Inspection device and inspection image generation method

Abstract

To increase the accuracy of extracting the outline of an inspection image.SOLUTION: According to an embodiment, an inspection device 1 includes: an imaging mechanism 10 for taking an image of a first image (an SEM image) of a sample 30; a first circuit 302 for calculating a first threshold value (an area threshold value) on the basis of the gradation level of the first image; a second circuit 303 for generating a first contour line based on the first threshold value in a second pixel (an area pixel) provided in the center of four first pixels arranged in a matrix of 2 rows and 2 columns in the first image; a third circuit 304 for calculating a first outline point in the second pixel by using the generated first contour line; and a comparison circuit 215 for comparing a reference image and the inspection image based on the first outline point.SELECTED DRAWING: Figure 5

Description

[Technical Field]

[0001] The present invention relates to an inspection apparatus for inspecting defects in a pattern formed on a sample and a method for generating an inspection image. [Background technology]

[0002] In the manufacturing process of semiconductor devices, a circuit pattern is transferred onto a semiconductor substrate by reduction exposure using an exposure apparatus (also called a "stepper" or "scanner"). The exposure apparatus uses a mask (also called a "reticle") on which an original pattern is formed to transfer the circuit pattern onto the semiconductor substrate (hereinafter also referred to as a "wafer").

[0003] For example, cutting-edge devices require the formation of circuit patterns with line widths of several nanometers. As circuit patterns become finer, the original patterns on masks also become finer. For this reason, electron beam defect inspection systems are required to have high defect detection performance that can handle fine patterns.

[0004] Defect inspection methods include the D-DB (Die to Database) method, which compares an inspection image based on an image (photographed image) of a sample (mask, etc.) with a reference image based on design data, and the DD (Die to Die) method, which compares multiple areas consisting of the same pattern formed on a sample.

[0005] The electron beam defect inspection system extracts the contour lines of a pattern from a captured image to generate an inspection image, and detects defects by comparing the contour lines of the pattern in the inspection image with the contour lines of the pattern in a reference image.

[0006] For example, Patent Document 1 discloses a method for extracting contours from a captured image using multiple two-dimensional spatial filter functions with different directions. In this case, filter processing is performed for each frame image (pixel) in different directions. If at least one of the values ​​(filtered intensity) obtained for each direction is greater than a threshold, the pixel is extracted as a candidate pixel containing a contour (contour pixel candidate).

[0007] Furthermore, for example, Patent Document 2 discloses a method in which brightness values ​​are interpolated for each rectangular region of an inspection image and a reference image, and the area of ​​the region where the interpolated brightness value is greater than a reference brightness value is calculated. The method then compares the area of ​​the inspection image with the area of ​​the reference image. In this case, the deviation between the inspection image and the reference image is detected based on the difference in area within the rectangular region. [Prior art documents] [Patent documents]

[0008] [Patent Document 1] Japanese Patent Application Publication No. 2022-16779 [Patent Document 2] Japanese Patent Application Publication No. 2020-183928 Summary of the Invention [Problem to be solved by the invention]

[0009] In an electron beam defect inspection device, when the edge strength of a pattern in a captured image decreases, the edge profile of the captured image becomes sluggish, making it difficult to stably extract the contour line of the pattern from the captured image. Furthermore, when the distance between the edges of the pattern becomes smaller than the size of the edge filter due to the miniaturization of the pattern, it becomes difficult to extract the contour line. If the length of the edge filter is reduced to address this, noise resistance and positional accuracy will decrease.

[0010] The present invention has been made in light of these points. That is, in the case of capturing an SEM image, when an electron beam is irradiated onto a sample, the intensity of the secondary electrons obtained varies depending on the type of material of the irradiated surface, i.e., the difference in the signal obtained at the edge of the pattern (hereinafter referred to as "material contrast") is large (hereinafter referred to as "material contrast rich"), and the intensity of the edge is low. It is therefore an object of the present invention to provide an inspection device and an inspection image generating method that can improve the accuracy of contour line extraction. [Means for solving the problem]

[0011] According to a first aspect of the present invention, an inspection device includes an imaging mechanism that captures a first image of a sample, a first circuit that calculates a first threshold value based on the gradation value of the first image, a second circuit that generates a first contour line based on the first threshold value at a second pixel located at the center of four first pixels arranged in 2 rows and 2 columns included in the first image, a third circuit that calculates a first contour point at the second pixel using the generated first contour line, and a comparison circuit that compares a reference image with an inspection image based on the first contour point.

[0012] According to a second aspect of the present invention, an inspection device includes an imaging mechanism that captures a first image of a sample and a second image of the sample to be compared with the first image; a first circuit that calculates a first threshold based on the gradation value of the first image and calculates a second threshold for the second image using the first threshold; a second circuit that generates a first contour line based on the first threshold at a second pixel located at the center of four first pixels arranged in 2 rows and 2 columns included in the first image, and generates a second contour line based on the second threshold at a fourth pixel located at the center of four third pixels arranged in 2 rows and 2 columns included in the second image; a third circuit that calculates a first contour point at the second pixel using the generated first contour line, and calculates a second contour point at the fourth pixel using the generated second contour line; and a comparison circuit that compares the inspection image based on the first contour points with a reference image based on the second contour points.

[0013] According to a third aspect of the present invention, a method for generating an inspection image includes the steps of capturing a first image of a sample, calculating a first threshold value based on the gradation value of the first image, generating a first contour line based on the first threshold value at a second pixel located at the center of four first pixels arranged in 2 rows and 2 columns in the first image, and calculating a first contour point at the second pixel using the generated first contour line. [Effects of the Invention]

[0014] According to the inspection device and the inspection image generating method of the present invention, the accuracy of extracting the contour line of the inspection image can be improved. [Brief explanation of the drawings]

[0015] [Figure 1] FIG. 1 is a diagram showing the overall configuration of an inspection device according to the first embodiment. [Figure 2] FIG. 2 is a block diagram of a contour extraction circuit included in the inspection device according to the first embodiment. [Figure 3] FIG. 3 is a flowchart of the inspection process in the inspection device according to the first embodiment. [Figure 4] FIG. 4 is a diagram showing a specific example of 4×4 pixels including the contour line of the inspection image and the contour line of the reference image in the comparison step in the inspection device according to the first embodiment. [Figure 5] FIG. 5 is a flowchart of generating an area image and generating contour data in the inspection device according to the first embodiment. [Figure 6] FIG. 6 is a specific example of a histogram showing the relationship between the gradation value and the number of pixels of an SEM image in the inspection device according to the first embodiment. [Figure 7] FIG. 7 is a diagram showing a specific example of calculation of the area of ​​an area pixel in the inspection device according to the first embodiment. [Figure 8] FIG. 8 is a diagram showing the relationship between an SEM image and an area image in the inspection device according to the first embodiment. [Figure 9] FIG. 9 is a diagram showing a specific example of the first filtering process in the inspection device according to the first embodiment. [Figure 10] FIG. 10 is a diagram showing a specific example of the second filtering process in the inspection device according to the first embodiment. [Figure 11] FIG. 11 is a diagram showing a specific example of calculation of contour points in the inspection apparatus according to the first embodiment. [Figure 12] FIG. 12 is a diagram showing a specific example of an area image in the inspection device according to the first embodiment. [Figure 13] FIG. 13 is a diagram showing a specific example of a near contour point in the inspection apparatus according to the first embodiment. [Figure 14] FIG. 14 is a diagram showing a specific example of an inspection image in the inspection device according to the first embodiment. [Figure 15] FIG. 15 is a flowchart of generating an area image and generating contour data in the inspection device according to the second embodiment. [Figure 16] FIG. 16 is a diagram showing a specific example of calculation of contour points in the inspection apparatus according to the second embodiment. [Figure 17] FIG. 17 is a flowchart of the inspection process in the inspection device according to the third embodiment. [Figure 18] FIG. 18 is a flowchart of generating an area image and generating contour data in the inspection device according to the third embodiment. [Figure 19] FIG. 19 is a diagram showing an example in which an inspection SEM image and a reference SEM image in the inspection device according to the third embodiment are each divided into four regions. [Figure 20] FIG. 20 is a flowchart of generating an area image and generating contour data in the inspection device according to the fourth embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0016] Hereinafter, embodiments will be described with reference to the drawings. The embodiments illustrate devices and methods for embodying the technical ideas of the invention. The drawings are schematic or conceptual, and the dimensions and proportions of each drawing are not necessarily the same as those of the actual objects. The technical ideas of the present invention are not specified by the shape, structure, arrangement, etc. of the components.

