Image processing apparatus, inspection apparatus, image processing method, and program

Abstract

The presence or absence of a defect in the pattern formed on the sample is evaluated based on the image.SOLUTION: An image reading part 1 reads image data IMG obtained by imaging a sample in which a plurality of regions having patterns formed based on the same design information are arranged. An evaluation value acquisition unit 3 acquires an evaluation value E (I, j) based on the luminance of pixels for a plurality of sampling images at the same sampling point in a plurality of regions among sampling images at a plurality of sampling points set in each of the plurality of regions. A reference evaluation value acquisition unit 4 acquires a reference evaluation value REFj for evaluation values of a plurality of sampling images at the same sampling point in a plurality of regions. A leveling evaluation value acquisition unit 5 acquires a leveling evaluation value EQ (I, j) indicating a degree of deviation of the evaluation value E (I, j) from a reference evaluation value REFj for each of the sampling images of the plurality of regions.SELECTED DRAWING: Figure 4

Description

[Technical Field]

[0001] The present disclosure relates to an image processing device, an inspection device, an image processing method, and a program. [Background technology]

[0002] For example, in order to detect defects in a sample such as a photomask, a technique is widely used in which the sample is inspected using an image captured by irradiating the sample with light (Patent Document 1).

[0003] Photomasks and other devices may have multiple chip patterns, or dies, based on the same design information. When forming multiple dies in this way, it is desirable that the pattern dimensions of the same part of the multiple dies, or so-called CD (Critical Dimension), are the same. However, it is known that variations in CD occur due to errors caused by the manufacturing process. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Publication No. 2017-187547 [Patent Document 2] Patent No. 6249513 Summary of the Invention [Problem to be solved by the invention]

[0005] As mentioned above, when CD variations become large and deviate from the specified dimension range, so-called CD defects occur. Therefore, in recent years, there has been a demand for CD defect inspection based on sample images.

[0006] If CD defects could be detected based on images, it might be possible to detect CD defects in equipment that also inspects other defects based on images. In this case, multiple types of inspections could be performed with a single equipment, which is expected to reduce inspection costs. [Means for solving the problem]

[0007] The image processing device according to the present disclosure includes an image reading means for reading image data of a sample having a plurality of regions arranged thereon, each having a pattern formed based on the same design information; an evaluation value acquisition means for acquiring evaluation values ​​based on pixel brightness for a plurality of sampling images at the same sampling point in the plurality of regions, among the sampling images at a plurality of sampling points set in each of the plurality of regions; a reference evaluation value acquisition means for acquiring a reference evaluation value for the evaluation values ​​for the plurality of sampling images at the same sampling point in the plurality of regions; and a leveling evaluation value acquisition means for acquiring a leveling evaluation value indicating the degree of deviation of the acquired evaluation value from the reference evaluation value for each of the sampling images in the plurality of regions.

[0008] The inspection device according to the present disclosure includes the image processing device described above, and a determination unit that detects defects in the patterns of the sampling images of the plurality of regions based on the leveling evaluation values.

[0009] The inspection device according to the present disclosure includes the image processing device described above and a judgment means for detecting defects smaller than the width of the pattern based on the difference between the evaluation value of the sampling image and the evaluation value of the sampling image in a reference image.

[0010] The image processing method according to the present disclosure reads image data of a sample in which multiple regions having patterns formed based on the same design information are arranged, obtains evaluation values ​​based on pixel brightness for multiple sampling images at the same sampling points in the multiple regions among the sampling images at multiple sampling points set in each of the multiple regions, obtains a reference evaluation value that obtains a reference evaluation value for the evaluation values ​​for the multiple sampling images at the same sampling points in the multiple regions, and obtains a leveled evaluation value that indicates the degree of deviation of the obtained evaluation value from the reference evaluation value for each of the sampling images in the multiple regions.

[0011] The program according to the present disclosure causes a computer to execute the following processes: reading image data of a sample having multiple regions arranged thereon, each having a pattern formed based on the same design information; acquiring evaluation values ​​based on pixel brightness for multiple sampling images at the same sampling points set in each of the multiple regions among the sampling images at the multiple sampling points in the multiple regions; acquiring a reference evaluation value that acquires a reference evaluation value for the evaluation values ​​for the multiple sampling images at the same sampling points in the multiple regions; and acquiring a leveled evaluation value that indicates the degree of deviation of the acquired evaluation value from the reference evaluation value for each of the sampling images in the multiple regions. [Effects of the Invention]

[0012] According to the present disclosure, the presence or absence of defects in a pattern formed on a sample can be evaluated based on an image. [Brief explanation of the drawings]

[0013] [Figure 1] FIG. 1 illustrates an example of a configuration of an inspection system according to a first embodiment. [Figure 2] FIG. [Figure 3]FIG. 1 is a top view showing the arrangement of sampling points on one die. [Figure 4] FIG. 1 is a diagram illustrating a configuration example of an image processing device according to a first embodiment. [Figure 5] 4 is a flowchart of the operation of the image processing device according to the first embodiment. [Figure 6] 1A and 1B are diagrams showing examples of patterns and edge regions of each die. [Figure 7] FIG. 10 is a diagram schematically illustrating a configuration of an image processing device according to a second embodiment. [Figure 8] FIG. 10 is a diagram illustrating an example of mapping of leveling evaluation values. [Figure 9] FIG. 10 is a block diagram schematically illustrating a configuration of an inspection device according to a third embodiment. [Figure 10] FIG. 1 is a diagram illustrating an example of the configuration of a computer for realizing an image processing device and an inspection device. DETAILED DESCRIPTION OF THE INVENTION

[0014] Hereinafter, a specific configuration of the present embodiment will be described with reference to the drawings. The following description shows a preferred embodiment of the present disclosure, and the scope of the present disclosure is not limited to the following embodiment. In the following description, parts with the same reference numerals indicate substantially the same content.

