e.g., by simulation) and may not contain known defects. As another example, the reference image can be an SCPM image capturing a different area of another wafer designed to have the same pattern as a different area of the wafer or an area captured by a plurality of inspection images 501_1 to 501_N. In some embodiments, the reference image can be the average image of a plurality of SCPM images capturing different areas of another wafer designed to have the same pattern as a different area of the wafer or an area captured by a plurality of inspection images 501_1 to 501_N.

[0044]

[0055] As another example, the average image 510 itself can be the reference image for detecting the first type of defects from the average image 510. If the average image 510 has a periodic pattern, a specific portion of the average image 510 can be compared with another portion of the average image 510 having the same pattern as this specific portion.

[0045]

[0056] In some embodiments, the difference image between the average image 510 and the reference image can be obtained based on the comparison. In some embodiments, the pixel value of the average image 510 can be compared with the pixel value of the reference image at the same relative position on the corresponding image. The pixel value of the difference image at a position without defects may be approximately zero or close to zero. In some embodiments, when the difference between the average image 510 and the reference image exceeds a predetermined threshold, potential defects (i.e., potential first defects) can be identified. In some embodiments, the threshold can be determined based on the noise levels of the average image 510 and the reference image, the defect detection sensitivity, etc. In some embodiments, post-processing can be performed on the identified potential defects to remove disturbing defects, classify the defects, etc.

[0046]

[0057] In FIG. 5B, the two detected first type defects 521_1 and 521_2 are shown on the average image 510 at corresponding positions. In some embodiments, when it is determined that a plurality of pixels adjacent to each other have abnormal pixel values, it can be determined that the plurality of pixels may constitute a single first type defect. Thus, in some embodiments, a single first type defect (e.g., first type defect 521_1 or 521_2) can include a plurality of pixels. In some embodiments, the first type defects 521_1 and 521_2 may refer to potential defects that may require post-processing such as removal of disturbing defects and classification of defects. Although the detected first type defects 521_1 and 521_2 are shown on the average image 510 in FIG. 5B, it will be understood that the first type defects can be provided as a defect list including defect identification, defect positions on the inspection image, defect types, and the like.

[0047]

[0058] Returning to FIG. 4, the mask generator 430 can generate a mask 530 that includes mask area(s) corresponding to the first type defects detected by the first type defect detector 420. As shown in FIG. 5C, the mask 530 can include mask areas corresponding to the detected first type defects. For example, the first mask area 531_1 corresponds to the first type defect 521_1, and the second mask area 531_2 corresponds to the second type defect 521_1. In some embodiments, the position and size of the mask area 531_1 or 531_2 can be determined to include the area including the corresponding first type defect 521_1 or 521_2. In some embodiments, the size of the mask area 531_1 or 531_2 can be determined to encompass the boundary of the first type defect 521_1 or 521_2 by making it larger than the size of the first type defect 521_1 or 521_2.

[0048]

[0059] Returning to FIG. 4, the second type of defect detector 440 can detect the second type of defect from a plurality of inspection images acquired by the inspection image acquirer 410. According to some embodiments of the present disclosure, the second type of defect detector 440 masks the plurality of inspection images 501_1 to 501_N with the mask 530 generated by the mask generator 430, and can detect the second type of defect for the unmasked areas of the plurality of inspection images 501_1 to 501_N. According to some embodiments of the present disclosure, the areas including the first type of defects 521_1 and 521_2 already identified by the first type of defect detector 420 are excluded when detecting the second type of defect. Since the gray levels around the defects are generally not stable and the boundaries of the defects are irregular, a considerable amount of computing time and computing resources may be required for defect detection for the defect areas. Therefore, when detecting the second type of defect, by excluding the areas including the already detected defects, the performance of defect detection and the overall efficiency of the system can be improved.