[0017] In the following, a defect inspection apparatus that uses a scanning electron microscope (hereinafter referred to as "SEM") to capture an electron beam image (hereinafter also referred to as "SEM image") of a sample will be described as an inspection apparatus. Note that the defect inspection apparatus may capture an image by irradiating the sample with an energy beam other than an electron beam. The defect inspection apparatus may use an optical microscope to capture an optical image of the sample, or may use a light-receiving element to capture an optical image of light reflected by or transmitted through the sample. Furthermore, in this embodiment, a case will be described in which the sample to be inspected is a mask, but the sample may also be any sample with a pattern on its surface, such as a wafer used in the manufacture of semiconductor devices or a substrate used in liquid crystal display devices, etc.

[0018] 1. First embodiment A first embodiment will be described below, in which the defect inspection method is a D-DB (Die to Database) method.

[0019] 1.1 Overall configuration of the inspection device First, an example of the overall configuration of an inspection device will be described with reference to Fig. 1. Fig. 1 is a diagram showing the overall configuration of an inspection device 1.

[0020] As shown in FIG. 1, the inspection device 1 includes an imaging mechanism 10 and a control mechanism 20.

[0021] The imaging mechanism 10 includes a sample chamber 11 and a lens barrel 12. The lens barrel 12 is installed above the sample chamber 11. For example, the lens barrel 12 has a cylindrical shape that extends perpendicular to the sample chamber 11. The sample chamber 11 and the lens barrel 12 have openings on their mutually contacting surfaces. The space formed by the sample chamber 11 and the lens barrel 12 is maintained in a vacuum (reduced pressure) state using a turbomolecular pump or the like.

[0022] In the sample chamber 11, a stage 13, a stage driving mechanism 14, and a detector 15 are provided.

[0023] A sample (mask) 30 is placed on the stage 13. The stage 13 is movable in an X direction parallel to the surface of the stage 13 and in a Y direction parallel to the surface of the stage 13 and intersecting the X direction. The stage 13 may also be movable in a Z direction perpendicular to the surface of the stage 13, or may be rotatable around a rotation axis on the XY plane, with the Z direction as the rotation axis.

[0024] The stage driving mechanism 14 has a driving mechanism for moving the stage 13 in the X direction and the Y direction. Note that the stage driving mechanism 14 may have, for example, a mechanism for moving the stage 13 in the Z direction, or a mechanism for rotating the stage 13 on the XY plane around a rotation axis in the Z direction.

[0025] The detector 15 detects secondary electrons or reflected electrons emitted from the sample, etc. The detector 15 transmits a signal of the detected secondary electrons or reflected electrons, etc., that is, imaging data of the SEM image, to the image acquisition circuit 213.

[0026] An electron gun 16 and an electron optical system 17, which are components of the SEM, are provided inside the electron column 12. In the example of Fig. 1, the configuration of the electron optical system 17 is shown, which irradiates a single beam onto a sample 30. Note that the electron optical system 17 may also be configured to irradiate the sample 30 with multiple beams.

[0027] The electron gun 16 is installed so as to emit an electron beam toward the sample chamber 11. The electron beam emitted by the electron gun 16 may be a single beam or multiple beams.

[0028] The electron optical system 17 focuses the electron beam emitted from the electron gun 16 onto a predetermined position on the sample 30 and irradiates the sample 30. For example, the electron optical system 17 includes a plurality of focusing lenses 101 and 102, a plurality of scanning coils 103 and 104, and an objective lens 105. The electron beam emitted from the electron gun 16 is accelerated and then focused as an electron spot on the surface of the sample 30 placed on the stage 13 by the focusing lenses 101 and 102 and the objective lens 105. The scanning coils 103 and 104 control the position of the electron spot on the sample 30.

[0029] The control mechanism 20 includes a control circuit 21 , a memory device 22 , a display device 23 , an input device 24 , and a communication device 25 .

[0030] The control circuit 21 controls the entire inspection apparatus 1. More specifically, the control circuit 21 controls the imaging mechanism 10 to acquire an SEM image (photographed image). The control circuit 21 also controls the control mechanism 20 to compare a reference image with an inspection image and detect defects. That is, the control circuit 21 is a processor for performing defect inspection. For example, the control circuit 21 includes a central processing unit (CPU), a random access memory (RAM), and a read-only memory (ROM), all of which are not shown. For example, the CPU loads a program stored in the ROM or the storage device 22, which serves as a non-transitory storage medium, into the RAM. The control circuit 21 then interprets and executes the program loaded into the RAM using the CPU to control the inspection apparatus 1. The control circuit 21 may be, for example, a CPU device such as a microprocessor, or a computer device such as a personal computer. In addition, the control circuit 21 may include a dedicated circuit (dedicated processor) in which at least some of the functions are performed by other integrated circuits such as an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), or a Graphics Processing Unit (GPU).

[0031] The control circuit 21 includes an unfolding circuit 211, a reference image generating circuit 212, an image acquiring circuit 213, a contour extracting circuit 214, and a comparing circuit 215. These may be configured by a program executed by the control circuit 21, by hardware or firmware provided in the control circuit 21, or by individual circuits controlled by the control circuit 21. The following describes a case where the control circuit 21 realizes the functions of the unfolding circuit 211, the reference image generating circuit 212, the image acquiring circuit 213, the contour extracting circuit 214, and the comparing circuit 215 by a program executed by the control circuit 21.

[0032] The expansion circuit 211 expands, for example, the design data 221 stored in the storage device 22 into data for each pattern (figure), and interprets the figure code and figure dimensions indicating the figure shape of the figure data. The expansion circuit 211 then expands the design data into a binary or multi-value (e.g., 8-bit) image (hereinafter also referred to as "expanded image") as a pattern arranged within a square, with a grid of a predetermined quantization dimension as a unit. The expansion circuit 211 calculates the occupancy rate of the figure for each pixel in the expanded image. The figure occupancy rate for each pixel calculated in this way is the pixel value. Below, a case will be described in which the pixel values ​​of the expanded image are expressed as 8-bit gradation value data. In this case, the pixel value of each pixel is expressed as a gradation value from 0 to 255. When the pixel value is 0, the figure occupancy rate is 0%, and when the pixel value is 255, the figure occupancy rate is 100%. If the defect inspection method of the inspection device 1 is a DD (Die to Die) method, the expansion circuit 211 may be omitted.