[0015] Embodiment 1 An image processing device according to embodiment 1 will be described. The image processing device according to this embodiment is configured to be incorporated into an optical device such as an inspection device used to inspect samples such as photomasks used in semiconductor manufacturing processes.

[0016] First, an optical device according to a first embodiment will be described. FIG. 1 is a diagram schematically illustrating an optical system of the optical device according to the embodiment. As shown in FIG. 1, the optical device 1000 according to the present embodiment is configured as a device that inspects a sample 90 to be inspected by irradiating the sample 90 with illumination light and detecting the reflected light. The sample 90 to be inspected by the optical device 1000 is, for example, an EUV (Extreme Ultraviolet) mask, and the optical device 1000 irradiates the sample 90 with EUV light. The sample 90 is not limited to an EUV mask, but may be any of various photomasks designed for light with a longer wavelength than EUV light, or any of various members on which a fine pattern is formed, such as a semiconductor wafer on which a circuit pattern is formed.

[0017] The optical device 1000 has an illumination optical system 10, a detection optical system 20, a monitor unit 30, and a processing unit 40. The illumination optical system 10 has a light source 11, an ellipsoidal mirror 12, an ellipsoidal mirror 13, and a drop mirror 14. The detection optical system 20 has a perforated concave mirror 21, a convex mirror 22, and a first detector 23. The perforated concave mirror 21 and the convex mirror 22 form a Schwarzschild magnification optical system. The monitor unit 30 has a cut mirror 31, a concave mirror 32, and a second detector 33.

[0018] The light source 11 emits, as illumination light L11, EUV light of 13.5 nm, which is the same as the exposure wavelength of the sample 90, which is an EUV mask. The illumination light L11 is not limited to EUV light, and may be light of another wavelength depending on the sample 90. The illumination light L11 emitted from the light source 11 is reflected by the ellipsoidal mirror 12. The illumination light L11 reflected by the ellipsoidal mirror 12 is focused at a focusing point IF1 at a position conjugate with the upper surface 91 of the sample 90, and then spreads out before being incident on a reflecting mirror such as the ellipsoidal mirror 13.

[0019] The illumination light L11 incident on the ellipsoidal mirror 13 is reflected by the ellipsoidal mirror 13. The illumination light L11 reflected by the ellipsoidal mirror 13 is condensed and incident on the drop-down mirror 14. That is, the ellipsoidal mirror 13 causes the illumination light L11 to be incident on the drop-down mirror 14 as convergent light. The drop-down mirror 14 is disposed directly above the sample 90. The illumination light L11 incident on the drop-down mirror 14, and the reflected illumination light L11 then enters the sample 90. That is, the illumination light L11 is reflected by the drop-down mirror 14 and enters the sample 90.

[0020] The ellipsoidal mirror 13 is designed and positioned to focus the illumination light L11 on the sample 90. The illumination optical system 10 is installed so that when the illumination light L11 illuminates the sample 90, an image of the light source 11 is formed on the upper surface 91 of the sample 90. Therefore, the illumination optical system 10 provides critical illumination. In this way, the illumination optical system 10 illuminates the inspection object using critical illumination by the illumination light L11 generated by the light source 11.

[0021] A sample 90 is placed on a stage 92. Here, a plane parallel to an upper surface 91 of the sample 90 is defined as an XY plane, and a direction perpendicular to the XY plane is defined as the Z direction. Illumination light L11 is incident on the sample 90 from a direction tilted with respect to the Z direction. That is, illumination light L11 is incident obliquely and illuminates the sample 90.

[0022] The stage 92 is an XYZ drive stage. By moving the stage 92 in the X and Y directions, it is possible to illuminate a desired area of ​​the sample 90. Furthermore, by moving the stage 92 in the Z direction, it is possible to perform focus adjustment.

[0023] Illumination light L11 from the light source 11 illuminates an inspection area of ​​the sample 90. The inspection area illuminated by the illumination light L11 is, for example, 0.5 mm square. Reflected light L12, which is incident from a direction tilted with respect to the Z direction and reflected by the sample 90, is incident on the perforated concave mirror 21. A hole 21a is provided in the center of the perforated concave mirror 21.

[0024] The reflected light L12 reflected by the perforated concave mirror 21 is incident on the convex mirror 22. The convex mirror 22 reflects the reflected light L12 incident from the perforated concave mirror 21 toward the hole 21a of the perforated concave mirror 21. The reflected light L12 passing through the hole 21a is detected by the first detector 23. The first detector 23 is a detector including a TDI (Time Delay Integration) sensor and acquires image data of the sample 90 to be inspected. The first detector 23 includes multiple image sensors arranged in a line in one direction. Linear image data captured by the multiple image sensors arranged in a line is called one-dimensional image data or one frame. The first detector 23 acquires multiple one-dimensional image data by scanning in a direction perpendicular to the one direction. The image sensor is, for example, a CCD (Charge Coupled Device). Note that the image sensor is not limited to a CCD.

[0025] In this way, the detection optical system 20 collects the reflected light L12 from the sample 90 illuminated by the illumination light L11, and the first detector 23 detects the collected reflected light L12 to obtain image data of the sample 90. The image data is, for example, one-dimensional image data.

[0026] The reflected light L12 contains information about defects in a pattern or the like formed on the sample 90. In this configuration, the reflected light L12, which is specularly reflected light of the illumination light L11 incident on the sample 90 from a direction tilted with respect to the Z direction, is detected by the detection optical system 20. If a defect exists in the sample 90, the defect is observed as a dark image. This observation method is called bright-field observation. The multiple one-dimensional image data of the sample 90 acquired by the first detector 23 are output to the processing unit 40 and processed into two-dimensional image data.

