Image processing apparatus and image processing method

Abstract

The present invention provides an image processing apparatus and image processing method that, with a simple configuration, can evaluate candidate defects found on a patterned surface, taking into account the impact when they are transferred to a wafer. [Solution] The image processing apparatus 10 according to this embodiment includes a first acquisition unit 11 that acquires an evaluation image A1 based on the imaging result of the pattern surface 52 on which the pattern 51 of the mask to be evaluated 50 is formed, a second acquisition unit 12 that acquires conversion parameters based on the relationship between the image B1 of the pattern surface 52 and the transferred image E1 when the pattern surface 52 is transferred to the wafer 54 by the exposure apparatus, and a third acquisition unit 13 that applies the conversion parameters to the evaluation image A1 to acquire a converted evaluation image F1.

Description

Technical Field

[0001] The present disclosure relates to an image processing apparatus and an image processing method.

Background Art

[0002] Patent Documents 1 and 2 describe techniques for inspecting defects in a mask on which a pattern is formed.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Patent Document 2

Non-Patent Documents

[0004]

Non-Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] By the way, it is preferable to be able to determine whether to regard as abnormal a defect candidate found on a pattern surface on which a pattern is formed, considering the influence when transferred to a wafer. Here, Patent Document 2 shows that a pattern including a defect is inspected and repaired in an apparatus having the same optical characteristics as an exposure apparatus.

[0006] However, it is difficult to individually create an inspection apparatus adapted to the optical conditions of an exposure apparatus.

[0007] This disclosure was made in view of the above problems and provides an image processing apparatus and an image processing method that can evaluate defect candidates found on a pattern surface, taking into account the impact when they are transferred to a wafer, with a simple configuration. [Means for solving the problem]

[0008] An image processing apparatus according to one aspect of this embodiment includes: a first acquisition unit that acquires an evaluation image based on the imaging result of a pattern surface on which a pattern of a mask to be evaluated is formed; a second acquisition unit that acquires conversion parameters based on the relationship between the image of the pattern surface and the transferred image obtained when the pattern surface is transferred to a wafer by an exposure apparatus; and a third acquisition unit that applies the conversion parameters to the evaluation image to acquire a converted evaluation image.

[0009] In the image processing apparatus described above, the second acquisition unit may acquire lithography conditions, including the optical conditions and resist conditions of the exposure apparatus, and determine the conversion parameters based on the lithography conditions.

[0010] In the image processing apparatus described above, the second acquisition unit may train and determine the conversion parameters by machine learning using the captured image and the transferred image as training data.

[0011] In the image processing apparatus described above, the second acquisition unit may determine a kernel that matches the transcribed image when applied to the captured image as the conversion parameter.

[0012] In the image processing apparatus described above, the evaluation image includes the captured image, and the third acquisition unit may acquire a converted captured image as the converted evaluation image by performing a convolution integral on the captured image multiplied by the kernel.

[0013] In the image processing apparatus described above, the second acquisition unit may determine the conversion parameters based on a function in an optical model for acquiring information about the pattern surface from the captured image, and a function in an optical model for acquiring the transfer image from the information about the pattern surface.

[0014] In the image processing apparatus described above, the first acquisition unit may acquire a difference image showing the difference between the captured image and a reference image corresponding to the captured image as the evaluation image, and the third acquisition unit may acquire a converted difference image obtained by applying the conversion parameters to the difference image as the converted evaluation image.

[0015] The image processing apparatus described above further includes a determination unit that determines whether the difference image satisfies predetermined conditions, and if the determination unit determines that the predetermined conditions are met, the third acquisition unit may acquire the converted difference image as the converted evaluation image.

[0016] The above-described image processing apparatus may include an evaluation unit that determines that there is a defect in the mask to be evaluated when a predetermined defective image is detected in the converted difference image.

[0017] The above-described image processing apparatus further includes a determination unit that determines the classification of the defect image appearing in the difference image, the second acquisition unit acquires a modified conversion parameter as the conversion parameter, which is obtained by modifying the conversion parameter according to the classification determined by the determination unit, and the third acquisition unit acquires a modified conversion difference image as the conversion evaluation image, which is obtained by applying the modified conversion parameter to the difference image.

[0018] The above-described image processing apparatus may further include a determination unit that determines that a defect image appearing in the converted difference image is a defect of a specific classification.

[0019] In the above-described image processing apparatus, the second acquisition unit may acquire a different parameter from the conversion parameter as a conversion parameter, the third acquisition unit may acquire a differentially converted image obtained by applying the different parameter to the difference image, and the determination unit may determine a defect image appearing in the converted difference image as a defect of the first classification and may determine a defect image appearing in the differentially converted image as a defect of the second classification.

[0020] The above-described image processing apparatus may further include a determination unit that determines whether the evaluation target mask satisfies a predetermined condition, and when the determination unit determines that the evaluation target mask satisfies the predetermined condition, the third acquisition unit may acquire the converted evaluation image.

[0021] In the above-described image processing apparatus, the predetermined condition may include at least one of illumination of the pattern surface of the evaluation target mask with a specific illumination distribution and a specific shape of the pattern surface in the exposure apparatus.

[0022] An image processing method according to an aspect of the present embodiment includes a step of acquiring an evaluation image based on an imaging result of a pattern surface on which a pattern of an evaluation target mask is formed, a step of acquiring a conversion parameter based on a relationship between an imaging image of the pattern surface and a transfer image when the pattern surface is transferred to a wafer by an exposure apparatus, and a step of acquiring a converted evaluation image by applying the conversion parameter to the evaluation image.

[0023] In the above-described image processing method, in the step of acquiring the conversion parameter, a lithography condition including an optical condition and a resist condition of the exposure apparatus may be acquired, and the conversion parameter may be determined based on the lithography condition.

[0024] In the above-described image processing method, in the step of acquiring the conversion parameter, the conversion parameter may be trained and determined by machine learning using the imaging image and the transfer image as a teacher data set.

[0025] In the image processing method described above, in the step of obtaining the conversion parameters, a kernel that matches the transcribed image when applied to the captured image may be determined as the conversion parameter.

[0026] In the above image processing method, the evaluation image includes the captured image, and in the step of obtaining the converted evaluation image, the converted captured image obtained by performing a convolution integral by multiplying the captured image by the kernel may be obtained as the converted evaluation image.

[0027] In the image processing method described above, in the step of obtaining the conversion parameters, the conversion parameters may be determined based on a function in an optical model that obtains information about the pattern surface from the captured image, and a function in an optical model that obtains the transfer image from the information about the pattern surface.

[0028] In the above image processing method, in the step of acquiring the evaluation image, a difference image showing the difference between the captured image and a reference image corresponding to the captured image may be acquired as the evaluation image, and in the step of acquiring the converted evaluation image, a converted difference image obtained by applying the conversion parameters to the difference image may be acquired as the converted evaluation image.

[0029] The above image processing method further includes a step of determining whether the difference image satisfies predetermined conditions, and if it is determined in the determination step that the predetermined conditions are met, the converted difference image may be acquired as the converted evaluation image in the step of acquiring the converted evaluation image.

[0030] The above image processing method may include a step of determining that there is a defect in the evaluation target mask when a predetermined defect image is detected in the converted difference image.

[0031] The above image processing method further includes a step of determining the classification of the defective images appearing in the difference image, and in the step of obtaining the conversion parameters, a modified conversion parameter obtained by modifying the conversion parameter according to the determined classification is obtained as the conversion parameter, and in the step of obtaining the post-conversion evaluation image, a modified post-conversion difference image obtained by applying the modified conversion parameter to the difference image is obtained as the post-conversion evaluation image.