[0033] The reference image generation circuit 212 performs resizing and corner rounding of the exfoliated image. The resizing is a process of resizing the graphic pattern of the exfoliated image. The corner rounding is a process of rounding the corners of the graphic pattern after the resizing process. The reference image generation circuit 212 then extracts the contour of the graphic pattern from the exfoliated image after the resizing and corner rounding processes to generate a reference image (contour image). The reference image generation circuit 212 transmits the generated reference image to the comparison circuit 215 and the storage device 22. Note that when the defect inspection method of the inspection device 1 is the DD (Die to Die) method, the reference image generation circuit 212 applies one of the contour images generated in the contour extraction circuit 214 as a reference image.

[0034] The image acquisition circuit 213 acquires imaging data from the detector 15 of the imaging mechanism 10. The image acquisition circuit 213 generates gradation value data of the SEM image based on the imaging data and transmits it to the contour extraction circuit 214 and the storage device 22.

[0035] The contour extraction circuit 214 extracts contour data of a pattern based on the SEM image data to generate an inspection image (contour image). The contour extraction circuit 214 of this embodiment generates an area image based on the SEM image. The area image is generated based on the gradation value of each pixel (hereinafter also referred to as "SEM pixel") of the SEM image. Details of the area image will be described later. The contour extraction circuit 214 extracts contour data of a pattern from the area image. The contour data includes information on the contour points of the pattern and the contour lines connecting the contour points. In other words, the contour data includes, for each pixel (pixel of the area image), a representative value of the coordinates through which the contour line passes, i.e., the coordinate position of the contour point, and information on the normal direction of the contour vector at the contour point. Details of the contour extraction circuit 214 will be described later.

[0036] The comparison circuit 215 detects defects by comparing the inspection image with the reference image. More specifically, the comparison circuit 215 aligns the inspection image with the reference image and calculates the shift amount of the inspection image relative to the reference image. The comparison circuit 215 measures the distortion amount of the inspection image from, for example, variations in the shift amount within the surface of the sample 30, and calculates a distortion coefficient. For example, it is preferable to express the distortion amount as a polynomial model of the coordinates (X, Y) within the image, and use the coefficients of that polynomial as the distortion coefficient. The comparison circuit 215 compares the inspection image with the reference image using an appropriate algorithm that takes into account the shift amount and the distortion coefficient. If the error between the inspection image and the reference image exceeds a predetermined value, the comparison circuit 215 determines that a defect exists at the corresponding coordinate position on the sample 30.

[0037] The storage device 22 stores data and programs related to defect inspection. For example, the storage device 22 stores design data 221, parameter information 222 of inspection conditions, inspection data 223, threshold data 224, and the like. For example, the parameter information 222 of inspection conditions includes imaging conditions of the imaging mechanism 10, reference image generation conditions, contour extraction conditions for SEM images, and defect detection conditions. The inspection data 223 includes image data (unfolded image, reference image, SEM image, and inspection image) and data related to detected defects (coordinates, size, etc.). The threshold data 224 includes data such as a frequency threshold, area threshold, variance threshold, and distance threshold, which will be described later. The storage device 22 may also store a defect inspection program 225 as a non-transitory storage medium. The defect inspection program 225 is a program for causing the control circuit 21 to execute defect inspection.

[0038] The storage device 22 may include various types of storage devices as external storage, such as a magnetic disk storage device (HDD: Hard Disk Drive) or a solid state drive (SSD). Furthermore, the storage device 22 may include, for example, a drive for reading a program stored on a non-transitory storage medium such as a CD (Compact Disc) or a DVD (Digital Versatile Disc).

[0039] The display device 23 includes, for example, a display screen (for example, an LCD (Liquid Crystal Display) or an EL (Electroluminescence) display), etc. Under the control of the control circuit 21, the display device 23 displays, for example, the defect detection results.

[0040] The input device 24 is an input device such as a keyboard, a mouse, a touch panel, or a button switch.

[0041] The communication device 25 is a device for connecting to a network to transmit and receive data to and from an external device. Various communication standards can be used for communication. For example, the communication device 25 receives design data 221 from an external device and transmits inspection data 223 and the like to the external device.

[0042] 1.2 Configuration of the contour extraction circuit Next, an example of the configuration of the contour extraction circuit 214 will be described with reference to Fig. 2. Fig. 2 is a block diagram of the contour extraction circuit 214. Note that the functions of each block of the contour extraction circuit 214 may be realized by the control circuit 21 executing firmware or the like, or may be realized by a dedicated circuit.

[0043] 2, the contour extraction circuit 214 includes a noise filter processing circuit 301, an area threshold calculation circuit 302, an area image generation circuit 303, a contour point calculation circuit 304, and a nearby contour point removal circuit 305. Data generated by each unit can be stored in the storage device 22 each time it is generated.

[0044] The noise filter processing circuit 301 is a circuit that reduces (removes) noise in SEM image data. The noise filter processing circuit 301 acquires SEM image data from the image acquisition circuit 213. The noise filter processing circuit 301 then reduces noise at the edges of the graphic pattern in the SEM image to smooth the shape of the pattern edges. For noise filtering, general filters such as a Gaussian filter or a bilateral filter can be used.

[0045] The area threshold calculation circuit 302 is a circuit that calculates an area threshold. The area threshold is a threshold used to generate an area image, which will be described later. The area threshold calculation circuit 302 generates a histogram of the gradation values ​​of the SEM image and calculates the area threshold from the histogram. Details of the method for calculating the area threshold will be described later. The area threshold can be stored in the storage device 22 as threshold data 224.

[0046] The area image generating circuit 303 is a circuit that generates an area image. More specifically, in a one-pixel region (hereinafter referred to as an "area pixel") in an SEM image, whose vertices are the center points of adjacent 2 (rows) × 2 (columns) SEM pixels, the area image generating circuit 303 linearly interpolates the gradation values ​​at each vertex to generate contour lines corresponding to the area threshold. The area image generating circuit 303 then calculates the area occupancy rate (simply referred to as "area") of the area pixel that is equal to or greater than the area threshold. The area of ​​the area pixel may be expressed as an occupancy rate of 0 to 1, or, like the SEM pixel, may be expressed as 8-bit gradation value data (gradation value of 0 to 255). For example, if the gradation values ​​of the four SEM pixels corresponding to the four vertices of the area pixel are all equal to or greater than the area threshold, the area of ​​the area pixel is 1. In this case, the area pixel is expressed as a white background. On the other hand, if the gradation values ​​of the four SEM pixels corresponding to the four vertices of the area pixel are all less than the area threshold, the area of ​​the area pixel is 0. In this case, the area pixel is represented by a black background. The area image generation circuit 303 calculates the area of ​​each area pixel that is equal to or greater than the area threshold while shifting the SEM pixel by one pixel, thereby generating an area image. The area image can be stored in the storage device 22 as inspection data 223.

[0047] Furthermore, the area image generating circuit 303 can perform two filtering processes to suppress extraction of false contour points in the area image.

[0048] The first filtering process is a process corresponding to isolated pixels. For example, in a 3×3 SEM pixel array of an SEM image, if the gradation value of the central SEM pixel is less than the area threshold and the gradation values ​​of the surrounding eight adjacent SEM pixels are equal to or greater than the area threshold, each of the 2×2 area pixels including the center point of the central SEM pixel as a vertex is determined to be a white background (i.e., an area of ​​"1"). In other words, if an SEM pixel with a gradation value less than the area threshold is isolated, i.e., if there is no SEM pixel with a gradation value less than the area threshold among the eight adjacent pixels, the area image generating circuit 303 determines that the area pixel based on the central SEM pixel is a white background. Note that the area image generating circuit 303 may also apply the first filtering process to 5×5 SEM pixels.