[0027] 1, the cut mirror 31 of the monitor unit 30 is disposed between the ellipsoidal mirror 13 and the drop mirror 14, and extracts a portion of the illumination light L11 between the ellipsoidal mirror 13 and the drop mirror 14. The cut mirror 31 reflects the illumination light L11 so as to slightly cut out a portion of the beam. The portion of the beam is, for example, the upper portion of the beam.

[0028] In the cross-sectional area of ​​the cross section perpendicular to the optical axis of the illumination light L11 at the position where the cut mirror 31 is disposed, the cross-sectional area of ​​a portion reflected by the cut mirror 31 is smaller than the cross-sectional area of ​​the remaining portion of the illumination light L11.

[0029] For example, if the cross-sectional area of ​​the cross section perpendicular to the optical axis of the illumination light L11 at the position where the cut mirror 31 is disposed is 100, then the cross-sectional area of ​​a portion of the illumination light L11 is approximately 1. The illumination light L11 extracted from the light source 11 has an extraction angle in a direction perpendicular to the optical axis of, for example, ±7°. The range of the angle used as the illumination light L11 for the sample 90 is, for example, ±6°. For use in the monitor unit 30, a small upper portion of the illumination light L11 beam, for example, a range of 1°, is extracted by the cut mirror 31. In this way, even if a small upper portion of the beam is extracted, the amount of illumination light L11 on the sample 90 does not decrease significantly. Therefore, a decrease in the accuracy of the inspection object can be suppressed.

[0030] The cut mirror 31 is disposed, for example, at a position close to the pupil of the illumination optical system 10. By extracting the illumination light L11 by the cut mirror 31 at a position close to the pupil of the illumination optical system 10, it is possible to obtain a good correlation between the image data acquired by the first detector 23 and the image data acquired by the second detector 33. Even if the numerical aperture (NA) for the first detector 23 and the NA for the second detector 33 are different and the point spread functions (PSFs) are different, the difference in NA does not affect this embodiment because the plasma size is sufficiently larger than the PSF size.

[0031] The illumination light L11 reflected by the cut mirror 31 is focused at a focusing point, and then enters the concave mirror 32 while diverging.

[0032] The concave mirror 32 and multiple mirrors (not shown) expand the beam of illumination light L11 extracted by the cut mirror 31. This allows the image data acquired by the second detector 33 to be obtained at a high magnification. For example, a magnification of 500 times can be achieved by using multiple mirrors.

[0033] In this embodiment, the magnification of the image data of the luminance distribution acquired by the monitor unit 30 is the same as the magnification of the image data of the inspection object acquired by the detection optical system 20. Note that the magnification of the image data of the luminance distribution acquired by the monitor unit 30 may be lower than the magnification of the image data of the inspection object acquired by the detection optical system 20. The solid angle required for extraction is the square of the ratio of the magnifications. For example, if the magnification of the first detector 23 is 20 times and the magnification of the second detector 33 is 2 times, the solid angle required for extraction by the cut mirror 31 is 1 / 100 of the solid angle of extraction from the light source 11. This is equivalent to 1 / 10 in terms of NA.

[0034] The illumination light L11 incident on the concave mirror 32 and reflected by the concave mirror 32 is detected by the second detector 33. The second detector 33 is a detector including a TDI sensor and acquires image data of the luminance distribution of the illumination light L11. The second detector 33 includes multiple imaging elements arranged in a line in one direction. Similar to the first detector 23, the linear image data acquired by the multiple imaging elements arranged in a line is one-dimensional image data or one frame. The second detector 33 acquires multiple one-dimensional image data by scanning in a direction perpendicular to the one direction. The one-dimensional image data acquired by the second detector 33 indicates the power fluctuation and luminance distribution of the illumination light L11. The imaging element is, for example, a CCD. However, the imaging element is not limited to a CCD.

[0035] For example, the optical system of the monitor unit 30 may be arranged so that an image of the light source 11 of the illumination light L11 is formed on the second detector 33. In this case, the first detector 23 and the second detector 33 are in a conjugate relationship. This allows the monitor unit 30 to obtain image data (hereinafter, sometimes referred to as "image data of power fluctuations and luminance distribution" or "monitor image") that can identify the power fluctuations and luminance distribution of the detected illumination light L11 by irradiating the second detector 33 with critical illumination using a portion of the illumination light L11. Therefore, the luminance distribution and power fluctuations can be corrected with high accuracy.

[0036] The monitor unit 30 outputs the acquired image data of the power fluctuation and luminance distribution of the illumination light L11 to the processing unit 40.

[0037] The processing unit 40 is connected to the detection optical system 20 and the monitor unit 30 by wire or wirelessly. The processing unit 40 receives image data of the inspection target from the first detector 23 in the detection optical system 20. The processing unit 40 also receives image data of the power fluctuation and luminance distribution of the illumination light L11 from the second detector 33 in the monitor unit 30.

[0038] The processing unit 40 may correct the image data of the sample 90 acquired by the detection optical system 20 based on the image data of the power fluctuation and luminance distribution acquired by the monitor unit 30. For example, the processing unit 40 may correct the luminance distribution of pixels included in the image data by shading correction to compensate for the influence of the luminance distribution of the critical illumination.

[0039] An overview of shading correction will be described. Assume that the original image data generated based on the detection results of the first detector 23 has a pixel brightness profile in a certain direction that is convex upward. In this case, the processing unit 40 performs shading correction by applying a gain having a profile that is convex downward to the pixel brightness profile. As a result, the shading-corrected profile has a flat shape. By making the pixel brightness profile flat, it is possible to more accurately inspect an object for defects based on, for example, the difference between the brightness of a pixel and the brightness of surrounding pixels. The gain profile used for shading correction may have any shape as long as it makes the pixel brightness profile have any shape (for example, a flatter shape) after shading correction.

