Wafer defect detection method, apparatus, electron beam scanning device, and storage medium

Abstract

To provide wafer defect inspection method and apparatus that improve the reliability of a defect detection result for each wafer as a detection target and prevent an erroneous defect detection result from being used in subsequent steps.SOLUTION: A method acquires a wafer image of a wafer as a detection target after starting scanning of an electron beam on a target defect detection area of the wafer as a detection target, calculates an image sharpness degree of the wafer image, acquires a sharpness evaluation value of the wafer image based on the image sharpness degree of the wafer image, when the sharpness evaluation value does not match a preset condition, describing that the quality of the acquired wafer image is poor, marks a defect detection result of the wafer as a detection target as an unreliable detection result, and distinguishes the marked defect detection result from a defect detection result of the wafer as a detection target whose wafer image quality is good. Then, the method stops defect detection of the wafer as a detection target, and timely stops a defect detection program of the wafer as a detection target, thereby avoiding meaningless execution steps.SELECTED DRAWING: Figure 2

Description

Technical Field

[0001] This application relates to the field of semiconductor technology, and particularly to a wafer defect detection method, apparatus, electron beam scanning device, and storage medium.

Background Art

[0002] With the development of the semiconductor industry, electron beam scanning devices have been widely applied in the semiconductor field to detect physical defects on wafers at the nanoscale and circuit on / off defects. The quality of wafer images directly affects the defect detection results. However, due to the basic principle of electron beam scanning devices, it is impossible to achieve accurate stability of wafer images, and it is difficult to ensure the quality of wafer images.

[0003] Currently, there are few image quality monitoring systems in electron beam scanning devices in the industry. For the detection of physical defects on wafers and circuit on / off defects, the image quality is poor, the defect detection results are incorrect, and the reliability of the defect detection results is low. As a result, incorrect defect detection results are used in subsequent processes. Although some electron beam scanning devices are equipped with image quality monitoring systems, a sample image is obtained before scanning, and based on the obtained sample image, the lens, electron beam, etc. of the scanning device are adjusted to obtain good wafer image quality during the defect detection process. However, this does not guarantee that the actual image quality will not cause false defect detections, and the reliability of the defect detection results is low.

Summary of the Invention

[0004] To solve the above problems, this application provides a wafer defect detection method, a wafer defect detection apparatus, an electron beam scanning device, and a computer-readable storage medium.

[0005] [[ID=二十七]] Performing electron beam scanning on a target defect detection area of a wafer to be detected, and detecting defects in the target defect detection area. The wafer to be detected has one or more defect detection regions, The process involves acquiring a wafer image of the wafer to be detected and calculating the image sharpness of the wafer image. Based on the image sharpness of the wafer image, a sharpness evaluation value of the wafer image is obtained. This includes determining whether the sharpness evaluation value of the wafer image meets a preset condition, and if the sharpness evaluation value of the wafer image does not meet the preset condition, marking the defect detection result of the wafer to be detected as an unreliable detection result.

[0006] In some embodiments, obtaining a sharpness evaluation value of a wafer image based on the image sharpness of the wafer image includes calculating a sharpness difference value between the image sharpness of the wafer image and a preset standard sharpness of the wafer image, and obtaining a sharpness evaluation value of the wafer image. The condition that the sharpness evaluation value of the wafer image does not meet a preset condition is that the sharpness evaluation value of the wafer image is greater than or equal to a first sharpness difference threshold.

[0007] In some embodiments, if the sharpness evaluation value of the wafer image does not meet the pre-set conditions, the defect detection of the wafer to be detected is stopped.

[0008] In some embodiments, after obtaining a sharpness evaluation value of the wafer image, the method further includes outputting the sharpness evaluation value of the wafer image to the statistical processing control system such that the statistical processing control system marks the defect detection result of the wafer to be detected as an unreliable detection result if the sharpness evaluation value of the wafer image does not meet a preset condition.

[0009] In some embodiments, the image sharpness of the wafer image is calculated using a relational expression.

[0010]

number

[0011] The method includes calculating the image sharpness of the wafer image based on the following: P is the image sharpness of the wafer image, m and n are the length and width of the wafer image, respectively, df is the grayscale change width of the pixel dots of the wafer image, dx is the increase in distance between the pixel dots of the wafer image, i is a pixel dot of the wafer image, and a is a neighboring point of the pixel dot.

[0012] In some embodiments, the wafer image is an image of a wafer region on the wafer to be detected that is not scanned by the electron beam, and is within a range less than a first preset distance from the target defect detection region.

[0013] In some embodiments, before performing an electron beam scan on a target defect detection region of the wafer to be detected, the method further includes: acquiring a brightness / contrast image of the wafer to be detected; acquiring the gradation distribution of the brightness / contrast image; acquiring a gradation distribution evaluation value of the brightness / contrast image based on the gradation distribution of the brightness / contrast image; and determining whether the gradation distribution evaluation value matches a preset condition, wherein if the gradation distribution evaluation value does not match the preset condition, the defect detection of the wafer to be detected is discontinued.

[0014] In some embodiments, obtaining the tone distribution of the brightness / contrast image includes obtaining a normal distribution of the number of pixel dots corresponding to each tone in the brightness / contrast image, and calculating the number of pixel dots corresponding to each tone within a predetermined number of sigma in the normal distribution in order to obtain the tone distribution of the brightness / contrast image. Obtaining a tone distribution evaluation value of the brightness / contrast image based on the tone distribution of the brightness / contrast image includes determining whether the tone is acceptable or unacceptable based on the number of pixel dots corresponding to the tone within a predetermined number of sigma in the normal distribution and the number of pixel dots corresponding to the same tone in a predetermined standard tone distribution, and calculating the pass rate of all tone within a predetermined number of sigma in the normal distribution as the tone distribution evaluation value of the brightness / contrast image.

[0015] In some embodiments, the predetermined number of sigma is 1 to 2 sigma.

[0016] In some embodiments, if the difference between the ratio of the number of pixel dots corresponding to a preset number of sigma tones in the normal distribution and the number of pixel dots corresponding to the same tone in the standard tone distribution and 100%, the tone is deemed acceptable, and / or If the aforementioned gradation distribution evaluation value does not meet the preset conditions, it means that the pass rate for all gradations is 95% or less. If the pass rate for all gradations is greater than 95%, the aforementioned gradation distribution evaluation value meets the preset conditions.

[0017] In some embodiments, if the grayscale distribution evaluation value matches a preset condition, the method includes: acquiring an astigmatism image of the wafer to be detected, calculating the image sharpness of the astigmatism image; acquiring a sharpness evaluation value of the astigmatism image based on the image sharpness of the astigmatism image; and determining whether the sharpness evaluation value of the astigmatism image matches a preset condition, wherein if the sharpness evaluation value of the astigmatism image does not match the preset condition, the defect detection of the wafer to be detected is discontinued; and if the sharpness evaluation value of the astigmatism image matches the preset condition, the electron beam scan is performed on the target defect detection region of the wafer to be detected.

[0018] In some embodiments, obtaining a sharpness evaluation value of an astigmatism image based on the image sharpness of the astigmatism image includes calculating a sharpness difference value between the image sharpness of the astigmatism image and a preset standard sharpness of the astigmatism image, and obtaining a sharpness evaluation value of the astigmatism image. If the sharpness evaluation value of the astigmatism image does not meet a preset condition, it means that the sharpness evaluation value of the astigmatism image is greater than or equal to a second sharpness difference threshold. If the sharpness evaluation value of the astigmatism image meets a preset condition, it means that the sharpness evaluation value of the astigmatism image is less than the second sharpness difference threshold.

[0019] In some embodiments, the method further includes, before performing an electron beam scan on a target defect detection region of the wafer to be detected, acquiring an astigmatism image of the wafer to be detected, calculating the image sharpness of the astigmatism image, acquiring a sharpness evaluation value of the astigmatism image based on the image sharpness of the astigmatism image, and determining whether the sharpness evaluation value of the astigmatism image matches a preset condition, wherein if the sharpness evaluation value of the astigmatism image does not match the preset condition, the defect detection of the wafer to be detected is discontinued.

[0020] In some embodiments, before performing an electron beam scan on a target defect detection region of the wafer to be detected, the method further includes: acquiring a focused image of the wafer to be detected; calculating the image sharpness of the focused image; acquiring a sharpness evaluation value of the focused image based on the image sharpness of the focused image; and determining whether the sharpness evaluation value of the focused image matches a preset condition, wherein if the sharpness evaluation value of the focused image does not match the preset condition, the defect detection of the wafer to be detected is discontinued.

[0021] In some embodiments, obtaining a sharpness evaluation value of the in-focus image based on the image sharpness of the in-focus image involves calculating a sharpness difference value between the image sharpness of the in-focus image and a preset standard sharpness of the in-focus image, and obtaining a sharpness evaluation value of the in-focus image. The condition that the sharpness evaluation value of the in-focus image does not meet the preset conditions is that the sharpness evaluation value of the in-focus image is greater than or equal to a third sharpness difference threshold.

[0022] In some embodiments, if the sharpness evaluation value of the focused image matches a preset condition, the method acquires a brightness / contrast image of the wafer to be detected, acquires the gradation distribution of the brightness / contrast image, obtains a gradation distribution evaluation value of the brightness / contrast image based on the gradation distribution of the brightness / contrast image, determines whether the gradation distribution evaluation value matches a preset condition, and if the gradation distribution evaluation value does not match a preset condition, discontinues defect detection of the wafer to be detected, and if the gradation distribution evaluation value matches a preset condition, the detection target The following steps are further included: acquiring an astigmatism image of the wafer, calculating the image sharpness of the astigmatism image; acquiring a sharpness evaluation value of the astigmatism image based on the image sharpness of the astigmatism image; determining whether the sharpness evaluation value of the astigmatism image matches a preset condition; if the sharpness evaluation value of the astigmatism image does not match a preset condition, discontinuing defect detection of the wafer to be detected; and if the sharpness evaluation value of the astigmatism image matches a preset condition, performing an electron beam scan of the target defect detection region of the wafer to be detected.