[0049] The second filtering process suppresses the extraction of false contours when the grayscale values ​​of each pixel are relatively close to the area threshold. For example, if the variance of grayscale values ​​of 3×3 SEM pixels in an SEM image is equal to or less than a predetermined variance threshold, the second filtering process determines each of the 2×2 area pixels based on the 3×3 SEM pixels as a white background (i.e., area “1”) or a black background (i.e., area “0”). For example, if the average value of the grayscale values ​​of the 3×3 SEM pixels is equal to or greater than the area threshold, the area image generating circuit 303 determines the target 2×2 area pixel as a white background. On the other hand, if the average value of the grayscale values ​​of the 3×3 SEM pixels is less than the area threshold, the area image generating circuit 303 determines the target 2×2 area pixel as a black background. Area pixels determined to be a white background or a black background do not include contour lines. The area image generating circuit 303 may also apply the second filtering process to 5×5 SEM pixels.

[0050] The contour point calculation circuit 304 is a circuit that calculates contour points of an area image. In this embodiment, the contour point calculation circuit 304 calculates the coordinate positions and normal direction vectors of contour points in area pixels (contour point candidate pixels) that include contour lines, for example, using a Sobel filter. That is, the contour point calculation circuit 304 calculates the coordinate positions and normal direction vectors of contour points in area pixels whose area is greater than 0 and less than 1. The contour point extraction method will be described in detail later.

[0051] The nearby contour point removal circuit 305 is a circuit that removes nearby contour points. Nearby contour points are contour points whose distance to adjacent contour points is equal to or less than a preset distance threshold. By removing nearby contour points, errors in the process of estimating contour lines are reduced.

[0052] 1.3 Overall flow of the inspection process Next, an example of the overall flow of the inspection process will be described with reference to Fig. 3. Fig. 3 is a flowchart of the inspection process.

[0053] As shown in FIG. 3, the inspection process roughly includes an inspection image acquisition process (step S1), a reference image generation process (step S2), and a comparison process (step S3).

[0054] 1.3.1 Inspection image acquisition process First, an example of the inspection image acquisition process in step S1 will be described. The image acquisition circuit 213 acquires an SEM image of the sample 30 from the imaging mechanism 10 (step S11). The image acquisition circuit 213 transmits the SEM image to the contour extraction circuit 214.

[0055] Next, the noise filtering circuit 301 of the contour extraction circuit 214 performs pre-filtering as pre-processing for generating an area image in order to remove (reduce) noise from the SEM image (step S12).

[0056] Next, the contour extraction circuit 214 generates an area image using the pre-filtered SEM image (step S13).

[0057] Next, the contour extraction circuit 214 generates contour data from the area image (step S14). That is, the contour extraction circuit 214 generates an inspection image (contour image).

[0058] The contour extraction circuit 214 transmits the generated inspection image to the comparison circuit 215 and the storage device 22 .

[0059] 1.3.2 Reference image acquisition process Next, an example of the reference image acquisition step will be described. For example, the inspection device 1 acquires the design data 221 via the communication device 25 (step S21). The acquired design data 221 is stored in the storage device 22, for example.

[0060] The expansion circuit 211 reads out the design data 221 stored in the storage device 22. Then, the expansion circuit 211 executes expansion processing to expand (convert) the design data 221 into, for example, 8-bit image data (expanded image) (step S22). Each pixel of the expanded image has a pixel value corresponding to the occupancy rate of the pixel occupied by the design data figure. For example, in the case of 8-bit image data, the pixel value is 0 when the occupancy rate of the design figure is 0%, and the pixel value is 255 when the occupancy rate is 100%. The expansion circuit 211 transmits the expanded image to the reference image generation circuit 212 and the storage device 22.

[0061] Next, the reference image generating circuit 212 executes resizing processing and corner rounding processing on the expanded image (step S23).

[0062] Next, the reference image generation circuit 212 generates pattern contour data from the expanded image that has been resized and corner-rounded (step S24). That is, the reference image generation circuit 212 generates a reference image (contour image). The reference image generation circuit 212 transmits the generated reference image to the comparison circuit 215 and the storage device 22.

[0063] 1.3.3 Comparison process Next, an example of the comparison process will be described. First, the comparison circuit 215 performs alignment using the inspection image and the reference image (step S31), and aligns the pattern in the inspection image with the pattern in the reference image. For example, the comparison circuit 215 calculates the relative vector between each contour position in the inspection image and the corresponding contour position in the reference image, and sets the average value of the relative vectors as the alignment shift amount. That is, the comparison circuit 215 calculates the alignment shift amount of the inspection image relative to the reference image.

[0064] Next, the comparison circuit 215 measures the amount of distortion of the inspection image (step S32) and calculates a distortion coefficient. For example, due to stage movement accuracy or distortion of the sample 30, a positional deviation may occur between the pattern coordinate information based on the design data 221 and the pattern coordinates calculated from the captured image. The comparison circuit 215 measures the amount of distortion of the inspection image from, for example, the distribution of local alignment shift amounts within the surface of the sample 30, and calculates the distortion coefficient.

[0065] Next, the comparison circuit 215 compares the inspection image with the reference image (step S33). The comparison circuit 215 detects defects based on the comparison result. In other words, the comparison circuit 215 calculates the amount of positional deviation between the contour line of the inspection image and the contour line of the reference image for each pixel based on the relative vector and the distortion coefficient. The comparison circuit 215 then detects defects based on the amount of positional deviation. The comparison result is output to the storage device 22 or the display device (monitor) 23.

[0066] A specific example of a comparison between an inspection image and a reference image is shown in Figure 4. Figure 4 shows a specific example of 4x4 pixels including the contour lines of the inspection image and the contour lines of the reference image.

[0067] 4, the comparison circuit 215 calculates the distance (amount of displacement) from each contour point of the inspection image to the contour line of the reference image, and determines that there is a defect when the amount of displacement exceeds a preset threshold.

[0068] After storing the results of the defect inspection in the storage device 22, the control circuit 21 may, for example, display the results on the display device 23 or output the results to an external device (for example, a review device) via the communication device 25.

[0069] 1.4 Details of the area image generation and contour data generation process Next, the area image generating process in step S13 and the contour data generating process in step S14 described with reference to Fig. 3 will be described in detail with reference to Fig. 5. Fig. 5 is a flowchart showing the area image generation and contour data generation.

[0070] 5, steps S101 to S104 are executed as an area image generation step (step S13). Then, steps S105 to S107 are executed as a contour data generation step (step S14). Each step will be described in detail.

[0071] [Step S101] The noise filtering circuit 301 performs filtering using a Gaussian filter on the pre-filtered SEM image. This reduces the effects of noise and white bands in the SEM image. Note that in step S101, filtering other than a Gaussian filter may be applied. Also, step S101 may be omitted.

[0072] [Step S102] The area threshold calculation circuit 302 generates a histogram of the gradation values ​​of the SEM image. From the histogram, the area threshold calculation circuit 302 determines the maximum and minimum gradation values ​​corresponding to the pattern. At this time, in order to reduce the influence of noise, the area threshold calculation circuit 302 determines the minimum and maximum values ​​within a range of gradation values ​​in which the number of pixels is greater than a preset frequency threshold. The area threshold calculation circuit 302 may also divide the SEM image into multiple regions and create a histogram for each region. In this case, the area threshold calculation circuit 302 calculates an area threshold for each region.

