Inspection device and inspection image generation method

Abstract

To improve extraction accuracy of a contour line of an inspection image.SOLUTION: An inspection device 1 according to an embodiment comprises: an imaging mechanism 10; a two-dimensional direction filter processing circuit 302 which executes filter processing by using a plurality of two-dimensional direction filters Fi for each pixel; a contour extraction circuit 214 which extracts a pixel in which at least one of intensities In_i for each filter angle calculated by the filter processing is greater than a threshold Th1 set for each filter angle as a contour point candidate pixel; a normal direction calculation circuit 303 which converts the intensity for each filter angle into polar coordinates in the contour point candidate pixel and calculates an angle in the first direction on the basis of the result of conversion into the polar coordinates; a contour point coordinate calculation circuit 306 which calculates coordinates of the contour point in the contour point candidate pixel on the basis of a one-dimensional profile in the first direction; a reference image generation circuit 212; and a comparison circuit 215.SELECTED DRAWING: Figure 10

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, mask defect inspection systems are required to have high defect detection performance that can handle fine original 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 defect inspection device extracts the contour lines of the pattern from the 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 the reference image.

[0006] For example, Cited 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, a filter process is performed for each direction on each frame image (pixel). 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). [Prior art documents] [Patent documents]

[0007] [Patent Document 1] Japanese Patent Application Publication No. 2022-16779 Summary of the Invention [Problem to be solved by the invention]

[0008] For example, if the image profile in a captured image is asymmetric, i.e., if the captured image has directional dependency, then the post-filtering intensity will have directional dependency. Therefore, if the post-filtering intensity in all directions is determined using the same intensity threshold, the intensity threshold used to determine the post-filtering intensity must be set to a relatively low value to match the direction with low intensity. However, if the intensity threshold is set low, noise is likely to be detected, increasing the likelihood of generating false contours. Furthermore, if the normal direction of a contour is determined based on the magnitude of the post-filtering intensity, then the directional dependency of the post-filtering intensity increases the likelihood of the normal direction being calculated incorrectly.

[0009] The present invention has been made in light of these points. That is, the present invention allows an inspection device to set different intensity thresholds for each direction. Then, the inspection device can calculate the angle of the major axis direction of an equivalent ellipse as the angle of the normal direction based on the result of converting the relationship between the filtered intensity and the filter angle into polar coordinates. As a result, contour detection and normal direction determination are processed independently, thereby providing an inspection device and an inspection image generation method that can improve the accuracy of contour extraction in an inspection image. [Means for solving the problem]

[0010] According to a first aspect of the present invention, an inspection apparatus includes an imaging mechanism for capturing an image of a sample, and for each pixel of the image, each other different Directional a two-dimensional directional filter processing circuit that performs filter processing using a plurality of two-dimensional directional filters; Indicates the directionality of each of multiple two-dimensional directional filters a contour extraction circuit that extracts pixels as contour point candidate pixels, where at least one of the intensities for each filter angle is greater than a threshold value set for each filter angle; and a contour extraction circuit that converts the intensities for each filter angle into polar coordinates for the contour point candidate pixels, and based on the results of the conversion into polar coordinates, The angle of the major axis of the equivalent ellipse in polar coordinates is Angle in the first direction as The device includes a normal direction calculation circuit that calculates the coordinates of a contour point at a contour point candidate pixel based on a one-dimensional profile in a first direction, a contour point coordinate calculation circuit that calculates the coordinates of a contour point at a contour point candidate pixel based on a one-dimensional profile in a first direction, a reference image generation circuit that generates a reference image, and a comparison circuit that compares the inspection image based on the contour points with the reference image.

[0011] According to a second aspect of the present invention, a method for generating an inspection image includes the steps of: each other different Directional A step of performing a filtering process using a plurality of two-dimensional directional filters; Indicates the directionality of each of multiple two-dimensional directional filters A step of extracting, as a contour point candidate pixel, a pixel in which at least one of the intensities for each filter angle is greater than a threshold value set for each filter angle; a step of converting the intensities for each filter angle in the contour point candidate pixel into polar coordinates; and based on the result of the conversion into polar coordinates, The angle of the major axis of the equivalent ellipse in polar coordinates is Angle in the first direction as and calculating coordinates of contour points at contour point candidate pixels based on the one-dimensional profile in the first direction. [Effects of the Invention]

[0012] 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]

