Image processing device, inspection device, image processing method, and inspection method

Abstract

To provide an image processing device, an inspection device, an image processing method, and an inspection method capable of improving measures against a change in luminance distribution because of a time variation factor.SOLUTION: An image processing device 40 includes: a specifying section 41 for specifying multiple peaks points 55 that become peaks for surrounding parameter values when comparing parameter values of illumination parameters of multiple points correlated with pixels of a captured image 53 obtained by imaging an object 50 at predetermined sampling time; a generation section 42 for generating distribution of parameter values by generating parameter values of multiple points among the peak points 55 on the basis of respective peak values of parameter values in the specified multiple peak points 55; and a correction section 46 for correcting the captured image 53 by correcting parameter values of at least partial pixels of the captured image 53 at predetermined sampling time on the basis of distribution of the generated parameter values.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent Document 1 describes that a reference image is acquired in advance, and a captured image is corrected based on the luminance distribution of the reference image due to illumination light. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent No. 6591348 Summary of the Invention [Problem to be solved by the invention]

[0004] When inspecting a sample such as a mask using illumination light, the luminance distribution of the illumination light may fluctuate over time, and it is desirable to improve the ability to deal with such changes in luminance distribution due to time factors. However, with conventional methods of correction using the luminance distribution of a reference image acquired in advance, it is assumed that the luminance distribution of the illumination at the time the reference image is acquired and the luminance distribution of the illumination at the time the inspection image is acquired may differ due to time factors, and in some cases this may not be able to be adequately addressed.

[0005] The present disclosure has been made in consideration of such problems, and provides an image processing device, an inspection device, an image processing method, and an inspection method that can improve the ability to deal with changes in brightness distribution due to time-varying factors. [Means for solving the problem]

[0006] An image processing device according to one aspect of this embodiment includes an identification unit that identifies a plurality of peak points that become peaks relative to surrounding parameter values ​​when comparing parameter values ​​of illumination parameters at a plurality of points corresponding to pixels of an image captured of an object at a predetermined sampling time; a generation unit that generates a distribution of the parameter values ​​by generating parameter values ​​for a plurality of points between the identified peak points based on each peak value of the parameter values ​​at the identified peak points; and a correction unit that corrects the captured image by correcting the parameter values ​​of at least some of the pixels of the captured image at the predetermined sampling time based on the generated distribution of parameter values.

[0007] In the image processing device, the generating section may generate the parameter values ​​of the plurality of points between the peak points by interpolating from the peak values.

[0008] In the image processing device, the correction section may correct the parameter values ​​by smoothing processing.

[0009] In the image processing device described above, the object may have a repeating pattern in which multiple patterns formed with approximately the same pattern width are arranged, and the identification unit may identify the peak point having the peak value that exceeds a threshold value of the parameter value set based on the pattern width.

[0010] In the image processing device described above, the object has a repeating pattern in which a plurality of patterns formed with approximately the same pattern width are arranged, and the device further includes a judgment unit that judges whether the distribution of the positions of the identified plurality of peak points corresponds to the distribution of the patterns, or whether the distributions of the parameter values ​​for each pattern are similar to each other, and when the judgment unit judges that they correspond or are similar, the correction unit may correct the captured image.

[0011] The image processing device may further include a determination unit that determines a degree of difference between a first distribution of parameter values ​​generated based on an image of the object at a sampling time earlier than the predetermined sampling time and a second distribution of parameter values ​​generated based on an image of the object at the predetermined sampling time, and when the determination unit determines that there is a difference, the correction unit may correct the image by correcting parameter values ​​of at least some pixels of the image of the object at the predetermined sampling time based on the second distribution.

[0012] The image processing device described above may further include an extraction unit that extracts each of the fluctuation characteristics of time-dependent temporal fluctuation and position-dependent spatial fluctuation in the distribution of the parameter values ​​based on the first distribution and the second distribution, and a prediction unit that predicts a predicted distribution of the parameter values ​​at a third sampling time after the predetermined sampling time based on each of the extracted fluctuation characteristics, wherein the generation unit generates the third distribution of the parameter values ​​based on an image of the object at the third sampling time, the determination unit determines a similarity between the predicted distribution and the third distribution by comparing them, and when the determination unit determines that they are similar, the correction unit corrects the image based on the third distribution.

[0013] In the image processing device described above, the determination unit may determine the similarity between the predictive distribution and the third distribution by comparing a high frequency component in the spatial variation characteristic of the predictive distribution with a high frequency component in the spatial variation characteristic of the third distribution.

[0014] In the above-mentioned image processing device, the parameter value of the illumination light used when imaging the object may decrease from the center of the captured image toward the edge, and the edge of the beam of the illumination light illuminating the object may be included in the captured image.

[0015] In the image processing device, the object may be a photomask on which a pellicle is formed, and the illumination light may include EUV light.

[0016] An image processing device according to one aspect of the present embodiment includes: an identification unit that identifies a plurality of peak points that are peaks relative to surrounding parameter values ​​when comparing parameter values ​​of illumination parameters at a plurality of points corresponding to pixels of a captured image of an object captured using a portion of illumination light at a predetermined sampling time; a generation unit that generates a fourth distribution of the parameter values ​​by generating the parameter values ​​of a plurality of points between the identified peak points based on each peak value of the parameter values ​​at the identified peak points, and generates a fifth distribution of the parameter values ​​of a plurality of points corresponding to pixels of a monitor image captured by receiving another portion of the illumination light; a determination unit that determines a similarity between the fourth distribution and the fifth distribution by comparing the fourth distribution and the fifth distribution; and a correction unit that corrects the captured image by correcting the parameter values ​​of at least a portion of pixels of the captured image at the predetermined sampling time based on the generated fourth distribution, and if the determination unit determines that the parameter values ​​are similar, the correction unit corrects the captured image.

[0017] In the image processing device, the determination unit may determine that there is an abnormality when it determines that there is no similarity.

[0018] In the image processing device, the determining unit may extract a difference between the fourth distribution and the fifth distribution, and the correcting unit may correct the fifth distribution based on the difference.

[0019] The image processing device may further include a memory unit that stores state parameters of the device that captured the image of the object at the sampling time when the predetermined difference was extracted, together with the predetermined difference, and when the determination unit determines that the same state parameters were acquired after the sampling time, the correction unit may correct the fifth distribution based on the predetermined difference.

[0020] An inspection device according to one aspect of this embodiment includes an illumination optical system that illuminates an object using illumination light, an imaging optical system that captures an image of the object illuminated by the illumination light, and the image processing device described above, and the image processing device inspects the object using the image-processed captured image.

[0021] An image processing method according to one aspect of this embodiment includes the steps of: identifying a plurality of peak points that are peaks relative to surrounding parameter values ​​when parameter values ​​of illumination parameters at a plurality of points corresponding to pixels of an image captured of an object at a predetermined sampling time are compared; generating a distribution of the parameter values ​​by generating parameter values ​​for a plurality of points between the identified peak points based on each peak value of the parameter values ​​at the identified peak points; and correcting the captured image by correcting the parameter values ​​of at least some of the pixels of the captured image at the predetermined sampling time based on the generated distribution of parameter values.