[0073] A specific example of a histogram will be described with reference to Fig. 6. Fig. 6 is a specific example of a histogram showing the relationship between the gradation value and the number of pixels of an SEM image.

[0074] As shown in Fig. 6, the horizontal axis of the graph represents the gradation value of the SEM image. The vertical axis of the graph represents the number of SEM pixels corresponding to each gradation value. In the example of Fig. 6, the frequency threshold is set to 200. The area threshold calculation circuit 302 determines the minimum and maximum values ​​from the gradation values ​​where the number of pixels exceeds the frequency threshold (200).

[0075] [Step S103] The area threshold calculation circuit 302 calculates an area threshold. The area threshold calculation circuit 302 performs the calculation of Equation (1) to calculate the area threshold Z.

[0076]

Number

[0077] Here, the variable k represents the ratio in the width between the minimum value and the maximum value. The variable k can be arbitrarily set within the range of 0 < k < 1. For example, when the area threshold Z is the midpoint between the minimum value and the maximum value, k = 0.5 is set.

[0078] [Step S104] The area image generation circuit 303 calculates the area of the area pixels. At this time, the area image generation circuit 303 executes the first filter process and the second filter process. That is, the area image generation circuit 303 executes the filter process of the area image. The area image generation circuit 303 generates an area image by repeatedly calculating the area of the area pixels while shifting the SEM pixels one by one.

[0079] Referring to FIGS. 7 to 10, a specific example of area image generation will be described. FIG. 7 is a diagram showing a specific example of calculating the area of area pixels. FIG. 8 is a diagram showing the relationship between the SEM image and the area image. FIG. 9 is a diagram showing a specific example of the first filter process. FIG. 10 is a diagram showing a specific example of the second filter process.

[0080] The figure on the left side of the paper in FIG. 7 shows 2×2 SEM pixels. For example, the gradation value of the SEM pixel in the upper left corner of the paper is 50, and its center point is set as “A”. The gradation value of the SEM pixel adjacent to the SEM pixel with the center point “A” in the X direction is 80, and its center point is set as “B”. The gradation value of the SEM pixel adjacent to the SEM pixel with the center point “A” in the Y direction is 100, and its center point is set as “C”. The gradation value of the SEM pixel adjacent to the SEM pixel with the center point “C” in the X direction is 150, and its center point is set as “D”.

[0081] The areal image generation circuit 303 generates areal pixels whose vertices are the center points "A" to "D" of the 2x2 SEM pixels. In other words, an areal pixel is generated in the center of the 2x2 SEM pixels. Therefore, the size of one SEM pixel is the same as the size of one areal pixel. Furthermore, the areal pixel is formed at a coordinate position shifted 1 / 2 pixel in both the X and Y directions from the SEM pixel.

[0082] The area image generating circuit 303 linearly interpolates the gradation values ​​of the four vertices "A" to "D" of the area pixel (i.e., the center points "A" to "D" of the four SEM pixels). More specifically, for example, when the area threshold Z is 90 (Z=90), the area image generating circuit 303 calculates the contour line y where Z=90 in the area pixel.

[0083] For example, the contour line y is expressed by equation (2).

[0084]

number

[0085] Here, the variables a to d are calculated using the following arithmetic expressions: a=DC, b=AC, c=C+BDA, and d=C. A to D are the gradation values ​​at vertices "A" to "D". In the example of FIG. 7, A=50, B=80, C=100, and D=150. Therefore, the variables a to d are a=50, b=-50, c=-20, and d=100.

[0086] The area image generating circuit 303 performs the calculation of equation (3) (that is, the integration of equation (2)) to calculate the area equal to or greater than the area threshold Z.

[0087]

number

[0088] In the example of FIG. 7, the area of ​​the area pixel corresponding to the area threshold of 90 is 0.5653.

[0089] When A+D=B+C, c=0. In this case, the contour line y is expressed by equation (4).

[0090]

number

[0091] In this case, the area image generating circuit 303 performs the calculation of equation (5) (that is, the integration of equation (4)) to calculate the area equal to or greater than the area threshold Z.

[0092]

number

[0093] Furthermore, if D=B and A=C, then b=0 and c=0. In this case, the contour lines are expressed by equation (6).

[0094]

number

[0095] 8, the area image generating circuit 303 generates an area image by repeatedly calculating the area of ​​the area pixel while shifting the SEM pixel by one pixel at a time. At this time, the area pixel is formed at a coordinate position shifted by 1 / 2 pixel in both the X direction and the Y direction relative to the SEM pixel. For example, the position of the area image is corrected in alignment (step S31) of the comparison step (step S3), and the area image is compared with the reference image.

[0096] Next, a specific example of the first filtering process will be described.

[0097] As shown in FIG. 9 , in a 3×3 SEM pixel array, the gradation values ​​of the top three SEM pixels are 117, 119, and 105, starting from the left side of the page. The gradation values ​​of the middle three SEM pixels are 108, 70, and 108, starting from the left side of the page. The gradation values ​​of the bottom three SEM pixels are 113, 113, and 109, starting from the left side of the page. For example, the area threshold is 80. In this case, the gradation value of the central SEM pixel is less than the area threshold, and the gradation values ​​of the surrounding eight SEM pixels are equal to or greater than the area threshold. In other words, SEM pixels with gradation values ​​less than the area threshold are isolated. In this case, the area image generation circuit 303 determines that the four 2×2 area pixels including the center point of the central SEM pixel contain a false contour. The area image generation circuit 303 then determines each area pixel to be white (i.e., area "1").

[0098] Next, a specific example of the second filtering process will be described.

[0099] As shown in FIG. 10 , in a 3×3 SEM pixel array, the gradation values ​​of the top three SEM pixels are 83, 85, and 88, starting from the left side of the page. The gradation values ​​of the middle three SEM pixels are 84, 78, and 85, starting from the left side of the page. The gradation values ​​of the bottom three SEM pixels are 86, 82, and 79, starting from the left side of the page. For example, the area threshold is 80. The area image generation circuit 303 calculates the variance of the gradation values ​​of the 3×3 pixels surrounding the pixel of interest. In the example of FIG. 10 , the variance V is 10.5. For example, if the variance threshold is 20, the variance V is smaller than the variance threshold. In this case, the area image generation circuit 303 determines that the four 2×2 area pixels including the center point of the central SEM pixel, which is the pixel of interest, contain a false contour. In the example of FIG. 10 , the average value of the gradation values ​​of the 3×3 SEM pixels is equal to or greater than the area threshold. In this case, the area image generation circuit 303 determines the target area pixel to be white (i.e., area "1"). On the other hand, if the average value of the gradation values ​​of the 3x3 SEM pixels is less than the area threshold, the area image generation circuit 303 determines the target area pixel to be black (i.e., area "0").

[0100] [Step S105] The contour point calculation circuit 304 calculates contour points from the area image using a Sobel filter.

[0101] A specific example of calculation of contour points will be described with reference to Fig. 11 and Fig. 12. Fig. 11 is a diagram showing a specific example of calculation of contour points. Fig. 12 is a diagram showing a specific example of an area image. In the example of Fig. 12, an area of ​​14 x 14 pixels is shown. In addition, in the example of Fig. 12, contour points and normal vectors are shown on the area pixels for which contour points have been calculated.