[0032] The above image processing method may further include a step of determining that the defect images appearing in the converted difference image are defects of a specific classification.

[0033] In the above image processing method, in the step of acquiring the conversion parameters, a different parameter from the conversion parameters may be acquired as the conversion parameters; in the step of acquiring the converted evaluation image, a different converted difference image may be acquired by applying the different parameter to the difference image; and in the determination step, the defect image appearing in the converted difference image may be determined to be a defect of the first category, and the defect image appearing in the different converted difference image may be determined to be a defect of the second category.

[0034] The above image processing method further includes a step of determining whether the mask to be evaluated satisfies predetermined conditions, and if it is determined in the determination step that the predetermined conditions are met, the converted evaluation image may be acquired in the step of acquiring the converted evaluation image.

[0035] In the image processing method described above, the predetermined conditions may include at least one of the following: the pattern surface of the mask to be evaluated is illuminated with a specific illumination distribution in the exposure apparatus, and the pattern surface has a specific shape. [Effects of the Invention]

[0036] According to this disclosure, it is possible to provide an image processing apparatus and an image processing method that can evaluate candidate defects found on a pattern surface, taking into account the impact when they are transferred to a wafer, using a simple configuration. [Brief explanation of the drawing]

[0037] [Figure 1] This is a block diagram illustrating an image processing device according to Embodiment 1. [Figure 2] This figure illustrates an evaluation image acquired by the first acquisition unit in the image processing apparatus according to Embodiment 1. [Figure 3] This figure illustrates an evaluation image acquired by the first acquisition unit in the image processing apparatus according to Embodiment 1, and the captured image is shown as the evaluation image. [Figure 4] This figure illustrates an evaluation image acquired by the first acquisition unit in the image processing apparatus according to Embodiment 1, and the evaluation image shown is a difference image. [Figure 5] This figure illustrates a reference image D1 used by the first acquisition unit to acquire a difference image in the image processing apparatus according to Embodiment 1. [Figure 6] This figure illustrates an image captured by the second acquisition unit for acquiring conversion parameters in the image processing apparatus according to Embodiment 1. [Figure 7] This figure illustrates a wafer image used by the second acquisition unit to acquire conversion parameters in the image processing apparatus according to Embodiment 1. [Figure 8] This is a schematic diagram illustrating a kernel acquired by the second acquisition unit as a conversion parameter in the image processing apparatus according to Embodiment 1. [Figure 9] The figure illustrates an optical model used by the second acquisition unit to acquire conversion parameters in the image processing apparatus according to Embodiment 1, where (a) is the optical model MI of an optical device that acquires an image of the pattern surface of the mask to be evaluated, and (b) is the optical model of an exposure device that performs a lithography process using the mask to be evaluated as a patterning device. [Figure 10]This figure illustrates a converted evaluation image acquired by the third acquisition unit in the image processing apparatus according to Embodiment 1. [Figure 11] This is a flowchart illustrating an image processing method according to Embodiment 1. [Figure 12] This is a block diagram illustrating an image processing device according to Embodiment 2. [Figure 13] This figure illustrates the conversion parameters acquired by the second acquisition unit and the converted evaluation image acquired by the third acquisition unit in the image processing apparatus according to Embodiment 2. [Figure 14] This figure illustrates the conversion parameters acquired by the second acquisition unit and the converted evaluation image acquired by the third acquisition unit in the image processing apparatus according to Embodiment 2. [Figure 15] This figure illustrates the conversion parameters acquired by the second acquisition unit and the converted evaluation image acquired by the third acquisition unit in the image processing apparatus according to Embodiment 2. [Figure 16] This is a flowchart illustrating an image processing method according to Embodiment 2. [Figure 17] This is a diagram illustrating an inspection device as an optical device according to Embodiment 3. [Modes for carrying out the invention]

[0038] Embodiments of the present disclosure will be described below with reference to the drawings. The following description illustrates preferred embodiments of the present disclosure and does not limit the scope of the present disclosure to the following embodiments. In the following description, the same reference numerals indicate substantially the same thing.

[0039] <Embodiment 1> The image processing apparatus and image processing method according to Embodiment 1 will be described. The image processing apparatus of this embodiment may be an evaluation apparatus for evaluating an object to be evaluated. The object to be evaluated includes, for example, a photomask. The photomask of the object to be evaluated is called the evaluation target mask. The photomask may have a pattern formed on it. Note that the evaluation apparatus may include not only a photomask, but also a semiconductor substrate, a semiconductor chip, and a semiconductor device as the object to be evaluated. The image processing apparatus may be part of an optical apparatus such as an inspection apparatus. The image processing apparatus may also be part of an apparatus (review apparatus) that displays the image obtained as a result of illuminating the object to be evaluated (for example, an captured image) on a display or the like. Furthermore, the image processing apparatus may be composed of multiple devices such as a server and a client communicating with each other.

[0040] Figure 1 is a block diagram illustrating an image processing apparatus 10 according to Embodiment 1. As shown in Figure 1, the image processing apparatus 10 includes a first acquisition unit 11, a second acquisition unit 12, and a third acquisition unit 13. The first acquisition unit 11, the second acquisition unit 12, and the third acquisition unit 13 function as acquisition means. As shown in Figure 1, the image processing apparatus 10 may also include an evaluation unit 14. The evaluation unit 14 functions as an evaluation means. In the following, an evaluation target mask will be described as an example of an evaluation target.

[0041] Figures 2 to 4 illustrate the evaluation image A1 acquired by the first acquisition unit 11 in the image processing apparatus 10 according to Embodiment 1, Figure 3 shows the captured image B1 as the evaluation image A1, and Figure 4 shows the difference image C1 as the evaluation image A1. Figure 5 illustrates the reference image D1 used by the first acquisition unit 11 to acquire the difference image C1 in the image processing apparatus 10 according to Embodiment 1. As shown in Figures 2 to 4, the first acquisition unit 11 acquires the evaluation image A1 based on the imaging results of the pattern surface 52 on which the pattern 51 of the mask to be evaluated 50 is formed.

[0042] As shown in Figure 3, the first acquisition unit 11 may acquire an image B1, which is an image of the pattern surface 52 of the mask 50 to be evaluated, as the evaluation image A1. In other words, the evaluation image A1 may include the image B1. Also, as shown in Figure 4, the first acquisition unit 11 may acquire a difference image C1 as the evaluation image A1. In other words, the evaluation image A1 may include the difference image C1. The difference image C1 may include the defective image 60. The difference image C1 is an image based on a comparison between the image B1 and a reference image D1 (see Figure 5) corresponding to the image B1, and as an example, it is an image showing the difference between the image B1 and the reference image D1 corresponding to the image B1. The reference image D1 may be a good product image representing an image in which there are no abnormalities or no critical abnormalities in the region of the mask 50 to be evaluated as captured in the image B1. Alternatively, the reference image D1 may be a comparison image that captures a region that is structurally equivalent to the region of the mask 50 to be evaluated as captured in the image B1. The difference image C1 may include in its pixel information the difference in brightness of pixels in the captured image B1 and the reference image D1 that are in the same relative position to each other. The difference image C1 may also include in its pixel information the ratio of brightness of pixels in the captured image B1 and the reference image D1 that are in the same relative position to each other. Hereafter, the difference image C1 will be defined as an image showing the difference between the captured image B1 and the reference image D1 corresponding to the captured image B1, and the pixel information of the difference image C1 will simply be referred to as brightness.