[0049]

[0060] FIG. 6A shows a first algorithm for detecting the second type of defect, which conforms to an embodiment of the present disclosure. As shown in FIG. 6A, the plurality of inspection images 501_1 to 501_N are masked with a mask 530 having mask areas 531_1 and 531_2 generated by the mask generator 430. According to some embodiments of the present disclosure, the second type of defect detector 440 can detect the second type of defect(s) by comparing the plurality of inspection images 501_1 to 501_N with each other. In some embodiments, any two individual masked inspection images among the plurality of inspection images 501_1 to 501_N can be compared with each other to detect the second type of defect. For example, as shown in FIG. 6A, the first inspection image 501_1 can be compared with the second inspection image 501_2, and similarly, any inspection image can be compared with an adjacent inspection image or any other inspection image. According to some embodiments of the present disclosure, the comparison between any two inspection images can be performed only for the unmasked areas. For example, the first inspection image 501_1 and the second inspection image 501_2 can be compared with each other for the areas other than the mask areas 531_1 and 531_2.

[0050]

[0061] In some embodiments, based on the comparison, for the non-mask area, a difference image between two masked images and an image (e.g., the first inspection image 501_1 and the second inspection image 501_2) can be obtained. In some embodiments, the pixel value of the first inspection image 501_1 can be compared with the pixel value of the second inspection image 501_2 at the same relative position on the corresponding image. The pixel value of the difference image at a position without defects may be approximately zero or a value close to zero. In some embodiments, when the difference between the first inspection image 501_1 and the second inspection image 501_2 exceeds a predetermined threshold, a potential defect, i.e., a potential second defect, can be identified. In some embodiments, the threshold can be determined based on the noise levels of the first inspection image 501_1 and the second inspection image 501_2, the defect detection sensitivity, etc. In some embodiments, post-processing for the identified potential defect can be performed to remove disturbing defects, classify the defects, etc. Although the first algorithm for detecting the second type of defect based on the comparison between the first inspection image 501_1 and the second inspection image 501_2 has been described, it will be understood that any two or more inspection images among the plurality of inspection images 501_1 to 501_N can be used to detect the second type of defect.

[0051]

[0062] According to the first algorithm, when detecting second-type defects, the defect detection performance can be improved by excluding the area including the already detected defects. However, when noise is included in individual inspection images, it may become difficult to detect defects by comparing individual inspection images. In particular, when detecting defects by comparing two images including noise, the disturbance rate may increase. This may be further deteriorated when the threshold for determining defects is lowered to detect weak defects. Furthermore, it is difficult to distinguish a permanent failure from a temporary failure by comparing individual inspection images. In the present disclosure, a failure means a sudden change in the electrical characteristics of a device, whereby the inspection signal corresponding to the area where the device has failed can change. When a failure occurs, a defective device that has generated a certain inspection signal indicating a defect in the device may not generate an inspection signal indicating a defect in subsequent frames (that is, the defective device may generate an inspection signal different from the original inspection signal indicating a defect). A permanent failure has a permanent effect, that is, there is a risk that the original inspection signal will not return when a permanent failure occurs, while the effect of a temporary failure lasts only for a certain period. In some cases, a temporary failure may exhibit periodic behavior. For example, a temporary failure may disappear after several frames, and then the device may restart from the initial state, and the temporary failure may appear again. To appropriately address or solve the problem of failures, it is important to identify which failure it is, such as whether it is a permanent failure or a temporary failure. However, there may be cases where permanent failures and temporary failures cannot be effectively distinguished even by comparing individual inspection images.

[0052]

[0063] Figure 6B shows a second algorithm for detecting second type defects that conforms to an embodiment of the present disclosure. As shown in Figure 6B, a plurality of inspection images 501_1 to 501_N are masked with a mask (e.g., mask 530 having mask areas 531_1 and 531_2 in Figure 5C) generated by a mask generator 430. In some embodiments, the average image 510 of the plurality of inspection images 501_1 to 501_N can also be masked with mask 530. According to some embodiments of the present disclosure, the second type defect detector 440 can detect second type defects by comparing any individual masked inspection image among the plurality of inspection images 501_1 to 501_N with the average image 510. For example, as shown in Figure 6B, each inspection image 501_1 to 501_N can be compared with the average image 510. According to some embodiments of the present disclosure, the comparison between the inspection images 501_1 to 501_N and the average image 510 can be performed only for non-mask areas. For example, the first inspection image 501_1 and the average image 510 can be compared with each other for areas other than the mask areas 531_1 and 531_2.