[0022] An inspection method according to one aspect of this embodiment includes the steps of illuminating an object using illumination light, capturing an image of the object illuminated by the illumination light, correcting the captured image using the image processing method described above, and inspecting the object using the image-processed captured image. [Effects of the Invention]

[0023] According to the present disclosure, it is possible to provide an image processing device, an inspection device, an image processing method, and an inspection method that can improve the way changes in luminance distribution due to time-varying factors are handled. [Brief explanation of the drawings]

[0024] [Figure 1] 1 is a configuration diagram illustrating an inspection device according to a first embodiment. [Figure 2]1 is a block diagram illustrating an image processing device according to a first embodiment. [Figure 3] 3 is a diagram illustrating a captured image acquired by the image processing device according to the first embodiment and parameter values ​​along line III-III. FIG. [Figure 4] 4 is a diagram illustrating a captured image in which a peak point is identified by an identifying unit and parameter values ​​along line IV-IV in the image processing device according to the first embodiment. FIG. [Figure 5] 3 is a diagram illustrating a distribution of parameter values ​​generated by a generating unit and parameter values ​​along a VV line of the generated distribution in the image processing device according to the first embodiment. FIG. [Figure 6] 6 is a diagram illustrating a captured image corrected by a correction unit and parameter values ​​along line VI-VI in the image processing device according to the first embodiment. FIG. [Figure 7] FIG. 2 is a flowchart illustrating an image processing method using the image processing device according to the first embodiment. [Figure 8] FIG. 2 is a flowchart illustrating an inspection method using the inspection device according to the first embodiment. [Figure 9] FIG. 10 is a block diagram illustrating an image processing device according to a first modification of the first embodiment. [Figure 10] FIG. 10 is a flowchart illustrating an image processing method using an image processing device according to Modification 1 of Embodiment 1. [Figure 11] FIG. 10 is a flowchart illustrating an image processing method using an image processing device according to another example of the first modification of the first embodiment. [Figure 12] FIG. 10 is a block diagram illustrating an image processing device according to a second modification of the first embodiment. [Figure 13] FIG. 10 is a flowchart illustrating an image processing method using an image processing device according to Modification 2 of Embodiment 1. [Figure 14] FIG. 10 is a configuration diagram illustrating an inspection device according to a second embodiment. [Figure 15] FIG. 10 is a configuration diagram illustrating a monitor unit in an inspection device according to a second embodiment. [Figure 16]FIG. 10 is a block diagram illustrating an image processing device according to a second embodiment. [Figure 17] FIG. 10 is a flowchart illustrating an image processing method using the image processing device according to the second embodiment. [Figure 18] FIG. 10 is a flowchart illustrating an inspection method using the inspection device according to the second embodiment. [Figure 19] FIG. 10 is a configuration diagram illustrating an inspection device according to a first modified example of the second embodiment. [Figure 20] FIG. 10 is a configuration diagram illustrating an inspection device according to a second modification of the second embodiment. [Figure 21] 10 is a plan view illustrating a detector of a monitor unit in an inspection device according to a second modification of the second embodiment. FIG. [Figure 22] 10 is a plan view illustrating a detector of a monitor unit in an inspection device according to a second modification of the second embodiment. FIG. DETAILED DESCRIPTION OF THE INVENTION

[0025] Hereinafter, embodiments of the present disclosure will be described with reference to the drawings. The following description shows preferred embodiments of the present disclosure, and the scope of the present disclosure is not limited to the following embodiments. In the following description, components with the same reference numerals indicate substantially similar content.

[0026] (Embodiment 1) An inspection device, an image processing device, an image processing method, and an inspection method according to a first embodiment will be described. First, in <Inspection Device>, the inspection device of this embodiment will be described. Next, in <Image Processing Device>, the image processing device provided in the inspection device will be described. After that, in <Image Processing Method> and <Inspection Method>, an image processing method using the image processing device and an inspection method using the inspection device will be described. Furthermore, in <Variation 1> and <Variation 2>, each variation will be described. Note that the image processing device or image processing method according to an example of the present disclosure may be used for an inspection device as described in the following embodiments, but is not limited to this. For example, the image processing device or image processing method according to an example of the present disclosure may be used in a device (review device) that displays an image (captured image) obtained by illuminating an object on a display or the like.

[0027] <Inspection equipment> FIG. 1 is a configuration diagram illustrating an inspection apparatus 1 according to a first embodiment. The inspection apparatus 1 includes an illumination optical system 10, an imaging optical system 20, and an image processing device 40. The illumination optical system 10 illuminates an object 50 using illumination light L11. The illumination optical system 10 includes, for example, a light source 11, an ellipsoidal mirror 12, an ellipsoidal mirror 13, and a drop mirror 14. The imaging optical system 20 captures an image of the object 50 illuminated by the illumination light L11. The imaging optical system 20 includes, for example, a perforated concave mirror 21, a convex mirror 22, and a detector 23. The perforated concave mirror 21 and the convex mirror 22 form a Schwarzschild magnification optical system. The illumination optical system 10 and the imaging optical system 20 may further include optical components other than those described above, or any of the optical components described above may be omitted.

[0028] The image processing device 40 processes the captured image of the object 50 captured by the imaging optical system 20. The image processing device 40 also uses the processed captured image to inspect the object 50. The image processing device 40 will be described later.

[0029] The inspection apparatus 1 is an apparatus that inspects an object 50 for defects, contamination, etc. The object 50 is, for example, an EUV mask compatible with EUV (Extra Ultra Violet) light. Note that the object 50 is not limited to an EUV mask, but may also be a photomask compatible with illumination light L11 having another wavelength. The object 50 is also not limited to a photomask, but may also be a semiconductor substrate, etc. Here, for convenience of explanation of the inspection apparatus 1, an XYZ Cartesian coordinate system is introduced. For example, a plane parallel to the stage surface of a stage 52 on which the object 50 is placed is defined as the XY plane, and a direction perpendicular to the stage surface is defined as the Z-axis direction. For convenience, the +Z-axis direction is referred to as upward.

[0030] The light source 11 generates illumination light L11. The illumination light L11 contains, for example, EUV light of 13.5 nm, which is the same as the exposure wavelength of the EUV mask that is the object 50. Note that the illumination light L11 may contain light of other wavelengths. The illumination light L11 generated from the light source 11 is reflected by the ellipsoidal mirror 12. The illumination light L11 reflected by the ellipsoidal mirror 12 travels while being narrowed, and is collected at a condensing point IF1. The condensing point IF1 is located at a position conjugate with the upper surface 51 of the object 50.

[0031] After passing through the focusing point IF1, the illumination light L11 travels while expanding and is incident on a reflecting mirror such as the ellipsoidal mirror 13. The illumination light L11 that is incident on the ellipsoidal mirror 13 is reflected by the ellipsoidal mirror 13, travels while being narrowed, and is 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 converging light. The drop-down mirror 14 is disposed above the object 50. The illumination light L11 that is incident on and reflected by the drop-down mirror 14 is incident on the object 50. That is, the drop-down mirror 14 causes the illumination light L11 to be incident on the object 50.

[0032] The ellipsoidal mirror 13 focuses the illumination light L11 onto the object 50. The illumination optical system 10 is installed so that when the illumination light L11 illuminates the object 50, an image of the light source 11 is formed on the upper surface 51 of the object 50. Therefore, the illumination optical system 10 is a critical illumination system. In this way, the illumination optical system 10 illuminates the object 50 using critical illumination by the illumination light L11 generated by the light source 11.

[0033] The object 50 is placed on a stage 52. The illumination light L11 is incident on the object 50 from a direction tilted from the Z-axis direction. That is, the illumination light L11 is incident obliquely and illuminates the object 50.

[0034] The stage 52 is an XYZ drive stage. By moving the stage 52 in the X-axis direction and the Y-axis direction, a desired area of ​​the object 50 can be illuminated. Furthermore, by moving the stage 52 in the Z-axis direction, focus adjustment can be performed. The stage 52 may also be rotated around the X-axis, Y-axis, and Z-axis as rotation axes. Note that instead of moving and rotating the stage 52 along the X-axis, Y-axis, and Z-axis directions, the illumination optical system 10 and the imaging optical system 20 may also be moved and rotated.

[0035] Illumination light L11 from the light source 11 illuminates an inspection area of ​​the object 50. Reflected light L12, which is incident from a direction tilted with respect to the Z-axis direction and reflected by the object 50, is incident on the perforated concave mirror 21. A hole 21a is provided in the center of the perforated concave mirror 21.

[0036] 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 that passes through the hole 21a is detected by the detector 23. The detector 23 may be a detector 23 including a TDI (Time Delay Integration) sensor. The detector 23 acquires image data of the object 50. The detector 23 includes multiple imaging elements arranged in a line in one direction. Linear image data captured by the multiple imaging elements arranged in a line is called one-dimensional image data or one frame. The detector 23 acquires multiple one-dimensional image data by scanning in a direction perpendicular to the one direction. The imaging element is, for example, a CCD (Charge Coupled Device). Note that the imaging element is not limited to a CCD.

[0037] In this way, the imaging optical system 20 collects the reflected light L12 from the object 50 illuminated by the illumination light L11, and detects the collected reflected light L12 with the detector 23 to obtain image data of the object 50. The image data is, for example, one-dimensional image data.