[0043] Figure 6 is a diagram illustrating an image B1 used by the second acquisition unit 12 to acquire conversion parameters in the image processing apparatus 10 according to Embodiment 1. Figure 7 is a diagram illustrating a wafer image E1 used by the second acquisition unit 12 to acquire conversion parameters in the image processing apparatus 10 according to Embodiment 1. The wafer image E1 is an example of a transfer image.

[0044] As shown in Figures 6 and 7, the second acquisition unit 12 acquires conversion parameters based on the relationship between the captured image B1 of the pattern surface 52 of the mask to be evaluated 50 and the wafer image E1. Here, as described above, the captured image B1 is an image of the pattern surface 52 of the mask to be evaluated 50 on which the pattern 51 is formed. The wafer image E1 is an image taken when the pattern 51 of the mask to be evaluated 50 is transferred to the wafer 54 by the exposure apparatus. The wafer image E1 includes an image of the pattern 55 on which the pattern 51 of the mask to be evaluated has been transferred to the wafer 54.

[0045] The conversion parameters may include the first to fourth parameters described below. The conversion parameters may also include a fifth parameter. The fifth parameter includes parameters other than the first to fourth parameters, based on the relationship between the captured image B1 and the wafer image E1. For example, the fifth parameter may include other parameters described later. Furthermore, the conversion parameters may include modified conversion parameters, which are modified versions of the conversion parameters described later. The modified conversion parameters are also based on the relationship between the captured image B1 and the wafer image E1.

[0046] The transformation parameters acquired by the second acquisition unit 12 are described below, divided into <first parameter: lithography conditions>, <second parameter: machine learning model>, <third parameter: kernel>, and <fourth parameter: Fourier transform and inverse Fourier transform functions>.

[0047] <Parameter 1: Lithography conditions> The second acquisition unit 12 may acquire lithography conditions, including the optical conditions and resist conditions of the exposure apparatus. The second acquisition unit 12 may then determine a conversion parameter (first parameter) based on the acquired lithography conditions. For example, an exposure apparatus may have different magnifications in one direction of the mask (e.g., the vertical direction) and in another direction perpendicular to that direction (e.g., the horizontal direction) to accommodate a high numerical aperture (NA). In this case, as an example of optical conditions, the magnification in the other direction relative to one direction is M times. In such a case, the second acquisition unit 12 acquires a first parameter as a conversion parameter, where the magnification in the other direction relative to one direction is M times.

[0048] Furthermore, the optical conditions may include not only the magnification in one direction relative to the other, but also the luminance distribution and contrast distribution of the illumination light. The resist conditions may also include the width between the patterns 51 formed by the resist.

[0049] <Second parameter: Machine learning model> The second acquisition unit 12 may train and determine the transformation parameters (second parameters) using machine learning with the captured image B1 and the wafer image E1 as training data. For example, the second acquisition unit 12 may have a machine learning model. The second acquisition unit 12 trains the machine learning model using the captured image B1 and the wafer image E1 as training data. In this way, the second acquisition unit 12 may acquire the trained machine learning model as the second parameter.

[0050] <Third parameter: kernel> The second acquisition unit may determine the coefficients of the kernel calculated by performing a convolution integral by multiplying the captured image B1 by the kernel to match the wafer image E1 as the conversion parameter (third parameter). The second acquisition unit 12 may determine the kernel that matches the wafer image E1 (transfer image) when applied to the captured image B1 as the conversion parameter.

[0051] Figure 8 is a schematic diagram illustrating a kernel W acquired as a conversion parameter by the second acquisition unit 12 in the image processing apparatus 10 according to Embodiment 1. As shown in Figure 8, the second acquisition unit 12 performs a convolution integral on the captured image B1 by multiplying it by the kernel W. The captured image B1 is an image composed of multiple pixel rows arranged in a first direction and multiple pixel rows arranged in a second direction orthogonal to the first direction. Here, the captured image B1 is assumed to be an image in which M pixels p are arranged in the first direction and N pixels p are arranged in the second direction.

[0052] A kernel W with two-dimensional parameters can be, for example, a kernel with 3 rows and 3 columns, i.e., 3 × 3 = 9 cells. The size of each element (sometimes called a coefficient or cell) of kernel W may virtually correspond to the size of a pixel p in the captured image B1.

[0053] The second acquisition unit 12 performs the process of applying kernel W to the captured image B1. For example, the second acquisition unit 12 performs the convolution process described in Non-Patent Literature 1. Specifically, at the pixel p corresponding to the center of kernel W, a convolution process is performed between the surrounding 3x3 pixel values ​​(e.g., luminance) and each cell of kernel W to filter each pixel in the captured image B1. For example, each cell of kernel W is represented by (i, j) using local coordinate axes including the x and y axes. If J(p) is the local operator that performs image filtering in the 3x3 neighborhood of each pixel p, then J(p) can be defined as the convolution between the luminance value I(x+i, y+j) around pixel p=(x,y) on the captured image B1 and W'(p), as shown in equation (1) below (the convolution is a two-dimensional discrete approximation).

[0054]

number

[0055] Here, W'(p) is the kernel obtained by inverting the original kernel W(p) with respect to both the x and y axes of the local coordinate system. i、jis the weight coefficient at each position (i, j) of the inverted kernel W'(p), and S>0 is the coefficient for normalization and scaling. If i and j extend beyond the boundary range of the captured image B1, a padding process may be performed beforehand to fill the surrounding area with pixel values ​​I(x+i, y+j). In this way, the second acquisition unit 12 performs a convolution process on each pixel of the captured image B1 and outputs the processed pixel value. The second acquisition unit 12 can sweep the entire captured image B1 with kernel W by sequentially changing the pixels of the captured image B1 where the central cell of kernel W is located. As a result, the second acquisition unit 12 acquires a modified captured image with kernel W applied to the entire captured image B1.

[0056] The corrected image is an image in which M pixels are arranged in the first direction and N pixels are arranged in the second direction, similar to the captured image B1. Each pixel (p') of the corrected image is... m、n Brightness I' m、n By applying kernel W, each pixel (p m、n ) Brightness I m、n It has been changed from [previous version].

[0057] The second acquisition unit 12 acquires a kernel W as a third parameter, in which the values ​​of each cell (coefficient) are adjusted so that the corrected image image matches the wafer image E1. For example, the second acquisition unit 12 may identify a coefficient that minimizes the difference between the brightness of each pixel in the corrected image image and the brightness of each pixel in the wafer image E1 when the values ​​of each cell (coefficient) of the kernel W are changed and applied, and determine the kernel W based on that coefficient.

[0058] <Fourth parameter: Functions for the Fourier transform and inverse Fourier transform> The second acquisition unit 12 may determine conversion parameters based on a function in the optical model MI that acquires information about the pattern surface 52 from the captured image B1, and a function in the optical model ML that acquires the wafer image E1 from the information about the pattern surface 52.

[0059] Figure 9 is a diagram illustrating an optical model used by the second acquisition unit 12 to acquire conversion parameters in the image processing apparatus 10 according to Embodiment 1. For the sake of explanation, Figure 9 uses a one-dimensional transmission optical model. In actual acquisition of conversion parameters, the one-dimensional model is extended to two dimensions. The same explanation can be applied even when using an optical model different from the transmission optical model. Figure 9(a) is the optical model MI of an optical device (which may be an evaluation device; hereinafter referred to as an inspection device) that acquires an image B1 of the pattern surface 52 of the mask to be evaluated 50. Figure 9(b) is the optical model ML of an exposure device that performs a lithography process using the mask to be evaluated 50 as a patterning device.

[0060] As shown in Figure 9(a), the optical model MI of the inspection device has optical elements 56I and 58I positioned between the pattern surface 52 of the mask 50 to be evaluated and the image surface 59I. Illumination light reflected from the pattern surface 52 is focused onto the image surface 59I via optical elements 56I and 58I. A pupil surface 57I is formed between optical elements 56I and 58I.