[0040] Furthermore, when critical illumination is used in the above-described optical device 1000, the luminance distribution at the first detector 23 is significantly affected by the state of the light source (bright spot). This is because, for example, when the light source (bright spot) moves in a direction parallel to the plane normal to the optical axis, the position (vertex position) at which the luminance profile at the first detector 23 becomes convex fluctuates. Patent Document 2 proposes shading correction that takes into account fluctuations in the luminance distribution at the first detector 23 due to the state of the light source, and the processing unit 40 may perform shading correction that takes into account fluctuations in the luminance distribution at the first detector 23 using a similar method.

[0041] When performing shading correction similar to that described in Patent Document 2, the processing unit 40 determines how to apply a predetermined gain to the luminance profile acquired by the first detector 23 based on the luminance profile acquired by the second detector 33. As one example, the processing unit 40 performs shading correction on the first detector 23 based on a gain obtained by shifting the predetermined gain by +x1 in the X-axis direction based on the result of determining that the peak position of the luminance profile acquired by the second detector 33 has shifted by +x1 in the +X-axis direction relative to the peak position of the reference luminance profile. As another example, the processing unit 40 performs shading correction on the first detector 23 based on a gain obtained by lowering the predetermined gain by −ΔI based on the result of determining that the intensity at the peak position of the luminance profile acquired by the second detector 33 is greater by ΔI than the intensity at the peak position of the reference luminance profile. Note that, although the comparison target in this description is the peak position of the luminance profile of the second detector 33, any point may be used as the comparison target. Furthermore, the movement of the predetermined gain in the X-axis direction or the intensity direction may be applied to the entire predetermined gain, or may be applied to a partial region of the predetermined gain, such as a field of view position (position on the X-axis) where fluctuations are particularly large. In this way, by performing shading correction similar to that in Patent Document 2, even if the luminance distribution of critical illumination changes over time, the luminance distribution can be corrected in accordance with the change.

[0042] The processing unit 40 outputs image data IMG including the image data corrected in this way to the image processing device 100.

[0043] The image data IMG generated by the optical device 1000 based on the imaging results of the sample 90 will be described. In this embodiment, the sample 90 is a plane array of multiple dies on which patterns based on the same design information are formed, such as a typical photomask or a semi-finished semiconductor wafer. The optical device 1000 images one or more identical positions on each die to inspect the in-plane manufacturing variations and defects of the sample 90 using die-to-die (D2D) or die-to-database (DDB). Then, image data IMG is generated as a data set of the captured images. This will be described in detail below.

[0044] 2 is a top view of the sample 90. In the top view, the horizontal direction of the paper is the X-axis direction, and the vertical direction of the paper is the Y-axis direction. In the sample 90, patterns of a plurality of dies (Dies) formed based on the same design information are arranged in an array in the X-axis and Y-axis directions. The sample 90 has p×q=M dies D1 to D2 with p columns in the X-axis direction and q rows in the Y-axis direction. M are arranged in a matrix, where p and q are integers equal to or greater than 2. However, the die arrangement in FIG. 2 is merely an example, and a different number of dies may be arranged in different positions on the sample 90.

[0045] Each of the M dies has a plurality of sampling points arranged in an array in the X-axis direction and the Y-axis direction. Figure 3 is a top view showing the arrangement of sampling points on one die. Here, when i is an integer between 1 and M, the i-th die is referred to as die D. i Let's say Die D i In the matrix, a × b = N sampling points S(i,1) to S(i,N) are arranged in a columns in the X-axis direction and b rows in the Y-axis direction, where a and b are integers equal to or greater than 2. That is, M dies D1 to D M Each of the dies has N sampling points, so M×N sampling points are arranged on the specimen 90. However, the arrangement of sampling points in Fig. 3 is only an example, and each die may have a different number of sampling points arranged at different positions.

[0046] Next, the configuration and operation of the image processing device 100 will be described. Fig. 4 is a diagram schematically illustrating an example of the configuration of the image processing device according to the first embodiment. Fig. 5 is a flowchart illustrating the operation of the image processing device according to the first embodiment. The image processing device 100 includes an image reading unit 1, a brightness correction unit 2, an evaluation value acquisition unit 3, a reference evaluation value acquisition unit 4, and a leveled evaluation value acquisition unit 5.

[0047] The image reading unit 1 reads image data IMG of the sample 90 imaged by the optical device 1000 (step S11 in FIG. 5). The image reading unit 1 may read the image data IMG from the optical device 1000, or may read the image data IMG stored in an arbitrary storage device.

[0048] The brightness correction unit 2 corrects the brightness variations of the M images at the sampling points at the same positions of the M dies (step S12 in FIG. 5). i The image at the j-th sampling point S(i,j) is referred to as a sampled image p(i,j). M The brightness of the pixels included in the j-th sampled image p(i,j) is normalized to a predetermined range. The brightness corrector 2 performs this correction process on each of the first to N-th sampled images.

[0049] When the optical device 1000 images the sample 90 using critical illumination, the luminance correction unit 2 may also correct the in-plane luminance variation of the pixels included in the sampled image by shading correction to compensate for the influence of the luminance distribution of the critical illumination in the luminance correction process. Similarly, for illumination other than critical illumination, the in-plane luminance variation of the pixels included in the sampled image may be corrected in accordance with the luminance distribution of the illumination light. Furthermore, various methods, such as the method disclosed in Patent Document 2, may be used to correct the in-plane luminance variation of the pixels included in the sampled image, taking into account the time fluctuation of the luminance distribution of the critical illumination due to differences in imaging timing.

[0050] After the brightness correction process, the evaluation value acquisition unit 3 acquires the average brightness value of pixels included in the edge region R, which is defined as the region in the sampled image p(i,j) where the edge of the pattern exists, as the evaluation value E(i,j) of the sampled image p(i,j) (step S13 in FIG. 5). The edge region R may be specified in advance for each sampling point based on the die design information. Note that, when the sampled image p(i,j) is a single pixel, the evaluation value E(i,j) may be the brightness value of the pixel of the sampled image p(i,j).