[0038] The reflected light L12 contains information about defects in the object 50. The specularly reflected light of the illumination light L11 that is incident on the object 50 from a direction tilted with respect to the Z-axis direction is detected by the imaging optical system 20. If a defect exists in the object 50, the defect is observed as a dark image. This observation method is called bright-field observation. The multiple one-dimensional image data of the object 50 acquired by the detector 23 are output to the image processing device 40 and processed into two-dimensional image data.

[0039] The image processing device 40 is connected to the imaging optical system 20 via a signal line or wirelessly. The image processing device 40 receives image data of the object 50 from the detector 23 in the imaging optical system 20. The image processing device 40 processes the image data of the object 50 received from the detector 23 into a two-dimensional captured image. The image processing device 40 inspects the object 50 using the processed captured image 53.

[0040] <Image processing device> Next, the image processing device 40 will be described. Fig. 2 is a block diagram illustrating the image processing device 40 according to the first embodiment. As shown in Fig. 2, the image processing device 40 includes an identification unit 41, a generation unit 42, and a correction unit 46. The identification unit 41, the generation unit 42, and the correction unit 46 function as an identification means, a generation means, and a correction means.

[0041] FIG. 3 illustrates a captured image 53 acquired by the image processing device 40 according to the first embodiment and parameter values ​​along line III-III. As shown in FIG. 3, the captured image 53 is an image of an object 50 captured in a field of view at a predetermined sampling time. The object 50 may have a repeating pattern in which multiple patterns 54 are arranged. The multiple patterns 54 may be formed with approximately the same pattern width. For ease of explanation of the captured image 53, an αβ Cartesian coordinate system is introduced. The direction in which the pattern 54 extends in the captured image 53 is defined as the β-axis direction. The pattern width indicates the length of the minor axis of the pattern 54. Therefore, the pattern width indicates the length in the α-axis direction. In the repeating pattern, the distance between the patterns 54 may be approximately the same. Note that in FIG. 3, the reference numerals of some of the patterns 54 are omitted to avoid cluttering the drawing.

[0042] The object 50 may include a photomask on which a pellicle is formed. The parameter values ​​of the illumination parameters of the illumination light L11 irradiated when capturing an image of the object 50 may decrease from one end of the captured image 53 to the other end, or may decrease from the center of the captured image 53 to the end of the captured image 53.

[0043] The illumination parameter is, for example, the intensity of the signal detected by the detector 23. Such signal intensity may reflect the brightness of the object 50 illuminated with the illumination light L11. In that case, the parameter value is a brightness value. The end of the beam of the illumination light L11 illuminating the object 50 may be included in the captured image 53. In other words, if the beam of the illumination light L11 is narrowed to about the field of view to prevent damage to the pellicle by the illumination light L11, the end of the beam of the illumination light L11 may be located at the edge of the captured image 53.

[0044] This results in a brightness distribution in which the parameter value (for example, brightness value) of the lighting parameter decreases from the center to the ends of the captured image 53. Alternatively, the brightness distribution may be such that the parameter value (for example, brightness value) of the lighting parameter decreases from one end of the captured image 53 to the other end.

[0045] 4 is a diagram illustrating a captured image 53 in which the identification unit 41 has identified peak points 55 and parameter values ​​along line IV-IV in the image processing device 40 according to the first embodiment. As shown in FIG. 4, the identification unit 41 identifies peak points 55 of the illumination parameters in the captured image 53. Note that in FIG. 4, the reference numerals of some of the peak points 55 are omitted to avoid cluttering the drawing.

[0046] The determination unit 41 compares the parameter values ​​of the illumination parameters at a plurality of points associated with the image of the captured image 53. For example, the determination unit 41 may compare the parameter values ​​of the illumination parameters at each point associated with each pixel of the captured image 53. The determination unit 41 corresponds each pixel of the captured image 53 to each point having coordinates. Specifically, for example, each point has coordinates (α, β). Therefore, the determination unit 41 corresponds the pixels of the captured image 53 to points having coordinates on the (α, β) plane.

[0047] The identification unit 41 identifies multiple peak points 55 when comparing the parameter values ​​of the illumination parameters at multiple points associated with pixels in the captured image 53. The peak points 55 are points having parameter values ​​greater than the parameter values ​​of the pixels corresponding to the surrounding points. Specifically, the peak points 55 include points where the Grad (gradient) of the parameter value is 0 with respect to the parameter values ​​of adjacent points. In other words, the peak points 55 are points that are peaks with respect to the surrounding parameter values. In this way, the identification unit 41 identifies multiple peak points 55 that are peaks with respect to the surrounding parameter values ​​when comparing the parameter values ​​of the illumination parameters at multiple points associated with pixels in the captured image 53 obtained by capturing an image of the object 50 at a predetermined sampling time.

[0048] When identifying the peak point 55, the identification unit 41 may set a threshold value for the parameter value of the lighting parameter. Then, the identification unit 41 may identify the peak point 55 having a peak value that exceeds the threshold value for the parameter value. For example, the identification unit 41 may set the smallest parameter value in the multiple patterns 54 as the threshold. Thus, the identification unit 41 identifies the peak point 55 from any point on the multiple patterns 54. Furthermore, the identification unit 41 may set the parameter value of a dark area between the patterns 54 in the captured image 53 as the threshold. In this case as well, the identification unit 41 identifies the peak point 55 from points on the multiple patterns 54.

[0049] The identification unit 41 may set the threshold value of the parameter value used when identifying the peak point 55 based on the pattern width. For example, when the pattern width is small, the identification unit 41 may set the threshold value to a small parameter value. When the pattern width becomes small, the area that receives and reflects the illumination light L11 becomes smaller. As a result, the parameter values ​​of the illumination parameters become smaller overall. When the pattern width approaches the focal depth of the ellipsoidal mirror 13, this tendency becomes more pronounced. Therefore, the peak value of the peak point 55 also becomes smaller. Therefore, the threshold value is set to a small parameter value.

[0050] On the other hand, when the pattern width is large, the identification unit 41 may set the threshold to a large parameter value. When the pattern width is large, the portion that receives and reflects the illumination light L11 becomes larger. As a result, the parameter values ​​of the illumination parameters become larger overall. Therefore, the threshold is set to a large parameter value. In this way, the identification unit 41 may identify a peak point 55 having a peak value that exceeds the parameter value threshold set based on the pattern width.

[0051] FIG. 5 shows the distribution of parameter values ​​generated by the generating unit 42 in the image processing device 40 according to the first embodiment, and the distribution of the generated parameter values. VV 5 is a diagram illustrating parameter values ​​along a line. As shown in FIG. 5, the generation unit 42 generates a distribution of parameter values ​​for each point based on each peak value of the parameter values ​​at the identified multiple peak points 55. Specifically, the generation unit 42 generates parameter values ​​for multiple points between the peak points 55 based on the peak values ​​of the peak points 55. For example, the generation unit 42 generates the distribution of parameter values ​​for each point by interpolating the parameter values ​​of the multiple points between the peak points 55 from the peak values. In this manner, the generation unit 42 generates the parameter value distribution by generating parameter values ​​for multiple points between the peak points 55 based on each peak value of the parameter values ​​at the identified multiple peak points 55. Note that the generation unit 42 may generate a distribution of parameter values ​​for each point corresponding to each pixel of the captured image 53 and record the parameter value at each point corresponding to each pixel determined based on the distribution. The parameter value determined at each point based on the distribution of parameter values ​​is used in the correction process by the correction unit 46, which will be described later.

[0052] FIG. 6 is a diagram illustrating a captured image 53 corrected by the correction unit 46 and parameter values ​​along the VI-VI line in the image processing device 40 according to the first embodiment. As shown in FIG. 6, the correction unit 46 corrects the captured image 53 by performing image processing on the parameter values ​​of each pixel of the captured image 53 at a predetermined sampling time based on the distribution of parameter values ​​generated based on the captured image 53 at the predetermined sampling time. This image processing may include smoothing processing. That is, the correction unit 46 corrects the captured image 53 by performing smoothing processing on the parameter values ​​of each pixel of the captured image 53 based on the distribution of parameter values ​​generated. Specifically, the correction unit 46 may divide the parameter value of each pixel of the captured image 53 by each parameter value in the distribution of parameter values ​​generated.