[0013] [Figure 1] FIG. 1 is a diagram showing the overall configuration of an inspection device according to an embodiment. [Figure 2] FIG. 2 is a block diagram of a contour extraction circuit included in the inspection apparatus according to one embodiment. [Figure 3] FIG. 3 is a diagram showing an example of a filter angle of a two-dimensional directional filter in an inspection device according to an embodiment. [Figure 4] FIG. 4 is a diagram showing an example of displaying pixel values ​​of 5×5 pixels with a pixel of interest at the center in an inspection device according to an embodiment. [Figure 5] FIG. 5 is a diagram showing an example of displaying matrix vectors of a two-dimensional directional filter in an inspection device according to an embodiment. [Figure 6] FIG. 6 is a diagram showing a specific example of two-dimensional directional filters F1 to F4 in the inspection device according to one embodiment. [Figure 7] FIG. 7 is a diagram showing specific examples of two-dimensional directional filters F5 to F8 in the inspection device according to one embodiment. [Figure 8] FIG. 8 is a flowchart of an inspection process in an inspection device according to one embodiment. [Figure 9] FIG. 9 is a diagram showing a specific example of 4×4 pixels including the contour lines of the inspection image and the contour lines of the reference image in the comparison step in the inspection device according to one embodiment. [Figure 10] FIG. 10 is a flowchart of a contour extraction process in an inspection apparatus according to an embodiment. [Figure 11] FIG. 11 is a graph showing the relationship between the filter angle and the filtered intensity in an inspection device according to one embodiment. [Figure 12] FIG. 12 is a graph showing the result of converting the relationship between the filtered intensity and the filter angle shown in FIG. 11 into polar coordinates. [Figure 13] FIG. 13 is a diagram showing a specific example of extraction of sampling points in an inspection device according to an embodiment. [Figure 14]FIG. 14 is a graph showing a specific example of gradation values ​​at sampling points in an inspection device according to an embodiment. [Figure 15] FIG. 15 is a graph showing a specific example of the filtered intensity after edge filtering of the one-dimensional profile shown in FIG. [Figure 16] FIG. 16 is a graph showing a specific example in which spline interpolation is applied to the filtered intensity shown in FIG. [Figure 17] FIG. 17 is a diagram showing a specific example in which the maximum value of the intensity after filtering by spline interpolation shown in FIG. 16 is set as a contour point. [Figure 18] FIG. 18 is a diagram showing a specific example of an isolated contour point in an inspection apparatus according to an embodiment. [Figure 19] FIG. 19 is a diagram showing a specific example of a near contour point in an inspection apparatus according to an embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0014] 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.

[0015] In the following, a defect inspection apparatus will be described as an 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 pattern to be measured. The defect inspection apparatus may use an optical microscope to capture an optical image of the pattern, or may use a light receiving element to capture an optical image of light reflected by or transmitted through the sample. In addition, in this embodiment, a case will be described in which the sample to be inspected is a mask, but the sample may 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.

[0016] 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.

[0017] As shown in FIG. 1, the inspection device 1 includes an imaging mechanism 10 and a control mechanism 20.

[0018] 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.

[0019] In the sample chamber 11, a stage 13, a stage driving mechanism 14, and a detector 15 are provided.

[0020] 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.

[0021] 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.

[0022] 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, data of the SEM image, to the image acquisition circuit 213.

[0023] The electron gun 16 and the 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 is shown in which a single beam is irradiated onto the sample 30. However, the SEM may also be configured to irradiate the sample 30 with multiple beams.

[0024] The electron gun 16 is installed so as to emit an electron beam toward the sample chamber 11 .

[0025] 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.

[0026] 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 .

[0027] 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).

[0028] The control circuit 21 includes an unfolding circuit 211, a reference image generating circuit 212, an image acquisition circuit 213, a contour extraction circuit 214, and a comparison circuit 215. These may be configured by a program executed by an integrated circuit such as a CPU, ASIC, FPGA, or GPU, or by hardware or firmware included in those integrated circuits, or by individual circuits controlled by those integrated circuits. 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 acquisition circuit 213, the contour extraction circuit 214, and the comparison circuit 215 by a program executed by the control circuit 21.

[0029] 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 in 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 of the expanded image. The calculated figure occupancy rate for each pixel is the pixel value. The following describes a case where the pixel values ​​of the expanded image are expressed as 8-bit gradation 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%.

[0030] 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 a contour 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.

[0031] The image acquisition circuitry 213 acquires data of the SEM image from the detector 15 of the imaging mechanism 10. The image acquisition circuitry 213 transmits the data of the SEM image to the contour extraction circuitry 214 and the storage device 22.

[0032] The contour extraction circuit 214 extracts contour data from the SEM image to generate an inspection image (contour image). The contour data includes information about the contour points of the pattern and the contour lines connecting the contour points. In other words, the contour data includes representative values ​​of the coordinates through which the contour lines pass for each pixel, i.e., the contour points, and information about the normal directions of the contour vectors at the contour points. The contour extraction circuit 214 will be described in detail later.

[0033] 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.

[0034] 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, etc. For example, the parameter information 222 of inspection conditions includes imaging conditions of the imaging mechanism 10, reference image generation conditions, contour extraction conditions of 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 intensity threshold data 224 is data of an intensity threshold used for contour extraction of SEM images. The storage device 22 also stores 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.

[0035] 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).

[0036] 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.

[0037] The input device 24 is an input device such as a keyboard, a mouse, a touch panel, or a button switch.

[0038] 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 from an external device and transmits defect inspection results and the like to the external device.

[0039] 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.

[0040] 2, the contour extraction circuit 214 includes a noise filter processing circuit 301, a two-dimensional direction filter processing circuit 302, a normal direction calculation circuit 303, a one-dimensional profile calculation circuit 304, an edge filter processing circuit 305, a contour point coordinate calculation circuit 306, an isolated contour point elimination circuit 307, and a nearby contour point elimination circuit 308. Data generated by each unit can be stored in the storage device 22 each time it is generated.

[0041] The noise filter processing circuit 301 removes (reduces) noise from the SEM image data. The noise filter processing circuit 301 acquires the SEM image data from the image acquisition circuit 213. The noise filter processing circuit 301 then removes (reduces) noise from 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.