[0102] As shown in Fig. 11, the contour point calculation circuit 304 uses a Sobel filter to determine the normal direction (normal vector) of the contour at the pixel of interest. More specifically, the contour point calculation circuit 304 performs convolution operations with the X-direction kernel and the Y-direction kernel of the Sobel filter on the area image. The contour point calculation circuit 304 then calculates the normal angle θ of the contour vector obtained by combining the calculated values ​​for the X and Y directions at the pixel of interest in the area pixel. For example, if the value in the X direction after Sobel filter processing is Fx and the value in the Y direction is Fy, the angle θ of the normal direction can be expressed by the formula θ = atan(Fy / Fx) (when Fx = 0, θ = π / 2).

[0103] Next, the contour point calculation circuit 304 calculates a half plane whose area is the same as the area of ​​the area pixel and whose boundary line is perpendicular to the normal vector found by the Sobel filter.

[0104] Next, the contour point calculation circuit 304 sets the midpoint of the boundary line as the coordinate position of the contour point. The coordinate position of the contour point is set in units of sub-pixels, which is obtained by dividing one area pixel into a plurality of sub-pixels.

[0105] The example in Fig. 12 shows a line and space pattern in which the line pattern extends in the X direction. The white area pixel region corresponds to the line pattern, and the black area pixels correspond to the space. In this case, the contour points and normal vectors are calculated for the area pixels sandwiched between the white area pixels and the black area pixels in the Y direction.

[0106] [Step S106] The adjacent contour point removal circuit 305 removes adjacent contour points. If the distance between the target contour point and an adjacent contour point is less than a preset distance threshold Lt, the adjacent contour point removal circuit 305 determines that the two contour points are in close proximity and removes one of the contour points as the close contour point. Of the two close contour points, the contour point to be removed is set arbitrarily. For example, of the two close contour points, the contour point farthest from the center of the area pixel may be removed. Furthermore, for example, a contour point corresponding to an SEM pixel (area pixel) that is scanned later in the electron beam scan (imaging) of the sample 30 may be removed.

[0107] A specific example of a nearby contour point is shown in Fig. 13. In the example of Fig. 13, a 5 x 5 pixel area centered on the nearby contour point is shown.

[0108] 13, for example, the nearby contour point elimination circuit 305 calculates the distances between contour point 2, which is the target of checking nearby contour points, and the adjacent contour points 1 and 3. In the example of FIG. 13, the distance L between contour point 1 and contour point 2 is 12 is less than the distance threshold Lt, so contour point 2 is removed.

[0109] [Step S107] The contour extraction circuit 214 defines area pixels including the contour points remaining after removing the neighboring contour points as contour pixels. The contour extraction circuit 214 then generates contour lines connecting the contour points. This generates an inspection image. The contour extraction circuit 214 stores the coordinate position of the contour point and the angle θ of the normal vector of each contour pixel, as well as the inspection image, in the storage device 22.

[0110] A specific example of an inspection image is shown in Fig. 14. Fig. 14 is a diagram showing a specific example of an inspection image. In the example of Fig. 14, an area of ​​4 x 4 pixels is shown.

[0111] As shown in FIG. 14, the contour extraction circuit 214 generates a contour line connecting the contour points of adjacent contour pixels.

[0112] 1.5. Effects of this embodiment For example, in an electron beam defect inspection system, if the distance between pattern edges is smaller than the size of the edge filter, it may be difficult to extract the contour line, resulting in a slow edge profile. Furthermore, brightness unevenness may occur due to charge-up of the sample, etc. Brightness unevenness can lead to false detection of the contour line.

[0113] In contrast, with the configuration according to this embodiment, the inspection device 1 can generate an area image based on an SEM image and generate contour data (inspection image) based on the area image. More specifically, the inspection device 1 can generate area pixels based on 2 x 2 SEM pixels. The inspection device 1 can generate contour lines based on an area threshold in the area pixels and calculate areas equal to or greater than the area threshold. The inspection device 1 can calculate contour points and normal vectors of the contour points in the area pixels. This reduces the effects of the generation of gradual edge profiles and uneven brightness. This improves the accuracy of extracting the contour lines of patterns in inspection images. Furthermore, by generating an area image, adjacent pixels can be processed in an overlapping manner, allowing contour points between adjacent pixels to be generated smoothly.

[0114] 2. Second embodiment Next, a second embodiment will be described. In the second embodiment, a method of generating contour data that is different from that of the first embodiment will be described. The following description will focus on the differences from the first embodiment.

[0115] 2.1 Details of the area image generation and contour data generation process The area image generating step in step S13 and the contour data generating step in step S14 described with reference to Fig. 3 will be described in detail with reference to Fig. 15. Fig. 15 is a flowchart of the area image generating step and the contour data generating step.

[0116] 15, in this embodiment, the processing of steps S101 to S103 and S110 is executed as an area image generation step (step S13). Then, steps S111, S106 and S107 are executed in order as a contour data generation step (step S14). Steps S101 to S103, S106 and S107 are the same as those in FIG. 5 of the first embodiment.

[0117] [Step S110] After the calculation of the area threshold value in step S103 is completed, the area image generation circuit 303 calculates the contour lines of the area pixels. That is, in this embodiment, the calculation of the area of ​​the area pixels is omitted. More specifically, the contour lines of the area pixels are calculated by equation (2) described in step S104 of the first embodiment, and the area calculation by equation (3) is omitted.

[0118] [Step S111] After the calculation of the contour lines of the area pixels is completed in step S110, the contour point calculation circuit 304 calculates contour points from the contour lines of the area pixels.

[0119] A specific example of calculation of contour points will be described with reference to Fig. 16. Fig. 16 is a diagram showing a specific example of calculation of contour points.

[0120] 16, the contour point calculation circuit 304 sets a contour point at the midpoint of the contour of the area pixel calculated in step S110, calculates its coordinate position, and then calculates the angle θ using the normal direction of the contour as the normal vector at the contour point.

[0121] 2.2 Effects of this embodiment The configuration according to this embodiment provides the same effects as those of the first embodiment.

[0122] Furthermore, with the configuration according to this embodiment, the contour point calculation circuit 304 can calculate contour points and their normal vectors from the contour lines of the area pixels. That is, the contour point calculation circuit 304 can calculate contour points and their normal vectors without using a Sobel filter to calculate normal vectors or contour points based on boundary lines. This allows the inspection device 1 to speed up the inspection image acquisition process.

[0123] 3. Third embodiment Next, a third embodiment will be described. In the third embodiment, a case where the first embodiment is applied to a DD system will be described. The following mainly describes the differences from the first and second embodiments.

[0124] 3.1 Overall flow of the inspection process First, an example of the overall flow of the inspection process will be described with reference to Fig. 17. Fig. 17 is a flowchart of the inspection process.

[0125] 17, the inspection process of this embodiment includes an image acquisition process (step S4) and a comparison process (step S3). The comparison process is similar to step S3 of the first embodiment shown in FIG.

[0126] An example of the image acquisition process of step S4 will be described. In this embodiment, an inspection image and a reference image are generated in the image acquisition process. In the following description, the SEM image used to generate the inspection image will also be referred to as an "inspection SEM image" or "left image." On the other hand, the SEM image used to generate the reference image will also be referred to as a "reference SEM image" or "right image."

[0127] The image acquisition circuitry 213 acquires an inspection SEM image and a reference SEM image from the imaging mechanism 10 (step S41). The image acquisition circuitry 213 transmits the inspection SEM image and the reference SEM image to the contour extraction circuitry 214.