[0053] Furthermore, the correction unit 46 may divide the parameter value of each pixel of the captured image 53 by the parameter value of each point determined based on the distribution of parameter values ​​recorded for each point corresponding to each pixel by the processing of the generation unit 42. In this way, the image processing device 40 corrects the captured image 53 at a predetermined sampling time using the correction unit 46, based on the distribution of parameter values ​​generated by the generation unit 42 based on the captured image 53 at the predetermined sampling time. This allows the image processing device 40 to respond with high real-time accuracy to fluctuations in the lighting parameter distribution (such as luminance distribution) due to time factors.

[0054] In the above description, the parameter values ​​are corrected based on the distribution of parameter values ​​for all pixels in the captured image 53, but this is not limitative and correction may be performed for some pixels in the captured image 53. In the above description, the parameter value distribution is generated for points corresponding to all pixels in the captured image 53, but this is not limitative and correction may be performed for a plurality of points corresponding to some pixels in the captured image 53.

[0055] <Image processing method> Next, an image processing method using the image processing device 40 will be described. FIG. 7 is a flowchart illustrating an image processing method using the image processing device 40 according to the first embodiment. As shown in step S11 of FIG. 7, the identification unit 41 identifies a plurality of peak points 55. For example, when comparing the parameter values ​​of the illumination parameters at each point corresponding to each pixel of a captured image 53 obtained by capturing an image of the object 50 at a predetermined sampling time, the identification unit 41 identifies a plurality of peak points 55 that are peaks relative to the surrounding parameter values. In step S11, the identification unit 41 may identify the peak points 55 having peak values ​​that exceed a parameter value threshold set based on the pattern width.

[0056] Next, as shown in step S12, the generation unit 42 generates a distribution of parameter values. For example, based on each peak value of the parameter values ​​at the identified multiple peak points 55, the generation unit 42 generates the distribution of parameter values ​​at each point by generating parameter values ​​at multiple points between the peak points 55. In step S12, the generation unit 42 may generate the parameter values ​​at multiple points between the peak points 55 by interpolating from the peak values.

[0057] Next, as shown in step S13, the correction unit 46 corrects the captured image 53. Specifically, the captured image 53 is corrected by performing image processing on the parameter values ​​of each pixel of the captured image 53 at a predetermined sampling time based on the distribution of parameter values ​​generated based on the captured image 53 at the predetermined sampling time. In step S13, the image processing may include a smoothing process. In this manner, the captured image 53 can be processed.

[0058] <Testing method> Next, an inspection method will be described as an operation of the inspection device 1 of this embodiment. The inspection device 1 corrects a captured image 53 of the object 50 and inspects the object 50 using the corrected captured image 53. FIG. 8 is a flowchart illustrating an inspection method using the inspection device 1 according to the first embodiment. As shown in step S101 of FIG. 8, first, the object 50 is illuminated using illumination light L11. For example, when illuminating the object 50 with illumination light L11 generated by the light source 11, the illumination optical system 10 is positioned so as to provide critical illumination. Then, the object 50 is illuminated with illumination light L11.

[0059] Next, as shown in step S102, a captured image 53 of the object 50 is acquired. For example, the imaging optical system 20 captures the captured image 53 of the object 50 illuminated by the illumination light L11. Then, the image processing device 40 acquires the captured image 53 of the object 50 from the detector 23 of the imaging optical system 20.

[0060] Next, as shown in step S103, the image processing device 40 corrects the captured image 53. Specifically, the image processing device 40 corrects the captured image 53 by performing processing based on the distribution of the generated parameter values.

[0061] Next, as shown in step S104, the object 50 is inspected using the corrected captured image 53 of the object 50. In this manner, the inspection device 1 can inspect the object 50.

[0062] Next, the effects of this embodiment will be described. The image processing device 40 of this embodiment generates a distribution of parameter values ​​based on peak values ​​of multiple peak points 55 identified in a captured image 53 at a predetermined sampling time. The image processing device 40 then corrects the captured image 53 based on the generated distribution of parameter values. In other words, the image processing device 40 can generate a distribution of parameter values ​​that reflects nonuniformity in parameter values ​​such as brightness values ​​at a predetermined sampling time, and then correct the captured image 53 at the same sampling time. Therefore, the nonuniformity at a predetermined sampling time can be used to correct the captured image 53 at the predetermined sampling time. This makes it possible to eliminate the effects of time fluctuations in the captured image 53, and improves the ability to handle changes in brightness distribution due to time fluctuations.

[0063] In contrast, a method of correcting a captured image using a reference image acquired in advance, such as that described in Patent Document 1, cannot suppress the time fluctuation between time t1 and time t2, because the time t1 when the reference image is acquired is different from the time t2 when the captured image is acquired.

[0064] On the other hand, in this embodiment, the time t3 at which the distribution of parameter values ​​reflecting non-uniformity appears is the same as the time t3 at which the captured image is captured, so that non-uniformity can be reduced.

[0065] When generating the distribution of parameter values, the generation unit 42 interpolates parameter values ​​between peak points 55 from the peak values. Furthermore, the correction unit 46 performs a smoothing process when correcting the captured image 53. This process can reduce non-uniformity in the parameter values ​​in the captured image 53, and can improve the ability to deal with changes in brightness distribution due to time-varying factors.

[0066] Since the peak point 55 is identified using a threshold value set based on the pattern width, it is possible to identify an appropriate peak point 55 when generating the distribution of parameter values.

[0067] <Variation 1> Next, an image processing device according to Modification 1 of Embodiment 1 will be described. In this modification, a captured image 53 is corrected after determining the correction conditions. FIG. 9 is a block diagram illustrating an image processing device 40a according to Modification 1 of Embodiment 1. As shown in FIG. 9, the image processing device 40a further includes a determination unit 45 in addition to an identification unit 41, a generation unit 42, and a correction unit 46. The determination unit 45 functions as a determination means.

[0068] The determination unit 45 determines whether the correction conditions are satisfied. The correction conditions include, for example, the correction conditions for the pattern 54 and sampling The parameter value distribution includes correction conditions related to the distribution of parameter values ​​before and after the time. Specifically, the determination unit 45 determines whether the distribution of the positions of the identified multiple peak points 55 corresponds to the distribution of pattern 54, or whether the distributions of parameter values ​​for each pattern 54 are similar to each other. In other words, the determination unit 45 determines whether there is a case where a peak point 55 is located at a position other than pattern 54. In this way, peak points 55 due to noise or the like are eliminated.

[0069] The determination unit 45 may determine the correspondence between the distribution of the positions of the identified multiple peak points 55 and the distribution of the pattern 54 based on whether the peak points 55 are located on the pattern 54. Furthermore, the determination unit 45 may determine the similarity of the distribution of the parameter values ​​for each pattern 54 based on, for example, the number of peak points 55 for each pattern 54, the peak values ​​of each peak point 55, and the arrangement shape of each peak point.

[0070] If the determination unit 45 determines that the distribution of the positions of the identified plurality of peak points 55 corresponds to the distribution of the pattern 54, the correction unit 46 corrects the captured image 53. Furthermore, if the determination unit 45 determines that the distributions of the parameter values ​​for each pattern 54 are similar to each other, the correction unit 46 corrects the captured image 53.

[0071] In addition, the determination unit 45 determines the degree of difference between the first distribution and the second distribution by comparing a first distribution of parameter values ​​at each point of the captured image 53 generated at a sampling time t1 that is earlier than the specified sampling time t2 with a second distribution of parameter values ​​at each point of the captured image 53 generated at the specified sampling time t2. sampling The second distribution of parameter values ​​at time t2 is sampling If the parameter value at time t1 does not differ from the first distribution, sampling At time t2, the captured image 53 is also corrected using the first distribution. sampling The second distribution of parameter values ​​at time t2 is sampling If the parameter value at time t1 differs from the first distribution, sampling At time t2, the captured image 53 is corrected using the second distribution.

[0072] For example, the determination unit 45 may determine the degree of dissimilarity based on the difference between the parameter value of a predetermined point in the first distribution and the parameter value of a predetermined point in the second distribution. The predetermined point may include a plurality of points. The determination unit 45 may set a threshold value for the degree of dissimilarity in advance. Then, the determination unit 45 may determine that the first distribution and the second distribution are different when the degree of dissimilarity exceeds the threshold. Furthermore, the determination unit 45 may determine that the first distribution and the second distribution are not different when the degree of dissimilarity is equal to or less than the threshold.