[0042] The two-dimensional directional filter processing circuit 302 performs two-dimensional directional filtering on each pixel of the image data after noise filtering. The two-dimensional directional filter processing circuit 302 has multiple two-dimensional directional filters with different directionality (hereinafter, the direction will also be referred to as "filter angle"). A common filter such as a Laplacian filter can be used as the two-dimensional directional filter. Details of the two-dimensional directional filter will be described later. The two-dimensional directional filter processing circuit 302 performs two-dimensional directional filtering for each direction (filter angle) and calculates an intensity value for each filter angle (hereinafter, the intensity value after filtering will be referred to as "filtered intensity"). The two-dimensional directional filter processing circuit 302 reads intensity threshold data 224 from the storage device 22 and compares the filtered intensity with the intensity threshold for each filter angle. Note that a different intensity threshold value can be set for each filter angle. For example, the intensity threshold is set for each filter angle based on the results of capturing an image of a calibration pattern before an inspection is performed. The two-dimensional direction filtering circuit 302 extracts pixels having a post-filtering intensity equal to or greater than the intensity threshold as candidate pixels containing contour points (hereinafter referred to as "contour point candidate pixels").

[0043] The normal direction calculation circuit 303 is a circuit that calculates the normal direction of the contour at the contour point candidate pixel. The normal direction calculation circuit 303 converts the filtered intensity distribution for each filter angle into polar coordinates. From the converted polar coordinate results, the normal direction calculation circuit 303 calculates the direction of the major axis of an ellipse (hereinafter referred to as an "equivalent ellipse") having the same second moment as the normal direction.

[0044] The one-dimensional profile calculation circuit 304 calculates a one-dimensional profile in the normal direction of the contour point candidate pixel.

[0045] The edge filter processing circuit 305 performs edge filtering on the one-dimensional profile in the normal direction.

[0046] The contour point coordinate calculation circuit 306 calculates the coordinates (positions) of contour points in contour point candidate pixels. The contour point coordinate calculation circuit 306 performs interpolation processing of the post-filtering intensity after the edge filter processing. The contour point coordinate calculation circuit 306 calculates the maximum value of the post-filtering intensity after the interpolation processing as the coordinates of the contour point.

[0047] The isolated contour point removal circuit 307 removes isolated contour points. An isolated contour point is a contour point where the number of contour points in an area (e.g., 3 (rows) × 3 (columns) pixels) including the contour point candidate pixel and its surrounding pixels, including the target contour point, is less than a predetermined number (e.g., 1 or 2).

[0048] The nearby contour point removal circuit 308 removes nearby contour points, which are contour points whose distance to adjacent contour points is equal to or less than a preset distance.

[0049] 3. 2D Directional Filtering Next, the two-dimensional directional filtering process will be described.

[0050] 3.1 Filter angle for 2D directional filtering First, an example of the filter angle of the two-dimensional directional filter will be described with reference to Fig. 3. Fig. 3 is a diagram showing an example of the filter angle of the two-dimensional directional filter.

[0051] As shown in FIG. 3, for example, eight two-dimensional directional filters corresponding to eight filter angles (directions) in the range of 0 to 180° are used. If each filter angle is numbered i (i is an integer from 1 to 8), when i=1, a two-dimensional directional filter corresponding to a filter angle of 0° is used. When i=2, a two-dimensional directional filter corresponding to a filter angle of 22.5° is used. When i=3, a two-dimensional directional filter corresponding to a filter angle of 45° is used. When i=5, a two-dimensional directional filter corresponding to a filter angle of 90° is used. When i=6, a two-dimensional directional filter corresponding to a filter angle of 112.5° is used. When i=7, a two-dimensional directional filter corresponding to a filter angle of 135° is used. When i=8, a two-dimensional directional filter corresponding to a filter angle of 157.5° is used.

[0052] 3.2 Example of display of pixel values ​​used in 2D directional filtering Next, a display example of the pixel values ​​of each pixel used in the two-dimensional directional filtering process will be described with reference to Fig. 4. Fig. 4 is a diagram showing a display example of pixel values ​​of 5 x 5 pixels centered on a pixel of interest.

[0053] As shown in Fig. 4, for example, two-dimensional directional filtering uses the gradation values ​​of 5 (rows) x 5 (columns) pixels centered around a pixel of interest. For example, the coordinates of the pixel of interest are (X, Y) = (I, J) (where I and J are integers). Then, the coordinates of each of the 5 x 5 pixels are expressed as (X, Y) = (I-2, J-2) to (I+2, J+2) from the upper left to the lower right of the page. In this case, the gradation value D of each pixel is defined as D(I-2, J-2) to D(I+2, J+2).

[0054] 3.3 Coordinates of matrix vectors of 2D directional filters Next, an example of coordinate display of matrix vectors of a two-dimensional directional filter will be described with reference to Fig. 5. Fig. 5 is a diagram showing an example of display of matrix vectors of a two-dimensional directional filter.

[0055] As shown in Fig. 5, for example, a 5x5 filter is used as the two-dimensional directional filter. For example, if the center coordinate is (0,0), each coordinate position is expressed as (X,Y) = (-2,-2) to (2,2) from the bottom left to the top right of the paper. For example, if the two-dimensional directional filter corresponding to the i-th filter angle is Fi, each value of the two-dimensional directional filter Fi is defined as Fi(-2,-2) to Fi(2,2).

[0056] 3.4 Example of a 2D directional filter Next, specific examples of two-dimensional directional filters will be described with reference to Fig. 6 and Fig. 7. Fig. 6 is a diagram showing specific examples of two-dimensional directional filters F1 to F4. Fig. 7 is a diagram showing specific examples of two-dimensional directional filters F5 to F8.