[0051] Figure 6 shows an example of the pattern and edge region of each die. In this example, the pattern of the letter A is formed in the sampled image p(i,j), with the brightness of the letter A being high and the brightness of the parts other than the letter being low. An edge region R, shown by a dotted line, is set to encompass the edge portion of the A. If the dimensions of the bright A pattern fluctuate due to CD variation, the average value of the pixels within the edge region R, i.e., the evaluation value, will also fluctuate.

[0052] 6 shows, as an example, a sampled image p_A in which the lines constituting the pattern are relatively thick, and a sampled image p_B in which the lines constituting the pattern are relatively thin. In this case, the edge region R of the sampled image p_A has more pixels with higher brightness than the edge region R of the sampled image p_B, so the evaluation value of the sampled image p_A is larger than the evaluation value of the sampled image p_B.

[0053] The evaluation value acquisition unit 3 performs evaluation value acquisition processing for all sampled images of all die images. That is, the evaluation value acquisition unit 3 acquires the evaluation value E(i,j) of the sampled image p(i,j) in the ranges of 1≦i≦M, 1≦j≦N.

[0054] The reference evaluation value acquisition unit 4 acquires the first to Mth dies D1 to D M The reference evaluation value REF is used as a comparison target for evaluating the evaluation values ​​E(1,j) to E(M,j) of the j-th sampled images p(1,j) to p(M,j). j (Step S14 in FIG. 5).

[0055] Here, the reference evaluation value acquisition unit 4 calculates a reference evaluation value REF based on the evaluation value of each sampled image acquired by the evaluation value acquisition unit 3. j For example, the reference evaluation value acquisition unit 4 acquires the first to Mth dies D1 to D M The average of the evaluation values ​​E(1,j) to E(M,j) of the j-th sampled images p(1,j) to p(M,j) is used as the reference evaluation value REF of the j-th sampled image. j Obtain as.

number

[0056] The reference evaluation value acquisition unit 4 calculates the reference and value for the evaluation values ​​E(1,j) to E(M,j) as a reference evaluation value REF j As far as possible, a value different from the average value mentioned above can be used as the reference evaluation value REF jFor example, the reference evaluation value acquisition unit 4 may acquire a value that is predetermined based on the design information of the sample 90 as the reference evaluation value REF j As an example, the reference evaluation value acquisition unit 4 may acquire a value obtained from an image corresponding to the j-th sampling point of the die among images generated from the design information of the sample 90 as the reference evaluation value REF j When used in combination with micro-defect inspection using DDB (described later), the value thus obtained based on the design information of the sample 90 may be used as the reference evaluation value REF j It is convenient to obtain it as

[0057] The leveled evaluation value acquisition unit 5 acquires the i-th die D i The smoothed evaluation value EQ(i,j) of the j-th sampled image p(i,j) is obtained based on the following equation (step S15 in FIG. 5).

number

[0058] In this way, according to the image processing device 100, when multiple dies with the same pattern are formed on the sample 90, a leveling evaluation value can be obtained that indicates how much the pattern at one sampling point set on each die deviates from the average pattern at the same sampling point on all dies.

[0059] As a result, the image processing device 100 can evaluate the CD variation of the pattern formed in the sample 90. Furthermore, by using the leveling evaluation value, the image processing device 100 can detect not only CD defects but also other defects, such as stains on the pattern.

[0060] Furthermore, the acquisition of the evaluation value in the image processing device 100 can be easily integrated into another image processing device that performs predetermined image processing on the image of the sample 90. Therefore, a multifunctional image processing device that acquires a leveled evaluation value and performs other image processing can be realized. Furthermore, by incorporating the configuration and functions of the image processing device according to this embodiment into an existing image processing device that performs other image processing based on the image of the sample 90, an image processing device that can acquire a leveled evaluation value and perform other image processing can be easily realized.

[0061] Embodiment 2 An image processing device according to a second embodiment will be described. The image processing device according to this embodiment is configured to visualize the distribution of the leveled evaluation values ​​in a sample 90. FIG. 7 is a diagram schematically showing the configuration of the image processing device according to the second embodiment. Compared to the image processing device 100 according to the first embodiment, the image processing device 200 according to the second embodiment further includes a map creating unit 6 and a map output unit 7.

[0062] The map creating unit 6 creates a heat map HM by mapping the M×N leveled evaluation values ​​acquired by the leveled evaluation value acquiring unit 5 for the N sampling points of each of the M dies onto a two-dimensional plane.

[0063] Fig. 8 is a diagram showing an example of mapping of the leveling evaluation value. As shown in Fig. 8, for example, the leveling evaluation value at a sampling point set on a sample can be displayed using a grayscale. In this case, the grayscale gradation may be set so that the darkest gradation corresponds to the maximum value on the positive side, the lightest gradation corresponds to the maximum value on the negative side, and an intermediate gradation corresponds to 0.

[0064] Furthermore, for example, the mapping may be performed using colors, such as using darker gradations as the absolute value of the leveling evaluation value increases, red for positive values, and blue for negative values.

[0065] The map output unit 7 provides the heat map created by the map creation unit 6 to the user of the image processing device as visible information. The map output unit 7 may be configured as a display device such as an LCD monitor that displays the heat map HM to the user, or as a printer that transfers the heat map HM onto a medium such as paper so that it can be viewed.

[0066] As described above, the image processing device 200 visually displays the CD variation of the pattern formed on the sample 90, allowing the user to efficiently grasp the CD variation of the pattern.

[0067] Embodiment 3 An inspection device according to a third embodiment will be described. The inspection device according to the present embodiment is configured to determine whether or not the CD of the pattern of each sampling image falls within a specified range, based on the leveling evaluation value acquired by the image processing device 100.