[0023] According to one aspect of the embodiments of the present application, a wafer defect detection device is disclosed. The wafer defect detection device includes a wafer scanning module for a detection target, an image sharpness calculation module, a sharpness evaluation value calculation module, a sharpness evaluation value judgment module, and a detection result marking module. Here, the wafer scanning module for the detection target performs an electron beam scan on a target defect detection area of a wafer to be detected, and is used to detect defects in the target defect detection area. Here, the wafer to be detected has one or more defect detection areas. The image sharpness calculation module acquires a wafer image of the wafer to be detected and is used to calculate the image sharpness of the wafer image. The sharpness evaluation value calculation module is used to obtain a sharpness evaluation value of the wafer image based on the image sharpness of the wafer image. The sharpness evaluation value judgment module is used to judge whether the sharpness evaluation value of the wafer image meets a preset condition. The detection result marking module is used to mark the defect detection result of the wafer to be detected as an unreliable detection result when the sharpness evaluation value of the wafer image does not meet the preset condition.

[0024] According to one aspect of the embodiments of the present application, an electron beam scanning device is disclosed. The electron beam scanning device includes one or more processors and a memory. When one or more computer programs are executed by the one or more processors, the memory stores the one or more computer programs for causing the processor to implement the wafer defect detection method described above.

[0025] According to one aspect of the embodiments of the present application, a computer-readable storage medium is disclosed. The computer-readable storage medium stores computer-readable instructions. When the computer-readable instructions are executed by a processor of a computer, the computer is caused to execute the wafer defect detection method described above.

[0026] The technical solution provided by the embodiments of the present application includes at least the following beneficial effects.

[0027] After starting the electron beam scanning of the target defect detection area of the wafer to be detected, the technical solution disclosed in the present application acquires a wafer image of the wafer to be detected, calculates the image sharpness of the wafer image, and based on the image sharpness of the wafer image, obtains a sharpness evaluation value of the wafer image. When the sharpness evaluation value does not meet the preset conditions, it is explained that the quality of the acquired wafer image is poor, marks the defect detection result of the wafer to be detected as an unreliable detection result, distinguishes the defect detection result of the wafer to be detected with good quality of the wafer image, enhances the reliability of the defect detection result of each wafer to be detected, and avoids using incorrect defect detection results in subsequent processes.

[0028] It should be noted that the above general description and the following detailed description are merely exemplary and do not limit the present application.

Brief Description of Drawings

[0029] The drawings here are incorporated into the specification and form a part of the specification, showing embodiments that conform to the present application and are used to interpret the principle of the present application together with the specification. [Figure 1] It is a schematic diagram showing detection results corresponding to image quality being poor and good image quality respectively according to an embodiment. [Figure 2] It shows a flowchart of a wafer defect detection method according to an embodiment of the present application. [Figure 3] It shows a flowchart of the quality detection step of a luminance / contrast image according to an embodiment of the present application. [Figure 4] It shows a schematic diagram of the tone distribution and the standard tone distribution obtained according to an embodiment of the present application. [Figure 5] It shows a flowchart of the quality detection step of an aberration image according to an embodiment of the present application. [Figure 6]This shows a flowchart of the quality detection step for a focused image according to one embodiment of the present invention. [Figure 7] This shows the overall flowchart of wafer defect detection according to one embodiment of the present invention. [Figure 8] This shows a block diagram of the wafer defect detection device according to one embodiment of the present invention. [Figure 9] This shows a block diagram of the configuration of an electron beam scanning device according to one embodiment of the present invention. [Figure 10] This shows a block diagram of a computer system configuration for realizing several embodiments of the present invention. [Modes for carrying out the invention]

[0030] The exemplary embodiments will be described in more detail below with reference to the drawings. However, the exemplary embodiments can be implemented in various forms and are not limited to the examples described herein. On the contrary, these exemplary embodiments are provided to make the description of the present application more comprehensive and complete and to comprehensively convey the concept of the exemplary embodiments to those skilled in the art.

[0031] Here, the terms “first,” “second,” and “third” are merely for descriptive purposes and should not be understood as indicating or implying relative importance, or implicitly indicating the number of designated technical features. Therefore, features defined as “first,” “second,” and “third” may explicitly or implicitly include one or more features.

[0032] Furthermore, the features, structures, or properties described can be combined in any suitable manner in one or more embodiments. The following description provides many specific details to give a complete understanding of the embodiments of the present application. However, those skilled in the art will be aware that the technical means of the present application can be actually implemented without one or more of the specific details, or that other methods, components, apparatus, steps, etc., can be employed. Otherwise, known methods, apparatus, implementations, or operations are not described or shown in detail to avoid obscuring any aspect of the present application.

[0033] The flowcharts shown in the diagrams are illustrative only and do not necessarily have to include all content and operations / steps, nor do they have to be performed in the order they are described. For example, some operations / steps may be broken down, and others may be combined or partially combined, so the actual order in which they are performed may vary depending on the actual situation.

[0034] EBI (Electron-Beam Inspection) is an important device for improving yield in chip manufacturing. It utilizes information excited when high-energy electrons interact with material on the wafer surface to form an image, and then uses image processing and computation to detect electrical and physical defects on the wafer. The quality of the image formed directly affects the defect detection results; poor image quality reduces the reliability of the defect detection results.

[0035] Some conventional EBI systems do not monitor image quality while scanning the wafer under detection with an electron beam. Generally, image quality drift occurs in less than 1% of cases, mainly due to failures in capturing image focus, brightness / contrast, and astigmatism, as well as sudden hardware failures of the device itself, causing the entire image to drift abruptly. This has the effect of defects not being detected during the detection process, and currently, there is no alarm mechanism, making it impossible for the defect detection department to guarantee the reliability of the defect detection results.

[0036] While some conventional EBI systems are equipped with image quality monitoring systems, these systems acquire a pre-embedded microchip image, i.e., a sample image, by the EBI machine before performing an electron beam scan on the wafer to be detected. Based on the acquired sample image, the scanning device's lens, electron beam, etc., are adjusted to obtain better wafer image quality during the defect detection process. However, this better wafer image quality does not guarantee sufficient quality to prevent false detection of defects. Furthermore, the sample image quality may not accurately represent the actual image quality of the wafer to be detected. In some cases, for example, if the thickness of the wafer to be detected differs from the thickness of the sample, the sample image quality may be good, but the actual image quality of the wafer to be detected may be poor. In this case as well, the reliability of the defect detection results is low, and incorrect detection results may be used in subsequent processes.

[0037] For example, if defects actually exist in the wafer being detected, and the image quality of the wafer obtained during the defect detection process is poor, the defects are not detected, resulting in zero defects as shown in Figure 1(a). On the other hand, if the image quality of the wafer obtained during the defect detection process is good, the defect detection result for the wafer being detected will show that there are defects in multiple crystal grains, marked with an "x," as shown in Figure 1(b).

[0038] As the critical dimension (CD) of semiconductor devices shrinks and industry demands for detection results become increasingly stringent, the reliability of defect detection results is becoming increasingly important. Therefore, this invention provides a wafer defect detection method that, after starting electron beam scanning of the target defect detection region of the wafer to be detected, acquires a wafer image of the wafer to be detected, analyzes the wafer image, determines the quality of the wafer image, marks the defect detection result of the wafer to be detected as unreliable if the quality of the wafer image is determined to be poor, distinguishes the defect detection results of wafers to be detected with good wafer image quality, and improves the reliability of the defect detection results of each wafer to be detected, thereby avoiding the use of incorrect defect detection results in subsequent processes.

[0039] The wafer defect detection method according to this application will be described in detail below with reference to specific embodiments.

[0040] Figure 2 shows a flowchart of a wafer defect detection method according to one embodiment of the present invention. As shown in Figure 2, the wafer defect detection method includes at least a wafer defect detection step, an image acquisition and sharpness calculation step, a sharpness evaluation value acquisition step, an image quality judgment step, a detection result marking step, etc., and corresponds to the following steps S110 to S150, which will be described in detail below.

[0041] In step S110, an electron beam scan is performed on the target defect detection region of the wafer to be detected, and defects in the target defect detection region are detected.

[0042] Here, the wafer to be detected has one or more defect detection regions. If the wafer to be detected has only one defect detection region, the target defect detection region is that defect detection region. If the wafer to be detected has multiple defect detection regions, the target defect detection region may be one of the multiple defect detection regions, or two or more of the multiple defect detection regions. For example, if the wafer to be detected has multiple defect detection regions, these multiple defect detection regions are located in the upper left corner region, the upper right corner region, the lower left corner region, and the lower right corner region of the wafer to be detected, respectively. In one embodiment, the target defect detection region includes the upper left corner region and the upper right corner region. In another embodiment, the target defect detection region includes the lower left corner region and the lower right corner region.

[0043] To make it clear, a target defect detection region is a defect detection region that requires electron beam scanning to detect defects. For a wafer to be detected that has multiple defect detection regions, electron beam scanning can be performed intermittently on multiple defect detection regions. For example, electron beam scanning can be performed first on the upper left and upper right corner regions as target defect detection regions, and after scanning the upper left and upper right corner regions is complete, electron beam scanning can be performed on the lower left and lower right corner regions as target defect detection regions. In this case, other steps, such as steps S120 to S140 below, can be inserted between the two electron beam scans. Of course, for a wafer to be detected that has multiple defect detection regions, electron beam scanning can be performed on multiple defect detection regions at once, that is, other steps cannot be inserted before electron beam scanning of all defect detection regions of the wafer to be detected is completed.