[0057] As shown in Figure 6, for example, in a two-dimensional directional filter F1, the values ​​of F1(-2,-2), F1(-1,-2), F1(0,-2), F1(1,-2), F1(2,-2), F1(-2,-1), F1(-1,-1), F1(0,-1), F1(1,-1), F1(2,-1), F1(-1,0), F1(1,0), F1(-2,1), F1(-1,1), F1(0,1), F1(1,1), F1(2,1), F1(-2,2), F1(-1,2), F1(0,2), F1(1,2), and F1(2,2) are set to 0. The values ​​of F1(-2,0) and F1(2,0) are set to -1. Let the value of F1(0,0) be 2.

[0058] For example, for the two-dimensional directional filter F2, let the values ​​of F2(-2,-2), F2(-1,-2), F2(0,-2), F2(1,-2), F2(2,-2), F2(-2,-1), F2(-1,-1), F2(0,-1), F2(0,1), F2(0,1), F2(1,1), F2(2,1), F2(-2,2), F2(-1,2), F2(0,2), F2(1,2), and F2(2,2) be 0. Let the values ​​of F2(1,-1) and F2(-1,1) be -0.1165. Let the values ​​of F2(2,-1) and F2(-2,1) be -0.6488. Let the values ​​of F2(-2,0) and F2(2,0) be -0.1989. Set the values ​​of F2(-1,0) and F2(1,0) to -0.0357. Set the value of F2(0,0) to 2.

[0059] For example, for the two-dimensional directional filter F3, the values ​​of F3(-2,-2), F3(-1,-2), F3(0,-2), F3(-2,-1), F3(-1,-1), F3(0,-1), F3(-2,0), F3(-1,0), F3(1,0), F3(2,0), F3(0,1), F3(1,1), F3(2,1), F3(0,2), F3(1,2), and F3(2,2) are set to 0. The values ​​of F3(1,-2), F3(2,-1), F3(-2,1), and F3(-1,2) are set to -0.2426. The values ​​of F3(2,-2) and F3(-2,2) are set to -0.1716. Set the values ​​of F3(1,-1) and F3(-1,1) to -0.3431. Set the value of F3(0,0) to 2.

[0060] For example, for the two-dimensional directional filter F4, let the values ​​of F4(-2,-2), F4(-1,-2), F4(2,-2), F4(-2,-1), F4(-1,-1), F4(2,-1), F4(-2,0), F4(-1,0), F4(1,0), F4(2,0), F4(-2,1), F4(1,1), F4(2,1), F4(-2,2), F4(1,2), and F4(2,2) be 0. Let the values ​​of F4(0,-2) and F4(0,2) be -0.19896. Let the values ​​of F4(1,-2) and F4(-1,2) be -0.6488. Let the values ​​of F4(0,-1) and F4(0,1) be -0.0357. Set the values ​​of F4(1,-1) and F4(-1,1) to -0.1165. Set the value of F4(0,0) to 2.

[0061] 7, for example, in the two-dimensional directional filter F5, the values ​​of F5(-2,-2), F5(-1,-2), F5(1,-2), F5(2,-2), F5(-2,-1), F5(-1,-1), F5(0,-1), F5(1,-1), F5(2,-1), F5(-2,0), F5(-1,0), F5(1,0), F5(2,0), F5(-2,1), F5(-1,1), F5(0,1), F5(1,1), F5(2,1), F5(-2,2), F5(-1,2), F5(1,2), and F5(2,2) are set to 0. The values ​​of F5(0,-2) and F5(0,2) are set to -1. Set the value of F5(0,0) to 2.

[0062] For example, for two-dimensional directional filter F2, the values ​​of F6(-2,-2), F6(1,-2), F6(2,-2), F6(-2,-1), F6(1,-1), F6(2,-1), F6(-2,0), F6(-1,0), F6(1,0), F6(2,0), F6(-2,1), F6(-1,1), F6(2,1), F6(-2,2), F6(-1,2), and F6(2,2) are set to 0. The values ​​of F6(-1,-2) and F6(1,2) are set to -0.6488. The values ​​of F6(0,-2) and F6(0,2) are set to -0.1989. The values ​​of F6(-1,-1) and F6(1,1) are set to -0.1165. Set the values ​​of F6(0,-1) and F6(0,1) to -0.0357. Set the value of F6(0,0) to 2.

[0063] For example, in the two-dimensional directional filter F7, the values ​​of F7(0,-2), F7(1,-2), F7(2,-2), F7(0,-1), F7(1,-1), F7(2,-1), F7(-2,0), F7(-1,0), F7(1,0), F7(2,0), F7(-2,1), F7(-1,1), F7(0,1), F7(-2,2), F7(-1,2), and F7(0,2) are set to 0. The values ​​of F7(-2,-2) and F7(2,2) are set to -0.1716. The values ​​of F7(-1,-2), F7(-2,-1), F7(2,1), and F7(1,2) are set to -0.2426. Set the values ​​of F7(-1,-1) and F7(1,1) to -0.3431. Set the value of F7(0,0) to 2.