[0061] In Figure 9(a), the position in one dimension is denoted by x, and the angular distribution by k. The intensity of the image plane 59I, the intensity of the pupil plane 57I, and the intensity of the pattern plane 52 are expressed by equations (2), (3), and (4) below, respectively. Here, i is an imaginary number.

[0062] gI(x) (2) FI(k)=vI(k)·∫gI(x)exp(-ikx)dx (3) f(x)=∫uI(k)·FI(k)exp(ikx)dx (4)

[0063] Here, uI(k) is a function dependent on the optical design of the inspection device, used to reproduce the intensity change from the pattern surface 52 to the pupil surface 57I by the optical element 56I of the inspection device. Also, vI(k) is a function dependent on the optical design of the inspection device, used to reproduce the intensity change from the pupil surface 57I to the image surface 59I by the optical element 58I of the inspection device.

[0064] gI(x) corresponds to the intensity in one dimension (x-direction) of the captured image B1. Therefore, based on the optical model MI of the inspection device, the Fourier transform shown in equation (3) is performed on gI(x), which represents the intensity of the captured image B1, and the inverse Fourier transform shown in equation (4) is performed on the obtained FI(k), thereby allowing the intensity of the pattern surface 52 of the mask 50 to be evaluated (which may correspond to the physical uneven shape) to be calculated from the captured image B1.

[0065] As shown in Figure 9(b), the optical model ML of the exposure apparatus has optical elements 56L and 58L positioned between the pattern surface 52 of the mask under evaluation 50 (which is a mask used as a patterning device) and the image surface 59L. Illumination light reflected from the pattern surface 52 is focused onto the image surface 59L via optical elements 56L and 58L. A pupil surface 57L is formed between optical elements 56L and 58L.

[0066] In Figure 9(b), the position in one dimension is denoted as x, and the angular distribution as k. If the intensity of the pattern surface 52 is f(x) as described above, then the intensity of the pupil surface 57L and the intensity of the image surface 59L are expressed by equations (5) and (6) below, respectively. Here, the intensity of the image surface 59L, gL(x), corresponds to the intensity of the wafer image E1 in one dimension (x direction).

[0067] FL(k)=uL(k)·∫f(x)exp(-ikx)dx (5) gL(x)=∫vL(k)·FL(k)exp(ikx)dx (6)

[0068] Here, uL(k) is a function dependent on the optical design of the exposure apparatus to reproduce the intensity change from the pattern surface 52 to the pupil surface 57L by the optical element 56L of the exposure apparatus. Also, vL(k) is a function dependent on the optical design of the exposure apparatus to reproduce the intensity change from the pupil surface 57L to the image surface 59L by the optical element 58L of the exposure apparatus.

[0069] f(x) is obtained based on equation (4) described above. Therefore, by performing the Fourier transform shown in equation (5) on f(x) based on the optical model ML of the exposure apparatus, and then performing the inverse Fourier transform shown in equation (6) on the obtained FL(k), the intensity of the wafer image E1 can be calculated from the intensity f(x) of the pattern surface 52 of the mask 50 to be evaluated.

[0070] In summary, the wafer image E1 can be calculated and obtained from the captured image B1 by performing the Fourier transform shown in equation (3) and the inverse Fourier transform shown in equation (4), based on the optical model MI of the inspection apparatus, on the intensity of the pattern surface 52 of the mask 50 to be evaluated, and then performing the Fourier transform shown in equation (5) and the inverse Fourier transform shown in equation (6), based on the optical model ML of the exposure apparatus.

[0071] The second acquisition unit 12 acquires a fourth parameter for converting the captured image B1 into a wafer image E1 based on the optical model. Specifically, the second acquisition unit 12 determines the fourth parameter based on a function in the optical model MI that acquires information about the pattern surface 52 from the captured image B1, and a function in the optical model ML that acquires the wafer image E1 from the information about the pattern surface 52. The fourth parameter may be a parameter determined based on the above uI(k), vI(k), uL(k), and vL(k), or it may be a parameter that includes the above uI(k), vI(k), uL(k), and vL(k) as internal coefficients.

[0072] Figure 10 is an example of a converted evaluation image F1 acquired by the third acquisition unit 13 in the image processing apparatus 10 according to Embodiment 1. As shown in Figure 10, the third acquisition unit 13 acquires the converted evaluation image F1 by applying conversion parameters to the evaluation image A1. The evaluation image A1 includes the captured image B1 and the difference image C1. The first acquisition unit 11 may acquire an image showing the difference between the captured image B1 and the converted reference image obtained by applying conversion parameters to the reference image D1 as the difference image C1. Therefore, the evaluation image A1 may include the difference image C1 between the captured image B1 and the converted reference image.

[0073] The conversion parameters include the first to fifth parameters and the modification conversion parameters.

[0074] The converted evaluation image F1 includes the converted captured image G1 and the converted difference image H1. For example, the third acquisition unit 13 may acquire the converted captured image G1 as the converted evaluation image F1 by applying conversion parameters to the captured image B1 (for example, by performing a convolution integral multiplied by the kernel W). In this case, the brightness of at least some of the pixels included in the converted captured image G1 has changed from the brightness of the pixels included in the captured image B1 based on the application of the conversion parameters. The third acquisition unit 13 may also acquire the converted difference image H1 as the converted evaluation image F1 by applying conversion parameters to the difference image C1 (for example, by performing a convolution integral multiplied by the kernel W). In this case, the brightness of at least some of the pixels included in the converted difference image H1 has changed from the brightness of the pixels included in the difference image C1 based on the application of the conversion parameters. The converted evaluation image F1 may also include the modified converted difference image J1 and the separate converted difference image K1, which will be described later.

[0075] The evaluation unit 14 evaluates the pattern surface 52 of the mask to be evaluated 50 based on the converted evaluation image F1. Specifically, the evaluation unit 14 evaluates the pattern surface 52 of the mask to be evaluated 50 based on the converted captured image G1, the converted difference image H1, the modified converted difference image J1, and the separate converted difference image K1. For example, the evaluation unit 14 may determine that the mask to be evaluated 50 has a defect if a predetermined defect image 60 is detected in the converted difference image H1 obtained by applying conversion parameters to the difference image C1 between the captured image B1 and the reference image D1. Here, the evaluation image A1 is the difference image C1, and the converted evaluation image F1 is the converted difference image H1.

[0076] Furthermore, the evaluation unit 14 may determine that there is a defect in the mask 50 to be evaluated if a predetermined defect image 60 is detected in the difference image C1 between the converted captured image G1 obtained by applying conversion parameters to the captured image B1 and the reference image D1 (which may also be a converted reference image). Here, the evaluation image A1 is the captured image B1, and the converted evaluation image F1 is the converted captured image G1. Thus, the evaluation unit 14 determining a defect in the mask 50 to be evaluated based on the difference image C1, which shows the difference between the converted captured image G1 (which is the converted evaluation image F1) and the reference image D1 (which may also be a converted reference image), is also included in the evaluation unit 14 evaluating the pattern surface 52 of the mask 50 to be evaluated based on the converted evaluation image F1.

[0077] The above-mentioned predetermined defect image 60 may include a defect image containing pixels with brightness outside the predetermined range, and a defect image containing a predetermined number or more of pixels with brightness outside the predetermined range.