[0044] In one embodiment, detecting defects in the target defect detection region involves performing an electron beam scan on the target defect detection region of the wafer to be detected. In the scan image obtained, two-level subtraction is performed on every two adjacent crystal grains within the wafer, comparing pairs of crystal grains. This method identifies the crystal grain that is different from the other two crystal grains among the three and identifies it as the defective crystal grain. This method provides high accuracy in defect detection results.

[0045] In step S120, a wafer image of the wafer to be detected is acquired, and the image sharpness of the wafer image is calculated.

[0046] In one embodiment, after completing the electron beam scan of the target defect detection region of the wafer to be detected, the wafer is scanned with the electron beam over the target defect detection region where the scan was completed to acquire a wafer image. Acquiring a wafer image after completing the scan of the target defect detection region is technically easy and readily achievable. Selectively, in other embodiments, a real-time wafer image may be acquired during the process of performing the electron beam scan of the target defect detection region of the wafer to be detected.

[0047] Here, the wafer image is not an image that covers the entire area of ​​the wafer to be detected, but may be a localized image of the wafer to be detected. In one embodiment, the acquired wafer image is an image of a wafer region on the wafer to be detected that is not scanned by the electron beam, and is within a range less than a first preset distance from the target defect detection region. Since traces remain in the wafer region scanned by the electron beam, affecting the image quality, by acquiring an image of a wafer region that is close to the target defect detection region and is not scanned by the electron beam, the image quality of the acquired wafer image can be made more accurate in the processing results of subsequent steps, as the image quality of the acquired wafer image represents the actual image quality of target defect detection during the scanning process.

[0048] Here, the first preset distance is a distance value that is sufficiently close to the preset target defect detection region. By using a wafer image of a region adjacent to the target defect detection region, it is possible to avoid the situation where the obtained wafer image cannot accurately represent the image quality of the target defect detection region during the scanning process due to minute errors between different regions of the wafer being detected.

[0049] In one embodiment, the relational expression

[0050]

number

[0051] The image sharpness of the wafer image is calculated based on the following, where P is the image sharpness of the wafer image, m and n are the length and width of the wafer image, respectively, df is the grayscale change range of the pixel dots of the wafer image, dx is the increase in distance between the pixel dots of the wafer image, i is a pixel dot of the wafer image, and a is a neighboring point of the pixel dot. Specifically, for each pixel dot in the wafer image, eight neighboring points are taken and subtracted from the corresponding grayscale value for each point. First, the weighted sum of the eight grayscale difference values ​​df obtained by subtracting the corresponding grayscale values ​​for each of the eight neighboring points is calculated. Here, the magnitude of the weight depends on the distance dx between the neighboring point and the pixel dot; the closer the neighboring point and the pixel dot, the larger the weight, and the farther the neighboring point and the pixel dot, the smaller the weight. For example, the weight of the grayscale difference values ​​in the 45° and 135° directions must be multiplied by 1 / √2 from the weight of the grayscale difference value in the 0° and 90° directions. Finally, the total number of pixel dots in the wafer image is calculated by adding the values ​​obtained for all pixel dots.

[0052]

number

[0053] The image sharpness P of the wafer image is obtained by dividing by this value. This relationship is a statistical representation of the degree of tonal diffusion around each pixel dot of the wafer image; in other words, the greater the degree of diffusion and the larger the value, the sharper the image can be considered to be.

[0054] In this embodiment, by reflecting the grayscale difference values ​​of the eight nearest points of the wafer image and the grayscale distribution of the image, it is possible to obtain accurate image sharpness calculation results that are less susceptible to parameter fluctuations due to factors such as noise. Of course, in other embodiments, the image sharpness of the wafer image may be calculated using other methods.

[0055] In step S130, a wafer image sharpness evaluation value is obtained based on the image sharpness of the wafer image.

[0056] Here, the sharpness evaluation value refers to a parameter related to the image sharpness of a wafer image, which can be used to evaluate the quality of the wafer image.

[0057] In one embodiment, a wafer image sharpness evaluation value is obtained based on the image sharpness of the wafer image; that is, the difference in sharpness between the image sharpness of the wafer image and a preset standard sharpness of the wafer image is calculated, and a wafer image sharpness evaluation value is obtained; that is, the difference in sharpness between the image sharpness of the wafer image and a preset standard sharpness of the wafer image is used as the wafer image sharpness evaluation value. The method for calculating the wafer image sharpness evaluation value is simple, and the image quality of the wafer image can be accurately evaluated.

[0058] In one embodiment, a wafer image sharpness evaluation value is obtained based on the image sharpness of the wafer image; that is, the image sharpness of the wafer image is used as the wafer image sharpness evaluation value. If the wafer image sharpness evaluation value is greater than or equal to the sum of the pre-set standard sharpness of the wafer image and the first sharpness difference value, the wafer image sharpness evaluation value does not meet the pre-set conditions.

[0059] Of course, this application is not limited to using the difference in sharpness between the image sharpness of the wafer image and a preset standard sharpness of the wafer image, or the image sharpness of the wafer image, as the wafer image sharpness evaluation value. In other embodiments, the wafer image sharpness evaluation value may be determined by any method.

[0060] In step S140, it is determined whether the sharpness evaluation value of the wafer image meets the pre-set conditions. If the sharpness evaluation value of the wafer image does not meet the pre-set conditions, the process proceeds to step S150.

[0061] In this embodiment, where the sharpness evaluation value of the wafer image is the difference in sharpness between the image sharpness of the wafer image and the preset standard sharpness of the wafer image, if the sharpness evaluation value of the wafer image is greater than or equal to the first sharpness difference threshold, i.e., if the sharpness evaluation value of the wafer image does not meet the preset conditions, the process proceeds to step S150. If the sharpness evaluation value of the wafer image is less than the first sharpness difference threshold, the sharpness evaluation value of the wafer image meets the preset conditions.

[0062] In an embodiment in which the image sharpness of a wafer image is used as the wafer image sharpness evaluation value, if the wafer image sharpness evaluation value is greater than or equal to the sum of the preset standard sharpness of the wafer image and the first sharpness difference threshold, that is, if the wafer image sharpness evaluation value does not meet the preset conditions, the process proceeds to step S150. If the wafer image sharpness evaluation value is less than the sum of the preset standard sharpness of the wafer image and the first sharpness difference threshold, the wafer image sharpness evaluation value meets the preset conditions.

[0063] In step S150, the defect detection result for the wafer to be detected is marked as an unreliable detection result.

[0064] If the wafer image sharpness evaluation value does not meet the preset conditions, i.e., if the wafer image quality is deemed poor, the defect detection result for the wafer being detected is marked as unreliable. This allows the defect detection department to distinguish between wafers with good image quality and those with good image quality. This enables the defect detection department to determine whether the defect detection result for each wafer is reliable, improve the reliability of the defect detection results for each wafer, and avoid using incorrect defect detection results in subsequent processes.

[0065] Selectively, in some embodiments, if it is determined in step S140 that the sharpness evaluation value of the wafer image meets a preset condition, i.e., the image quality of the wafer is good, the further step of marking the defect detection result of the wafer to be detected as a reliable detection result may be performed. In these embodiments, the defect detection results corresponding to wafers with poor image quality and wafers with excellent image quality are marked, respectively, and it is distinguished whether the defect detection result of each wafer to be detected is reliable or not.

[0066] Here, marking a wafer's defect detection result as an unreliable detection result may be done by directly marking the wafer, in which case the wafer may be marked by marking its wafer ID, or it may be marked indirectly by marking its wafer number in the corresponding wafer lot, i.e., which wafer it is in a given wafer lot, in which case it is not necessary to obtain the wafer ID of the wafer, making the program simpler.

[0067] In one embodiment, if the image quality determination step determines that the sharpness evaluation value of the wafer image does not meet the pre-set conditions, a defect detection cancellation step is further executed, which includes canceling defect detection of the wafer to be detected.

[0068] If the wafer to be detected has multiple defect detection areas, and the target defect detection area does not include all of the defect detection areas of the wafer to be detected, some defect detection areas may be detected after the image quality judgment step. If the wafer to be detected has the target defect detection areas, defect detection of the wafer to be detected can be stopped, and the defect detection program for the wafer with poor image quality can be stopped in a timely manner, avoiding meaningless execution steps and improving the effective utilization rate of the EBI.

[0069] Canceling defect detection on the wafer to be detected may also involve outputting a control command to cancel the defect detection program on the wafer to be detected, causing the machine to stop defect detection on the wafer based on the control command. In this embodiment, if the image quality judgment step determines that the sharpness evaluation value of the wafer image does not meet the preset conditions, the defect detection cancellation step is executed directly, which is highly efficient.

[0070] After the image quality determination step, there is a possibility that the wafer to be detected does not contain the target defect detection area. In some embodiments, if the image quality determination step determines that the sharpness evaluation value of the wafer image does not meet the pre-set conditions, the detection target confirmation step may be performed first. That is, it is determined whether or not the target defect detection area exists on the wafer to be detected. If the target defect detection area exists on the wafer to be detected, the defect detection cancellation step is then performed to cancel defect detection on the wafer to be detected.

[0071] In one embodiment, the image quality judgment step and the detection result marking step are performed by an SPC (Statistical Process Control) system. After the sharpness evaluation value acquisition step, a sharpness evaluation value output step is performed to output the sharpness evaluation value of the wafer image to the SPC system. The SPC system then performs an image quality judgment step to determine whether the sharpness evaluation value of the wafer image meets a preset condition. If it determines that the sharpness evaluation value of the wafer image does not meet the preset condition, it performs a detection result marking step to mark the defect detection result of the wafer to be detected as an unreliable detection result.

[0072] In an embodiment of the wafer defect detection method that includes a defect detection termination step, the SPC system further executes the defect detection termination step, that is, if the sharpness evaluation value of the wafer image does not match a preset condition, it further executes a step of terminating defect detection of the wafer to be detected.

