JP2023009348A - Pattern edge evaluation method - Google Patents

Abstract

To provide a method for correctly evaluating a defect of a pattern edge even when a part of the pattern edge on an image cannot be detected.SOLUTION: A method includes generating an image 60 of a pattern 54, overlaying a CAD pattern 50 corresponding to the pattern 54 with the pattern 54 on the image 60, creating a plurality of intensity profiles along a plurality of normals 65 perpendicular to the edges of the CAD pattern 50, determining a plurality of profile indices representing the shapes of the plurality of intensity profiles, calculating an edge evaluation value that is a representative value of the plurality of profile indices, and comparing the edge evaluation value with a defect threshold.SELECTED DRAWING: Figure 7

Description

The present invention relates to a method of evaluating edges of a pattern formed on a workpiece such as a wafer used in the manufacture of semiconductor devices, and more particularly to a method of detecting edge defects in a pattern on an image. .

A semiconductor device pattern inspection method using a die to database technique is known (see, for example, Patent Document 1). In this pattern inspection method, an image of a pattern on a workpiece such as a wafer or a glass substrate is generated by a scanning electron microscope, the pattern on the image and the CAD pattern are superimposed, and the edges of the pattern on the image and the edges of the CAD pattern are detected. and the distance to detect pattern edge defects on the workpiece. A CAD pattern is a virtual pattern created based on pattern design information (position, length, size, etc.) included in design data (also referred to as CAD data).

Prior to detecting pattern edge defects, the edges of the pattern on the image are detected as follows. The computer superimposes CAD pattern 505 onto pattern 501 on image 500, as shown in FIG. The computer then generates a plurality of search lines 507 extending normal to the edges of the CAD pattern 505 and creates an intensity profile of the image 500 along these search lines 507 . In FIG. 12, only some of the plurality of search lines 507 are drawn to simplify the drawing.

FIG. 13 shows the luminance profile along the search line 507 shown in FIG. The vertical axis in FIG. 13 represents the luminance, and the horizontal axis represents the position on the search line 507 . The computer detects edge points 510 on the luminance profile whose luminance is equal to a predetermined reference value. The computer repeats similar operations to determine multiple edge points on the intensity profile along all search lines 507 . A line connecting these edge points is determined as an edge of the pattern 501 on the image 500 .

The edges of the pattern 501 on the image 500 thus determined are compared with the edges of the CAD pattern 505, and defects in the edges of the pattern 501 are detected based on the comparison result.

JP 2011-17705 A

However, if the edge of the pattern 501 on the image 500 and the edge of the CAD pattern 505 deviate too much, the edge defect of the pattern 501 may not be detected. For example, in the example shown in FIG. 14, a part of the edge of pattern 501 is missing and the edge of pattern 501 does not exist on search line 507 . In the example shown in FIG. 15, parts of two adjacent patterns 501 are connected to form a bridge, and no edge exists on the search line 507 within the bridge.

14 and 15 show examples of pattern edge defects, and it is important to detect such defects. However, in these examples, since the edge of the pattern 501 itself cannot be detected, the edge of the CAD pattern 505 and the edge of the pattern 501 cannot be compared, and as a result, the defect of the pattern edge cannot be detected.

Accordingly, the present invention provides a method that can correctly evaluate pattern edge defects even when part of pattern edges on an image cannot be detected.

In one aspect, a method of evaluating edges of a pattern on a workpiece comprises: generating an image of said pattern; superimposing a CAD pattern corresponding to said pattern on said pattern on said image; creating a plurality of luminance profiles along a plurality of normals perpendicular to the , determining a plurality of profile indices representing shapes of the plurality of luminance profiles, respectively, and determining an edge evaluation value that is a representative value of the plurality of profile indices; A method is provided for calculating and comparing the edge estimate to a defect threshold.

In one aspect, each of the plurality of profile indices is a contrast value representing a difference between a peak value and a bottom value of the corresponding luminance profile.
In one aspect, each of the plurality of profile indicators is the slope of the corresponding luminance profile.
In one aspect, the edge evaluation value is an average value of the plurality of profile indices.
In one aspect, the edge evaluation value is a standard deviation of the plurality of profile indices.
In one aspect, each of the plurality of profile indicators is an edge detection indicator indicating the presence of an edge on the corresponding normal, or an edge missing indicator indicating the absence of an edge on the corresponding normal.
In one aspect, the edge evaluation value is a ratio of the number of the edge missing indicators included in the plurality of profile indicators to the number of the plurality of profile indicators.