[0053]

[0064] In some embodiments, based on the comparison, for non-mask areas, a difference image between the inspection images 501_1 to 501_N and the average image 510 can be obtained. In some embodiments, the pixel value of the first inspection image 501_1 can be compared with the pixel value of the average image 510 at the same relative position on the corresponding image. In some embodiments, when the difference between the first inspection image 501_1 and the average image 510 exceeds a predetermined threshold, a potential defect (i.e., a potential second type defect) can be identified. In some embodiments, the threshold can be determined based on the noise levels of the first inspection image 501_1 and the average image 510, defect detection sensitivity, etc.

[0054]

[0065] Although the second algorithm for detecting the second type of defect has been described based on the comparison between the first inspection image 501_1 and the average image 510, it will be understood that any of the plurality of inspection images 501_1 to 501_N can be used to detect the second type of defect. According to the second algorithm, since the average image 510 with less noise is used as the reference image, the disturbance rate can be reduced when detecting the second type of defect, and thus the defect detection accuracy can be improved.

[0055]

[0066] FIG. 7A shows the detected second type of defect that conforms to the embodiment of the present disclosure. In FIG. 7A, two second type of defects 711_1 and 711_2 detected by the second type of defect detector 440 are shown. In some embodiments, the second type of defects 711_1 and 711_2 can be defects detected according to either the first algorithm or the second algorithm. In some embodiments, when it is determined that a plurality of pixels adjacent to each other have pixel values exceeding a threshold for determining the second type of defect, it can be determined that the plurality of pixels can constitute a single second type of defect. Therefore, in some embodiments, a single second type of defect (for example, the second type of defect 711_1 or 711_2) can include a plurality of pixels. In some embodiments, the second type of defects 711_1 and 711_2 may refer to potential defects that may require post-processing such as removal of disturbance defects and classification of defects.

[0056]

[0067] Returning to FIG. 4, the defect combiner 450 can combine the first type of defect(s) detected by the first type of defect detector 420 and the second type of defect(s) detected by the second type of defect detector 440. FIG. 7B shows the combined first and second type of defects that conform to an embodiment of the present disclosure. In FIG. 7B, the first type of defects 521_1 and 521_2 and the second type of defects 711_1 and 711_2 are shown together at their respective corresponding positions. FIG. 7B shows the first and second type of defects detected at their respective corresponding positions on the frame, but it will be understood that the detected first and second type of defects can be combined to create a defect list including defect identification, defect position on the inspection image, defect type, etc.

[0057]

[0068] FIG. 8 is a process flowchart representing an exemplary transient defect detection method that conforms to an embodiment of the present disclosure. The steps of method 800 can be executed on a computer device (e.g., the controller 109 of FIG. 1) or performed by a system that uses the functions of the computer device (e.g., the system 400 of FIG. 4). It is understood that the illustrated method 800 can be modified to change the order of the steps and include additional steps.

[0058]

[0069] In step S810, a plurality of inspection images are acquired. Step S810 can be performed, for example, by the inspection image acquirer 410 among others. In some embodiments, the inspection image is an SCPM image of a sample or a wafer. FIG. 5A shows a plurality of inspection images 501_1 to 501_N of a region of a wafer (e.g., a single image area or die of the wafer). In some embodiments, the plurality of inspection images 501_1 to 501_N are taken over a certain period of time. In some embodiments, the plurality of inspection images 501_1 to 501_N are taken over time series.

[0059]

[0070] In step S820, the first type of defect can be detected. Step S820 can be performed, for example, particularly by the first type of defect detector 410. In some embodiments, the first type of defect can be detected from the plurality of inspection images 501_1 to 501_N obtained in step S810. In some embodiments of the present disclosure, an average image of the plurality of inspection images 501_1 to 501_N can be obtained. FIG. 5B shows the average image 510 of the plurality of inspection images 501_1 to 501_N. In some embodiments, the average image 510 can be obtained by averaging the plurality of inspection images 501_1 to 501_N for each pixel. According to some embodiments of the present disclosure, the first type of defect can be detected by comparing the average image 510 with a reference image. Since the process of detecting the first type of defect has been described with respect to FIG. 4, for the sake of brevity, detailed description is omitted here.