[0128] Next, the noise filtering circuit 301 of the contour extraction circuit 214 performs pre-filtering on the inspection SEM image and the reference SEM image (step S42).

[0129] Next, the contour extraction circuit 214 generates an area image (hereinafter also referred to as "inspection area image") using the pre-filtered inspection SEM image. Also, the contour extraction circuit 214 generates an area image (hereinafter also referred to as "reference area image") using the pre-filtered reference SEM image (step S13).

[0130] Next, the contour extraction circuit 214 generates contour data from each of the inspection area image and the reference area image (step S14). That is, the contour extraction circuit 214 generates an inspection image (contour image) and a reference image (contour image).

[0131] The contour extraction circuit 214 transmits the test image and the reference image to the comparison circuit 215 and the storage device 22 .

[0132] 3.2 Details of the area image generation and contour data generation process The area image generating step and the contour data generating step will be described in detail with reference to Fig. 18. Fig. 18 is a flowchart of the area image generating step and the contour data generating step.

[0133] 18, steps S401 to S407 are executed as an area image generating step (step S43). Then, steps S408 to S410 are executed as a contour data generating step (step S44). Each step will be described in detail.

[0134] [Step S401] The noise filtering circuit 301 performs Gaussian filtering on the pre-filtered SEM image for inspection, similar to step S101 in Fig. 5 of the first embodiment. Note that in step S401, filtering other than Gaussian filtering may be applied. Also, step S401 may be omitted.

[0135] [Step S402] The area threshold calculation circuit 302 generates a histogram of the gradation values ​​of the inspection SEM image, similar to step S102 in FIG. 5 of the first embodiment. The area threshold calculation circuit 302 determines the maximum and minimum gradation values ​​corresponding to the pattern from the histogram. At this time, in order to reduce the influence of noise, the area threshold calculation circuit 302 determines the minimum and maximum values ​​within a range of gradation values ​​in which the number of pixels is greater than a preset frequency threshold. Note that the area threshold calculation circuit 302 may divide the SEM image into multiple regions (frames) and create a histogram for each region. In this case, the area threshold calculation circuit 302 calculates an area threshold for each region.

[0136] [Step S403] The area threshold calculation circuit 302 calculates the area threshold Z L (hereinafter referred to as "inspection area threshold Z L Calculate the inspection area threshold Z L The calculation method is the same as that in step S103 of FIG. 5 in the first embodiment.

[0137] [Step S404] The area threshold calculation circuit 302 calculates the standard deviation and average value of the grayscale values ​​in the designated areas of the inspection SEM image (left image) and the reference SEM image (right image).

[0138] [Step S405] The area threshold calculation circuit 302 performs the calculation of equation (7) to obtain the area threshold Z R (hereinafter referred to as "reference area threshold Z R The left image and the right image are images having almost the same properties, that is, the same patterns. Therefore, the area threshold calculation circuit 302 calculates the reference area threshold Z R can be calculated.

[0139]

number

[0140] where Z L is the area threshold of the left image (inspection SEM image). μ L is the average value of the gradation value of the Left image. L is the standard deviation of the grayscale values ​​of the Left image. R is the average value of the gradation value of the right image. R is the standard deviation of the gradation values ​​of the Right image.

[0141] A specific example of a case where an SEM image is divided into a plurality of regions will be described with reference to Fig. 19. Fig. 19 is a diagram showing an example where an inspection SEM image and a reference SEM image are each divided into four regions.

[0142] As shown in FIG. 19, the left and right images are divided into four regions (region F A , area F B , area F C , area F D ) are divided into the left image and the right image. A is the area F of the right image A Here, the area F of the left image corresponds to A The average value of the gradation value in μ LA and the standard deviation is σ LA Similarly, the area F of the right image A The average value of the gradation value in μ RA and the standard deviation is σ RA In this case, the area F of the right image A Reference area threshold Z RA is calculated using equation (8).

[0143]

number

[0144] Similarly, the region F of the left image B is the area F of the right imageB Corresponds to the region F in the left image. B The average value of the gradation value in μ LB and the standard deviation is σ LB Then, the area F of the right image B The average value of the gradation value in μ RB and the standard deviation is σ RB In this case, the area F of the right image B Reference area threshold Z RB is calculated using equation (9).

[0145]

number

[0146] Region F of the Left image C is the area F of the right image C Corresponds to the region F in the left image. C The average value of the gradation value in μ LC and the standard deviation is σ LC Then, the area F of the right image C The average value of the gradation value in μ RC and the standard deviation is σ RC In this case, the area F of the right image C Reference area threshold Z RC is calculated using equation (10).

[0147]

number

[0148] Also, the area F of the left image D is the area F of the right image D Corresponds to the region F in the left image. D The average value of the gradation value in μ LD and the standard deviation is σ LD Then, the area F of the right image D The average value of the gradation value in μ RD and the standard deviation is σ RDIn this case, the area F of the right image D Reference area threshold Z RD is calculated using equation (11).

[0149]

number

[0150] [Step S406] The area image generating circuit 303 calculates the inspection area threshold Z L The area image generating circuit 303 calculates the area of ​​an area pixel (hereinafter also referred to as "inspection area pixel") from the inspection SEM image using the filter. At this time, the area image generating circuit 303 may execute a first filter process and a second filter process. The area image generating circuit 303 generates an inspection area image by repeatedly calculating the area of ​​the area pixel while shifting the SEM pixel by one pixel.

[0151] [Step S407] As in step S406, the area image generating circuit 303 calculates the reference area threshold Z R (or Z RA ~Z RD ) to calculate the area of ​​an area pixel (hereinafter also referred to as "reference area pixel") from the reference SEM image. At this time, the area image generation circuit 303 may execute a first filter process and a second filter process. The area image generation circuit 303 generates the reference area image by repeatedly calculating the area of ​​the area pixel while shifting the SEM pixel by one pixel. Note that steps S406 and S407 may be executed simultaneously, or their order may be reversed.

[0152] [Step S408] The contour point calculation circuit 304 calculates contour points from the area image using a Sobel filter, similar to step S105 of Fig. 5 in the first embodiment. The contour point calculation circuit 304 calculates contour points in each of the area image for inspection and the area image for reference.

[0153] [Step S409] The nearby contour point removal circuit 305 removes nearby contour points from each of the inspection area image and the reference area image, similar to step S106 in FIG. 5 of the first embodiment.

[0154] [Step S410] The contour extraction circuit 214 generates contour lines connecting contour points in each of the area image for inspection and the area image for reference, similar to step S107 in FIG. 5 of the first embodiment. This generates an inspection image and a reference image. The contour extraction circuit 214 transmits the inspection image and the reference image to the comparison circuit 215.

[0155] 3.3 Effects of this embodiment The configuration according to this embodiment provides the same effects as those of the first embodiment.

[0156] With the configuration according to this embodiment, contour extraction can be performed even when the defect inspection method is the DD method.

[0157] Furthermore, with the configuration according to this embodiment, it is possible to divide an image region (frame) and set a different area threshold for each region, thereby correcting brightness variations between dies, i.e., between the inspection SEM image and the reference SEM image.

[0158] This embodiment can also be applied to the D-DB method, which generates a reference image by simulating an SEM image from design data.

[0159] 4. Fourth embodiment Next, a fourth embodiment will be described. In the fourth embodiment, a case where the second embodiment is applied to the DD system will be described. The following mainly describes the differences from the first to third embodiments.