[0068] 9 is a block diagram schematically illustrating the configuration of an inspection device according to the third embodiment. The inspection device 3000 includes an image processing device 100 and a determination unit 300.

[0069] The determination unit 300 compares the smoothing evaluation value of each sampled image acquired by the image processing device 100 with a determination criterion range, and if the smoothing evaluation value of each sampled image falls outside the determination criterion range, determines that the pattern of the sampled image corresponding to the compared smoothing evaluation value has a CD defect. This type of determination is called a determination based on the magnitude of the abnormality. The determination unit 300 then outputs the determination result DET(i,j). In this way, the determination unit 300 can detect CD defects such as CD variations of patterns.

[0070] In the above description, the inspection system 3000 has been described as detecting CD defects in the pattern of the sampled image, but this is merely an example. As mentioned above, it is preferable that a single inspection system be able to inspect multiple types of defects. Therefore, for example, the inspection system 3000 may be configured to detect defects (microdefects) smaller than the pattern width on the sample 90 based on the brightness of pixels included in the image of the sample 90.

[0071] Next, a method for detecting minute defects in the inspection device 3000 will be described. The determination unit 300 determines the die Ds to be used as the inspection reference and the die Ds to be inspected. i If the difference between the evaluation value E(s,j) in the sampled image p(s,j) and the evaluation value E(i,j) in the sampled image p(i,j) at the j-th sampling point common to both is outside the predetermined judgment criterion range, the die D is to be inspected. i The determination unit 300 determines that there is a micro-defect at the sampling point S(i,j). The determination unit 300 then outputs the determination result DET(i,j). Here, the evaluation value E(s,j) may be referred to as the evaluation value of the sampled image in the reference image. This type of defect determination method is generally called die-to-die (D2D) micro-defect inspection.

[0072] The determination unit 300 also determines the die D i When the difference between the evaluation value E(i, j) of the sampled image p(i, j) at the j-th sampling point and the evaluation value acquired from the sampled image in the image generated from the design information of the sample 90 falls outside a predetermined judgment criterion range, the die D to be inspected is i It may be determined that there is a micro-defect at the sampling point S(i,j). Here, the evaluation value obtained based on the image generated from the design information of the sample 90 may be referred to as the evaluation value of the sampled image in the reference image. This type of defect determination method is generally called micro-defect inspection using DDB (die-to-database).

[0073] In the inspection apparatus 3000, the size of the image to be used as a sampled image, i.e., the number of pixels to be included in the sampled image, may be set for each type of defect to be judged. For example, the number of pixels to be included in the sampled image used in the detection of minute defects by the judgment unit 300 may be smaller than the number of pixels to be included in the sampled image used in the detection of CD defects.

[0074] Furthermore, the number of pixels included in the sampled image used in detecting a minute defect may be one. That is, by using a single pixel as the sampled image p(i, j) and the luminance value of the single pixel as the evaluation value E(i, j), minute defects can be suitably detected.

[0075] As described above, the inspection device 3000 can automatically and efficiently detect CD defects in the patterns of each sampled image of the sample 90.

[0076] Furthermore, the inspection system 3000 can detect CD defects and defects smaller than the pattern width on the sample 90 using a single system, which is expected to reduce inspection costs.

[0077] Furthermore, the CD defect inspection in the inspection apparatus 3000 can be easily integrated into another inspection apparatus that performs other types of pattern defect inspection based on images of the sample 90. Therefore, it is possible to realize a multifunctional inspection apparatus that performs CD defect inspection and other defect inspection. Furthermore, by installing the image processing device and judgment unit according to this embodiment in an existing inspection apparatus that performs other types of pattern defect inspection based on images of the sample 90, it is possible to easily realize an inspection apparatus that is capable of CD defect inspection.

[0078] Other embodiments Although the present disclosure has been described above with reference to the embodiments, the present disclosure is not limited to the above-described embodiments. Various modifications that can be understood by those skilled in the art can be made to the configuration and details of the present disclosure within the scope of the present disclosure. Furthermore, each embodiment can be combined with other embodiments as appropriate.

[0079] In the inspection device according to the third embodiment, the image processing device 100 may be replaced with the image processing device 200 to generate and output the heat map HM.

[0080] In the inspection device according to the third embodiment, the judgment unit 300 compares the smoothing evaluation value of each sampled image acquired by the image processing device 100 with a judgment criterion range, and if the smoothing evaluation value of each sampled image falls outside the judgment criterion range, judges that the pattern of the sampled image corresponding to the compared smoothing evaluation value has a CD defect; however, the judgment unit 300 may also judge that there is a CD defect by other methods.

[0081] For example, a pattern may be determined to have a CD defect based on the number or percentage of sampled images whose smoothing evaluation values ​​fall outside the judgment criteria range among a predetermined number of adjacent sampled images (referred to as a specified number). As an example, if five or more sampled images (the majority of the nine) out of a total of nine adjacent 3x3 sampled images have smoothing evaluation values ​​that fall outside the judgment criteria range, the area including those nine sampled images may be determined to have a CD defect. This type of determination is called a determination based on the appearance density of anomalies.

[0082] In the determination based on the magnitude of the anomaly, the determination unit 300 may determine that there is a CD defect when the smoothing evaluation value of the sampled image deviates from the determination reference range by more than a first threshold, and in the determination based on the appearance density of the anomaly, the determination unit 300 may determine that there is a CD defect when the number or proportion of sampled images whose smoothing evaluation value deviates from the determination reference range by more than a second threshold among a specified number of sampled images exceeds a predetermined value. In this case, the first threshold may be greater than the second threshold.