[0060]

[0071] In step S830, a mask is generated. Step S830 can be performed, for example, particularly by the mask generator 430. In step S830, a mask (for example, mask 530) including the mask area(s) corresponding to the first type of defect detected in step S820 is generated. As shown in FIG. 5C, the mask 530 can include the mask area corresponding to the detected first type of defect. In some embodiments, the position and size of the mask area 531_1 or 531_2 can be determined to include the area including the corresponding first type of defect 521_1 or 521_2. In some embodiments, the size of the mask area 531_1 or 531_2 can be determined to encompass the boundary of the first type of defect 521_1 or 521_2 by making it larger than the size of the first type of defect 521_1 or 521_2.

[0061]

[0072] In step S840, the second type of defect is detected. Step S840 can be performed, for example, particularly by the second type defect detector 440. In step S840, the second type of defect can be detected from the plurality of inspection images obtained in step S810. According to some embodiments of the present disclosure, the mask generated in step S830 can be used to mask the plurality of inspection images 501_1 to 501_N, and for the non-masked areas of the plurality of inspection images 501_1 to 501_N, the second type of defect can be detected.

[0062]

[0073] According to some embodiments of the present disclosure, the second type of defect(s) can be detected by comparing the plurality of inspection images 501_1 to 501_N with each other, which is shown as the first algorithm with reference to FIG. 6A. According to some embodiments of the present disclosure, the second type of defect can be detected by comparing any individual masked inspection image among the plurality of inspection images 501_1 to 501_N with the average image 510, which is shown as the second algorithm with reference to FIG. 6B. Since the first algorithm and the second algorithm are described with respect to FIGS. 6A and 6B, for the sake of brevity, detailed descriptions are omitted here.

[0063]

[0074] In step S850, the first type of defect and the second type of defect are combined. Step S850 can be performed, for example, particularly by the defect combiner 450. In step S850, the first type of defect(s) detected in step S820 and the second type of defect(s) detected in step S840 are combined. FIG. 7B shows the combined first type of defect and second type of defect that conforms to the embodiment of the present disclosure. In FIG. 7B, the first type of defects 521_1 and 521_2 and the second type of defects 711_1 and 711_2 are shown together at the corresponding positions respectively. Although FIG. 7B shows the first type of defect and the second type of defect detected at the corresponding positions on the frame, it will be understood that the detected first type of defect and the second type of defect can be combined to create a defect list including defect identification, the position of the defect on the inspection image, the type of the defect, etc.

[0064]

[0075] A non-transitory computer-readable medium may be provided, and this non-transitory computer-readable medium stores instructions for causing a processor of a controller (e.g., the controller 109 in FIG. 1) to execute, among other things, image inspection, image acquisition, stage positioning, beam focusing, electric field adjustment, beam bending, condenser lens adjustment, activation of a charged particle source, beam deflection, and method 800. General forms of non-transitory media include, for example, floppy disks, flexible disks, hard disks, solid state drives, magnetic tapes, or any other magnetic data recording media, CD-ROM (Compact Disc Read Only Memory), any other optical data recording media, any physical medium having a pattern of holes, RAM (Random Access Memory), PROM (Programmable Read Only Memory), and EPROM (Erasable Programmable Read Only Memory), FLASH-EPROM or any other flash memory, NVRAM (Non-Volatile Random Access Memory), caches, registers, any other memory chips or cartridges, and networked versions of the foregoing.

[0065]