According to the present invention, edge defects can be evaluated based on edge evaluation values even when part of edges of a pattern on an image cannot be detected.

1 is a schematic diagram illustrating an embodiment of an image generation system; FIG. It is a schematic diagram which shows an example of a CAD pattern. FIG. 3 is a schematic diagram showing an example of an image of an actual pattern on a workpiece formed according to the CAD pattern shown in FIG. 2; FIG. 4 is a diagram showing a state in which an actual pattern on an image and a CAD pattern are superimposed; FIG. 10 is a diagram illustrating a process of arranging a plurality of normals on edges of a CAD pattern within an evaluation target area; FIG. 6(a) shows an example of a luminance profile along a normal placed at a defect-free position, and FIG. 6(b) shows an example of a luminance profile along a normal placed at a defect FIG. 4 is a diagram showing an example of a luminance profile; Each of FIGS. 7(a) and 7(b) shows an example of a pattern with another type of defect and the corresponding luminance profile. Figure 4 is a flow chart describing one embodiment of a method for detecting edge defects in a pattern on an image; FIG. 5 is a diagram for explaining an embodiment in which the slope of an edge luminance profile is used as a profile index; FIG. 10(a) is a schematic diagram showing an example where there is no defect in the edge of the pattern, and FIG. 10(b) is a schematic diagram showing an example where there is a defect in the edge of the pattern. FIGS. 11A to 11C are diagrams for explaining the edge detection index and edge missing index. FIG. 4 is a diagram showing a state in which a CAD pattern is superimposed on a pattern on an image; FIG. 13 shows a luminance profile along the search line shown in FIG. 12; FIG. 4 is a diagram showing an example of pattern edge defects; FIG. 10 is a diagram showing another example of pattern edge defects;

BEST MODE FOR CARRYING OUT THE INVENTION Hereinafter, embodiments of the present invention will be described with reference to the drawings. FIG. 1 is a schematic diagram showing one embodiment of an image generation system. The image generation system includes a scanning electron microscope 1 that generates an image of the workpiece W and an operation control section 5 that controls the operation of the scanning electron microscope 1 . Examples of workpieces W include wafers, masks, panels, substrates, etc. used in the manufacture of semiconductor devices.

The operation control unit 5 is composed of at least one computer. The operation control unit 5 includes a storage device 5a in which programs are stored, and a processing device 5b that executes operations according to instructions included in the programs. The storage device 5a includes a main storage device such as a random access memory (RAM) and an auxiliary storage device such as a hard disk drive (HDD) and solid state drive (SSD). Examples of the processing device 5b include a CPU (central processing unit) and a GPU (graphic processing unit). However, the specific configuration of the operation control unit 5 is not limited to this embodiment.

The operation control unit 5 may be an edge server connected to the scanning electron microscope 1 via a communication line, or a cloud server connected to the scanning electron microscope 1 via a communication network such as the Internet or a local network. or a fog computing device (gateway, fog server, router, etc.) installed in a network connected to the scanning electron microscope 1 . The operation control unit 5 may be a combination of multiple servers. For example, the operation control unit 5 may be a combination of an edge server and a cloud server connected to each other by a communication network such as the Internet or a local network. In another example, the operation control unit 5 may include multiple servers (computers) that are not connected via a network.

The scanning electron microscope 1 includes an electron gun 15 for emitting an electron beam, a focusing lens 16 for converging the electron beam emitted from the electron gun 15, an X deflector 17 for deflecting the electron beam in the X direction, and an electron beam in the Y direction. It has a Y deflector 18 for deflection, an objective lens 20 for focusing an electron beam on a work piece W, which is an example of a sample, and a stage 31 for supporting the work piece W. FIG. The configuration of the electron gun 15 is not particularly limited. For example, a field emitter type electron gun, a semiconductor photocathode type electron gun, or the like can be used as the electron gun 15 .

The electron beam emitted from the electron gun 15 is converged by the converging lens 16, deflected by the X deflector 17 and the Y deflector 18, converged by the objective lens 20, and irradiated onto the surface of the workpiece W. When the workpiece W is irradiated with the primary electrons of the electron beam, the workpiece W emits electrons such as secondary electrons and reflected electrons. Electrons emitted from the workpiece W are detected by an electron detector 26 . Electron detection signals from the electron detector 26 are input to an image acquisition device 28 and converted into an image. The scanning electron microscope 1 thus produces an image of the surface of the workpiece W. FIG. The image acquisition device 28 is connected to the operation control section 5 .

