Pattern matching method

Abstract

The method accumulates the measured values of the widths of the reference patterns (210A) belonging to the first group to determine a first accumulated value, accumulates the measured values of the widths of the reference patterns (210B) belonging to the second group to determine a second accumulated value, performs a second matching of a plurality of patterns on the image of the second region with a corresponding plurality of CAD patterns, accumulates the measured values of the widths of the patterns (220A) belonging to the first group to determine a third accumulated value, accumulates the measured values of the widths of the patterns (220B) belonging to the second group to determine a fourth accumulated value, and determines that the second matching was performed correctly when the magnitude relationship of the third accumulated value and the fourth accumulated value is the same as the magnitude relationship of the first accumulated value and the second accumulated value.

Description

TECHNICAL FIELD

[0001] The present application relates to a method of matching a pattern formed on a surface of a workpiece such as a wafer or a glass substrate with a CAD pattern produced based on design data of the pattern in manufacturing a semiconductor device. BACKGROUND

[0002] A chip-to-database method is a pattern matching method of matching a pattern formed on a surface of a workpiece such as a wafer or a glass substrate with a CAD pattern produced based on design data of the pattern. More specifically, the chip-to-database method is a method of acquiring coordinates of a region to be checked from the design data, moving a stage on which the workpiece is placed to the coordinates, generating an image of the pattern on the workpiece by electron beam irradiation, matching the image with the CAD pattern produced based on the design data, producing a curve of a gray scale of the image in a range in which an edge of the CAD pattern is set as a starting point, determining an edge of the pattern on the image based on the curve of the gray scale, and determining a matching position in which a positional deviation of the determined edge and an edge of the corresponding CAD pattern is minimized.

[0003] PRIOR ART DOCUMENTS

[0004] PATENT DOCUMENTS

[0005] Patent Document 1: Japanese Patent Laid-Open No. 5-324836

[0006] Patent Document 2: Japanese Patent Publication No. 2012-519391 SUMMARY

[0007] PROBLEMS TO BE SOLVED BY THE INVENTION

[0008] Recently, double patterning, which is a technique capable of forming a high integration circuit with narrower pattern intervals, has attracted attention. The double patterning is a technique of forming a first pattern and a second pattern alternately arranged in two steps. However, in a certain specific region, a deviation in a size (CD, Critical Dimension) of the pattern is likely to occur. Thus, in such a region, accurate statistical data of the CD (Critical Dimension) is required for optimization of a process and monitoring of a process variation.

[0009] A technique of measuring CDs of a pattern 1 and a pattern 2 using only a feature amount of a pattern profile curve of an SEM image is disclosed in Patent Document 2. However, in this technique, information capable of being added to design information is not obtained in addition to the measured values of the CDs, and thus it is difficult to analyze problems in design or a process.

[0010] The following operation can be achieved by the conventional chip-to-database method: directly specifying a region where the influence of the expected double patterning process variation is large on the CAD coordinate system within the design data to acquire CD statistical data, thereby performing CD bias evaluation within the memory cell. Further, the chip-to-database method can measure the CD while correcting the influence of the magnification variation or rotation of the SEM image and the like using the design data, so that a large number of accurate differences between the design value and the CD can be obtained. In addition, additional information such as peripheral pattern information can be obtained from the design data, so that the chip-to-database method is most suitable for CD measurement data analysis for the purpose of mask or process parameter modification.

[0011] Generally, at the end portion of the memory cell, the pattern is easily deformed largely due to the influence of the optical proximity effect and the like. Therefore, to acquire data of the CD variation caused by double patterning, it is desirable to perform CD measurement at the central portion of the memory cell. However, it is difficult to determine whether the pattern matching is correct at the central portion of the memory cell. For example, as shown in FIG. 5, there is a case where the repeated pattern 505 on the SEM image 500 is consistent with the CAD pattern 510 on the design data, but a pitch offset occurs. Figure 16

[0012] Therefore, the present application provides a pattern matching method capable of achieving correct matching of a pattern formed by multiple patterning and a corresponding CAD pattern.