[0078] Next, the image processing method of this embodiment will be described. Figure 11 is a flowchart illustrating the image processing method according to Embodiment 1. As shown in Figure 11, the image processing method of this embodiment includes a step S11 for acquiring an evaluation image A1, a step S12 for acquiring conversion parameters, and a step S13 for acquiring a converted evaluation image F1. As shown in Figure 11, the image processing method of this embodiment may further include an evaluation step S14.

[0079] In step S11, the first acquisition unit 11 acquires an evaluation image A1 based on the imaging result of the pattern surface 52 on which the pattern 51 of the mask 50 to be evaluated is formed. The evaluation image A1 may include the captured image B1, or it may include the difference image C1. The first acquisition unit 11 may acquire a difference image C1 as the evaluation image A1, which shows the difference between the captured image B1 and a reference image D1 corresponding to the captured image B1. Alternatively, the first acquisition unit 11 may acquire an image as the difference image C1 that shows the difference between the captured image B1 and a converted reference image obtained by applying conversion parameters to the reference image D1 corresponding to the captured image B1.

[0080] In step S12, the second acquisition unit 12 acquires conversion parameters based on the relationship between the captured image B1 and the wafer image E1. The second acquisition unit 12 may acquire lithography conditions, including the optical conditions and resist conditions of the exposure apparatus, and determine the conversion parameters based on the lithography conditions. Alternatively, the second acquisition unit 12 may train and determine the conversion parameters using machine learning with the captured image B1 and the wafer image E1 as training data. Furthermore, the second acquisition unit 12 may determine the conversion parameters as the coefficients of a kernel calculated to match the wafer image E1 by performing a convolution integral by multiplying the captured image B1 by the kernel. Furthermore, the second acquisition unit 12 may determine the conversion parameters based on a function in the optical model MI that acquires information on the pattern surface 52 from the captured image B1, and a function in the optical model ML that acquires the wafer image E1 from the information on the pattern surface 52.

[0081] In step S13, the third acquisition unit 13 applies conversion parameters to the evaluation image A1 to obtain the converted evaluation image F1. For example, the third acquisition unit 13 may obtain the converted captured image G1, which is obtained by performing a convolution integral by multiplying the captured image B1 by a kernel, as the converted evaluation image F1. Alternatively, the third acquisition unit 13 may obtain the converted difference image H1, which is obtained by applying conversion parameters to the difference image C1, as the converted evaluation image F1.

[0082] In step S14, the evaluation unit 14 evaluates the pattern surface 52 of the mask to be evaluated based on the converted evaluation image F1.

[0083] Next, the effects of this embodiment will be described. The image processing apparatus 10 of this embodiment acquires conversion parameters based on the relationship between the captured image B1 of the pattern surface 52 of the mask to be evaluated 50 and the wafer image E1 obtained when the pattern surface 52 is transferred to the wafer 54 by an exposure apparatus. The image processing apparatus 10 then evaluates the pattern surface 52 of the mask to be evaluated 50 based on the converted evaluation image F1 obtained by applying the conversion parameters to the evaluation image A1. Thus, the image processing apparatus 10 can evaluate candidate defects found on the pattern surface 52, taking into account the impact when they are transferred to the wafer 54, with a simple configuration.

[0084] When defects or defect candidates appearing in the captured image B1 or difference image C1 are transferred to the wafer 54 by the exposure apparatus, they may not adversely affect the formation on the wafer 54. The image processing apparatus 10 evaluates whether the defects and defect candidates appearing in the captured image B1 or difference image C1 adversely affect the formation on the wafer 54 using a converted evaluation image F1 that has been converted in advance using conversion parameters. This makes it possible to evaluate whether or not there will be an adverse effect without actually transferring the mask 50 to be evaluated to the wafer 54 by the exposure apparatus.

[0085] The second acquisition unit 12 of the image processing device 10 may determine the transformation parameters based on the lithography conditions of the exposure device, or it may train and determine the transformation parameters using machine learning. Alternatively, the second acquisition unit 12 may determine the coefficients of the kernel W as the transformation parameters, or it may determine the transformation parameters based on the Fourier transform and inverse Fourier transform functions of the optical model. This can improve the evaluation accuracy of the mask 50 to be evaluated.

[0086] <Embodiment 2> Next, an image processing apparatus according to Embodiment 2 will be described. The image processing apparatus of this embodiment includes a determination unit that determines predetermined conditions and the classification of defective images 60. Figure 12 is a block diagram illustrating an image processing apparatus 20 according to Embodiment 2. As shown in Figure 12, the image processing apparatus 20 further includes a determination unit 11a compared to the image processing apparatus 10 described above. The determination unit 11a has the function of a determination means. The evaluation unit 14 may include the determination unit 11a. The determination unit 11a may include the evaluation unit 14.

[0087] The determination unit 11a determines whether the evaluation image A1 satisfies predetermined conditions. The predetermined conditions include, for example, that the pattern surface 52 in the captured image B1 has a specific shape, and that the pattern surface 52 is illuminated with a specific illumination distribution in the exposure apparatus. The specific shape of the pattern surface 52 includes, for example, a linear shape such as line and space, and the shape of a hole. Illumination with a specific illumination distribution includes, for example, an illumination distribution with a bipolarized light source. When exposing a line and space pattern 51, illumination from a bipolarized light source such as a dipole may be used to increase contrast. In this embodiment, if the determination unit 11a determines that the evaluation image A1 satisfies such predetermined conditions, the second acquisition unit 12 acquires conversion parameters, and the third acquisition unit 13 acquires the converted evaluation image F1. The determination unit 11a may also determine whether the evaluation target mask 50 satisfies predetermined conditions. If the determination unit 11a determines that the mask 50 to be evaluated satisfies such predetermined conditions, the second acquisition unit 12 may acquire the conversion parameters, and the third acquisition unit 13 may acquire the converted evaluation image F1.

[0088] Furthermore, the determination unit 11a may determine whether the difference image C1 satisfies predetermined conditions. The predetermined conditions may include conditions relating to at least one of the size, brightness, brightness change, and shape of the defect image 60 in the difference image C1. If the determination unit 11a determines that the difference image C1 satisfies the predetermined conditions, the second acquisition unit 12 acquires the conversion parameters, and the third acquisition unit 13 acquires the converted difference image H1 as the converted evaluation image F1. The evaluation unit 14 evaluates the pattern surface 52 of the mask 50 to be evaluated based on the converted difference image H1.

[0089] Therefore, the image processing device 20 operates the second acquisition unit 12, the third acquisition unit 13, and the evaluation unit 14 when the captured image B1 and the evaluation image A1, such as the difference image C1, satisfy predetermined conditions. If the predetermined conditions are not met, the image processing device 20 does not need to operate the second acquisition unit 12, the third acquisition unit 13, and the evaluation unit 14, thereby reducing costs.

[0090] Depending on the size, brightness, brightness change, and shape of the defect image 60 in the difference image C1, it may be preferable to apply conversion parameters to the difference image C1 as the evaluation image F1 and perform the evaluation based on the converted difference image H1. Therefore, by setting conditions such as the size, brightness, brightness change, and shape of such defect images 60, the evaluation accuracy of the mask 50 to be evaluated can be improved.

[0091] Furthermore, the determination unit 11a may classify the defective images 60 that appear in the difference image C1. For example, the determination unit 11a may classify the brightness changes in the region of the defective image 60. Specifically, the determination unit 11a may classify the defective images 60 into those with gradual brightness changes and those with steep brightness changes. In addition, the determination unit 11a may classify the size, brightness, and shape of the defective images 60. The determination unit 11a may further classify the defective images 60 with gradual brightness changes and those with steep brightness changes using a threshold value for the rate of change in brightness.