[0073] When the determination unit 45 compares the first distribution with the second distribution and determines that the two distributions are different, the correction unit 46 corrects the captured image 53 by performing image processing based on the second distribution.

[0074] Fig. 10 is a flowchart illustrating an image processing method using the image processing device 40 according to Modification 1 of Embodiment 1. Fig. 11 is a flowchart illustrating an image processing method using the image processing device 40 according to another example of Modification 1 of Embodiment 1. Steps S21, S22, and S24 in Fig. 10 are similar to the above-described steps S11, S12, and S13, respectively. Steps S31 and S32 in Fig. 11 are similar to the above-described steps S11 and S12, respectively. Note that steps S21, S22, S23, S31, and S32 may be performed at sampling time t1, which is earlier than the predetermined sampling time t2, and at the predetermined sampling time t2.

[0075] As shown in step S23, in this modification, it is determined whether a correction condition is satisfied. For example, the determination unit 45 determines whether the distribution of the positions of the identified plurality of peak points 55 corresponds to the distribution of the pattern 54, or whether the distributions of the parameter values ​​for each pattern 54 are similar to each other. If the determination unit 45 determines in step S23 that the distribution of the positions of the identified plurality of peak points 55 corresponds to the distribution of the pattern 54, or if it determines that the distributions of the parameter values ​​for each pattern 54 are similar to each other, the correction unit 46 corrects the captured image 53 as shown in step S24. Then, the process ends.

[0076] On the other hand, in step S23, if the judgment unit 45 determines that the distribution of the positions of the identified multiple peak points 55 does not correspond to the distribution of the pattern 54, or if it determines that the distributions of the parameter values ​​for each pattern 54 are not similar to each other, the processing is terminated.

[0077] Furthermore, in step S33, a first distribution of parameter values ​​at each point of the captured image 53 generated at sampling time t1, which is earlier than the predetermined sampling time t2, may be compared with a second distribution of parameter values ​​at each point of the captured image 53 generated at the predetermined sampling time t2, to determine the degree of difference between the first distribution and the second distribution. If it is determined that the two distributions differ, as shown in step S34, the correction unit 46 corrects the captured image 53 by performing image processing based on the second distribution generated from the captured image 53 at the predetermined sampling time t2.

[0078] On the other hand, in step S33, if the judgment unit 45 determines that the first distribution and the second distribution do not differ, the correction unit 46 corrects the captured image 53 by correcting the parameter values ​​based on the first distribution generated from the captured image 53 at sampling time t1.

[0079] According to this modification, the captured image 53 is corrected in accordance with the determination of the correction conditions, thereby making it possible to correct the captured image 53 in accordance with the correction conditions that have been set in advance.

[0080] <Variation 2> Next, an image processing device according to Modification 2 of Embodiment 1 will be described. Fig. 12 is a block diagram illustrating an image processing device 40b according to Modification 2 of Embodiment 1. As shown in Fig. 12, the image processing device 40b further includes an extraction unit 43, a prediction unit 44, and a determination unit 45 in addition to an identification unit 41, a generation unit 42, and a correction unit 46. The extraction unit 43, the prediction unit 44, and the determination unit 45 function as extraction means, prediction means, and determination means, respectively.

[0081] The extraction unit 43 extracts the fluctuation characteristics of the time fluctuation that depends on time and the spatial fluctuation that depends on the position of each point in the degree of difference between the first distribution and the second distribution.

[0082] The predictor 44 predicts the distribution of parameter values ​​at a sampling time t3 that is later than the predetermined sampling time t2 based on the extracted fluctuation characteristics. The predicted distribution at this time is called a predicted distribution.

[0083] The generation unit 42 generates a distribution of parameter values ​​at each point at sampling time t3 by the processing described above based on the captured image 53 at sampling time t3. The distribution generated at this time is called a third distribution.

[0084] The determination unit 45 compares the predicted distribution for sampling time t3 predicted by the prediction unit 44 with a third distribution, which is the distribution of parameter values ​​at sampling time t3 generated by the generation unit 42, to determine the similarity between the two distributions. The determination of similarity may be performed in the same manner as the determination of dissimilarity. That is, if the dissimilarity exceeds a threshold, the two distributions may be determined to be dissimilar, and if the dissimilarity is equal to or less than the threshold, the determination unit 45 may determine the similarity between the two distributions by comparing high-frequency components in the spatial variation characteristics of the predicted distribution with high-frequency components in the spatial variation characteristics of the third distribution. If the determination unit 45 determines that the two distributions are similar as a result of comparing the predicted distribution with the third distribution, the correction unit 46 corrects the captured image 53 by performing image processing based on the third distribution.

[0085] Fig. 13 is a flowchart illustrating an image processing method using an image processing device 40b according to Modification 2 of Embodiment 1. Steps S41, S42, and S47 in Fig. 13 are similar to the above-described steps S11, S12, and S13, respectively.

[0086] As shown in step S43, in this modification, the extraction unit 43 extracts the fluctuation characteristics of the time fluctuation that depends on time and the spatial fluctuation that depends on the position of each point in the degree of difference between the first distribution and the second distribution.

[0087] Next, as shown in step S44, the prediction unit 44 predicts the distribution of parameter values ​​at a sampling time t3 that is later than the predetermined sampling time t2, based on the extracted fluctuation characteristics. Next, as shown in step S45, the generating unit 42 generates a third distribution of parameter values ​​at sampling time t3.

[0088] Next, as shown in step S46, the determination unit 45 compares the generated third distribution with the predicted predicted distribution to determine the similarity between the two distributions. In step S46, the determination unit 45 may determine the similarity between the two distributions by comparing the high-frequency components in the spatial variation characteristics of the predicted distribution with the high-frequency components in the spatial variation characteristics of the third distribution. If the determination unit 45 determines that the two distributions are similar by comparing the predicted distribution with the third distribution, the correction unit 46 corrects the captured image by performing image processing based on the third distribution as shown in step S47.

[0089] On the other hand, in step S46, when the determination unit 45 compares the predicted distribution with the third distribution and determines that the two distributions are not similar, the process ends.

[0090] According to this modification, it is possible to predict the distribution of parameter values ​​at sampling time t3. Therefore, it is possible to predict the influence of time fluctuations from sampling time t2 to sampling time t3 and reduce the influence of time fluctuations in advance. Furthermore, since the determination unit 45 determines the similarity in the fluctuation characteristics of time fluctuations and spatial fluctuations, it is possible to improve the determination of correction conditions.

[0091] (Embodiment 2) Next, the inspection device, image processing device, image processing method, and inspection method according to embodiment 2 will be described in <Inspection device>, <Image processing device>, <Image processing method>, and <Inspection method>, respectively. After that, each modification will be described in <Modification 1> and <Modification 2>.

[0092] <Inspection equipment> The inspection device of this embodiment will be described. The inspection device of this embodiment further includes a monitor unit. FIG. 14 is a configuration diagram illustrating an inspection device 2 according to embodiment 2. As shown in FIG. 14, the inspection device 2 further includes a monitor unit 30. The monitor unit 30 has a cut mirror 31, a concave mirror 32, and a detector 33.

[0093] FIG. 15 is a configuration diagram illustrating the monitor unit 30 in the inspection device 2 according to the second embodiment. FIG. 15 also shows an enlarged view of the vicinity of the concave mirror 32. As shown in FIGS. 14 and 15, 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.

[0094] In the cross-sectional area 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.

[0095] For example, if the cross-sectional area of ​​the cross section of the illumination light L11 perpendicular to the optical axis 15 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 15 of, for example, ±7°. The range of the angle used as the illumination light L11 for the EUV mask is, for example, ±6°. For use in the monitor unit 30, a small portion of the upper part 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 portion of the upper part of the beam is extracted, the amount of illumination light L11 on the EUV mask does not decrease significantly. Therefore, a decrease in the inspection accuracy of the object 50 can be suppressed.

[0096] 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 detector 23 and the image data acquired by the detector 33. Even if the numerical aperture (NA) for the detector 23 and the NA for the detector 33 differ and the point spread functions (PSFs) also differ, the difference in NA does not affect this embodiment because the plasma size is sufficiently larger than the PSF size.

[0097] The illumination light L11 reflected by the cut mirror 31 travels while being narrowed down and is converged at a convergence point IF2. Thereafter, the illumination light L11 is incident on the concave mirror 32 while diverging.