[0064] For example, for the two-dimensional directional filter F8, let the values ​​of F8(-2,-2), F8(-1,-2), F8(0,-2), F8(1,-2), F8(2,-2), F8(0,-1), F8(1,-1), F8(2,-1), F8(-2,1), F8(-1,1), F8(0,1), F8(-2,2), F8(-1,2), F8(0,2), F8(1,2), and F8(2,2) be 0. Let the values ​​of F8(-2,-1) and F8(2,1) be -0.6488. Let the values ​​of F8(-1,-1) and F8(1,1) be -0.1165. Let the values ​​of F8(-2,0) and F8(2,0) be -0.19896. Set the values ​​of F8(-1,0) and F8(1,0) to -0.0357. Set the value of F8(0,0) to 2.

[0065] 3.5 2D Directional Filtering Equation Next, the calculation formula for the filter processing in each of the two-dimensional directional filters F1 to F8 will be explained. The two-dimensional directional filtering circuit 302 performs the calculation of formula (1) and performs a convolution calculation between the two-dimensional directional filter Fi and the pixel of interest.

[0066]

number

[0067] Here, In_i indicates the intensity after filtering by the two-dimensional directional filter Fi. D indicates the gradation value of the pixel. The asterisk (*) indicates a convolution operation. If the coordinates of the pixel of interest are (X, Y) = (I, J) as explained in Figure 4, the operation of equation (1) can be expressed as equation (2).

[0068]

number

[0069] Here, k is an integer between −2 and 2 that indicates the amount of shift from coordinate I. l is an integer between −2 and 2 that indicates the amount of shift from coordinate J.

[0070] More specifically, the formula (2) is expressed by the following formula: In_i=D(I-2,J-2)Fi(-2,-2)+D(I-1,J-2)Fi(-1,-2)+D(I,J-2)Fi(0,-2)+D(I+1,J-2)Fi(1,-2)+D(I+2,J-2)Fi(2,-2)+D(I-2,J-1)Fi( -2,-1)+D(I-1,J-1)Fi(-1,-1)+D(I,J-1)Fi(0,-1)+D(I+1,J-1)Fi(1,-1)+D(I+2,J-1)Fi(2,-1)+D(I-2,J)Fi(-2,0)+D(I-1,J)Fi(-1,0) )+D(I,J)Fi(0,0)+D(I+1,J)Fi(1,0)+D(I+2,J)Fi(2,0)+D(I-2,J+1)Fi(-2,1)+D(I-1,J+1)Fi(-1,1)+D(I,J+1)Fi(0,1)+D(I+1,J+1)F i(1,1)+D(I+2,J+1)Fi(2,1)+D(I-2,J+2)Fi(-2,2)+D(I-1,J+2)Fi(-1,2)+D(I,J+2)Fi(0,2)+D(I+1,J+2)Fi(1,2)+D(I+2,J+2)Fi(2,2)

[0071] 4 Overall flow of the inspection process Next, an example of the overall flow of the inspection process will be described with reference to Fig. 8. Fig. 8 is a flowchart of the inspection process.

[0072] As shown in FIG. 8, 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).

[0073] 4.1 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.

[0074] Next, the contour extraction circuit 214 executes noise filtering to remove noise from the SEM image (step S12).

[0075] Next, the contour extraction circuit 214 extracts the contour of the pattern from the SEM image after the noise filtering process (step S13) to generate an inspection image (contour image). That is, the contour extraction circuit 214 extracts a contour line and a plurality of contour points for each figure pattern.

[0076] The contour extraction circuit 214 transmits the generated inspection image to the comparison circuit 215 and the storage device 22 .

[0077] 4.2 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.

[0078] 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.

[0079] Next, the reference image generating circuit 212 executes resizing processing and corner rounding processing on the expanded image (step S23).

[0080] Next, the reference image generation circuit 212 extracts the pattern contour from the expanded image that has been resized and corner-rounded (step S24) 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.

[0081] 4.3 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.

[0082] 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.

[0083] 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.

[0084] A specific example of a comparison between an inspection image and a reference image is shown in Figure 9. Figure 9 shows a specific example of 4x4 pixels including the contour lines of the inspection image and the contour lines of the reference image.

[0085] 9, 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.

[0086] 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.

[0087] 5 Details of the contour extraction process Next, the contour extraction process of step S13 will be described in detail with reference to Fig. 10. Fig. 10 is a flowchart of the contour extraction process.

[0088] 10, the contour extraction process involves steps S101 to S109, which are executed for each pixel of the SEM image (photographed image) after noise filtering. Each step will now be described in detail.

[0089] [Step S101] The two-dimensional direction filter processing circuit 302 performs two-dimensional direction filter processing corresponding to each of a plurality of directions (filter angles) on each pixel of the SEM image after noise filtering. Then, the two-dimensional direction filter processing circuit 302 compares the filtered intensity with an intensity threshold for each filter angle.

[0090] A specific example of two-dimensional directional filtering using eight-directional two-dimensional directional filters F1 to F8 will be described with reference to Fig. 11. Fig. 11 is a graph showing the relationship between the filter angle and the post-filtering intensity.

[0091] As shown in Fig. 11, the two-dimensional directional filter processing circuit 302 performs filtering at each of eight filter angles and calculates the filtered intensity. In the example of Fig. 11, the filtered intensity value is maximum when the filter angle is 90° using the two-dimensional directional filter F5.

[0092] A different intensity threshold Th1 is set for each filter angle. In the example of Fig. 11, the intensity threshold Th1 is set to the lowest at a filter angle of 22.5° (i=2), and the intensity threshold Th1 is set to the highest at a filter angle of 67.5° (i=4).