[0092] Figures 13 and 14 illustrate the conversion parameters acquired by the second acquisition unit 12 and the converted evaluation image F1 acquired by the third acquisition unit 13 in the image processing apparatus 20 according to Embodiment 2. As shown in Figures 13 and 14, the second acquisition unit 12 acquires conversion parameters to be applied to the difference image C1 according to the classification of the defect image 60 determined by the determination unit 11a. For example, as shown in Figure 13, in the case of classification 61, where the brightness change in the region of the defect image 60 is steep, the second acquisition unit 12 acquires predetermined conversion parameters based on the relationship between the captured image B1 and the wafer image E1. Then, the third acquisition unit 13 applies the conversion parameters to the difference image C1 to acquire the converted difference image H1.

[0093] On the other hand, as shown in Figure 14, in the case of classification 62, where the brightness change in the region of the defective image 60 is gradual, the second acquisition unit 12 acquires a modified conversion parameter, which is the conversion parameter, by correcting the conversion parameter. Then, the third acquisition unit 13 applies the modified conversion parameter to the difference image C1 as the conversion parameter. As a result, the third acquisition unit 13 acquires the modified conversion difference image J1 as the converted evaluation image F1. The modified conversion difference image J1 is an image obtained by applying the modified conversion parameter to the difference image C1.

[0094] For example, the second acquisition unit 12 may acquire modified conversion parameters by adding components of a moving average filter and / or a sharpening filter to the conversion parameters according to the classification of the defect image 60. The second acquisition unit 12 may also acquire modified conversion parameters by adding components of other filters according to the classification of the defect image 60 to the conversion parameters. In the difference image C1, defects detected as classification 62 defect images 60 with a gradual change in brightness may be difficult to detect in the wafer image E1 transferred by the exposure apparatus. In such cases, it may be difficult to properly judge the influence of classification 62 defect images. Therefore, in the case of classification 62 defect images 60 with a gradual change in brightness, modified conversion parameters with a greater sharpening filter component added to the conversion parameters are used. As described above, the second acquisition unit 12 may acquire conversion parameters according to the classification of the defect image 60. This makes it possible to evaluate the pattern surface 52 of the evaluation target mask 50 while eliminating the influence of the surroundings of the defect image 60.

[0095] Furthermore, the determination unit 11a may classify the defect image 60 not only based on the change in brightness in the region of the defect image 60, but also on the size of the defect image 60, the shape of the defect image 60, the brightness of the defect image 60, the material of the mask 50 to be evaluated, the material of the wafer 54, etc. Also, in the above, it was stated that a conversion parameter is applied when the classification 61 is that the change in brightness in the region of the defect image 60 is steep, and a correction conversion parameter is applied when the classification 62 is that the change in brightness in the region of the defect image 60 is gradual, but this relationship may be reversed. In addition, the classification distinction is just an example. Therefore, the third acquisition unit 13 may acquire a converted difference image H1 by applying the conversion parameter to the difference image C1 if the defect image 60 in the difference image C1 is a defect of the first classification (defect image), and acquire a corrected converted difference image J1 by applying the correction conversion parameter to the difference image C1 if the defect image 60 in the difference image C1 is a defect of the second classification (defect image).

[0096] Figure 15 illustrates the conversion parameters acquired by the second acquisition unit 12 and the converted evaluation image F1 acquired by the third acquisition unit 13 in the image processing apparatus 20 according to Embodiment 2. As shown in Figure 15, the third acquisition unit 13 may acquire a converted difference image H1 by applying the conversion parameters to the difference image C1. Alternatively, the third acquisition unit 13 may acquire a modified converted difference image J1 by applying modified conversion parameters to the difference image C1.

[0097] Thus, the image processing device 20 may acquire two evaluation images F1 without the determination unit 11a performing a determination on the defective image 60. The evaluation unit 14 may determine that the defective image 60 detected in the converted difference image H1 to which the conversion parameters have been applied is a defective image 60 of classification 61. The evaluation unit 14 may also determine that the defective image 60 detected in the modified converted difference image J1 to which the modification conversion parameters have been applied is a defective image 60 of classification 62. As mentioned above, the evaluation unit 14 may include the determination unit 11a. Therefore, the determination unit 11a may determine that the defective image 60 appearing in the converted difference image H1 is a defect of a specific classification. The determination unit 11a may also determine that the defective image 60 appearing in the converted difference image H1 is a defect of classification 61, and that the defective image 60 appearing in the modified converted difference image J1 is a defect of classification 62.

[0098] By using conversion parameters tailored to classifications 61 and 62 of the defect image 60, the appearance of the defect image 60 may become more pronounced. In other words, by applying conversion parameters, it is possible to characteristically extract defect images 60 of a specific classification. Therefore, by changing the conversion parameters, it is possible to selectively detect specific defect images 60. Furthermore, the image processing method by the image processing device 20 in Figure 15 eliminates the need for prior classification of the defect image 60 by the determination unit 11a.

[0099] The second acquisition unit 12 may acquire a different parameter as the conversion parameter, which is different from the conversion parameter. The third acquisition unit 13 may acquire a different converted difference image K1, obtained by applying the different parameter to the difference image C1, as the converted evaluation image F1. Alternatively, the third acquisition unit 13 may apply the conversion parameter to the difference image C1 to obtain a converted difference image H1, and then apply the corrected conversion parameter to the converted difference image H1 to obtain a corrected converted difference image J1. In this case, the result of calculating the conversion parameter and the corrected conversion parameter may be a different parameter. Furthermore, the different parameter may be the same as the corrected conversion parameter, and the corrected converted difference image J1 may be the same as the different converted difference image K1. The determination unit 11a may determine that the defect image 60 appearing in the converted difference image H1 is a defect of the first category, and that the defect image appearing in the different converted difference image K1 is a defect of the second category.

[0100] Figure 16 is a flowchart illustrating an image processing method according to Embodiment 2. As shown in Figure 16, the image processing method of this embodiment includes step S11a, compared to the image processing method described above. In step S11a, the determination unit 11a determines whether the evaluation image A1 satisfies predetermined conditions. The determination unit 11a may also determine whether the difference image C1 satisfies predetermined conditions. Alternatively, the determination unit 11a may determine the classification of the defect image 60 that appeared in the difference image C1.

[0101] If the determination unit 11a determines that the evaluation image A1 or the difference image C1 satisfies predetermined conditions, the second acquisition unit 12 may acquire conversion parameters in step S12. In addition, for the defect image 60 that appeared in the difference image C1, the second acquisition unit 12 acquires conversion parameters or corrected conversion parameters according to the classification determined by the determination unit 11a.

[0102] According to this embodiment, the image processing device 20 includes a determination unit 11a that determines the classification of the defective image 60 in the difference image C1. Therefore, the image processing device 20 can apply conversion parameters corresponding to the classification of the defective image 60 to the difference image C1. Thus, the accuracy of the evaluation can be improved. Furthermore, since the image processing device 20 includes a determination unit 11a that determines predetermined conditions, the evaluation image A1 can be determined before applying the conversion parameters to the evaluation image A1. This makes it possible to determine whether or not to acquire the converted evaluation image F1. Thus, processing costs can be reduced. As mentioned above, the determination unit 11a may determine the classification of the defective image 60 based on the defective image 60 identified based on the converted evaluation image F1 obtained by applying specific conversion parameters to the evaluation image A1. In this case, step S11a may be provided after step S13.

[0103] <Embodiment 3> Next, an optical device according to Embodiment 3 will be described. The optical device of this embodiment includes an image processing device. The optical device may include, for example, an inspection device for inspecting the mask 50 to be evaluated and an image processing device. Alternatively, the optical device may include a review device and an image processing device. The image processing device may be part of the inspection device and part of the review device, and the optical device may be the inspection device and the review device themselves, respectively. Below, an inspection device including an image processing device will be described as an example of an optical device.