[0160] 4.1 Details of the area image generation and contour data generation process The area image generating step in step S43 and the contour data generating step in step S44 described with reference to Fig. 17 will be described in detail with reference to Fig. 20. Fig. 20 is a flowchart of the area image generating step and the contour data generating step.

[0161] As shown in Fig. 20, steps S401 to S405, S420, and S421 are executed as an area image generation step (step S43). Then, steps S422, S409, and S410 are executed in order as a contour data generation step (step S44). Steps S401 to S405, S409, and S410 are the same as those in Fig. 18 of the third embodiment.

[0162] [Step S420] In step S405, the reference area threshold Z R After the calculation of the inspection area threshold Z L The contour lines of the area pixel for inspection are calculated using the following equation: In other words, in this embodiment, the calculation of the area of ​​the area pixel is omitted.

[0163] [Step S421] As in step S420, the area image generating circuit 303 calculates the reference area threshold Z R (or Z RA ~Z RD ) is used to calculate the contour lines of the reference area pixels. Note that steps S420 and S421 may be executed simultaneously, or their order may be reversed.

[0164] [Step S422] After the calculation of the contour lines of the reference area pixels is completed in step S421, the contour point calculation circuit 304 calculates the contour points of the inspection area image and the reference area image from the contour lines of the area pixels, similar to step S111 in Figure 15 of the second embodiment.

[0165] 4.2 Effects of this embodiment The configuration according to this embodiment provides the same effects as those of the second and third embodiments.

[0166] Similar to the third embodiment, this embodiment can also be applied to the D-DB method of generating a reference image by simulating an SEM image from design data.

[0167] 5. Modifications, etc. In the above embodiment, the case where an inspection image is generated in an inspection device has been described, but the method for generating an inspection image is not limited to an inspection device and may be applied to other devices that generate an inspection image based on image data, such as a measuring device.

[0168] The present invention is not limited to the above-described embodiments, and various modifications can be made in the implementation stage without departing from the spirit of the invention. Furthermore, the embodiments may be implemented in appropriate combinations, in which case the combined effects can be obtained. Furthermore, the above-described embodiments include various inventions, and various inventions can be extracted by combining selected elements from the disclosed elements. For example, if the problem can be solved and the desired effect can be obtained even if some elements are deleted from all elements shown in the embodiments, the configuration from which these elements are deleted can be extracted as an invention. [Explanation of symbols]

[0169] 1...inspection apparatus, 10...imaging mechanism, 11...sample chamber, 12...optical column, 13...stage, 14...stage driving mechanism, 15...detector, 16...electron gun, 17...electron optical system, 20...control mechanism, 21...control circuit, 22...storage device, 23...display device, 24...input device, 25...communication device, 30...sample, 101, 102...focusing lens, 103, 104...scanning coil, 105...objective lens, 211...expansion circuit, 212...reference image generation circuit, 213...image acquisition circuit, 214...contour extraction circuit, 215...comparison circuit, 221...design data, 222...parameter information, 223...inspection data, 224...threshold data, 225...defect inspection program, 301...noise filter processing circuit, 302...area threshold calculation circuit, 303...area image generation circuit, 304...contour point calculation circuit, 305...nearby contour point removal circuit

Claims

1. an imaging mechanism for capturing a first image of the sample; a first circuit for calculating a first threshold value based on the gradation value of the first image; a second circuit that generates a first contour line based on the first threshold value at a second pixel that is provided at the center of four first pixels arranged in two rows and two columns and included in the first image; a third circuit for calculating a first contour point at the second pixel using the generated first contour line; a comparison circuit for comparing a reference image with an inspection image based on the first contour points; An inspection device comprising:

2. the second circuit calculates a first area of ​​the second pixel that is surrounded by the first contour line and is equal to or greater than the first threshold; the third circuit calculates a first normal vector of the second pixel using a Sobel filter, forms a first half-plane at the second pixel that is orthogonal to the first normal vector and has the same first area as the second pixel, and calculates a midpoint of a line segment that constitutes the first half-plane of the second pixel as the first contour point. The inspection device according to claim 1 .

3. the third circuit determines a midpoint of the first contour line of the second pixel as the first contour point; The inspection device according to claim 1 .

4. the first circuit generates a histogram of the gradation values ​​of the first image, determines a minimum value and a maximum value of gradation values ​​having a number of pixels equal to or greater than a second threshold value set in advance using the histogram, and calculates the first threshold value using the minimum value and the maximum value; The inspection device according to any one of claims 1 to 3.

5. an imaging mechanism that captures a first image of a sample and a second image of the sample to be compared with the first image; a first circuit that calculates a first threshold value based on a gradation value of the first image and calculates a second threshold value of the second image using the first threshold value; a second circuit that generates a first contour line based on the first threshold value for a second pixel that is provided at the center of four first pixels that are arranged in two rows and two columns and that are included in the first image, and generates a second contour line based on the second threshold value for a fourth pixel that is provided at the center of four third pixels that are arranged in two rows and two columns and that are included in the second image; a third circuit that calculates a first contour point at the second pixel using the generated first contour line and calculates a second contour point at the fourth pixel using the generated second contour line; a comparison circuit for comparing an inspection image based on the first contour points with a reference image based on the second contour points; An inspection device comprising:

6. the second circuit calculates a first area surrounded by the first contour line in the second pixel, the first area being equal to or greater than the first threshold, and calculates a second area surrounded by the second contour line in the fourth pixel, the second area being equal to or greater than the second threshold; the third circuit uses a Sobel filter to calculate a first normal vector of the second pixel and a second normal vector of the fourth pixel, and forms, at the second pixel, a first half-plane that is orthogonal to the first normal vector and has the same calculated first area as the second pixel, and calculates a midpoint of a line segment that constitutes the first half-plane of the second pixel as the first contour point; and, at the fourth pixel, forms a second half-plane that is orthogonal to the second normal vector and has the same calculated second area as the fourth pixel, and calculates a midpoint of a line segment that constitutes the second half-plane of the fourth pixel as the second contour point. The inspection device according to claim 5 .

7. the third circuit determines a midpoint of the first contour line of the second pixel as the first contour point, and a midpoint of the second contour line of the fourth pixel as the second contour point; The inspection device according to claim 5 .

8. capturing a first image of the sample; calculating a first threshold value based on the gradation value of the first image; generating a first contour line based on the first threshold value at a second pixel provided at the center of four first pixels arranged in two rows and two columns included in the first image; calculating a first contour point at the second pixel using the generated first contour line; A method for generating an inspection image, comprising:

9. The method further includes a step of calculating a first area of ​​the second pixel that is surrounded by the first contour line and is equal to or greater than the first threshold value; The step of calculating the first contour points includes: calculating a normal vector at the second pixel using a Sobel filter; forming a first half-plane at the second pixel, the first half-plane being orthogonal to the normal vector and having the same calculated first area as the second pixel, and calculating a midpoint of a line segment constituting the first half-plane of the second pixel as the first contour point; The method of generating an inspection image of claim 8 , comprising:

10. further comprising a step of setting a midpoint of the first contour line of the second pixel as the first contour point. The method for generating an inspection image according to claim 8.

11. The step of calculating the first threshold value includes: generating a histogram of the tone values ​​of the first image; determining a minimum value and a maximum value of gradation values ​​having a number of pixels equal to or greater than a second threshold value set in advance using the histogram; calculating the first threshold value using the minimum value and the maximum value; The method of generating an inspection image according to any one of claims 8 to 10, comprising:

Images