[0093] The two-dimensional direction filtering circuit 302 compares the filtered intensity with the intensity threshold Th1 at each filter angle. In the example of Fig. 11, the filtered intensity is higher than the intensity threshold Th1 at filter angles of 67.5° (i=4), 90° (i=5), and 112.5° (i=6).

[0094] [Step S102] The contour extraction circuit 214 checks whether or not there is a post-filtering intensity greater than the intensity threshold Th1. If there is no filter angle at which the post-filtering intensity is greater than the intensity threshold Th1 (No in step S102), the contour extraction circuit 214 determines that the pixel does not correspond to a contour point candidate pixel, and terminates contour extraction for the pixel.

[0095] On the other hand, if there is a filter angle at which the post-filtering intensity is higher than the intensity threshold value Th1 (Yes in step S102), the contour extraction circuit 214 extracts the pixel as a contour point candidate pixel.

[0096] [Step S103] The normal direction calculation circuit 303 displays the intensity distribution by regarding the filtered intensity and the filter angle as polar coordinates. For example, the i-th filtered intensity is In_i, and the filter angle is Si. When polar coordinates (In_I, Si) are converted into XY coordinates, they are expressed as (X, Y) = (In_i cos(Si), In_i sin(Si)). In this case, since the filter angle is in the range of 0° to 180°, the filter angles of 0° to 180° are set as filter angles symmetrical about the origin for angles of 180° to 360°. The filtered intensity is then set to a value equal to the filter angle symmetrical about the origin. More specifically, the eight filter angles in the range of 0° to 180° described with reference to FIG. 3 are numbered i=1 to 8, and the filter angles symmetrical about the origin are numbered i=9 to 16. i=9 is symmetrical about the origin of i=1. The filter angle for i=9 is S9=0°(S1)+180°=180°, and the filtered intensities are In_9=In_1. i=10 is symmetrical with i=2 about the origin. The filter angle for i=10 is S10=22.5°(S2)+180°=202.5°, and the filtered intensities are In_10=In_2. i=11 is symmetrical with i=3 about the origin. The filter angle for i=11 is S11=45°(S3)+180°=225°, and the filtered intensities are In_11=In_3. i=12 is symmetrical with i=4 about the origin. The filter angle for i=12 is S12=67.5°(S4)+180°=247.5°, and the filtered intensities are In_12=In_4. i=13 is symmetric with i=5 around the origin. The filter angle for i=13 is S13 = 90° (S5) + 180° = 270°, and the filtered intensities are In_13 = In_5. i=14 is symmetric with i=6 around the origin. The filter angle for i=14 is S14 = 112.5° (S6) + 180° = 292.5°, and the filtered intensities are In_14 = In_6. i=15 is symmetric with i=7 around the origin. The filter angle for i=15 is S15 = 135° (S7) + 180° = 315°, and the filtered intensities are In_15 = In_7. i=16 is symmetric with i=8 around the origin.The filter angle for i=16 is S16=157.5°(S8)+180°=337.5°, and the filtered intensities have the relationship In_16=In_8.

[0097] A specific example of a display converted into polar coordinates is shown in Fig. 12. Fig. 12 is a graph showing the results of converting the relationship between filtered intensity and filter angle shown in Fig. 11 into polar coordinates.

[0098] By converting to polar coordinates, the intensity distribution becomes bipolar, as shown in Figure 12. The dashed ellipse is the equivalent ellipse for the bipolar intensity distribution.

[0099] [Step S104] A normal direction calculation circuit 303 calculates the angle of the major axis direction of the equivalent ellipse from the second moment of the filtered intensity converted into polar coordinates as the angle of the normal direction to the contour line.

[0100] More specifically, the normal direction calculation circuit 303 performs the calculation of equation (3) to calculate the second moment M20 in the X direction, that is, the variance of X.

[0101]

number

[0102] The normal direction calculation circuit 303 performs the calculation of equation (4) to calculate the second moment M02 in the Y direction, that is, the Y variance.

[0103]

number

[0104] The normal direction calculation circuit 303 performs the calculation of equation (5) to calculate the XY intersection moment M11, that is, the covariance of XY.

[0105]

number

[0106] Next, the normal direction calculation circuit 303 performs the calculation of equation (6) to calculate the angle θ of the principal axis direction, that is, the normal direction.

[0107]

number

[0108] Here, mod denotes the modulo function. When mod(a, b) is used, the answer is the remainder when a is divided by b. If a and b are not integers, the general formula is mod(a, b) = a - floor(a / b) × b. Here, floor(c) denotes the nearest integer smaller than c, and "×" denotes multiplication and " / " denotes division. atan2 denotes the arctangent function. The result of the atan2 function is expressed in radians ranging from -π to π. The order of the arguments in parentheses for atan2 in equation (6) corresponds to programming languages ​​such as C, C++, and Fortran. pi denotes the constant π. Converting the value obtained from equation (6) to degrees (°), the normal angle θ is approximately 88° in the example shown in Figure 12.

[0109] [Step S105] The one-dimensional profile calculation circuit 304 calculates a one-dimensional profile in the normal direction with the center of the contour point candidate pixel as the origin. More specifically, the one-dimensional profile calculation circuit 304 first extracts sampling points at one-pixel intervals in the normal direction with the center of the contour point candidate pixel as the origin.