[0073] As described above, this invention starts electron beam scanning of the target defect detection area of ​​the wafer to be detected, acquires a wafer image of the wafer to be detected, calculates the image sharpness of the wafer image, acquires a wafer image sharpness evaluation value based on the image sharpness of the wafer image, explains that the acquired wafer image quality is poor if the sharpness evaluation value does not meet the preset conditions, marks the defect detection result of the wafer to be detected as an unreliable detection result, distinguishes the defect detection results of wafers to be detected with good image quality, improves the reliability of the defect detection results of each wafer to be detected, and avoids using incorrect defect detection results in subsequent processes. Furthermore, if the sharpness evaluation value does not meet the preset conditions, defect detection of the wafer to be detected is stopped, the defect detection program for the wafer with poor image quality is stopped in a timely manner, meaningless detections are avoided, and the effective utilization rate of EBI can be improved.

[0074] In some embodiments, before performing the wafer defect detection step, a brightness / contrast image quality detection step is first performed. As shown in Figure 3, this brightness / contrast image quality detection step includes a brightness / contrast image acquisition and gradation distribution calculation step, a gradation distribution evaluation value acquisition step, and a brightness / contrast image quality determination step, which correspond to steps S210 to S230 below, and are described in detail below.

[0075] In step S210, a brightness / contrast image of the wafer to be detected is acquired, and the grayscale distribution of the brightness / contrast image is obtained.

[0076] In one embodiment, acquiring a brightness / contrast image of the wafer to be detected involves scanning a wafer region of a non-defect detection area on the wafer to be detected with an electron beam, which is outside the target defect detection area and within a range less than a second preset distance from the target defect detection area, and acquiring a brightness / contrast image of the wafer region.

[0077] The second preset distance is a distance value that is sufficiently close to the preset target defect detection area. By using a brightness / contrast image of an area adjacent to the target defect detection area, it is possible to avoid situations where the obtained brightness / contrast image cannot accurately represent the image quality of the target defect detection area due to minute errors between different areas of the wafer being detected.

[0078] In one embodiment, obtaining the tonal distribution of a luminance / contrast image includes obtaining the normal distribution of the number of pixel dots corresponding to each tone in the luminance / contrast image, and calculating the number of pixel dots corresponding to each tone within a predetermined number of sigma values ​​in the normal distribution to obtain the tonal distribution of the luminance / contrast image. According to statistical theory, a normal distribution is observed when the sample size is large, and this embodiment uses statistical theory to calculate the tonal distribution of the luminance / contrast image, thereby accurately obtaining the tonal distribution of the luminance / contrast image.

[0079] In step S220, a gradation distribution evaluation value of the luminance / contrast image is obtained based on the gradation distribution of the luminance / contrast image.

[0080] Here, the tonal distribution evaluation value is a parameter related to the tonal distribution of a luminance / contrast image, which can be used to evaluate the quality of the luminance / contrast image.

[0081] In one embodiment, obtaining the tone distribution of a brightness / contrast image includes obtaining the normal distribution of the number of pixel dots corresponding to each tone in the brightness / contrast image, calculating the number of pixel dots corresponding to each tone within a predetermined number of σ (Sigma) in the normal distribution, and obtaining the tone distribution of the brightness / contrast image. Obtaining a tone distribution evaluation value of the brightness / contrast image based on the tone distribution of the brightness / contrast image includes determining whether the tone is acceptable based on the number of pixel dots corresponding to the tone within a predetermined number of σ in the normal distribution and the number of pixel dots corresponding to the same tone in a predetermined standard tone distribution, and calculating the acceptance rate of all tone within the predetermined number of σ in the normal distribution to obtain a tone distribution evaluation value of the brightness / contrast image. The tone distribution evaluation value obtained by this method is useful for accurately determining the image quality of the brightness / contrast image in a later step.

[0082] In detail, determining whether a tone is acceptable based on the number of pixel dots corresponding to a predetermined number of σ in a normal distribution and the number of pixel dots corresponding to the same tone in a predetermined standard tone distribution includes: calculating the ratio of the number of pixel dots corresponding to a predetermined number of σ in a normal distribution to the number of pixel dots corresponding to the same tone in a predetermined standard tone distribution; subtracting the obtained ratio from 100% to obtain the difference value; comparing the obtained difference value with the magnitude of a predetermined threshold; determining that the tone is acceptable if the obtained difference value is smaller than the predetermined threshold, and that the tone is unacceptable if the obtained difference value is greater than or equal to the predetermined threshold. By comparing the actually obtained tone distribution with the standard tone distribution, it is possible to accurately determine whether each tone is acceptable or not.

[0083] For example, if the preset threshold is 5%, that is, if the difference between the ratio of the number of pixel dots corresponding to a preset number of σ in a normal distribution and the number of pixel dots corresponding to the same gradation in a preset standard gradation distribution and 100%, the gradation is judged to be acceptable, and if the difference between the ratio of the number of pixel dots corresponding to a preset number of σ in a normal distribution and the number of pixel dots corresponding to the same gradation in a preset standard gradation distribution and 100%, the gradation is judged to be unacceptable.

[0084] In one embodiment, the preset number of σ is 1 to 2 σ. 1 to 2 σ already provides a sufficiently large number of samples, allowing for relatively accurate gradation distribution evaluation values ​​to be obtained. Furthermore, it reduces unnecessary calculations, improves the efficiency of calculating gradation distribution evaluation values, and thereby improves the efficiency of image quality detection. Specifically, the preset number of σ may be, for example, 1 σ, 1.5 σ, 2 σ, etc. Of course, the preset number of σ may be 2 or more σ.

[0085] For example, Figure 4(a) shows a schematic diagram of the gradation distribution of the luminance / contrast image acquired in step S210, and Figure 4(b) shows a schematic diagram of the standard gradation distribution. The number of gradations within a preset σ is, for example, 110 to 170 gradations. In step S220, the difference between the ratio of the number of pixel dots corresponding to Figure 4(a) for 110 gradations and the number of pixel dots corresponding to Figure 4(b) for 110 gradations, and 100%, is calculated. If the difference is less than 5%, 110 gradations are judged to be acceptable; otherwise, 110 gradations are judged to be unacceptable, and the same applies to the other gradations. After determining whether all gradations are acceptable or not, the total number of acceptable gradations within 110 to 170 gradations is divided by the total number of gradations within 110 to 170 gradations. That is, the acceptance rate of all gradations within the preset number of σ, i.e., the gradation distribution evaluation value of the luminance / contrast image, can be obtained.

[0086] It should be noted that using the pass rate of all gradations within a predetermined number of σ in a normal distribution as the gradation distribution evaluation value of the brightness / contrast image is only one embodiment of this invention, and in other embodiments, the gradation distribution evaluation value may be calculated using other methods.

[0087] In step S230, it is determined whether the gradation distribution evaluation value of the brightness / contrast image matches the preset conditions. If the gradation distribution evaluation value of the brightness / contrast image does not match the preset conditions, the process proceeds to the defect detection cancellation step, i.e., step S240.

[0088] In one embodiment, determining whether the gradation distribution evaluation value of the luminance / contrast image meets the pre-set conditions involves determining whether the gradation distribution evaluation value of the luminance / contrast image is greater than a pre-set threshold, and if the gradation distribution evaluation value of the luminance / contrast image is greater than a pre-set threshold, the gradation distribution evaluation value meets the pre-set conditions, or if the gradation distribution evaluation value of the luminance / contrast image is less than or equal to a pre-set threshold, the gradation distribution evaluation value does not meet the pre-set conditions.

[0089] For example, if the preset threshold is 95%, that is, if the pass rate of all gradations within the preset number of σ is greater than 95%, the gradation distribution evaluation value matches the preset conditions, and the image quality of the brightness / contrast image is judged to be good. If the pass rate of all gradations within the preset number of σ is 95% or less, the gradation distribution evaluation value does not match the preset conditions, and the image quality of the brightness / contrast image is judged to be poor, the process proceeds to step S240.

[0090] In step S240, defect detection of the wafer to be detected is stopped.

[0091] If the image quality of the brightness / contrast image is determined to be poor, the program proceeds directly to the defect detection abort step, stopping the defect detection program for subsequent wafers to be detected, avoiding meaningless execution steps, and improving the effective utilization rate of the EBI.

[0092] In one embodiment, in step S230, it is determined that the gradation distribution evaluation value of the brightness / contrast image matches a preset condition, and the astigmatism image quality detection step is then executed. As shown in Figure 5, the astigmatism image quality detection step includes an astigmatism image acquisition and sharpness calculation step, a sharpness evaluation value acquisition step, and an astigmatism image quality determination step, which correspond to the following steps S310 to S330, and are described in detail below.

[0093] In step S310, an astigmatism image of the wafer to be detected is acquired, and the image sharpness of the astigmatism image is calculated.

[0094] In one embodiment, acquiring an astigmatism image of the wafer to be detected involves scanning a wafer region of the non-defect detection region on the wafer to be detected with an electron beam, which is outside the target defect detection region and within a third preset distance from the target defect detection region, and acquiring an astigmatism image of the said wafer region.

[0095] The third preset distance is a distance value that is sufficiently close to the preset target defect detection region. By using the astigmatism image as an image of a region adjacent to the target defect detection region, it is possible to avoid the situation where the obtained astigmatism image cannot accurately represent the image quality of the target defect detection region due to minute errors between different regions of the wafer being detected.