Design data of patterns for forming electronic circuits such as wiring formed on the workpiece W is stored in the storage device 5a. The design data includes pattern design information such as the coordinates of the vertices of the pattern formed on the workpiece W, the position, shape and size of the pattern, and the number of the layer to which the pattern belongs. The operation control unit 5 can read the design data of the pattern formed on the workpiece W from the storage device 5a.

Next, an embodiment of a method for detecting edge defects in a pattern on an image based on a comparison between the pattern on the image generated by the scanning electron microscope 1 and the corresponding CAD pattern on the design data will be described. do. A CAD pattern is a virtual pattern defined by pattern design information included in design data, and has a polygonal shape. A pattern on the workpiece W is formed according to the corresponding CAD pattern. CAD is an abbreviation for computer-aided design. FIG. 2 is a schematic diagram showing an example of a CAD pattern. Reference numeral 50 in FIG. 2 indicates a CAD pattern. In the following description, the pattern already formed on the work piece W may be referred to as the actual pattern.

The operation control unit 5 issues a command to the scanning electron microscope 1 to generate an image of the actual pattern on the workpiece W. FIG. FIG. 3 is a schematic diagram showing an example of an image 60 of the actual pattern 54 on the work piece W formed according to the CAD pattern 50 shown in FIG. In the example shown in FIG. 3, only a portion of the actual pattern 54 formed according to the CAD pattern 50 shown in FIG. 2 appears on the image 60. In FIG.

As mentioned above, the actual pattern 54 on the work piece W is formed according to the CAD pattern 50 but does not have exactly the same shape as the CAD pattern 50 . In the example shown in FIG. 3, a part of the edge of the actual pattern 54 is missing, and such edge missing should be detected as an edge defect of the actual pattern 54 .

The operation control unit 5 acquires an image 60 of the actual pattern 54 shown in FIG. 3 from the scanning electron microscope 1 . Next, the motion control unit 5 performs matching (also referred to as pattern matching) between the actual pattern 54 on the image 60 and the CAD pattern 50 created from the design data. FIG. 4 is a schematic diagram showing the result of pattern matching, showing a state in which the actual pattern 54 on the image 60 and the CAD pattern 50 are superimposed.

The pattern matching is performed according to known methods. For example, the operation control unit 5 superimposes the actual pattern 54 on the image 60 and the CAD pattern 50, creates a brightness profile of the image 60 within a range set with the edge of the CAD pattern 50 as a starting point, and uses the brightness profile to create the image. The edge of the real pattern 54 on 60 is determined, and the matching position that minimizes the bias value between the determined edge position and the corresponding edge position of the CAD pattern 50 is determined. The bias value is an index value indicating the amount of deviation (distance) between the edge determined from the luminance profile and the edge of the CAD pattern 50 .

As shown in FIG. 5, the operation control section 5 arranges a plurality of normal lines 65 on the edges of the CAD pattern 50 within a predetermined evaluation target area 63 . These normals 65 are perpendicular to the edges of CAD pattern 50 . The normals 65 are line segments having the same length and are evenly spaced on the edge of the CAD pattern 50 . The size and shape of the evaluation target area 63 are set in advance.

The motion control unit 5 creates a plurality of brightness profiles along a plurality of normal lines 65 arranged on the edge of the CAD pattern 50 . FIG. 6(a) shows an example of the luminance profile along the normal 65 positioned at the defect-free position shown in FIG. A luminance profile is an array of luminances distributed on the normal 65 . Brightness is a number from 0 to 255, for example, in a grayscale image. This numerical range of luminance from 0 to 255 is an example, and other numerical ranges may be used.

In general, the edges of the pattern on the image produced by the scanning electron microscope 1 have high brightness due to the so-called edge effect. Therefore, as can be seen from FIG. 6(a), the luminance profile has peak values near the edges. On the other hand, areas where no pattern is present have low brightness, so the brightness profile has a bottom value outside the edge.

FIG. 6(b) shows an example of the intensity profile along the normal 65 placed at the defect shown in FIG. As can be seen from FIG. 6(b), the luminance profile along the normal 65 located where the pattern edge is missing is generally flat and has a generally low luminance. The shape of the luminance profile shown in FIG. 6(b) is clearly different from the shape of the luminance profile shown in FIG. 6(a).