[0083] In the above description, the illumination light L11 is described as being EUV light, but this is merely an example, and the illumination light L11 may be light of other wavelengths, such as UV light other than EUV, visible light, or infrared light, depending on the sample 90. Furthermore, although the optical device 1000 is described as being configured as a reflective optical system, the optical device may be configured as having a refractive optical system or a catadioptric system as long as it can guide the illumination light L11 in a similar manner.

[0084] In the above-described embodiments, the image processing device and inspection device according to the present disclosure have been described primarily as hardware configurations, but this is not limiting. The image processing device and inspection device according to the present disclosure can also be realized by having a computer execute a computer program to perform any desired processing. These processes may be realized by having a computer including at least one processor (e.g., a microprocessor, CPU, GPU, MPU, or DSP (Digital Signal Processor)) execute the program. Specifically, one or more programs including instructions for causing a computer to perform these algorithms related to transmission signal processing or reception signal processing may be created, and the programs may be supplied to the computer.

[0085] A computer program can be stored and supplied to a computer using various types of non-transitory computer-readable media. Non-transitory computer-readable media include various types of tangible storage media. Examples of non-transitory computer-readable media include magnetic storage media (e.g., flexible disks, magnetic tapes, hard disk drives), magneto-optical storage media (e.g., magneto-optical disks), CD-ROMs (Read Only Memory), CD-Rs, CD-R / Ws, and semiconductor memories (e.g., mask ROMs, PROMs (Programmable ROMs), EPROMs (Erasable PROMs), flash ROMs, and RAMs (Random Access Memory)). The program may also be supplied to a computer by various types of transitory computer-readable media. Examples of transitory computer-readable media include electrical signals, optical signals, and electromagnetic waves. The transitory computer-readable media can supply the program to a computer via a wired communication path such as an electric wire or optical fiber, or via a wireless communication path.

[0086] An example of the configuration of a computer for realizing an image processing device and an inspection device is shown below. FIG. 10 is a diagram showing an example of the configuration of a computer for realizing an image processing device and an inspection device. The image processing device and inspection device can be realized by a computer 9000 such as a dedicated computer or a personal computer (PC). However, the computer does not need to be physically single, and may be multiple when performing distributed processing. As shown in FIG. 10, the computer 9000 has, for example, a processor 9001, a ROM (Read Only Memory) 9002, a RAM (Random Access Memory) 9003, a storage unit 9004, a communication interface 9005, and a user interface 9006.

[0087] The processor 9001, ROM 9002, RAM 9003, storage unit 9004, communication interface 9005, and user interface 9006 are connected to each other so as to be able to communicate with each other via a bus 9007. Note that although explanation of OS software for operating the computer is omitted, it is also installed in the computer 9000 as appropriate.

[0088] The ROM is configured by, for example, a nonvolatile semiconductor memory device, etc. The ROM 9002 stores information such as various programs used by the computer 9000.

[0089] The storage unit 9004 is configured by various storage devices such as a hard disk, a solid state disk, etc. Furthermore, the storage unit 9004 is not limited to a storage device installed in the computer 9000, but may be a storage device external to the computer 9000. The external storage device may be a cloud storage connected to the computer 9000 via various communication means, for example, a network. The storage unit 9004 stores information such as various programs and data used by the computer 9000.

[0090] The RAM 9003 is configured by a volatile semiconductor memory device, etc. Programs, data, and other information used by the processor 9001 are loaded into the RAM 9003 from one or both of the ROM 9002 and the storage unit 9004 as appropriate.

[0091] The processor 9001 may be configured with, for example, a CPU (Central Processing Unit). The processor 9001 may also include not only a CPU but also a GPU (Graphics Processing Unit). A GPU is suitable for performing routine processing in parallel, and when applied to processing in a neural network, for example, it can improve processing speed compared to a CPU. The processor 9001 executes various processes based on various programs stored in the ROM 9002 or various programs and data held in the RAM 9003, as appropriate. The processor 9001 may also store data generated by processing in the RAM 9003 or the storage unit 9004, as appropriate.

[0092] The communication interface 9005 is an interface that connects the computer 9000 to a communication network such as the Internet or an intranet via various wired communication means or wireless communication means, etc. This allows the computer 9000 to communicate with other devices, systems, sensors, etc. that are connected to the communication network.

[0093] The user interface 9006 includes, for example, a display unit that provides information so that the user can recognize it using a display device or the like, and an audio output unit that outputs audio. The user interface 9006 also includes an input unit that allows the user to input information to the computer 9000 by operating a keyboard, mouse, touch panel, or the like. The user interface 9006 may also include devices such as sensors that obtain information useful to the user.

[0094] Although the computer 9000 has been described as a single device here, this is merely an example. The computer 9000 may be configured as multiple physically separated devices. Some of the multiple devices may be portable devices, and other devices may be stationary devices.

[0095] Although the present disclosure has been described above with reference to the embodiments, the present disclosure is not limited to the above-described embodiments. Various modifications that can be understood by those skilled in the art can be made to the configuration and details of the present disclosure within the scope of the present disclosure. Furthermore, each embodiment can be combined with other embodiments as appropriate.