[0076] The following clauses can be used to further describe the embodiments. 1. A method of transient defect inspection using inspection images, comprising: obtaining a plurality of inspection images; generating an average image of the plurality of inspection images; detecting first type defects in the average image; determining a mask area corresponding to the first type defects; determining whether the plurality of inspection images have second type defects within the non-mask area. method. 2. Determining whether the plurality of inspection images have second type defects The method according to claim 1, comprising comparing a first inspection image among a plurality of inspection images with an average image for a non-mask area. 3. Determining whether a plurality of inspection images have second type defects further includes generating a difference image between the first inspection image and the average image for a non-mask area; and determining whether the first inspection image has second type defects based on the difference image. The method according to claim 2. 4. Determining whether a plurality of inspection images have second type defects includes masking the average image and the first inspection image among the plurality of inspection images for an area corresponding to a mask area; and comparing the first inspection image among the plurality of inspection images with the average image for a non-mask area. The method according to claim 1. 5. Determining whether a plurality of inspection images have second type defects includes comparing the first inspection image among the plurality of inspection images with the second inspection image among the plurality of inspection images for a non-mask area. The method according to claim 1. 6. Determining whether a plurality of inspection images have second type defects further includes generating a difference image between the first inspection image and the second inspection image for a non-mask area; and determining whether the plurality of inspection images have second type defects based on the difference image. The method according to claim 5. 7. Determining whether a plurality of inspection images have second type defects includes masking the first inspection image and the second inspection image among the plurality of inspection images for an area corresponding to a mask area; and comparing the first inspection image with the second inspection image for a non-mask area. The method according to claim 1. 8. The method according to any one of claims 1 to 7, further including combining the first type defects and the second type defects. 9. Determining a mask area corresponding to the first type defects The method according to any one of clauses 1 to 8, including determining a mask area so as to include an area containing the first type of defect. 10. A method for transient defect inspection using inspection images, comprising: acquiring a plurality of inspection images; generating an average image of the plurality of inspection images; detecting a first type of defect in the average image; determining a mask area corresponding to the first type of defect; for the non-mask area, comparing an individual inspection image among the plurality of inspection images with the average image; determining whether the plurality of inspection images have a second type of defect in the non-mask area based on the comparison, the method. 11. Comparing an individual inspection image among the plurality of inspection images with the average image includes generating a difference image between a first inspection image among the plurality of inspection images and the average image for the non-mask area, the method according to clause 10. 12. Determining whether the plurality of inspection images have a second type of defect includes determining whether the first inspection image has a second type of defect based on the difference image, the method according to clause 11. 13. Comparing an individual inspection image among the plurality of inspection images with the average image includes masking the average image and a first inspection image among the plurality of inspection images for an area corresponding to the mask area, and comparing a first inspection image among the plurality of inspection images with the average image for the non-mask area, the method according to clause 10. 14. The method according to any one of clauses 10 to 13, further including combining the first type of defect and the second type of defect. 15. Determining a mask area corresponding to the first type of defect includes determining a mask area so as to include an area containing the first type of defect, the method according to any one of clauses 10 to 14. 16. An apparatus for transient defect inspection using inspection images, comprising: a memory storing a set of instructions; at least one processor configured to execute the set of instructions to cause the apparatus to acquire a plurality of inspection images; generate an average image of the plurality of inspection images; detect first type defects in the average image; determine a mask area corresponding to the first type defects; and determine whether the plurality of inspection images have second type defects in the non-mask area; the apparatus. 17. When determining whether the plurality of inspection images have second type defects, the at least one processor is further configured to execute the set of instructions to cause the apparatus to compare a first inspection image among the plurality of inspection images with the average image for the non-mask area, the apparatus according to clause 16. 18. When determining whether the plurality of inspection images have second type defects, the at least one processor is further configured to execute the set of instructions to cause the apparatus to generate a difference image between the first inspection image and the average image for the non-mask area; and determine whether the first inspection image has second type defects based on the difference image, the apparatus according to clause 17. 19. When determining whether the plurality of inspection images have second type defects, the at least one processor is further configured to execute the set of instructions to cause the apparatus to mask the average image and a first inspection image among the plurality of inspection images for an area corresponding to the mask area; and compare the first inspection image among the plurality of inspection images with the average image for the non-mask area, the apparatus according to clause 16. 20. When determining whether the plurality of inspection images have second type defects, the at least one processor is further configured to execute the set of instructions to cause the apparatus to The apparatus according to clause 16, configured to cause comparison of a first inspection image among a plurality of inspection images with a second inspection image among the plurality of inspection images for a non-mask area. 21. When determining whether a plurality of inspection images have second-type defects, at least one processor executes a set of instructions to cause the apparatus to generate a difference image between the first inspection image and the second inspection image for a non-mask area, and determine whether the plurality of inspection images have second-type defects based on the difference image. The apparatus according to clause 20 is further configured to perform the above. 22. When determining whether a plurality of inspection images have second-type defects, at least one processor executes a set of instructions to cause the apparatus to mask the first inspection image and the second inspection image among the plurality of inspection images for an area corresponding to a mask area, and compare the first inspection image with the second inspection image for a non-mask area. The apparatus according to clause 16 is further configured to perform the above. 23. At least one processor executes a set of instructions to cause the apparatus to further combine a first-type defect and a second-type defect. The apparatus according to any one of clauses 16 to 22 is further configured to perform the above. 24. When determining a mask area corresponding to a first-type defect, at least one processor executes a set of instructions to cause the apparatus to determine the mask area to include an area including a first-type defect as a target. The apparatus according to any one of clauses 16 to 23 is further configured to perform the above. 