The operation control unit 5 determines a plurality of profile indices that respectively represent the shapes of the plurality of luminance profiles. Each profile index in this embodiment is obtained by digitizing the shape of each luminance profile. More specifically, the profile index representing the shape of the brightness profile is a contrast value representing the difference between the peak value and the bottom value of the brightness profile. The difference between the peak value and the bottom value of the luminance profile shown in FIG. 6(a) is large to some extent, whereas the difference between the peak value and the bottom value of the luminance profile shown in FIG. 6(b) is almost 0. . Therefore, a profile index made up of contrast values can be used as an index representing the shape of the luminance profile.

The operation control unit 5 calculates an edge evaluation value, which is a representative value of a plurality of profile indices representing the shapes of a plurality of luminance profiles along a plurality of normals 65 respectively. In this embodiment, the edge evaluation value is the average value of a plurality of profile indices (a plurality of contrast values in this embodiment). The motion control unit 5 can calculate the edge evaluation value by dividing the total value of the plurality of profile indices by the number of the plurality of profile indices (that is, the number of the plurality of normal lines 65).

Each of FIGS. 7(a) and 7(b) shows an example of a pattern with another type of defect and the corresponding luminance profile. In the example shown in FIGS. 7(a) and 7(b), there are two adjacent CAD patterns 50 running parallel to each other. However, there is a defect that two adjacent actual patterns 54 that should not be connected are connected by a bridge. The size of the bridge is different in FIGS. 7(a) and 7(b). Due to such a difference in bridge size, the edge evaluation value, which is the average value of multiple profile indices (multiple contrast values), also changes.

As can be seen from FIGS. 6 and 7, the smaller the edge evaluation value, the greater the degree of defect. Therefore, the operation control unit 5 is configured to compare the edge evaluation value with the defect threshold. The defect threshold is a predetermined value and can be arbitrarily set based on past evaluations, experiments, and the like. When the edge evaluation value is smaller than the defect threshold, the operation control unit 5 determines that the edge of the pattern 54 on the image 60 has a defect. In the example of FIG. 7 , the portion of the pattern 54 where the edge defect exists is within the evaluation target area 63 . The motion control unit 5 similarly calculates the edge evaluation value while moving the evaluation target area 63 little by little along the edge of the CAD pattern 50, and compares the edge evaluation value with the defect threshold value. The entire edge of pattern 54 on image 60 can be evaluated.

The edge evaluation value is not limited to the average value of a plurality of profile indices as long as it represents a representative value of the plurality of profile indices. In one embodiment, the edge metric may be the standard deviation of multiple profile metrics. As can be seen from FIGS. 7A and 7B, if there is a defect (bridge) at the edge of the pattern 54, variations in the plurality of profile indices within the evaluation target area 63 increase. The larger the standard deviation that indicates the variation of the plurality of profile indices, the greater the degree of defects. Therefore, the operation control unit 5 may be configured to compare the edge evaluation value, which is the standard deviation, with the defect threshold. The defect threshold is a predetermined value and can be arbitrarily set based on past evaluations, experiments, and the like.

ｎは、評価対象エリア６３内のプロファイル指標の総数（すなわち法線６５の総数）である。 The operation control unit 5 determines that a defect exists in the edge of the pattern 54 on the image 60 when the edge evaluation value, which is the standard deviation, is larger than the defect threshold value. Standard deviation is calculated from the following formula.

n is the total number of profile indicators (ie, the total number of normals 65) in the area under evaluation 63;

FIG. 8 is a flowchart illustrating one embodiment of a method for detecting edge defects in pattern 54 on image 60 based on a comparison of pattern 54 on image 60 with a corresponding CAD pattern 50 .

In step 1, the motion controller 5 commands the scanning electron microscope 1 to generate an image 60 of the pattern 54 on the workpiece W. FIG. The motion controller 5 acquires the image 60 of the pattern 54 from the scanning electron microscope 1 .
In step 2 , the motion control section 5 reads out the CAD pattern 50 corresponding to the pattern 54 on the image 60 from the storage device 5 a and superimposes the CAD pattern 50 on the pattern 54 on the image 60 . More specifically, the motion controller 5 performs pattern matching between the CAD pattern 50 and the pattern 54 on the image 60 .