[0096] In one embodiment, the relational expression

[0097]

number

[0098] The image sharpness of the astigmatism image is calculated based on the following, where P is the image sharpness of the astigmatism image, m and n are the length and width of the astigmatism image, respectively, df is the gradation change range of the pixel dots of the astigmatism image, dx is the increase in distance between the pixel dots of the astigmatism image, i is a pixel dot of the astigmatism image, and a is a neighboring point of the pixel dot. Specifically, for each pixel in the astigmatism image, eight neighboring points are taken and subtracted from the corresponding tonal value for each point. The weighted sum of the eight tonal difference values ​​df obtained by subtracting the corresponding tonal values ​​for each of the eight neighboring points is then calculated. Here, the magnitude of the weight depends on the distance dx between the neighboring point and the pixel, with a larger weight for closer neighboring points and a smaller weight for farther neighboring points. For example, the weight of the tonal difference values ​​in the 45° and 135° directions must be multiplied by 1 / √2 from the weight of the tonal difference value in the 0° and 90° directions. Finally, the total number of pixel dots in the astigmatism image is calculated by adding the values ​​obtained for all pixel dots.

[0099]

number

[0100] By dividing by this, the image sharpness P of the astigmatic image is obtained. This relationship is a statistical representation of the degree of tonal diffusion around each pixel dot of the astigmatic image; in other words, the greater the degree of diffusion and the larger the value, the sharper the image can be considered to be.

[0101] In this embodiment, by reflecting the tonal difference values ​​of the eight nearest neighbors of the astigmatic image and the tonal distribution of the image, it is possible to obtain accurate image sharpness calculation results that are less susceptible to parameter fluctuations due to factors such as noise. Of course, in other embodiments, the image sharpness of the astigmatic image may be calculated using other methods.

[0102] In step S320, a sharpness evaluation value for the astigmatism image is obtained based on the image sharpness of the astigmatism image.

[0103] In one embodiment, a sharpness evaluation value for an astigmatism image is obtained based on the image sharpness of the astigmatism image. Specifically, the difference in sharpness between the image sharpness of the astigmatism image and a preset standard sharpness of the astigmatism image is calculated, and a sharpness evaluation value for the astigmatism image is obtained. Specifically, the difference in sharpness between the image sharpness of the astigmatism image and a preset standard sharpness of the astigmatism image is used as the sharpness evaluation value for the astigmatism image. The method for calculating the sharpness evaluation value for the astigmatism image is simple, and the image quality of the astigmatism image can be accurately evaluated.

[0104] In one embodiment, a sharpness evaluation value for the astigmatism image is obtained based on the image sharpness of the astigmatism image; that is, the image sharpness of the astigmatism image is used as the sharpness evaluation value for the astigmatism image. If the sharpness evaluation value for the astigmatism image is greater than or equal to the sum of the pre-set standard sharpness of the astigmatism image and a second sharpness difference value, the sharpness evaluation value for the astigmatism image does not meet the pre-set conditions.

[0105] Of course, this application is not limited to using the difference in sharpness between the image sharpness of the astigmatism image and a preset standard sharpness of the astigmatism image, or the image sharpness of the astigmatism image, as the sharpness evaluation value of the astigmatism image. In other embodiments, the sharpness evaluation value of the astigmatism image may be determined by any method.

[0106] In step S330, it is determined whether the sharpness evaluation value of the astigmatism image matches a preset condition. If the sharpness evaluation value of the astigmatism image does not match the preset condition, the defect detection abort step, i.e., step S340, is executed. If the sharpness evaluation value of the astigmatism image matches the preset condition, the step of performing an electron beam scan on the target defect detection area of ​​the wafer to be detected is further executed.

[0107] In this embodiment, where the sharpness evaluation value of the astigmatism image is the difference in sharpness between the image sharpness of the astigmatism image and the preset standard sharpness of the astigmatism image, if the sharpness evaluation value of the astigmatism image is greater than or equal to the second sharpness difference threshold, i.e., if the sharpness evaluation value of the astigmatism image does not meet the preset conditions, the process proceeds to step S340. If the sharpness evaluation value of the astigmatism image is less than the second sharpness difference threshold, the sharpness evaluation value of the astigmatism image meets the preset conditions.

[0108] In an embodiment in which the image sharpness of an astigmatism image is used as the sharpness evaluation value of the astigmatism image, if the sharpness evaluation value of the astigmatism image is greater than or equal to the sum of the preset standard sharpness of the astigmatism image and the second sharpness difference threshold, that is, if the sharpness evaluation value of the astigmatism image does not meet the preset conditions, the process proceeds to step S340. If the sharpness evaluation value of the astigmatism image is less than the sum of the preset standard sharpness of the astigmatism image and the second sharpness difference threshold, the sharpness evaluation value of the astigmatism image meets the preset conditions.

[0109] In step S340, defect detection of the wafer to be detected is stopped.

[0110] If the image quality of an astigmatic aberration image is determined to be poor, the system proceeds directly to the defect detection abort step, stopping the defect detection program for subsequent wafers to be detected, avoiding meaningless execution steps, and improving the effective utilization rate of the EBI.

[0111] In some embodiments, if it is determined in step S230 that the gradation distribution evaluation value of the brightness / contrast image matches a preset condition, the step of directly performing an electron beam scan on the target defect detection area of ​​the wafer to be detected and subsequent related steps may be performed without performing the quality detection step of the astigmatism image. In some embodiments, only the quality detection step of the astigmatism image may be performed, and the quality detection step of the brightness / contrast image may not be performed.

[0112] In some embodiments, before performing the wafer defect detection step, a focus image quality detection step is first performed. As shown in Figure 6, this focus image quality detection step includes a focus image acquisition and sharpness calculation step, a sharpness evaluation value acquisition step, and a focus image quality determination step, which correspond to steps S410 to S430 below, and are described in detail below.

[0113] In step S410, a focused image of the wafer to be detected is acquired, and the image sharpness of the focused image is calculated.

[0114] In one embodiment, acquiring a focused image of the wafer to be detected involves scanning a wafer region of a non-defect detection region on the wafer to be detected with an electron beam, which is outside the target defect detection region and within a range less than a fourth preset distance from the target defect detection region, and acquiring a focused image of the said wafer region.

[0115] The fourth preset distance is a distance value that is sufficiently close to the preset target defect detection region. By making the focused image an image of the region adjacent to the target defect detection region, it avoids the situation where the obtained focused image cannot accurately represent the image quality of the target defect detection region due to minute errors between different regions of the wafer being detected.

[0116] In one embodiment, the relational expression

[0117]

number

[0118] The image sharpness of the in-focus image is calculated based on the following, where P is the image sharpness of the in-focus image, m and n are the length and width of the in-focus image, respectively, df is the grayscale change range of the pixel dots in the in-focus image, dx is the increase in distance between the pixel dots in the in-focus image, i is a pixel dot in the in-focus image, and a is a neighboring point of the pixel dot. Specifically, for each pixel in the in-focus image, eight neighboring points are taken and subtracted from the corresponding tonal value for each point. The weighted sum of the eight tonal difference values ​​df obtained by subtracting the corresponding tonal values ​​for each of the eight neighboring points is then calculated. Here, the magnitude of the weight depends on the distance dx between the neighboring point and the pixel; the closer the distance, the larger the weight, and the farther the distance, the smaller the weight. For example, the weight of the tonal difference values ​​in the 45° and 135° directions must be multiplied by 1 / √2 from the weight of the tonal difference value in the 0° and 90° directions. Finally, the total number of pixel dots in the in-focus image is calculated by adding the values ​​obtained for all pixel dots.

[0119]

number

[0120] Dividing by this value, the image sharpness P of the in-focus image is obtained. This relationship is a statistical representation of the degree of tonal diffusion around each pixel dot of the in-focus image; in other words, the greater the degree of diffusion and the larger the value, the sharper the image can be considered to be.

[0121] In this embodiment, by reflecting the tonal difference values ​​of the eight nearest points of the focused image and the tonal distribution of the image, it is possible to obtain accurate image sharpness calculation results that are less susceptible to parameter fluctuations due to factors such as noise. Of course, in other embodiments, the image sharpness of the focused image may be calculated using other methods.

[0122] In step S420, a sharpness evaluation value of the focused image is obtained based on the image sharpness of the focused image.

[0123] In one embodiment, a sharpness evaluation value of the focused image is obtained based on the image sharpness of the focused image. Specifically, the difference in sharpness between the image sharpness of the focused image and a preset standard sharpness of the focused image is calculated, and a sharpness evaluation value of the focused image is obtained. Specifically, the difference in sharpness between the image sharpness of the focused image and a preset standard sharpness of the focused image is used as the sharpness evaluation value of the focused image. The method for calculating the sharpness evaluation value of the focused image is simple, and the image quality of the focused image can be accurately evaluated.

[0124] In one embodiment, a sharpness evaluation value of the in-focus image is obtained based on the image sharpness of the in-focus image; that is, the image sharpness of the in-focus image is used as the sharpness evaluation value of the in-focus image. If the sharpness evaluation value of the in-focus image is greater than or equal to the sum of the pre-set standard sharpness of the in-focus image and the third sharpness difference value, the sharpness evaluation value of the in-focus image does not meet the pre-set conditions.

[0125] Of course, this application is not limited to using the difference in sharpness between the image sharpness of the focused image and a preset standard sharpness of the focused image, or the image sharpness of the focused image, as the sharpness evaluation value of the focused image. In other embodiments, the sharpness evaluation value of the focused image may be determined by any method.

[0126] In step S430, it is determined whether the sharpness evaluation value of the focused image matches a preset condition. If the sharpness evaluation value of the focused image does not match the preset condition, the defect detection abort step, i.e., step S440, is executed. If the sharpness evaluation value of the focused image matches the preset condition, the step of performing an electron beam scan on the target defect detection area of ​​the wafer to be detected is further executed.

[0127] In this embodiment, where the sharpness evaluation value of the focused image is the difference in sharpness between the image sharpness of the focused image and the preset standard sharpness of the focused image, if the sharpness evaluation value of the focused image is greater than or equal to the third sharpness difference threshold, that is, if the sharpness evaluation value of the focused image does not meet the preset conditions, the process proceeds to step S440. If the sharpness evaluation value of the focused image is less than the third sharpness difference threshold, the sharpness evaluation value of the focused image meets the preset conditions.