25. An apparatus for transient defect inspection using inspection images, a memory storing a set of instructions, at least one processor, which executes a set of instructions to cause the apparatus to acquire a plurality of inspection images, generate an average image of the plurality of inspection images, and detect a first-type defect in the average image. Determine a mask area corresponding to the first type of defect, For the non-mask area, compare an individual inspection image among a plurality of inspection images with the average image, Based on the comparison, determine whether the plurality of inspection images have a second type of defect within the non-mask area, and at least one processor configured to cause the execution of the method, Device. 26. When comparing an individual inspection image among a plurality of inspection images with the average image, the at least one processor executes a set of instructions to cause the device to, For the non-mask area, generate a difference image between the first inspection image among the plurality of inspection images and the average image, the device according to clause 25. 27. When determining whether the plurality of inspection images have a second type of defect, the at least one processor executes a set of instructions to cause the device to, Based on the difference image, determine whether the first inspection image has a second type of defect, the device according to clause 26. 28. When comparing an individual inspection image among a plurality of inspection images with the average image, the at least one processor executes a set of instructions to cause the device to, For the area corresponding to the mask area, mask the average image and the first inspection image among the plurality of inspection images, For the non-mask area, compare the first inspection image among the plurality of inspection images with the average image, the device according to clause 25. 29. The at least one processor executes a set of instructions to cause the device to, Further combine the first type of defect and the second type of defect, the device according to any one of clauses 25 to 28. 30. When determining a mask area corresponding to the first type of defect, the at least one processor executes a set of instructions to cause the device to, An apparatus according to any one of clauses 25 to 29, configured to cause determination of a mask area so as to include an area containing a first type of defect. 31. A non-transitory computer-readable medium storing a set of instructions executable by at least one processor of a computer device to cause the computer device to perform a transient defect inspection method using inspection images, the method comprising: acquiring a plurality of inspection images; generating an average image of the plurality of inspection images; detecting a first type of defect in the average image; determining a mask area corresponding to the first type of defect; determining whether the plurality of inspection images have a second type of defect within the non-mask area. The computer-readable medium. 32. When determining whether the plurality of inspection images have a second type of defect, the set of instructions executable by at least one processor of the computer device causes the computer device to compare a first inspection image among the plurality of inspection images with the average image for the non-mask area. The computer-readable medium according to clause 31. 33. When determining whether the plurality of inspection images have a second type of defect, the set of instructions executable by at least one processor of the computer device causes the computer device to generate a difference image between the first inspection image and the average image for the non-mask area; further determine whether the first inspection image has a second type of defect based on the difference image. The computer-readable medium according to clause 32. 34. When determining whether the plurality of inspection images have a second type of defect, the set of instructions executable by at least one processor of the computer device causes the computer device to mask the average image and a first inspection image among the plurality of inspection images for an area corresponding to the mask area; The computer-readable medium according to clause 31, which causes, for a non-mask area, a first inspection image among a plurality of inspection images to be compared with an average image. 35. When determining whether a plurality of inspection images have a second type of defect, a set of instructions executable by at least one processor of a computer device causes the computer device to The computer-readable medium according to clause 31, which causes, for a non-mask area, a first inspection image among a plurality of inspection images to be compared with a second inspection image among the plurality of inspection images. 36. When determining whether a plurality of inspection images have a second type of defect, a set of instructions executable by at least one processor of a computer device causes the computer device to For a non-mask area, generating a difference image between a first inspection image and a second inspection image; The computer-readable medium according to clause 35, which further causes, based on the difference image, determining whether the plurality of inspection images have a second type of defect. 37. When determining whether a plurality of inspection images have a second type of defect, a set of instructions executable by at least one processor of a computer device causes the computer device to For an area corresponding to a mask area, masking a first inspection image and a second inspection image among the plurality of inspection images; The computer-readable medium according to clause 31, which causes, for a non-mask area, a first inspection image to be compared with a second inspection image. 38. A set of instructions executable by at least one processor of a computer device causes the computer device to The computer-readable medium according to any one of clauses 31 to 37, which further causes a first type of defect and a second type of defect to be combined. 39. When determining a mask area corresponding to a first type of defect, a set of instructions executable by at least one processor of a computer device causes the computer device to A computer-readable medium according to any one of clauses 31 to 38, which causes a mask area to be determined so as to include an area containing a first type of defect. 40. A non-transitory computer-readable medium storing a set of instructions executable by at least one processor of the computer device, which causes the computer device to perform a method for transient defect inspection using inspection images, the method comprising: Obtaining a plurality of inspection images; Generating an average image of the plurality of inspection images; Detecting a first type of defect in the average image; Determining a mask area corresponding to the first type of defect; Comparing an individual inspection image among the plurality of inspection images with the average image for non-mask areas; Based on the comparison, determining whether the plurality of inspection images have a second type of defect in the non-mask areas. Computer-readable medium. 41. When comparing an individual inspection image among the plurality of inspection images with the average image, the set of instructions executable by at least one processor of the computer device causes the computer device to Generate a difference image between a first inspection image among the plurality of inspection images and the average image for non-mask areas, according to clause 40. 42. When determining whether the plurality of inspection images have a second type of defect, the set of instructions executable by at least one processor of the computer device causes the computer device to Determine whether the first inspection image has a second type of defect based on the difference image, according to clause 41. 43. When comparing an individual inspection image among the plurality of inspection images with the average image, the set of instructions executable by at least one processor of the computer device causes the computer device to Mask the average image and a first inspection image among the plurality of inspection images for an area corresponding to the mask area. The computer-readable medium according to clause 40, which causes a first inspection image among a plurality of inspection images to be compared with an average image for a non-mask area. 44. A set of instructions executable by at least one processor of a computer device causes the computer device to The computer-readable medium according to any one of clauses 40 to 43, which further causes a combination of a first type of defect and a second type of defect to be performed. 45. When determining a mask area corresponding to a first type of defect, a set of instructions executable by at least one processor of a computer device causes the computer device to The computer-readable medium according to any one of clauses 40 to 44, which causes a mask area to be determined so as to include an area including the first type of defect as a target.