[0096] Each drawing is merely an example for describing one or more embodiments. Each drawing may relate not only to one particular embodiment, but also to one or more other embodiments. As will be understood by those skilled in the art, various features or steps described with reference to any one drawing can be combined with features or steps shown in one or more other drawings to create, for example, an embodiment not explicitly shown or described. Not all features or steps shown in any one drawing are necessary to describe an exemplary embodiment, and some features or steps may be omitted. The order of steps described in any drawing may be changed as appropriate. [Explanation of symbols]

[0097] 1 Image reading section 2. Brightness correction section 3. Evaluation value acquisition unit 4. Reference evaluation value acquisition unit 5. Leveling evaluation value acquisition unit 6. Map Creation Department 7 Map output section 10 Illumination optical system 11 Light source 12, 13 Ellipsoidal mirror 14. Mirror 20 Detection optics 21 Perforated concave mirror 21a hole 22 Convex mirror 23 Detector 1 30 Monitor section 31 Cut mirror 32 concave mirror 33 Second detector 40 Processing section 90 samples 91 Top surface 92 Stages 100, 200 Image processing device 300 Judgment section 1000 optical equipment 3000 Inspection Equipment 9000 computers 9001 processor 9002 ROM 9003 RAM 9004 Storage section 9005 Communication Interface 9006 User Interface 9007 Bus DET judgment result IF1 Focus point IMG image data L11 illumination light L12 reflected light

Claims

1. an image reading means for reading image data obtained by imaging a sample on which a plurality of regions each having a pattern formed based on the same design information are arranged; an evaluation value acquisition means for acquiring evaluation values ​​based on pixel luminance for a plurality of sampled images at the same sampling point in each of the plurality of regions, among the sampled images at a plurality of sampling points set in each of the plurality of regions; a reference evaluation value acquisition means for acquiring a reference evaluation value for the evaluation values ​​of a plurality of sampled images at the same sampling point in the plurality of regions; and a leveled evaluation value acquisition means for acquiring a leveled evaluation value indicating a degree of deviation of the acquired evaluation value from the reference evaluation value for each of the sampling images of the plurality of regions. Image processing device.

2. the evaluation value acquisition means acquires the evaluation value based on the brightness of pixels in a predetermined area set as a region where an edge portion of the pattern exists for each of the sampling images. The image processing device according to claim 1 .

3. the evaluation value acquisition means acquires an average value of luminance of pixels within the predetermined region as the evaluation value; The image processing device according to claim 2 .

4. the reference evaluation value acquisition means acquires an average value of the evaluation values ​​of the plurality of sampling images at the same sampling point in the plurality of regions as the reference evaluation value for the plurality of sampling images at the same sampling point; The image processing device according to claim 3 .

5. the reference evaluation value is provided to the reference evaluation value acquisition means as a value obtained from an image generated based on design information of the sample. The image processing device according to claim 3 .

6. the leveling evaluation value acquisition means acquires the leveling evaluation value for each of the sampling images of the plurality of regions based on a difference between the reference evaluation value and the acquired evaluation value; 6. The image processing device according to claim 4 or 5.

7. the leveling evaluation value acquisition means acquires, for each of the sampling images of the plurality of regions, a value obtained by dividing a difference between the reference evaluation value and the acquired evaluation value by the reference evaluation value, as the leveling evaluation value; The image processing device according to claim 6 .

8. a map creating means for creating a two-dimensional map showing the distribution of the smoothed evaluation values ​​obtained for each of the sampling images of the plurality of regions; and a map output means for outputting the two-dimensional map.

3. The image processing device according to claim 1 or 2.

9. a brightness correction means for normalizing the brightness of pixels of the plurality of sampled images at the same sampling point within a predetermined range; the evaluation value acquisition means acquires evaluation values ​​of the plurality of sampled images at the same sampling point after luminance correction.

3. The image processing device according to claim 1 or 2.

10. the image data is obtained by correcting the luminance of pixels included in the image data based on the luminance distribution of the illumination used to image the sample; The image processing device according to claim 9 .

11. the image data is obtained by correcting the luminance of the pixels included in the image data based on a time variation in the luminance distribution of the illumination; The image processing device according to claim 10.

12. the luminance correction means corrects the luminance of pixels included in the image data based on the luminance distribution of the illumination used to image each sampling point of the sample. The image processing device according to claim 9 .

13. the luminance correction means corrects the luminance of the pixel included in the image data based on a time variation of the luminance distribution of the illumination. The image processing device according to claim 12.

14. The illumination is critical illumination. The image processing device according to claim 10.

15. The image processing device according to any one of claims 1 to 5; a determining unit that detects defects in the patterns of the sampling images of the plurality of regions based on the leveling evaluation value; Inspection equipment.

16. the determining means determines that a defect exists in the pattern when the smoothing evaluation value of each of the sampling images is outside a predetermined range.

16. The inspection device according to claim 15.

17. the determining means determines that a defect exists in the pattern based on the number of sampled images whose smoothing evaluation value is outside a predetermined range among a predetermined number of adjacent sampled images.

17. The inspection device according to claim 16.

18. the sample is a photomask, The defects include CD defects of the pattern and contamination of the pattern.

16. The inspection device according to claim 15.

19. The image processing device according to any one of claims 1 to 5; a determining unit that detects defects smaller than the width of the pattern based on a difference between an evaluation value of the sampled image and an evaluation value of the sampled image in a reference image; Inspection equipment.

20. reading image data obtained by capturing an image of a sample in which a plurality of regions having patterns formed based on the same design information are arranged; obtaining evaluation values ​​based on pixel brightness for a plurality of sampled images at the same sampling point in each of the plurality of regions among the sampled images at a plurality of sampling points set in each of the plurality of regions; acquiring a reference evaluation value for the evaluation values ​​of a plurality of sampling images at the same sampling point in the plurality of regions; acquiring a leveled evaluation value indicating a degree of deviation of the acquired evaluation value from the reference evaluation value for each of the sampling images of the plurality of regions; Image processing methods.

21. A process of reading image data obtained by capturing an image of a sample on which a plurality of regions having patterns formed based on the same design information are arranged; a process of acquiring evaluation values ​​based on pixel luminance for a plurality of sampled images at the same sampling point in each of the plurality of regions, among the sampled images at a plurality of sampling points set in each of the plurality of regions; a reference evaluation value acquisition process for acquiring a reference evaluation value for the evaluation values ​​for a plurality of sampling images at the same sampling point in the plurality of regions; and acquiring a leveled evaluation value indicating a degree of deviation of the acquired evaluation value from the reference evaluation value for each of the sampling images of the plurality of regions. program.

Images