[0098] The concave mirror 32 and multiple mirrors (not shown) magnify the illumination light L11 extracted by the cut mirror 31. The distance G1 is between the condensing point IF2 and the concave mirror 32, and the distance G2 is between the condensing point IF2 and the detector 33. The image data acquired by the detector 33 can also be highly magnified. However, to obtain a high magnification (up to 500), the distance G2 must be very large. For example, if the distance G1 is up to 5 mm, the distance G2 must be set to 2500 mm, resulting in a magnification of 500x. For example, a magnification of 500x can be achieved by using multiple mirrors.

[0099] In this embodiment, the magnification of the image data of the illumination parameter distribution (e.g., luminance distribution) acquired by the monitor unit 30 is the same as the magnification of the image data of the object 50 acquired by the imaging optical system 20. Note that the magnification of the image data of the illumination parameter distribution acquired by the monitor unit 30 may be lower than the magnification of the image data of the object 50 acquired by the imaging 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 detector 23 is 20 times and the magnification of the 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.

[0100] The illumination light L11 incident on the concave mirror 32 and reflected by the concave mirror 32 is detected by the detector 33. The detector 33 includes, for example, a TDI sensor. The detector 33 acquires a monitor image such as the luminance distribution of the illumination light L11. The detector 33 includes a plurality of imaging elements arranged in a line in one direction. Linear image data acquired by the plurality of imaging elements arranged in a line is one-dimensional image data or one frame, similar to the detector 23. The detector 33 acquires a plurality of one-dimensional image data by scanning in a direction perpendicular to the one direction. The one-dimensional image data acquired by the detector 33 indicates the power fluctuation and luminance distribution of the illumination light L11. The imaging element is, for example, a CCD (Charge Coupled Device). Note that the imaging element is not limited to a CCD.

[0101] For example, the optical system is arranged so that an image of the light source 11 of the illumination light L11 is formed on the detector 33. As a result, the monitor unit 30 acquires image data of the luminance distribution of the illumination light L11 detected by illuminating the detector 33 with critical illumination using part of the illumination light L11. This makes it possible to accurately correct the luminance distribution and power fluctuations.

[0102] In this way, the monitor unit 30 collects a portion of the illumination light L11 and detects the collected illumination light L11 with the detector 33 to obtain image data including the distribution of illumination parameters such as the power fluctuation and luminance distribution of the illumination light L11. The image data obtained by the detector 33 is output to the image processing device 40c and processed into two-dimensional image data.

[0103] The image processing device 40c is connected to the monitor unit 30 by a signal line or wirelessly. The image processing device 40c receives image data including the distribution of illumination parameters from the detector 33 in the monitor unit 30. The image processing device 40c processes the image data of the object 50 received from the detector 33 into a two-dimensional monitor image.

[0104] <Image processing device> Next, an image processing device according to a second embodiment will be described. Fig. 16 is a block diagram illustrating an image processing device 40c according to the second embodiment. As shown in Fig. 16, the image processing device 40c includes an identification unit 41, a generation unit 42, a determination unit 45, a correction unit 46, and a storage unit 47. The storage unit 47 functions as a storage unit.

[0105] In this embodiment, the identifying unit 41 identifies multiple peak points 55 that are peaks relative to the surrounding parameter values ​​when comparing the parameter values ​​of the illumination parameters at each point corresponding to each pixel of a captured image 53 obtained by capturing an image of the object 50 using a portion of the illumination light L11 at a predetermined sampling time t2. Here, the portion of the illumination light L11 is a portion that was not extracted by the cut mirror 31.

[0106] The generation unit 42 generates a distribution of parameter values ​​at each point (hereinafter referred to as a fourth distribution) by generating parameter values ​​at multiple points between the identified peak points 55 based on each peak value of the parameter values ​​at the multiple peak points 55. At the same time, the generation unit 42 generates a distribution of parameter values ​​at each point (hereinafter referred to as a fifth distribution) that corresponds to each pixel of a monitor image captured by receiving another portion of the illumination light L11. Here, the other portion of the illumination light L11 is the portion extracted by the cut mirror 31.

[0107] The determination unit 45 compares the fourth distribution of parameter values ​​for each point in the captured image 53 with the fifth distribution of parameter values ​​for each point in the monitor image. Then, the determination unit 45 determines the similarity between the two distributions. If the determination unit 45 determines that the two distributions are similar, the correction unit 46 corrects the captured image 53. Specifically, the correction unit 46 corrects the captured image 53 by performing image processing on the parameter values ​​of each pixel of the captured image 53 at a predetermined sampling time t2 based on the generated fourth distribution of parameter values.

[0108] The similarity between the fourth distribution and the fifth distribution in this way indicates that the fourth distribution generated by the generation unit 42 is similar to the fifth distribution of the illumination light L11 acquired by the monitor unit 30. In other words, this indicates that the fourth distribution matches the results monitored by the monitor unit 30. This indicates the validity of image processing the captured image 53 based on the fourth distribution.

[0109] If the determination unit 45 determines that the fourth distribution and the fifth distribution are not similar, the determination unit 45 may determine an abnormality, for example, an abnormality in the inspection device 2. Furthermore, the determination unit 45 may extract a difference between the fourth distribution of parameter values ​​of each point in the captured image 53 and the fifth distribution of parameter values ​​of each point in the monitor image. The correction unit 46 may correct the fifth distribution based on the extracted difference. This indicates that the reliability of the fourth distribution generated from the captured image 53 is higher than the reliability of the fifth distribution acquired by the monitor unit 30 of the inspection device 2. Note that if the reliability of the fifth distribution is high, the correction unit 46 may correct the fourth distribution based on the extracted difference.

[0110] The storage unit 47 stores the state parameters of the inspection device 2 at the sampling time when the predetermined difference was extracted, in association with the difference between the fourth distribution and the fifth distribution extracted by the determination unit 45. The state parameters of the inspection device 2 include, for example, the temperature of predetermined members of the inspection device 2 and the temperature inside the housing. Note that the state parameters of the inspection device 2 are not limited to temperature, and may also include the humidity of the inspection device 2, the output of the light source 11, the output of the illumination light L11, etc. When the determination unit 45 determines that the same state parameters have been acquired after the sampling time, the correction unit 46 may correct at least one of the fourth distribution and the fifth distribution based on the difference between the distributions recorded in association with the state parameters.

[0111] <Image processing method> Next, an image processing method using the image processing device 40c of this embodiment will be described. Fig. 17 is a flowchart illustrating an image processing method using the image processing device 40c according to embodiment 2. As shown in step S51 in Fig. 17, the identification unit 41 identifies a plurality of peak points 55 that are peaks relative to surrounding parameter values ​​when comparing the parameter values ​​of the illumination parameters at each point corresponding to each pixel of a captured image 53 obtained by capturing an image of an object 50 using a portion of the illumination light L11 at a predetermined sampling time.

[0112] Next, as shown in step S52, the generation unit 42 generates a fourth distribution of parameter values ​​for each point by generating parameter values ​​for multiple points between the peak points 55 based on each peak value of the parameter values ​​at the identified multiple peak points 55. At the same time, the generation unit 42 generates a fifth distribution of parameter values ​​for each point, which corresponds to each pixel of the monitor image captured by the monitor unit 30 by receiving another part of the illumination light L11, based on, for example, the luminance of each point.

[0113] Next, as shown in step S53, it is determined whether the correction condition is satisfied. Specifically, the determination unit 45 compares the fourth distribution of parameter values ​​of each point in the captured image 53 with the fifth distribution of parameter values ​​of each point in the monitor image. Then, the determination unit 45 determines the similarity between the two distributions. If the determination unit 45 determines that the two distributions are similar, the correction unit 46 corrects the captured image 53 as shown in step S54. Specifically, the correction unit 46 corrects the captured image 53 by performing image processing on the parameter values ​​of each pixel of the captured image 53 at a predetermined sampling time based on the fourth distribution. Thereafter, the processing ends.

[0114] On the other hand, if the determination unit 45 determines in step S53 that the fourth distribution and the fifth distribution are not similar, then as shown in step S55, the determination unit 45 may determine that there is an abnormality, for example, that there is an abnormality in the inspection device 2. Then, the process ends.