In step 3, the motion controller 5 creates a plurality of brightness profiles along a plurality of normals 65 perpendicular to the edges of the CAD pattern 50 (see FIG. 6).
In step 4, the operation control unit 5 determines a plurality of profile indices representing the shapes of the plurality of luminance profiles. Each profile index is, for example, a contrast value that indicates the difference between the peak value and bottom value of the luminance profile.
At step 5, the operation control unit 5 calculates an edge evaluation value, which is a representative value of a plurality of profile indices. The edge evaluation value is, for example, the average value or standard deviation of multiple profile indices.

At step 6, the operation control unit 5 compares the edge evaluation value with the defect threshold.
At step 7, the operation control unit 5 determines that the edge of the pattern 54 on the image 60 has a defect if the edge evaluation value is smaller (or larger) than the defect threshold.
In this manner, the operation control unit 5 can correctly detect (evaluate) the defect of the pattern edge based on the edge evaluation value even if the edge of the defective portion of the pattern 54 on the image 60 cannot be detected.

Each profile index is a numerical value representing the shape of each luminance profile. In the above-described embodiment, the contrast value, which is the difference between the peak value and the bottom value of the luminance profile, is used as the profile index. However, the profile index is not limited to the contrast value as long as it changes according to the shape of the luminance profile. In one embodiment, the profile index may be the slope of the luminance profile, as described below.

FIG. 9 is a diagram for explaining an embodiment in which the slope of the luminance profile of the edge of pattern 54 on image 60 is used as the profile index. Details of this embodiment that are not specifically described are the same as those of the embodiment described above with reference to FIGS.

As shown in FIG. 9, in one embodiment, the slope of the luminance profile of the pattern edge is the slope of the straight line connecting the peak and bottom points of the luminance profile. More specifically, the slope of the luminance profile is calculated from the following equation.
Slope of luminance profile = (peak value of luminance profile - bottom value of luminance profile) / (position of peak value - position of bottom value)

However, the method of calculating the slope of the brightness profile is not limited to the method using the above formula, and for example, the slope of the tangent line of the brightness profile may be calculated.

The operation control unit 5 calculates a plurality of slopes as a plurality of profile indices representing the shapes of a plurality of luminance profiles along a plurality of normals 65, respectively, and calculates an edge evaluation value that is an average value of the calculated plurality of slopes. Calculate.

FIG. 10A is a schematic diagram showing an example in which the edge of the pattern 54 has no defect, and FIG. 10B is a schematic diagram showing an example in which the edge of the pattern 54 has a defect. When the edges of the pattern 54 are defect-free, as shown in FIG. 10(a), the slope of the intensity profile along each normal 65 is large, but there are edge defects, as shown in FIG. 10(b). The slope of the intensity profile along the placed normal 65 is small. As a result, when there is a defect in the edge, the edge evaluation value, which is the average value of multiple slopes, is small.

The motion controller 5 is configured to compare the edge evaluation value with a defect threshold. The defect threshold is a predetermined value and can be arbitrarily set based on past evaluations, experiments, and the like. When the edge evaluation value is smaller than the defect threshold, the operation control unit 5 determines that the edge of the pattern 54 on the image 60 has a defect. In one embodiment, the edge metric may be the standard deviation of multiple slopes. In this case, if the edge evaluation value is greater than the defect threshold, the operation control unit 5 determines that the edge of the pattern 54 on the image 60 has a defect.

Next, an embodiment will be described in which an edge detection index and an edge missing index, which will be described below, are used as each profile index instead of the contrast value or gradient of the luminance profile. The details of this embodiment that are not specifically described are the same as those of the embodiment described above with reference to FIGS.

The edge detection index is an index indicating that an edge exists on the normal line 65 , and the edge missing index is an index indicating that an edge does not exist on the normal line 65 . The edge detection index and the edge missing index will be described below with reference to FIGS. 11(a) to 11(c). In the example shown in FIG. 11A, no defect exists at the edges of the pattern 54 within the evaluation target area 63 . Therefore, all of the profile indices of luminance profiles along multiple normals 65 within the evaluation target area 63 are edge detection indices. On the other hand, in the examples shown in FIGS. 11(b) and 11(c), a defect exists in a part of the edge of the pattern 54 within the evaluation target area 63 . Therefore, some of the profile indices of the luminance profile along the plurality of normals 65 within the evaluation target area 63 are edge missing indices, and others are edge detection indices.