[0110] A specific example of sampling point extraction is shown in Fig. 13. In the example of Fig. 13, 5 x 5 pixels are shown with a contour point candidate pixel at the center.

[0111] As shown in FIG. 13, the one-dimensional profile calculation circuit 304 defines the normal direction, with the center of the contour point candidate pixel as the origin (0), as a one-dimensional coordinate. The one-dimensional profile calculation circuit 304 then extracts multiple sampling points at one-pixel intervals in the positive and negative directions. If the one-pixel interval is L1, the distance between two sampling points is L1. In the example of FIG. 13, sampling points with coordinates (1), (2), and (3) are extracted with the right side of the page as the positive direction from the origin. Similarly, sampling points with coordinates (-1), (-2), and (-3) are extracted with the left side of the page as the negative direction. It is preferable that the number of sampling points extracted be 11 or more, including the contour point candidate pixel, taking into account the processing described below.

[0112] Next, the one-dimensional profile calculation circuit 304 calculates the gradation value (brightness value) at each sampling point and creates a one-dimensional profile in the normal direction. More specifically, the positions of the sampling points are set in sub-pixel units, which are obtained by dividing one pixel into multiple sub-pixels. For example, the one-dimensional profile calculation circuit 304 calculates the gradation value at the sampling point using a bicubic interpolation method using surrounding 4 x 4 pixels (16 pixels).

[0113] A specific example of the gradation values ​​at the sampling points is shown in Fig. 14. Fig. 14 is a graph showing a specific example of the gradation values ​​at the sampling points.

[0114] In the example of Fig. 14, the sampling point of the contour point candidate pixel is set to coordinate (0), and 11 sampling points from (-5) to (5) are extracted. The sampling point with coordinate (-1) has the highest gradation value, and the sampling point with coordinate (4) has the lowest gradation value.

[0115] [Step S106] The edge filter processing circuit 305 performs edge filtering on the gradation values ​​of the sampling points.

[0116] A specific example of edge filtering is shown in Fig. 15. Fig. 15 is a graph showing a specific example of filtered intensity after edge filtering of the one-dimensional profile shown in Fig. 14.

[0117] The example in FIG. 15 shows the result of performing a convolution operation using a 1×5 one-dimensional filter (−1, 0, 2, 0, 1) as edge filtering.

[0118] [Step S107] The contour point coordinate calculation circuit 306 performs interpolation of the post-filtering intensity after the edge filtering process to calculate the coordinates of the contour points. More specifically, first, it performs interpolation of the post-filtering intensity at each sampling point. For example, spline interpolation is used as the interpolation process. The contour point coordinate calculation circuit 306 calculates the coordinates of the contour points where the post-filtering intensity is maximum based on the result of the interpolation process.

[0119] A specific example of spline interpolation is shown in Fig. 16. Fig. 16 is a graph showing a specific example of spline interpolation applied to the filtered intensity shown in Fig. 15.

[0120] As shown in Fig. 16, for example, the contour point coordinate calculation circuit 306 determines the coordinate where the post-filtering intensity is maximum after spline interpolation as (A). The distance from the origin (0) to the coordinate (A) is then determined as L2. In the example of Fig. 16, the post-filtering intensity is maximum at the coordinate (A) located at a distance L2 in the negative direction.

[0121] The contour point coordinate calculation circuit 306 converts the coordinate (A) at the distance L2 in the normal direction from polar coordinates (L2, θ) to XY coordinates, and calculates the coordinates of the contour point.

[0122] Fig. 17 shows a specific example of the coordinates of a contour point. Fig. 17 is a diagram showing a specific example in which the maximum value of the intensity after filtering by spline interpolation shown in Fig. 16 is set as a contour point. The example in Fig. 17 shows 5 x 5 pixels with a contour point candidate pixel at the center.

[0123] As shown in FIG. 17, a contour point is calculated at a position at a distance L2 in the normal direction in the negative direction.

[0124] [Step S108] The isolated contour point removal circuit 307 removes isolated contour points.

[0125] A specific example of an isolated contour point is shown in Fig. 18. In the example of Fig. 18, 5 x 5 pixels with an isolated contour point at the center are shown.

[0126] As shown in Figure 18, for example, the isolated contour point elimination circuit 307 selects 3x3 pixels surrounding contour point CP1, which is the target pixel for confirmation of an isolated contour point. The isolated contour point elimination circuit 307 then compares the number of contour points contained in the target pixel with a preset threshold value. For example, the threshold value is set to 2. In the example of Figure 18, there is only one contour point in the target pixel, which is lower than the threshold value. Therefore, the isolated contour point elimination circuit 307 removes contour point CP1 as an isolated contour point.

[0127] [Step S109] The nearby contour point removal circuit 308 removes nearby contour points. If the distance between the target contour point and an adjacent contour point is less than a preset threshold, the nearby contour point removal circuit 308 removes the target contour point as a nearby contour point.

[0128] A specific example of a nearby contour point is shown in Fig. 19. In the example of Fig. 19, 5 x 5 pixels centered on the nearby contour point are shown.

[0129] 19, for example, the nearby contour point elimination circuit 308 calculates the distance between a contour point CPn (n is an arbitrary integer) that is the target of nearby contour point confirmation and the adjacent contour points CPn-1 and CPn+1. In the example of Fig. 19, the distance Lcp between the contour points CPn and CPn+1 is less than the threshold value, so the contour point CPn is eliminated.