[0115] In step S53, the determination unit 45 may extract a difference between the fourth distribution of parameter values ​​of each point in the captured image 53 and the fifth distribution of parameter values ​​of each point in the monitor image. In this case, in step S54, the correction unit 46 may correct the fifth distribution based on the extracted difference.

[0116] Furthermore, in step S53, the determination unit 45 may determine whether or not a state parameter identical to the state parameter stored in the storage unit 47 has been acquired after the sampling time. When the determination unit 45 determines that the identical state parameter has been acquired after the sampling time, the correction unit 46 corrects at least one of the fourth distribution and the fifth distribution based on the difference between the distributions recorded in association with the state parameter.

[0117] <Testing method> Next, an inspection method using the inspection device 2 of this embodiment will be described. Fig. 18 is a flow chart illustrating an inspection method using the inspection device 2 according to embodiment 2. Steps S201, S202, and S205 in Fig. 18 are the same as steps S101, S102, and S104 in the inspection method using the inspection device 1 described above.

[0118] As shown in step S203, the distribution of parameter values ​​of the monitor image is acquired from the monitor image captured by receiving another part of the illumination light L11.

[0119] Next, as shown in step S204, the captured image 53 is subjected to image processing. Specifically, the captured image 53 is subjected to image processing by the image processing method using the image processing device 40c described above. In this manner, the object 50 can be inspected.

[0120] According to this embodiment, the reliability of the fourth distribution of parameter values ​​can be confirmed using the monitor unit 30. Furthermore, by determining the similarity between the fourth distribution and the fifth distribution, the reliability of the monitor unit 30 can be confirmed. In this way, this embodiment can mutually complement the function of detecting non-uniformity of the illumination light L11 with the monitor unit 30.

[0121] <Variation 1> Next, an inspection device according to Modification 1 of Embodiment 2 will be described. This modification includes a modification of the monitor unit 30. FIG. 19 is a structural diagram illustrating an inspection device 2a according to Modification 1 of Embodiment 2. As shown in FIG. 19, in the inspection device 2a, the monitor unit 30a includes a concave mirror 32a and a detector 33a. In this modification, instead of extracting another portion of the illumination light L11 using a cut mirror 31, the illumination light L11 is directly separated from the light source 11. Specifically, the light source 11 emits the illumination light L11 to the ellipsoidal mirror 12 and also to the concave mirror 32a. The concave mirror 32a reflects the illumination light L11 to the detector 33a. The detector 33a acquires image data of parameter values ​​such as the luminance distribution of the illumination light L11. The detector 33a outputs the image data to an image processing device 40c. In this manner, the image processing device 40c can acquire a captured image 53 and a monitor image.

[0122] <Variation 2> Next, an inspection device according to Modification 2 of Embodiment 2 will be described. Fig. 20 is a configuration diagram illustrating an inspection device 2b according to Modification 2 of Embodiment 2. Figs. 21 and 22 are plan views illustrating a detector 33b of a monitor unit 30b in the inspection device 2b according to Modification 2 of Embodiment 2.

[0123] 20, in an inspection device 2b, a detector 33b of a monitor unit 30b may be disposed on the optical path of illumination light L11. The detector 33b may be disposed at a light condensing point IF1 on the optical path of illumination light L11. 、Alternatively, the detector 33b may be positioned away from the focal point IF1. As shown in FIGS. 21 and 22, the detector 33b may be a multi-cell detector 33b having multiple detection units. As shown in FIG. 21, the detector 33b may be a hollow multi-cell detector 33ba, or as shown in FIG. 22, it may be a transmission multi-cell detector 33bb. The hollow multi-cell detector 33ba may be positioned at the focal point IF1 on the optical path of the illumination light L11, and the transmission multi-cell detector 33bb may be positioned away from the focal point IF1. In this way, the image processing device 40c can acquire a monitor image.

[0124] Although the embodiments of the present disclosure have been described above, the present disclosure includes appropriate modifications that do not impair the objects and advantages thereof, and is not limited to the above-described embodiments. Furthermore, combinations of the configurations of Embodiments 1 and 2 and their modifications are also within the scope of the technical concept of the present disclosure. Furthermore, the following configurations are also within the scope of the technical concept of the embodiments.

[0125] (Appendix 1) a step of identifying a plurality of peak points that are peaks relative to surrounding parameter values ​​when comparing parameter values ​​of illumination parameters at a plurality of points corresponding to pixels of an image captured of an object at a predetermined sampling time; generating a distribution of the parameter values ​​by generating parameter values ​​at a plurality of points between the identified peak points based on the respective peak values ​​of the parameter values ​​at the identified peak points; correcting the captured image by correcting the parameter values ​​of at least some pixels of the captured image at the predetermined sampling time based on the generated distribution of the parameter values; An image processing method comprising: (Appendix 2) In the step of generating a distribution of parameter values, generating the parameter values ​​for a plurality of points between the peak points by interpolating from the peak values; 2. The image processing method according to claim 1. (Appendix 3) In the step of correcting the captured image, correcting the parameter values ​​by smoothing processing; 2. The image processing method according to claim 1. (Appendix 4) the target object has a repeating pattern in which a plurality of patterns formed with approximately the same pattern width are arranged, In the step of identifying a plurality of peak points, identifying the peak point having the peak value exceeding the parameter value threshold set based on the pattern width; 2. The image processing method according to claim 1. (Appendix 5) the target object has a repeating pattern in which a plurality of patterns formed with approximately the same pattern width are arranged, determining whether a distribution of the positions of the identified plurality of peak points corresponds to a distribution of the patterns, or whether distributions of the parameter values ​​for each pattern are similar to each other; Furthermore, correcting the captured image when it is determined in the determining step that the captured image corresponds or is similar; 2. The image processing method according to claim 1. (Appendix 6) a step of comparing a first distribution of the parameter values ​​generated based on an image of the object at a sampling time prior to the predetermined sampling time with a second distribution of the parameter values ​​generated based on an image of the object at the predetermined sampling time, thereby determining a degree of difference between the first distribution and the second distribution; Furthermore, performing a step of correcting the captured image by correcting parameter values ​​of at least some pixels of the captured image of the object at the predetermined sampling time based on the second distribution when it is determined in the determining step that there is a difference; 2. The image processing method according to claim 1. (Appendix 7) extracting each of the variation characteristics of the time variation and the position variation in the distribution of the parameter values ​​based on the first distribution and the second distribution; predicting a predictive distribution of the parameter values ​​at a third sampling time that is later than the predetermined sampling time based on each of the extracted fluctuation characteristics; generating a third distribution of the parameter values ​​based on the captured image of the object at the third sampling time; determining a similarity between the predictive distribution and the third distribution by comparing the predictive distribution with the third distribution; Furthermore, When it is determined that the two images are similar in the step of determining the similarity, 3rd distribution and correcting the captured image by performing correction based on the 7. An image processing method according to claim 6. (Appendix 8) In the step of determining the similarity, determining a similarity between the prediction distribution and the third distribution by comparing a high frequency component in the spatial variation characteristic of the prediction distribution with a high frequency component in the spatial variation characteristic of the third distribution; 8. The image processing method according to claim 7. (Appendix 9) the parameter value of the illumination light used to illuminate the object when capturing an image of the object decreases from the center of the captured image toward the edge of the captured image, an end of the beam of illumination light illuminating the object is included in the captured image; 2. The image processing method according to claim 1. (Appendix 10) the target object is a photomask on which a pellicle is formed, and the illumination light includes EUV. 10. The image processing method according to claim 9. (Appendix 11) a step of identifying a plurality of peak points that are peaks relative to surrounding parameter values ​​when comparing parameter values ​​of illumination parameters at a plurality of points corresponding to pixels of an image obtained by capturing an image of an object using a portion of the illumination light at a predetermined sampling time; generating a fourth distribution of the parameter values ​​by generating the parameter values ​​of a plurality of points between the identified peak points based on the respective peak values ​​of the parameter values ​​at the identified peak points, and generating a fifth distribution of the parameter values ​​of a plurality of points corresponding to pixels of a monitor image captured by receiving another portion of the illumination light; determining a similarity between the fourth distribution and the fifth distribution by comparing the fourth distribution and the fifth distribution; correcting the captured image by correcting the parameter values ​​of at least some pixels of the captured image at the predetermined sampling time based on the generated fourth distribution of the parameter values; Equipped with performing a step of correcting the captured image when it is determined that the captured image is similar in the step of determining the similarity; Image processing methods. (Appendix 12) The method further includes a step of determining that the distributions are abnormal when the distributions are determined to be not similar in the step of determining the similarity of the distributions. 12. The image processing method according to claim 11. (Appendix 13) In the step of determining the similarity of the distributions, a difference between the fourth distribution and the fifth distribution is extracted; In the step of correcting the captured image, the fifth distribution is corrected based on the difference. 12. The image processing method according to claim 11. (Appendix 14) correcting the fifth distribution based on the difference when it is determined in the step of determining the similarity of the distributions that the state parameters stored in the storage unit in which the difference is stored are identical to the state parameters of the inspection device that captured the image of the object at the sampling time when the difference was extracted, and that the state parameters were acquired after the sampling time. 14. The image processing method according to claim 13. [Explanation of symbols]