In one embodiment, the presence or absence of an edge of pattern 54 on each normal 65 is determined by the difference between the peak and bottom values of the luminance profile along each normal 65 (or the slope of the luminance profile) and a predetermined edge detection reference value. More specifically, when the difference between the peak value and the bottom value of the luminance profile along a certain normal line 65 (or the gradient of the luminance profile) is greater than the edge detection reference value (for example, (a)) determines that an edge of the pattern 54 exists on the normal 65, and the difference between the peak value and the bottom value of the luminance profile along a certain normal 65 (or the gradient of the luminance profile) is the edge If it is smaller than the detection reference value (see, for example, FIG. 6B), it is determined that the edge of the pattern 54 does not exist on the normal line 65 .

As can be seen from FIGS. 11(a) to 11(c), each of the plurality of profile indicators is an edge detection indicator that indicates the existence of an edge on the corresponding normal 65, or an edge detection indicator on the corresponding normal 65. Either one of the missing edge indicators indicating that the edge is not present. That is, in the example shown in FIG. 11A, all of the plurality of profile indices are edge detection indices, but in the examples shown in FIGS. 11B and 11C, the plurality of profile indices are edge detection indices. Includes both indices and missing edge indices. In other words, each one of the plurality of profile indicators is either an edge detection indicator or an edge missing indicator.

The operation control unit 5 calculates an edge evaluation value, which is the ratio of the number of edge missing indicators to the number of profile indicators in the evaluation target area 63 . Furthermore, the motion control unit 5 is configured to compare the edge evaluation value with a defect threshold. The defect threshold is a predetermined value and can be arbitrarily set based on past evaluations, experiments, and the like. When the edge evaluation value is greater than the defect threshold, the operation control unit 5 determines that the edge of the pattern 54 on the image 60 has a defect.

As described above, according to each of the above-described embodiments, the motion control unit 5 determines the edge of the pattern 54 on the image 60 based on the edge evaluation value even if part of the edge of the pattern 54 on the image 60 is not detected. Defects can be evaluated.

The operation control unit 5 may evaluate edge defects using any one of the edge evaluation values in the above-described multiple embodiments, or may evaluate edge defects using the multiple edge evaluation values used in the respective embodiments. Edge defects may be evaluated in combination.

The above-described embodiments are described for the purpose of enabling a person having ordinary knowledge in the technical field to which the present invention belongs to implement the present invention. Various modifications of the above embodiments can be made by those skilled in the art, and the technical idea of the present invention can be applied to other embodiments. Accordingly, the present invention is not limited to the described embodiments, but is to be construed in its broadest scope in accordance with the technical spirit defined by the claims.

W Work piece 1 Scanning electron microscope 5 Motion controller 5a Storage device 5b Processing device 15 Electron gun 16 Focusing lens 17 X deflector 18 Y deflector 20 Objective lens 26 Electron detector 31 Stage 50 CAD pattern 54 Pattern on image 60 Image 63 Evaluation target area 65 Normal line

Claims (7)

A method of evaluating edges of a pattern on a workpiece, comprising:
generating an image of the pattern;
superimposing a CAD pattern corresponding to the pattern on the pattern on the image;
creating a plurality of intensity profiles along a plurality of normals perpendicular to edges of the CAD pattern;
determining a plurality of profile indices each representing a shape of the plurality of luminance profiles;
calculating an edge evaluation value that is a representative value of the plurality of profile indices;
A method, wherein the edge evaluation value is compared to a defect threshold. 2. The method of claim 1, wherein each of said plurality of profile indicators is a contrast value representing the difference between peak and bottom values of the corresponding luminance profile. 2. The method of claim 1, wherein each of said plurality of profile indicators is the slope of a corresponding luminance profile. 4. The method according to any one of claims 1 to 3, wherein said edge evaluation value is an average value of said plurality of profile indicators. 4. The method of any one of claims 1-3, wherein the edge estimate value is the standard deviation of the plurality of profile measures. 2. The method of claim 1, wherein each of the plurality of profile indicators is an edge detection indicator that indicates the presence of an edge on the corresponding normal or an edge missing indicator that indicates the absence of an edge on the corresponding normal. Method. 7. The method of claim 6, wherein the edge evaluation value is a ratio of the number of the missing edge indicators included in the plurality of profile indicators to the number of the plurality of profile indicators.

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