[0104] Figure 17 is a diagram illustrating an inspection device 100 as an optical device according to Embodiment 3. The inspection device 100 comprises an illumination optical system 110, an imaging optical system 120, and an image processing device 10. The image processing device 10 may be an image processing device 20. The illumination optical system 110 illuminates the mask 50 to be evaluated using illumination light L111. The illumination optical system 110 includes, for example, a light source 111, an ellipsoidal mirror 112, an ellipsoidal mirror 113, and a recessed mirror 114.

[0105] The imaging optical system 120 captures an image B1 of the evaluation target mask 50 illuminated by illumination light L111. The imaging optical system 120 includes, for example, a perforated concave mirror 121, a convex mirror 122, and a detector 123. The perforated concave mirror 121 and the convex mirror 122 constitute a Schwarzschild magnifying optical system. The illumination optical system 110 and the imaging optical system 120 may further include optical components other than those described above, or any of the above optical components may be omitted.

[0106] Here, for the sake of explaining the inspection device 100, we introduce the XYZ Cartesian coordinate system. For example, the plane parallel to the stage surface of the stage 152 on which the mask to be evaluated 50 is placed will be the XY plane, and the direction perpendicular to the stage surface will be the Z axis direction. For convenience, the +Z axis direction will be called "up," and the -Z axis direction will be called "down." Note that "up" and "down" are for the sake of explaining the inspection device 100 and do not indicate the actual direction in which the inspection device 100 is placed.

[0107] The light source 111 generates illumination light L111. The illumination light L111 contains, for example, EUV light at 13.5 nm, the same wavelength as the exposure wavelength of the EUV mask, which is the mask to be evaluated 50. The illumination light L111 generated from the light source 111 is reflected by the ellipsoidal mirror 112. The illumination light L111 reflected by the ellipsoidal mirror 112 travels while being focused and is concentrated at the focal point IF1. The focal point IF1 is positioned conjugate to the pattern surface 52 of the mask to be evaluated 50.

[0108] The illumination light L111, after passing through the focal point IF1, spreads out as it travels and enters a reflecting mirror such as the ellipsoidal mirror 113. The illumination light L111 that enters the ellipsoidal mirror 113 is reflected by the ellipsoidal mirror 113, and as it travels, it is focused and enters the recessed mirror 114. In other words, the ellipsoidal mirror 113 causes the illumination light L111 to enter the recessed mirror 114 as a focused beam. The recessed mirror 114 is positioned above the mask 50 to be evaluated. The illumination light L111 that enters the recessed mirror 114 and is reflected enters the mask 50 to be evaluated. That is, the recessed mirror 114 causes the illumination light L111 to enter the mask 50 to be evaluated.

[0109] The ellipsoidal mirror 113 focuses the illumination light L111 onto the mask 50 under evaluation. The illumination optical system 110 is positioned so that when the illumination light L111 illuminates the mask 50 under evaluation, the image of the light source 111 is projected onto the pattern surface 52 of the mask 50 under evaluation. Therefore, the illumination optical system 110 provides critical illumination. In this way, the illumination optical system 110 illuminates the mask 50 under evaluation using critical illumination provided by the illumination light L111 generated by the light source 111.

[0110] The mask to be evaluated 50 is placed on the stage 152. The illumination light L111 is incident on the mask to be evaluated 50 from a direction inclined from the Z-axis direction. That is, the illumination light L111 is incident on the mask to be evaluated 50 at an oblique angle as oblique incidence illumination. The illumination light L111 may illuminate the mask to be evaluated 50 as such oblique incidence illumination.

[0111] Stage 152 is an XYZ driven stage. By moving Stage 152 in the X-axis and Y-axis directions, a desired area of ​​the mask 50 to be evaluated can be illuminated. Furthermore, by moving Stage 152 in the Z-axis direction, focus adjustment can be performed. Stage 152 may also be rotated using the X-axis, Y-axis, and Z-axis as rotation axes. Alternatively, instead of moving and rotating Stage 152 along the X-axis, Y-axis, and Z-axis directions, the illumination optical system 110 and the imaging optical system 120 may be moved and rotated.

[0112] Illumination light L111 from light source 111 illuminates the imaging area of ​​the mask 50 to be evaluated. Reflected light L112, which is incident from a direction inclined with respect to the Z axis and reflected by the mask 50 to be evaluated, is incident on the perforated concave mirror 121. A hole 121a is provided in the center of the perforated concave mirror 121.

[0113] The reflected light L112 reflected by the perforated concave mirror 121 is incident on the convex mirror 122. The convex mirror 122 reflects the reflected light L112 incident from the perforated concave mirror 121 toward the hole 121a of the perforated concave mirror 121. The reflected light L112 that has passed through the hole 121a is detected by the detector 123. The detector 123 may be a detector 123 that includes a TDI (Time Delay Integration) sensor.

[0114] The detector 123 acquires image data of the mask 50 to be evaluated. The detector 123 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 detector 123 acquires multiple one-dimensional image data by scanning in a direction orthogonal to one direction. The image sensors are, for example, CCDs (Charge Coupled Devices). However, the image sensors are not limited to CCDs.

[0115] In this manner, the imaging optical system 120 collects the reflected light L112 from the evaluation target mask 50 illuminated by the illumination light L111, and detects the collected reflected light L112 with the detector 123 to acquire image data of the evaluation target mask 50. The image data is, for example, two-dimensional image data.

[0116] The reflected light L112 contains information such as the defect image 60 of the mask 50 under evaluation. The specularly reflected light of the illumination light L111 incident on the mask 50 under evaluation from a direction tilted with respect to the Z axis is detected by the imaging optical system 120. If a defect exists in the mask 50 under evaluation, the defect image 60 is observed as a dark image. This observation method is called bright-field observation. Multiple one-dimensional image data of the mask 50 under evaluation acquired by the detector 123 are output to the image processing device 10 and processed into two-dimensional image data.

[0117] The image processing device 10 is connected to the imaging optical system 120 by a signal line or wirelessly. The image processing device 10 receives image data of the mask to be evaluated 50 from the detector 123 in the imaging optical system 120. The image processing device 10 processes the image data of the mask to be evaluated 50 received from the detector 123 as a two-dimensional captured image B1. Specifically, the image processing device 10 converts the captured image B1 into a converted captured image G1 based on conversion parameters.

[0118] The mask 50 to be evaluated is, for example, an EUV mask that corresponds to EUV light. However, the mask 50 to be evaluated is not limited to an EUV mask. The mask 50 to be evaluated may also be a photomask that corresponds to illumination light L111 having other wavelengths. Furthermore, as mentioned above, the mask 50 to be evaluated may also be a semiconductor substrate, a semiconductor chip, or a semiconductor device.