[0128] In an embodiment in which the image sharpness of the focused image is used as the sharpness evaluation value of the focused image, if the sharpness evaluation value of the focused image is greater than or equal to the sum of the preset standard sharpness of the focused image and the third sharpness difference threshold, that is, if the sharpness evaluation value of the focused image does not meet the preset conditions, the process proceeds to step S440. If the sharpness evaluation value of the focused image is less than the sum of the preset standard sharpness of the focused image and the third sharpness difference threshold, the sharpness evaluation value of the focused image meets the preset conditions.

[0129] In step S440, defect detection of the wafer to be detected is stopped.

[0130] If the image quality of the focused image is determined to be poor, the process proceeds directly to the defect detection abort step, stopping the defect detection program for subsequent wafers to be detected, avoiding meaningless execution steps, and improving the effective utilization rate of EBI.

[0131] In one embodiment, in step S430, if it is determined that the sharpness evaluation value of the focused image matches a preset condition, then a brightness / contrast image quality detection step is performed, which specifically refers to the description in steps S210 to S230, and in step S230, if it is determined that the gradation distribution evaluation value of the brightness / contrast image matches a preset condition, then an astigmatism image quality detection step is performed, which specifically refers to the description in steps S310 to S330, and in step S330, if it is determined that the sharpness evaluation value of the astigmatism image matches a preset condition, then a step of performing an electron beam scan on the target defect detection area of ​​the wafer to be detected is performed, and in step S330, if it is determined that the sharpness evaluation value of the astigmatism image does not match a preset condition, then a step of canceling defect detection of the wafer to be detected is performed.

[0132] Focus, brightness / contrast, and astigmatism are three important evaluation metrics in electron beam scanning images. Focus image quality, brightness / contrast image quality, and astigmatism image quality are judged in that order. If any of these metrics are poor, defect detection on the wafer to be detected is stopped. If all three metrics are good, the step of scanning the target defect detection area of ​​the wafer with the electron beam is executed. This minimizes the need for meaningless execution steps and improves the effective utilization rate of EBI.

[0133] In some embodiments, if it is determined in step S430 that the sharpness evaluation value of the focused image matches a preset condition, one or both of the brightness / contrast image quality detection step and the astigmatism image quality detection step may be omitted. In some embodiments, the execution order of the focus image quality detection step, the brightness / contrast image quality detection step and the astigmatism image quality detection step may be changed.

[0134] Figure 7 shows an overall flowchart of wafer defect detection according to one embodiment of the present invention. As shown in Figure 7, wafer defect detection includes a recipe setting step and a wafer detection step. In the recipe setting step, a focused image, a brightness / contrast image, an astigmatism image, and a wafer image are defined sequentially, and the standard sharpness of the focused image, the standard grayscale distribution of the brightness / contrast image, the standard sharpness of the astigmatism image, and the standard sharpness of the wafer image are defined.In the wafer detection stage, first, a focused image of the wafer to be detected is acquired, the image sharpness of the focused image is calculated, the sharpness difference value between the image sharpness of the focused image and the standard sharpness of the focused image is calculated, and this sharpness difference value is transmitted to the SPC system, where the SPC system determines whether this sharpness difference value is smaller than a third sharpness difference threshold. If NO, defect detection of the wafer to be detected is stopped. If YES, a brightness / contrast image of the wafer to be detected is acquired further, the grayscale distribution of the brightness / contrast image is calculated, the pass rate of all grayscales within a predetermined number of σ in the grayscale distribution is calculated, and the pass rate is transmitted to the SPC system, where the SPC system determines whether this pass rate is larger than a predetermined threshold. If YES, defect detection of the wafer to be detected is stopped. If NO, an astigmatism image of the wafer to be detected is acquired further, the image sharpness of the astigmatism image is calculated, and astigmatism The process calculates the difference in sharpness between the image sharpness and the standard sharpness of the astigmatism image, transmits this difference to the SPC system, and determines whether this difference is smaller than a second sharpness difference threshold. If the result is NO, the process stops detecting defects in the wafer to be detected. If the result is YES, the process proceeds to the wafer defect detection step, where an electron beam scan is performed on the target defect detection area of ​​the wafer to be detected. After the scan is completed, fixed-point drawing is performed to acquire a wafer image of the wafer to be detected. The image sharpness of the wafer image is calculated, the difference in sharpness between the image sharpness of the wafer image and the standard sharpness of the wafer image is calculated, transmits this difference to the SPC system, and determines whether this difference is smaller than a first sharpness difference threshold. If the result is NO, the process stops detecting defects in the wafer to be detected, and the defect detection result for the wafer to be detected is marked as unreliable.

[0135] In the embodiment shown in Figure 7, the quality of the focused image, the brightness / contrast image, and the astigmatism image are judged in order. If any of the indicators are poor, defect detection of the wafer to be detected is stopped. Only when all three indicators are good, i.e., when it is confirmed that there is no malfunction in the EBI and no problem with the wafer to be detected (for example, the wafer to be detected is not properly placed), is the step of scanning the target defect detection area of ​​the wafer to be detected performed with the electron beam. This avoids the unreliability of subsequent defect detection results due to reasons such as EBI failure or the wafer not being properly placed, minimizes the avoidance of meaningless execution steps, and improves the effective utilization rate of the EBI. The EBI may have problems during the detection stage, and after the scan is completed, image quality is detected secondarily to avoid inaccurate defect detection results due to poor image quality caused by problems with the EBI or wafer during the scanning stage, and improves the reliability of the final defect detection result. Furthermore, this invention performs image quality detection based on the actual wafer to be detected, resulting in high accuracy of image quality monitoring.

[0136] Next, as shown in Figure 8, this embodiment provides a wafer defect detection device 800, which mainly includes a wafer scanning module 801, an image sharpness calculation module 802, a sharpness evaluation value calculation module 803, a sharpness evaluation value determination module 804, and a detection result mark module 805.

[0137] Here, the wafer scanning module 801 to be detected is used to perform electron beam scanning on the target defect detection region of the wafer to be detected and to detect defects in the target defect detection region, and the wafer to be detected has one or more defect detection regions.

[0138] The image sharpness calculation module 802 acquires a wafer image of the wafer to be detected and calculates the image sharpness of the wafer image.

[0139] In one embodiment, the image sharpness calculation module 802 is related to the formula

[0140]

number

[0141] The image sharpness of the wafer image is calculated based on the following, where P is the image sharpness of the wafer image, m and n are the length and width of the wafer image, respectively, df is the grayscale change range of the pixel dots of the wafer image, dx is the increase in distance between the pixel dots of the wafer image, i is a pixel dot of the wafer image, and a is a neighboring point of the pixel dot.

[0142] In one embodiment, the wafer image is an image of a wafer region on the wafer to be detected that is not scanned by the electron beam, and is within a range less than a first preset distance from the target defect detection region.

[0143] The sharpness evaluation value calculation module 803 is used to obtain a sharpness evaluation value of a wafer image based on the image sharpness of the wafer image.

[0144] The sharpness evaluation value determination module 804 is used to determine whether the sharpness evaluation value of the wafer image meets the pre-set conditions. The detection result mark module 805 is used to mark the defect detection result of the wafer being detected as an unreliable result if the sharpness evaluation value of the wafer image does not meet the pre-set conditions.

[0145] In one embodiment, the sharpness evaluation value calculation module 803 is configured to calculate the sharpness difference value between the image sharpness of the wafer image and the preset standard sharpness of the wafer image, and to obtain the sharpness evaluation value of the wafer image. If the sharpness evaluation value of the wafer image does not meet the preset conditions, it means that the sharpness evaluation value of the wafer image is greater than or equal to a first sharpness difference threshold.

[0146] In one embodiment, the sharpness evaluation value calculation module 803 outputs the sharpness evaluation value of the wafer image to the SPC system, and the detection result mark module 805 functions as part of the SPC system.

[0147] In one embodiment, the wafer defect detection apparatus 800 further includes a defect detection cancellation module 806, which is configured to cancel defect detection of the wafer to be detected if the sharpness evaluation value of the wafer image does not match a preset condition.

[0148] In one embodiment, the wafer defect detection apparatus 800 further includes a grayscale distribution calculation module 807, a grayscale distribution evaluation value calculation module 808, and a grayscale distribution evaluation value determination module 809, wherein the grayscale distribution calculation module 807 is configured to acquire a brightness / contrast image of the wafer to be detected and to acquire the grayscale distribution of the brightness / contrast image; the grayscale distribution evaluation value calculation module 808 is configured to acquire a grayscale distribution evaluation value of the brightness / contrast image based on the grayscale distribution of the brightness / contrast image; the grayscale distribution evaluation value determination module 809 is used to determine whether the grayscale distribution evaluation value matches a preset condition; and the defect detection cancellation module 806 is configured to cancel defect detection of the wafer to be detected if the grayscale distribution evaluation value of the brightness / contrast image does not match a preset condition.

[0149] In one embodiment, the tone distribution calculation module 807 is configured to obtain a normal distribution of the number of pixel dots corresponding to each tone in a brightness / contrast image, calculate the number of pixel dots corresponding to each tone within a preset number of sigma in the normal distribution, and obtain the tone distribution of the brightness / contrast image. The tone distribution evaluation value calculation module 808 is configured to determine whether the tone is acceptable or not based on the number of pixel dots corresponding to the tone within a preset number of sigma in the normal distribution and the number of pixel dots corresponding to the same tone in a preset standard tone distribution, and calculate the acceptance rate of all tone within a preset number of sigma in the normal distribution as the tone distribution evaluation value of the brightness / contrast image.

[0150] In one embodiment, the preset number of sigma is 1 to 2 sigma values.