[0066]

[0077] The block diagrams included in the drawings may illustrate the architecture, functions, and operations of possible embodiments of a system, method, and computer hardware or software product according to various exemplary embodiments of the present disclosure. In this regard, each block of the schematic diagram may represent a specific arithmetic operation process or logical operation process that can be implemented using hardware such as an electronic circuit. A block may also represent a module, segment, or portion of code that includes one or more executable instructions for implementing a predetermined logical function. It should be understood that in some alternative embodiments, the functions shown in the blocks may occur in an order different from the order described in the figure. For example, two consecutively shown blocks may be executed or implemented substantially simultaneously, or depending on the related functions, the two blocks may be executed in the reverse order. Also, some blocks may be omitted. Furthermore, it should be understood that each block of the block diagram, and combinations of blocks, may be implemented by a dedicated hardware-based system that performs a predetermined function or operation, or by a combination of dedicated hardware and computer instructions.

[0067]

[0078] It should be understood that the embodiments of the present disclosure are not limited to the configurations themselves described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from the scope of the present invention. Although the present disclosure has been described in connection with various embodiments, other embodiments of the present invention will be apparent to those skilled in the art upon consideration of the specification and implementation of the invention disclosed herein. The specification and examples are intended to be regarded as merely illustrative, and the true scope and spirit of the present invention are indicated by the following claims.