[0119] While embodiments of this disclosure have been described above, this disclosure includes appropriate modifications that do not impair its purpose and advantages, and is not limited by the embodiments described above. Furthermore, the technical concept of this disclosure also includes appropriate omissions and combinations of the configurations of Embodiments 1 to 3. [Explanation of Symbols]

[0120] 10, 20 Image processing equipment 11 First acquisition part 11a Judgment part 12 Second acquisition part 13 Third acquisition part 14. Evaluation Department 50 masks to be evaluated 51 patterns 52 patterned surfaces 54 wafers 55 patterns 56 Optical elements 57 Pupil plane 58 optical elements 59 Image plane 60 Defective Image 100 Inspection device 110 Illumination optical system 111 Light source 112 Ellipsoidal mirror 113 Ellipsoidal mirror 114 Recessed Mirror 120 Imaging Optical System 121 Perforated concave mirror 121a Hole 122 Convex mirror 123 Detector 152 stages A1 Evaluation Image B1 Acquired image C1 Difference Image D1 Reference Image E1 Wafer image (transfer image) F1 converted evaluation image G1 converted image H1 converted difference image IF1 Focus point J1 Corrected and converted difference image K1 Differential Conversion Difference Image L111 illumination light L112 Reflected light MI, ML Optical Models

Claims

1. A first acquisition unit acquires an evaluation image based on the imaging results of the pattern surface on which the pattern of the mask to be evaluated is formed, A second acquisition unit acquires conversion parameters based on the relationship between the captured image of the pattern surface and the transferred image obtained when the pattern surface is transferred to the wafer by an exposure apparatus. A third acquisition unit that applies the conversion parameters to the aforementioned evaluation image to acquire a converted evaluation image, Equipped with an image processing device.

2. The aforementioned second acquisition unit is, The lithography conditions, including the optical conditions and resist conditions of the exposure apparatus, are obtained. Based on the lithography conditions, the conversion parameters are determined. The image processing apparatus according to claim 1.

3. The second acquisition unit trains and determines the conversion parameters by machine learning using the captured image and the transferred image as training data. The image processing apparatus according to claim 1.

4. The second acquisition unit determines a kernel that, when applied to the captured image, will match the transcribed image, as the conversion parameter. The image processing apparatus according to claim 1.

5. The evaluation image includes the captured image, The third acquisition unit acquires the converted image, which is the converted evaluation image, by performing a convolution integral on the captured image multiplied by the kernel. The image processing apparatus according to claim 4.

6. The second acquisition unit determines the conversion parameters based on a function in an optical model that acquires information about the pattern surface from the captured image, and a function in an optical model that acquires the transfer image from the information about the pattern surface. The image processing apparatus according to claim 1.

7. The first acquisition unit acquires a difference image showing the difference between the captured image and a reference image corresponding to the captured image as the evaluation image. The third acquisition unit acquires the converted difference image obtained by applying the conversion parameters to the difference image as the converted evaluation image. The image processing apparatus according to claim 1.

8. The system further includes a determination unit that determines whether the difference image satisfies predetermined conditions, If the determination unit determines that the predetermined conditions are met, The third acquisition unit acquires the converted difference image as the converted evaluation image. The image processing apparatus according to claim 7.

9. The system includes an evaluation unit that determines that there is a defect in the mask to be evaluated when a predetermined defect image is detected in the converted difference image. The image processing apparatus according to claim 7.

10. The system further includes a determination unit that determines the classification of defect images appearing in the difference image, The second acquisition unit acquires a modified conversion parameter as the conversion parameter, which is obtained by modifying the conversion parameter according to the classification determined by the determination unit. The third acquisition unit acquires the modified and converted difference image, obtained by applying the modification and conversion parameters to the difference image, as the converted evaluation image. The image processing apparatus according to claim 7.

11. The system further includes a determination unit that determines that the defect image appearing in the converted difference image is a defect of a specific classification. The image processing apparatus according to claim 7.

12. The second acquisition unit acquires a different parameter from the conversion parameter as a conversion parameter, The third acquisition unit acquires a different converted difference image by applying the different parameters to the difference image, The determination unit determines that the defect image appearing in the converted difference image is a defect of the first category, and determines that the defect image appearing in the other converted difference image is a defect of the second category. The image processing apparatus according to claim 11.

13. The system further includes a determination unit that determines whether the mask to be evaluated satisfies predetermined conditions, If the determination unit determines that the predetermined conditions are met, The third acquisition unit acquires the converted evaluation image. The image processing apparatus according to claim 1.

14. The predetermined conditions include at least one of the following: the pattern surface of the mask to be evaluated is illuminated with a specific illumination distribution in the exposure apparatus, and the pattern surface has a specific shape. The image processing apparatus according to claim 13.

15. A step of obtaining an evaluation image based on the imaging results of the patterned surface on which the pattern of the mask to be evaluated is formed, A step of obtaining conversion parameters based on the relationship between the captured image of the pattern surface and the transferred image obtained when the pattern surface is transferred to the wafer by an exposure apparatus, The steps include applying the conversion parameters to the evaluation image to obtain a converted evaluation image, An image processing method equipped with [a specific feature].

16. In the step of obtaining the aforementioned conversion parameters, The lithography conditions, including the optical conditions and resist conditions of the exposure apparatus, are obtained. Based on the lithography conditions, the conversion parameters are determined. The image processing method according to claim 15.

17. In the step of obtaining the aforementioned conversion parameters, The transformation parameters are trained and determined by machine learning using the captured image and the transferred image as training datasets. The image processing method according to claim 15.

18. In the step of obtaining the aforementioned conversion parameters, A kernel that matches the transcribed image when applied to the captured image is determined as the conversion parameter. The image processing method according to claim 15.

19. The evaluation image includes the captured image, In the step of obtaining the converted evaluation image, The converted image obtained by performing a convolution integral by multiplying the aforementioned captured image by the kernel is acquired as the converted evaluation image. The image processing method according to claim 18.

20. In the step of obtaining the aforementioned conversion parameters, The transformation parameters are determined based on a function in an optical model that acquires information about the pattern surface from the captured image, and a function in an optical model that acquires the transfer image from the information about the pattern surface. The image processing method according to claim 15.

21. In the step of acquiring the aforementioned evaluation image, A difference image showing the difference between the captured image and a reference image corresponding to the captured image is obtained as the evaluation image. In the step of obtaining the converted evaluation image, The converted difference image obtained by applying the conversion parameters to the aforementioned difference image is acquired as the converted evaluation image. The image processing method according to claim 15.

22. The step further comprises determining whether the difference image satisfies predetermined conditions, In the above determination step, If it is determined that the above-mentioned predetermined conditions are met, In the step of obtaining the converted evaluation image, The converted difference image is acquired as the converted evaluation image. The image processing method according to claim 21.

23. The system includes a step of determining that there is a defect in the evaluation target mask when a predetermined defect image is detected in the converted difference image. The image processing method according to claim 21.

24. The step further comprises determining the classification of defect images that appear in the difference image, In the step of obtaining the aforementioned conversion parameters, Depending on the determined classification, the modified conversion parameters obtained by correcting the conversion parameters are acquired as the conversion parameters. In the step of obtaining the converted evaluation image, The modified and converted difference image obtained by applying the modification and conversion parameters to the difference image is acquired as the converted evaluation image. The image processing method according to claim 21.

25. The method further includes a step of determining that the defect image appearing in the converted difference image is a defect of a specific classification. The image processing method according to claim 21.

26. In the step of obtaining the aforementioned conversion parameters, A different parameter from the aforementioned conversion parameter is obtained as the aforementioned conversion parameter, In the step of obtaining the converted evaluation image, A different transformed difference image is obtained by applying the other parameters to the difference image. In the above determination step, The defect image appearing in the converted difference image is determined to be a defect of the first category, and the defect image appearing in the other converted difference image is determined to be a defect of the second category. The image processing method according to claim 25.

27. The process further includes a step of determining whether the mask to be evaluated satisfies predetermined conditions. In the above determination step, If it is determined that the above-mentioned predetermined conditions are met, In the step of obtaining the converted evaluation image, The converted evaluation image is obtained. The image processing method according to claim 15.

28. The predetermined conditions include at least one of the following: the pattern surface of the mask to be evaluated is illuminated with a specific illumination distribution in the exposure apparatus, and the pattern surface has a specific shape. The image processing method according to claim 27.

Images