[0151] In one embodiment, the tone distribution evaluation value calculation module 808 is configured to determine that a tone is acceptable if the difference between the ratio of the number of pixel dots corresponding to a preset number of tones within a sigma in a normal distribution and the number of pixel dots corresponding to the same tone in a standard tone distribution, and 90%, is less than 5%. The tone distribution evaluation value does not meet the preset conditions if the pass rate for all tones is 95% or less, and the tone distribution evaluation value meets the preset conditions if the pass rate for all tones is greater than 95%.

[0152] In one embodiment, the image sharpness calculation module 802 is further configured to acquire an astigmatism image of the wafer to be detected and calculate the image sharpness of the astigmatism image; the sharpness evaluation value calculation module 803 is further configured to acquire a sharpness evaluation value of the astigmatism image based on the image sharpness of the astigmatism image; the sharpness evaluation value determination module 804 is configured to determine whether the sharpness evaluation value of the astigmatism image conforms to a preset condition; the defect detection cancellation module 806 is configured to cancel defect detection of the wafer to be detected if the sharpness evaluation value of the astigmatism image does not conform to a preset condition; and the wafer scanning module 801 is configured to perform an electron beam scan of the target defect detection region of the wafer to be detected if the sharpness evaluation value of the astigmatism image conforms to a preset condition.

[0153] In one embodiment, the sharpness evaluation value calculation module 803 is configured to calculate the sharpness difference value between the image sharpness of the astigmatism image and the pre-set standard sharpness of the astigmatism image, and to obtain the sharpness evaluation value of the astigmatism image. If the sharpness evaluation value of the astigmatism image does not meet the pre-set conditions, it means that the sharpness evaluation value of the astigmatism image is greater than or equal to the second sharpness difference threshold, and if the sharpness evaluation value of the astigmatism image meets the pre-set conditions, it means that the sharpness evaluation value of the astigmatism image is less than the second sharpness difference threshold.

[0154] In one embodiment, the image sharpness calculation module 802 is further configured to acquire a focused image of the wafer to be detected and calculate the image sharpness of the focused image; the sharpness evaluation value calculation module 803 is further configured to acquire a sharpness evaluation value of the focused image based on the image sharpness of the focused image; the sharpness evaluation value determination module 804 is configured to determine whether the sharpness evaluation value of the focused image matches a preset condition; and the defect detection cancellation module 806 is configured to cancel defect detection of the wafer to be detected if the sharpness evaluation value of the focused image does not match a preset condition.

[0155] In one embodiment, the sharpness evaluation value calculation module 803 is configured to calculate the sharpness difference value between the image sharpness of the in-focus image and the preset standard sharpness of the in-focus image, and to obtain the sharpness evaluation value of the in-focus image. If the sharpness evaluation value of the in-focus image does not meet the preset conditions, it means that the sharpness evaluation value of the in-focus image is greater than or equal to a third sharpness difference threshold.

[0156] The process for realizing the functions and operations of each module in the wafer defect detection apparatus 800 described above will be explained by referring specifically to the process for realizing the corresponding steps in the wafer defect detection method described above, and will not be explained here.

[0157] As shown in Figure 9, this embodiment provides an electron beam scanning device 900 which includes one or more processors 901 and a memory 902, the memory 902 which stores one or more programs, and when one or more programs are executed by one or more processors 901, the electron beam scanning device 900 implements the wafer defect detection method of the present invention.

[0158] The electron beam scanning device 900 may also be equipped with the aforementioned SPC system.

[0159] This invention redesigns the EBI image quality monitoring system to pre-detect image sharpness and grayscale distribution, perform secondary detection after scanning is complete, and mark the defect detection results of wafers with poor image quality as unreliable detection results. This distinguishes the defect detection results of wafers with good image quality from those with poor image quality, thereby improving the reliability of the defect detection results for each wafer. Furthermore, it promptly stops the defect detection program for wafers with poor image quality, avoiding meaningless execution steps and improving the effective utilization rate of the EBI.

[0160] Figure 10 shows a block diagram of a computer system configuration for realizing several embodiments of the present application. The computer system shown in Figure 10 is merely an example and does not impose any limitations on the functions and scope of use of the embodiments of the present application.

[0161] As shown in Figure 10, the computer system 1000 includes a CPU (Central Processing Unit) 1001 and can perform various appropriate operations and processes according to programs stored in the ROM (Read-Only Memory) 1002 or programs loaded from the storage unit 1008 into the RAM (Random Access Memory) 1003, for example, to perform the wafer defect detection method in the above embodiment. The RAM 1003 also stores various programs and data necessary for system operation. The CPU 1001, ROM 1002, and RAM 1003 are interconnected by a bus 1004. The I / O (Input / Output) interface 1005 is also connected to the bus 1004.

[0162] The I / O interface 1005 is connected to an input unit 1006 including a keyboard and mouse, an output unit 1007 including a CRT (Cathode Ray Tube), an LCD (Liquid Crystal Display), and speakers, a storage unit 1008 including a hard disk, and a communication unit 1009 including a LAN (Local Area Network) card and a network interface card such as a modem. The communication unit 1009 performs communication processing via a network such as the Internet. A driver 1010 is also connected to the I / O interface 1005 as needed. Removable media 1011 such as magnetic disks, optical disks, magneto-optical disks, and semiconductor memory are loaded into the drive 1010 as needed, and computer programs read from them are installed in the storage unit 1008 as needed.

[0163] In particular, according to embodiments of the present application, the process described with reference to the flowchart above may be implemented as a computer software program. For example, embodiments of the present application include a computer program product, which includes a computer program loaded onto a computer-readable medium, and which includes a computer program for performing all or some of the steps shown in the flowchart of the wafer defect detection method. In such embodiments, the computer program may be downloaded and installed from a network via a communication unit 1009 and / or installed from removable media 1011. When this computer program is executed by a central processing unit (CPU) 1001, various limited functions in the system of the present application are performed.

[0164] The computer-readable media shown in the embodiments of this application may be a computer-readable signal medium, a computer-readable storage medium, or any combination of both. The computer-readable storage medium may be, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of more than these. More specific examples of computer-readable storage media include, but are not limited to, electrical connections having one or more wires, portable computer magnetic disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In this application, the computer-readable storage medium may be any tangible medium containing or storing a program, the program may be instructed to perform actions such as being used in conjunction with or in use with a system, apparatus, or device. In this application, a computer-readable signal medium may include data signals propagating as part of a baseband or carrier wave, and carry a computer-readable computer program. Such propagating data signals may include, but are not limited to, electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium may be any computer-readable medium other than a computer-readable storage medium, and such computer-readable medium may transmit, propagate or transmit an instruction execution system, apparatus or device, or a program used in combination therewith. The computer program contained in the computer-readable medium may be transmitted by any suitable medium, including, but not limited to, wireless, wired, or any suitable combination thereof.

[0165] The flowcharts and block diagrams in the drawings illustrate the feasible system architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present application. Here, each block in a flowchart or block diagram may represent a module, block, or part of code, the module, block, or part of code containing one or more executable instructions for implementing a predefined logical function. Alternatively, it should be noted that the functions described in a block may be generated in an order different from the order shown in the drawings. For example, two consecutively shown blocks may actually be executed essentially in parallel, or they may be executed in reverse order, as determined by the functions involved. It should be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented by a system with dedicated hardware that performs a predefined function or operation, or by a combination of dedicated hardware and computer instructions.

[0166] The units referred to in the embodiments of this application may be implemented in software or in hardware, and the described units may be provided on a processor. However, the names of these units do not limit the units themselves in any given case.

[0167] In another embodiment, the present invention further provides a computer-readable storage medium which may be included in the electron beam scanning device described in the above embodiment, or which may exist independently and not be incorporated into the electron beam scanning device. The computer-readable storage medium carries one or more programs, and when the one or more programs are executed by one of the electron beam scanning devices, the electron beam scanning device implements the method of the above embodiment.

[0168] While the above detailed description mentions several modules or units of the device for performing operations, such division is not mandatory. In fact, according to embodiments of the present invention, the features and functions of two or more modules or units described above may be embodied in a single module or unit. Conversely, the features and functions of a single module or unit described above may be embodied by multiple modules or units.

[0169] The above description of embodiments will facilitate the understanding of those skilled in the art that the exemplary embodiments described herein may be implemented by software or by combining the necessary hardware with software. Accordingly, the technical solutions according to the embodiments of the present application may be embodied in the form of a software product which may be stored on a non-volatile storage medium (which may be a CD-ROM, U disk, portable hard disk, etc.) or on a network and include a number of instructions which cause a computing device (which may be a personal computer, server, touch terminal, or network device, etc.) to perform the method according to the embodiments of the present application.

[0170] A person skilled in the art will readily conceive of other embodiments of the Application after practicing the Specification and the Inventions Disclosed herein. The Application is intended to cover any variations, uses, or adaptive changes of the Application, which will follow the general principles of the Application and include common or conventional art means in the Art not disclosed herein. This Specification and Examples should be considered merely illustrative, and the true scope and spirit of the Application are indicated by the appended Claims. [Explanation of symbols]

[0171] 800, Wafer defect detection device, 801, Wafer scanning module for detection, 802, Image sharpness calculation module, 803, Sharpness evaluation value calculation module, 804, Sharpness evaluation value judgment module, 805, Detection result mark module, 806, Defect detection cancellation module, 807, Grayscale distribution calculation module, 808, Grayscale distribution evaluation value calculation module, 809, Grayscale distribution evaluation value judgment module, 900, Electron beam scanning device, 901, Processor, 902, Memory, 1000, Computer system, 1001, CPU, 1002, ROM, 1003, RAM, 1004, Bus, 1005, I / O interface, 1006, Input unit, 1007, Output unit, 1008, Storage unit, 1009, Communication unit, 1010, Drive, 1011, Removable media.