Claims

**Claim 1** An apparatus for transient defect inspection using inspection images, comprising: a memory storing a set of instructions; at least one processor configured to execute the set of instructions to cause the apparatus to: acquire a plurality of inspection images; generate an average image of the plurality of inspection images; detect first type defects in the average image; determine a mask area corresponding to the first type defects; determine whether the plurality of inspection images have second type defects in the non-mask area; and an apparatus. **Claim 2** When determining whether the plurality of inspection images have second type defects, the at least one processor is further configured to execute the set of instructions to cause the apparatus to: compare a first inspection image of the plurality of inspection images with the average image for the non-mask area. The apparatus according to claim 1. **Claim 3** When determining whether the plurality of inspection images have second type defects, the at least one processor is further configured to execute the set of instructions to cause the apparatus to: generate a difference image between the first inspection image and the average image for the non-mask area; determine whether the first inspection image has second type defects based on the difference image. The apparatus according to claim 2. **Claim 4** When determining whether the plurality of inspection images have second type defects, the at least one processor is further configured to execute the set of instructions to cause the apparatus to: mask the average image and the first inspection image of the plurality of inspection images for the area corresponding to the mask area; compare the first inspection image of the plurality of inspection images with the average image for the non-mask area. The apparatus according to claim 1. **Claim 5** When determining whether the plurality of inspection images have second type defects, the at least one processor is further configured to execute the set of instructions to cause the apparatus to: compare a first inspection image of the plurality of inspection images with a second inspection image of the plurality of inspection images for the non-mask area. The apparatus according to claim 1. **Claim 6** When determining whether the plurality of inspection images have the second type of defect, the at least one processor executes the set of instructions to cause the apparatus to generate a difference image between the first inspection image and the second inspection image for the non-mask area; further determine, based on the difference image, whether the plurality of inspection images have the second type of defect. The apparatus according to claim 5 is configured to perform the above steps. **Claim 7** When determining whether the plurality of inspection images have the second type of defect, the at least one processor executes the set of instructions to cause the apparatus to mask the first inspection image and the second inspection image among the plurality of inspection images for the area corresponding to the mask area; compare the first inspection image with the second inspection image for the non-mask area. The apparatus according to claim 1 is configured to perform the above steps. **Claim 8** The at least one processor executes the set of instructions to cause the apparatus to further combine the first type of defect and the second type of defect. The apparatus according to claim 1 is configured to perform the above steps. **Claim 9** When determining the mask area corresponding to the first type of defect, the at least one processor executes the set of instructions to cause the apparatus to determine the mask area so as to include the area including the first type of defect as a target. The apparatus according to claim 1 is configured to perform the above steps. **Claim 10** An apparatus for transient defect inspection using inspection images, comprising: a memory storing a set of instructions; at least one processor, which executes the set of instructions to cause the apparatus to acquire a plurality of inspection images; generate an average image of the plurality of inspection images; detect a first type of defect in the average image; determine a mask area corresponding to the first type of defect; compare an individual inspection image among the plurality of inspection images with the average image for the non-mask area; determine, based on the comparison, whether the plurality of inspection images have a second type of defect in the non-mask area. The at least one processor is configured to perform the above steps. An apparatus comprising the above components. **Claim 11** When comparing an individual inspection image of the plurality of inspection images to the average image, the at least one processor executes the set of instructions to cause the apparatus to: The apparatus of claim 10 , configured to cause generating a difference image between a first inspection image of the plurality of inspection images and the average image for the unmasked area.

12. In determining whether the plurality of inspection images has the second type of defects, the at least one processor executes the set of instructions to cause the apparatus to: The apparatus of claim 11 , configured to determine whether the first inspection image has the second type of defect based on the difference image.

13. When comparing an individual inspection image of the plurality of inspection images to the average image, the at least one processor executes the set of instructions to cause the apparatus to: masking the average image and a first inspection image of the plurality of inspection images for a region corresponding to the mask area; and comparing the first test image of the plurality of test images to the average image for the unmasked area.

14. The at least one processor executes the set of instructions to cause the device to: The apparatus of claim 10 , further configured to combine the first type defects and the second type defects.

15. In determining the mask areas corresponding to the first type defects, the at least one processor executes the set of instructions to cause the apparatus to: The apparatus of claim 10 , configured to cause the mask area to be determined to cover an area containing the first type of defect.

Images