[0126] 1, 2, 2a, 2b Inspection equipment 10 Illumination optical system 11 Light source 12 Ellipsoidal mirror 13 Ellipsoidal mirror 14 Drop-in mirror 15 Optical axis 20 Imaging optical system 21 Perforated concave mirror 21a hole 22 Convex mirror 23 Detector 30, 30a, 30b Monitor section 31 Cut mirror 32, 32a concave mirror 33, 33a, 33b detectors 33ba Hollow Multi-Cell Detector 33bb transmission multi-cell detector 40, 40a, 40b, 40c Image processing device 41 Specific part 42 Generation part 43 Extraction part 44 Prediction Department 45 Judgment section 46 Correction unit 47 Memory section 50 Objects 51 Top side 52 Stages 53 Captured images 54 patterns 55 Peak Point IF1, IF2 focal point L11 illumination light L12 reflected light

Claims

1. an identifying unit that identifies a plurality of peak points that are peaks relative to surrounding parameter values ​​when comparing parameter values ​​of illumination parameters at a plurality of points corresponding to pixels of an image captured of an object at a predetermined sampling time; a generating unit that generates a distribution of the parameter values ​​by generating the parameter values ​​at a plurality of points between the identified peak points based on the respective peak values ​​of the parameter values ​​at the identified peak points; a correction unit that corrects the captured image by correcting the parameter values ​​of at least some pixels of the captured image at the predetermined sampling time based on the generated distribution of the parameter values; Equipped with the generated distribution of parameter values ​​reflects the non-uniformity of the distribution of the lighting parameter at the given sampling time; the correction by the correction unit includes correction to reduce the non-uniformity based on a distribution of the parameter values; Image processing device.

2. The correction by the correction unit includes dividing the parameter values ​​of at least some pixels of the captured image at the predetermined sampling time by the generated distribution of the parameter values. The image processing device according to claim 1 .

3. The illumination parameters at the predetermined sampling time have at least one of a distribution that decreases from the center of the captured image toward the edges, or a distribution that decreases from one edge of the captured image toward the other edge. The image processing device according to claim 1 .

4. the generating unit generates the parameter values ​​of the plurality of points between the peak points by interpolating from the peak values. The image processing device according to claim 1 .

5. the target object has a repeating pattern in which a plurality of patterns formed with approximately the same pattern width are arranged, the identifying unit identifies the peak point having the peak value exceeding the threshold value of the parameter value set based on the pattern width. The image processing device according to claim 1 .

6. the target object has a repeating pattern in which a plurality of patterns formed with approximately the same pattern width are arranged, a determination unit that determines whether a distribution of the positions of the identified plurality of peak points corresponds to a distribution of the pattern, or whether distributions of the parameter values ​​for each pattern are similar to each other; When the determination unit determines that the captured image corresponds or is similar, the correction unit corrects the captured image. The image processing device according to claim 1 .

7. a determination unit that determines a degree of difference between a first distribution of the parameter values ​​generated based on an image of the object at a sampling time before the predetermined sampling time and a second distribution of the parameter values ​​generated based on an image of the object at the predetermined sampling time, When the determination unit determines that there is a difference, the correction unit corrects the captured image by correcting parameter values ​​of at least some pixels of the captured image of the object at the predetermined sampling time based on the second distribution. The image processing device according to claim 1 .

8. an extracting unit that extracts each of the fluctuation characteristics of a time fluctuation that depends on time and a spatial fluctuation that depends on position in the distribution of the parameter values ​​based on the first distribution and the second distribution; a prediction unit that predicts a predicted distribution of the parameter values ​​at a third sampling time that is later than the predetermined sampling time based on each of the extracted fluctuation characteristics; Furthermore, the generation unit generates a third distribution of the parameter values ​​based on a captured image of the object at the third sampling time; the determination unit determines a similarity between the predictive distribution and the third distribution by comparing the predictive distribution and the third distribution; When the determination unit determines that the captured image is similar, the correction unit corrects the captured image based on the third distribution. The image processing device according to claim 7 .

9. the determination unit determines a similarity between the prediction distribution and the third distribution by comparing a high frequency component in the spatial variation characteristic of the prediction distribution with a high frequency component in the spatial variation characteristic of the third distribution. The image processing device according to claim 8 .

10. the parameter value of the illumination light used to illuminate the object when capturing an image of the object decreases from the center of the captured image toward the edge of the captured image, an end of the beam of illumination light illuminating the object is included in the captured image; The image processing device according to claim 1 .

11. The image processing apparatus according to claim 10 , wherein the object is a photomask on which a pellicle is formed, and the illumination light includes EUV light.

12. an identification unit that identifies a plurality of peak points that are peaks relative to surrounding parameter values ​​when comparing parameter values ​​of illumination parameters at a plurality of points corresponding to pixels of an image obtained by capturing an image of an object using a portion of the illumination light at a predetermined sampling time; a generation unit that generates a fourth distribution of the parameter values ​​by generating the parameter values ​​of a plurality of points between the identified peak points based on the respective peak values ​​of the parameter values ​​at the identified peak points, and that generates a fifth distribution of the parameter values ​​of a plurality of points corresponding to pixels of a monitor image captured by receiving another portion of the illumination light; a determination unit that determines a similarity between the fourth distribution and the fifth distribution by comparing the fourth distribution with the fifth distribution; a correction unit that corrects the captured image by correcting the parameter values ​​of at least some pixels of the captured image at the predetermined sampling time based on the generated fourth distribution; Equipped with When the determination unit determines that the captured image is similar, the correction unit corrects the captured image; the generated distribution of parameter values ​​reflects the non-uniformity of the distribution of the lighting parameter at the given sampling time; the correction by the correction unit includes correction to reduce the non-uniformity based on a distribution of the parameter values; Image processing device.

13. The determination unit determines that there is an abnormality when determining that there is no similarity. The image processing device according to claim 12.

14. the determination unit extracts a difference between the fourth distribution and the fifth distribution, the correction unit corrects the fifth distribution based on the difference. The image processing device according to claim 12.

15. a storage unit that stores state parameters of the device that captured the image of the object at the sampling time when the predetermined difference was extracted, together with the predetermined difference; Furthermore, when it is determined by the determination unit that the same state parameter has been acquired after the sampling time, the correction unit corrects the fifth distribution based on the predetermined difference. The image processing device according to claim 14.

16. an illumination optical system that illuminates an object using illumination light; an imaging optical system that captures an image of the object illuminated by the illumination light; An image processing device according to any one of claims 1 to 15; Equipped with the image processing device inspects the object using the image-processed captured image; Inspection equipment.

17. a step of identifying a plurality of peak points that are peaks relative to surrounding parameter values ​​when comparing parameter values ​​of illumination parameters at a plurality of points corresponding to pixels of an image captured of an object at a predetermined sampling time; generating a distribution of the parameter values ​​by generating parameter values ​​at a plurality of points between the identified peak points based on the respective peak values ​​of the parameter values ​​at the identified peak points; correcting the captured image by correcting the parameter values ​​of at least some pixels of the captured image at the predetermined sampling time based on the generated distribution of the parameter values; Equipped with the generated distribution of parameter values ​​reflects the non-uniformity of the distribution of the lighting parameter at the given sampling time; the correction includes a correction that reduces the non-uniformity based on a distribution of the parameter values. Image processing methods.

18. illuminating an object with illumination light; capturing an image of the object illuminated by the illumination light; correcting the captured image by the image processing method according to claim 17; Inspecting the object using the image-processed captured image; An inspection method comprising:

Images