Claims

1. The process involves performing an electron beam scan on a target defect detection region of a wafer to be detected, and detecting defects in the target defect detection region, wherein the wafer to be detected has one or more defect detection regions. The process involves acquiring a wafer image of the wafer to be detected and calculating the image sharpness of the wafer image. Based on the image sharpness of the wafer image, a sharpness evaluation value of the wafer image is obtained. This includes determining whether the sharpness evaluation value of the wafer image meets a preset condition, and if the sharpness evaluation value of the wafer image does not meet the preset condition, marking the defect detection result of the wafer to be detected as an unreliable detection result. A wafer defect detection method characterized by the features described above.

2. Obtaining a sharpness evaluation value of the wafer image based on the image sharpness of the wafer image includes calculating the sharpness difference between the image sharpness of the wafer image and a preset standard sharpness of the wafer image, and obtaining a sharpness evaluation value of the wafer image. The sharpness evaluation value of the wafer image does not meet the preset conditions if the sharpness evaluation value of the wafer image is greater than or equal to the first sharpness difference threshold. The wafer defect detection method according to claim 1.

3. If the sharpness evaluation value of the wafer image does not match the preset conditions, the defect detection of the wafer to be detected is canceled. The wafer defect detection method according to claim 1.

4. After obtaining the sharpness evaluation value of the wafer image, the wafer defect detection method is The sharpness evaluation value of the wafer image is further output to the statistical processing control system so that the statistical processing control system marks the defect detection result of the wafer to be detected as an unreliable detection result if the sharpness evaluation value of the wafer image does not meet the preset conditions. The wafer defect detection method according to claim 1.

5. Calculating the image sharpness of the wafer image is: Relationship [Math 1] This includes calculating the image sharpness of the wafer image based on the above, P is the image sharpness of the wafer image, m and n are the length and width of the wafer image, respectively, df is the grayscale change range of the pixel dots of the wafer image, dx is the increase in distance between the pixel dots of the wafer image, i is a pixel dot of the wafer image, and a is a neighboring point of the pixel dot. The wafer defect detection method according to claim 1.

6. The wafer image is an image of a wafer region on the wafer to be detected that is not scanned by the electron beam, and is located within a range less than a first preset distance from the target defect detection region. The wafer defect detection method according to claim 1.

7. Before performing an electron beam scan on the target defect detection region of the wafer to be detected, the wafer defect detection method: The process involves acquiring a brightness / contrast image of the wafer to be detected, and obtaining the grayscale distribution of the brightness / contrast image. Based on the gradation distribution of the luminance / contrast image, an evaluation value of the gradation distribution of the luminance / contrast image is obtained. The process involves determining whether the grayscale distribution evaluation value matches the preset conditions, and further includes discontinuing defect detection of the wafer to be detected if the grayscale distribution evaluation value does not match the preset conditions. A wafer defect detection method according to any one of claims 1 to 6, characterized by the features described above.

8. Obtaining the grayscale distribution of the aforementioned brightness / contrast image is, Obtaining a normal distribution of the number of pixel dots corresponding to each grayscale in the aforementioned brightness / contrast image, To obtain the gradation distribution of the luminance / contrast image, the method includes calculating the number of pixel dots corresponding to each gradation within a predetermined number of sigma values ​​in the normal distribution. Based on the gradation distribution of the luminance / contrast image, obtaining a gradation distribution evaluation value of the luminance / contrast image is: The pass / fail status of the tone is determined based on the number of pixel dots corresponding to a predetermined number of tones within sigma in the normal distribution and the number of pixel dots corresponding to the same tone as the predetermined number of tones within sigma in the predetermined standard tone distribution. This includes calculating the pass rate of all gradations within a predetermined number of sigma values ​​in the normal distribution as the gradation distribution evaluation value of the brightness / contrast image. The wafer defect detection method according to feature 7.

9. The aforementioned pre-set number of sigma symbols is 1 to 2 sigma symbols. The wafer defect detection method according to claim 8.

10. If the difference between the ratio of the number of pixel dots corresponding to a preset number of sigma tones in the normal distribution and the number of pixel dots corresponding to the same tone as the preset number of sigma tones in the standard tone distribution, and 100%, is less than 5%, then the tone is judged to be acceptable, and / or The condition that the aforementioned tone distribution evaluation value does not meet the predetermined conditions is that the pass rate of all tone levels within the predetermined number of sigma in the normal distribution is 95% or less, and if the pass rate of all tone levels within the predetermined number of sigma in the normal distribution is greater than 95%, then the aforementioned tone distribution evaluation value meets the predetermined conditions. The wafer defect detection method according to claim 8.

11. If the grayscale distribution evaluation value matches the preset conditions, the wafer defect detection method is The process involves acquiring an astigmatism image of the wafer to be detected and calculating the image sharpness of the astigmatism image. Based on the image sharpness of the aforementioned astigmatism image, a sharpness evaluation value of the aforementioned astigmatism image is obtained, The sharpness evaluation value of the astigmatism image is determined to meet the preset conditions, and further includes the step of stopping defect detection of the wafer to be detected if the sharpness evaluation value of the astigmatism image does not meet the preset conditions, and performing an electron beam scan of the target defect detection region of the wafer to be detected if the sharpness evaluation value of the astigmatism image meets the preset conditions. The wafer defect detection method according to feature 7.

12. Obtaining a sharpness evaluation value of the astigmatism image based on the image sharpness of the astigmatism image includes calculating a sharpness difference value between the image sharpness of the astigmatism image and a preset standard sharpness of the astigmatism image, and obtaining a sharpness evaluation value of the astigmatism image. The sharpness evaluation value of the astigmatism image does not meet the preset conditions if the sharpness evaluation value of the astigmatism image is greater than or equal to the second sharpness difference threshold, and the sharpness evaluation value of the astigmatism image meets the preset conditions if the sharpness evaluation value of the astigmatism image is less than the second sharpness difference threshold. The wafer defect detection method according to claim 11.

13. Before performing an electron beam scan on the target defect detection region of the wafer to be detected, the wafer defect detection method: The process involves acquiring an astigmatism image of the wafer to be detected and calculating the image sharpness of the astigmatism image. Based on the image sharpness of the aforementioned astigmatism image, a sharpness evaluation value of the aforementioned astigmatism image is obtained, The process further includes determining whether the sharpness evaluation value of the astigmatism image matches the preset conditions, and if the sharpness evaluation value of the astigmatism image does not match the preset conditions, discontinuing defect detection of the wafer to be detected. A wafer defect detection method according to any one of claims 1 to 6, characterized by the features described above.

14. Before performing an electron beam scan on the target defect detection region of the wafer to be detected, the wafer defect detection method: The process involves obtaining a focused image of the wafer to be detected and calculating the image sharpness of the focused image. Based on the image sharpness of the focused image, a sharpness evaluation value of the focused image is obtained. The process involves determining whether the sharpness evaluation value of the focused image matches the preset conditions, and further includes discontinuing defect detection of the wafer to be detected if the sharpness evaluation value of the focused image does not match the preset conditions. A wafer defect detection method according to any one of claims 1 to 6, characterized by the features described above.

15. Obtaining a sharpness evaluation value of the focused image based on the image sharpness of the focused image includes calculating the sharpness difference between the image sharpness of the focused image and a preset standard sharpness of the focused image, and obtaining a sharpness evaluation value of the focused image. The sharpness evaluation value of the in-focus image does not meet the preset conditions if the sharpness evaluation value of the in-focus image is greater than or equal to the third sharpness difference threshold. The wafer defect detection method according to feature 14.

16. If the sharpness evaluation value of the focused image matches the preset conditions, the wafer defect detection method is The process involves acquiring a brightness / contrast image of the wafer to be detected, and obtaining the grayscale distribution of the brightness / contrast image, Based on the gradation distribution of the luminance / contrast image, an evaluation value of the gradation distribution of the luminance / contrast image is obtained. To determine whether the aforementioned grayscale distribution evaluation value matches the predetermined conditions, If the grayscale distribution evaluation value does not match the preset conditions, defect detection of the wafer to be detected is stopped. If the grayscale distribution evaluation value matches the preset conditions, an astigmatism image of the wafer to be detected is acquired, and the image sharpness of the astigmatism image is calculated. Based on the image sharpness of the aforementioned astigmatism image, a sharpness evaluation value of the aforementioned astigmatism image is obtained, Determining whether the sharpness evaluation value of the astigmatic image matches the preset conditions, If the sharpness evaluation value of the astigmatism image does not match the preset conditions, the defect detection of the wafer to be detected will be stopped. If the sharpness evaluation value of the astigmatism image matches the preset conditions, the step of performing an electron beam scan on the target defect detection region of the wafer to be detected further includes: The wafer defect detection method according to feature 14.

17. Used to perform electron beam scanning on a target defect detection region of a wafer to be detected and to detect defects in the target defect detection region, wherein the wafer to be detected includes a wafer scanning module having one or more defect detection regions, An image sharpness calculation module for acquiring a wafer image of the wafer to be detected and calculating the image sharpness of the wafer image, A sharpness evaluation value calculation module for obtaining a sharpness evaluation value of the wafer image based on the image sharpness of the wafer image, A sharpness evaluation value determination module for determining whether the sharpness evaluation value of the wafer image meets pre-set conditions, The wafer image sharpness evaluation value does not match the preset conditions, and the detection result mark module marks the defect detection result of the wafer to be detected as an unreliable detection result. A wafer defect detection apparatus characterized by the following features.

18. Includes one or more processors and memory, The memory is for storing the one or more computer programs that enable the processor to implement the wafer defect detection method described in any one of claims 1 to 6 when the one or more computer programs are executed by the one or more processors. An electron beam scanning device characterized by the following features.

19. The system stores computer-readable instructions, and when a computer-readable instruction is executed by a computer processor, it causes the computer processor to execute the wafer defect detection method described in any one of claims 1 to 6. A computer-readable storage medium characterized by the following features.

Images