[0130] The contour extraction circuit 214 determines the pixels containing the remaining contour points as contour pixels, and then generates a contour line from the contour points of the contour pixels.

[0131] 6. Effects of this embodiment When extracting pattern contours and contour points from a captured image in an inspection system, asymmetry can occur in the image profile due to factors such as the electron beam scanning direction or the pattern shape. In such cases, the post-filter intensity after two-dimensional directional filtering becomes filter-angle dependent. For example, when calculating the post-filter intensity from an image of a perfectly circular hole, ideally, the post-filter intensity would be constant regardless of the filter angle. However, asymmetry in the image profile causes the post-filter intensity to vary depending on the filter angle. If the intensity threshold is constant regardless of the filter angle, it is necessary to set the intensity threshold to a filter angle with low sensitivity to the post-filter intensity. This results in a relatively low intensity threshold, which increases the likelihood of erroneously extracting candidate contour pixels. In other words, it increases the likelihood of generating false contours. Furthermore, the method of using the filter angle at which the post-filter intensity value is maximized as the normal angle has low angular resolution in the normal direction. Furthermore, the asymmetry in the image profile increases the likelihood of calculating an incorrect filter angle as the normal angle.

[0132] In contrast, with the configuration according to this embodiment, the inspection device can set a different intensity threshold for each filter angle. This makes it possible to reduce errors in extracting candidate pixels for contour points even if the image profile is asymmetric. Furthermore, the inspection device can calculate the angle of the major axis direction of the equivalent ellipse as the angle of the normal direction based on the result of converting the relationship between the filtered intensity and the filter angle into polar coordinates. This improves the angular resolution in the normal direction and reduces errors in calculating the angle of the normal direction. This improves the accuracy of extracting contour lines from the inspection image.

[0133] 7. 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.

[0134] 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]

[0135] 1...inspection device, 10...imaging mechanism, 11...sample chamber, 12...optical tube, 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...focusing lens, 103...scanning coil, 105...objective lens, 211...development circuit, 212...reference image generating circuit, 213...image acquisition circuit, 214...contour Extraction circuit, 215...comparison circuit, 221...design data, 222...parameter information, 223...inspection data, 224...intensity threshold data, 225...defect inspection program, 301...noise filter processing circuit, 302...two-dimensional direction filter processing circuit, 303...normal direction calculation circuit, 304...one-dimensional profile calculation circuit, 305...edge filter processing circuit, 306...contour point coordinate calculation circuit, 307...isolated contour point removal circuit, 308...adjacent contour point removal circuit

Claims

1. an imaging mechanism for capturing an image of the sample; a two-dimensional directional filter processing circuit that performs filtering for each pixel of the image using a plurality of two-dimensional directional filters having different directivities; a contour extraction circuit that extracts, as a contour point candidate pixel, a pixel for which at least one of the intensities for each filter angle that indicate the directionality of each of the plurality of two-dimensional directional filters calculated by the filter processing is greater than a threshold value set for the filter angle; a normal direction calculation circuit that converts the intensity for each filter angle into polar coordinates for the contour point candidate pixel, and calculates an angle of a major axis direction of an equivalent ellipse in the polar coordinates as an angle of a first direction based on the result of the conversion into polar coordinates; a contour point coordinate calculation circuit that calculates coordinates of contour points at the contour point candidate pixels based on the one-dimensional profile in the first direction; a reference image generating circuit for generating a reference image; a comparison circuit for comparing the inspection image based on the contour points with the reference image; An inspection device comprising:

2. the inspection device further includes a one-dimensional profile calculation circuit that sets a plurality of sampling points at pixel size intervals along the first direction, with the center position of the contour point candidate pixel as an origin, and calculates a pixel value for each of the sampling points. The inspection device according to claim 1 .

3. the contour point coordinate calculation circuit performs edge filtering on the one-dimensional profile, and then performs spline interpolation on the values ​​calculated by the edge filtering.

3. The inspection device according to claim 1 or 2.

4. the contour point coordinate calculation circuit determines the position where the result of the spline interpolation is the maximum value as the coordinate of the contour point; The inspection device according to claim 3 .

5. performing a filtering process for each pixel of an image of the sample using a plurality of two-dimensional directional filters having different directionalities; extracting, as contour point candidate pixels, pixels for which at least one of the intensities for each filter angle indicating the directionality of each of the plurality of two-dimensional directional filters calculated by the filtering process is greater than a threshold value set for the filter angle; converting the intensity for each filter angle into polar coordinates for the contour point candidate pixel; calculating an angle of a major axis direction of the equivalent ellipse in the polar coordinates as an angle of a first direction based on the result of the conversion into the polar coordinates; calculating coordinates of contour points at the contour point candidate pixels based on the one-dimensional profile in the first direction; A method for generating an inspection image, comprising:

6. In the step of calculating the one-dimensional profile, a plurality of sampling points are set at intervals of the size of the pixel along the first direction, with the center position of the contour point candidate pixel as the origin, and a pixel value is calculated for each of the sampling points. The method for generating an inspection image according to claim 5 .

7. performing edge filtering of the one-dimensional profile; performing spline interpolation of the values ​​calculated by the edge filtering process; The method of claim 5 further comprising:

8. In the step of calculating the coordinates of the contour points, a position where the result of the spline interpolation is a maximum value is calculated as the coordinates of the contour points. The method for generating an inspection image according to claim 7.

Images