[0013] Technical means for solving the problem

[0014] ​In one aspect, there is provided a pattern matching method, which includes generating an image of a first region within a pattern region including a pattern formed by multiple patterning, performing a first matching of a plurality of reference patterns on the image of the first region with a plurality of first CAD patterns classified into a first group and a second group in advance according to layer information, classifying the plurality of reference patterns into the first group and the second group according to a layer classification of the first CAD patterns, measuring widths of the plurality of reference patterns belonging to the first group and widths of the plurality of reference patterns belonging to the second group, accumulating the measured values of the widths of the plurality of reference patterns belonging to the first group to determine a first accumulated value, accumulating the measured values of the widths of the plurality of reference patterns belonging to the second group to determine a second accumulated value, determining a size relationship between the first accumulated value and the second accumulated value, generating an image of a second region within the pattern region, performing a second matching of a plurality of patterns on the image of the second region with a plurality of second CAD patterns classified into a first group and a second group in advance according to layer information, classifying the plurality of patterns on the image of the second region into the first group and the second group according to a layer classification of the second CAD patterns, measuring widths of the plurality of patterns belonging to the first group and widths of the plurality of patterns belonging to the second group, accumulating the measured values of the widths of the plurality of patterns belonging to the first group to determine a third accumulated value, accumulating the measured values of the widths of the plurality of patterns belonging to the second group to determine a fourth accumulated value, determining a size relationship between the third accumulated value and the fourth accumulated value, and determining that the second matching is correctly performed when the size relationship between the third accumulated value and the fourth accumulated value is consistent with the size relationship between the first accumulated value and the second accumulated value.

[0015] In one aspect, the first region is an edge region including an edge of the pattern region, and the second region is within the pattern region and located inwardly compared to the first region.

[0016] In one aspect, the pattern formed by the multiple patterning is a repeating pattern.

[0017] In one aspect, the pattern matching method further includes the following steps: when the size relationship between the third accumulated value and the fourth accumulated value is not consistent with the size relationship between the first accumulated value and the second accumulated value, relatively moving the plurality of patterns on the image of the second region by one pitch with respect to the plurality of second CAD patterns and performing the second matching again.

[0018] In one aspect, an absolute value of a difference between the first accumulated value and the second accumulated value and an absolute value of a difference between the third accumulated value and the fourth accumulated value are greater than a predetermined value.

[0019] In one aspect, there is provided a pattern matching method, which includes generating an image of a first region within a pattern region including a pattern formed by multiple patterning, performing a first matching of a plurality of reference patterns on the image of the first region with a plurality of first CAD patterns previously classified into a first group and a second group according to layer information, classifying the plurality of reference patterns into the first group and the second group according to a layer classification of the first CAD patterns, calculating a slope of a brightness curve of an edge of the plurality of reference patterns belonging to the first group and a slope of a brightness curve of an edge of the plurality of reference patterns belonging to the second group, accumulating the calculated values of the slope of the brightness curve of the edge of the plurality of reference patterns belonging to the first group to determine a first accumulated value, accumulating the calculated values of the slope of the brightness curve of the edge of the plurality of reference patterns belonging to the second group to determine a second accumulated value, determining a size relationship between the first accumulated value and the second accumulated value, generating an image of a second region within the pattern region, performing a second matching of a plurality of patterns on the image of the second region with a plurality of second CAD patterns previously classified into the first group and the second group according to layer information, classifying the plurality of patterns on the image of the second region into the first group and the second group according to a layer classification of the second CAD patterns, calculating a slope of a brightness curve of an edge of the plurality of patterns belonging to the first group and a slope of a brightness curve of an edge of the plurality of patterns belonging to the second group, accumulating the calculated values of the slope of the brightness curve of the edge of the plurality of patterns belonging to the first group to determine a third accumulated value, accumulating the calculated values of the slope of the brightness curve of the edge of the plurality of patterns belonging to the second group to determine a fourth accumulated value, determining a size relationship between the third accumulated value and the fourth accumulated value, and determining that the second matching is correctly performed when the size relationship between the third accumulated value and the fourth accumulated value is consistent with the size relationship between the first accumulated value and the second accumulated value.

[0020] In one aspect, the first region is an edge region including an edge of the pattern region, and the second region is within the pattern region and located inwardly compared to the first region.

[0021] In one aspect, the pattern formed by the multiple patterning is a repeating pattern.

[0022] In one aspect, the pattern matching method further includes the following steps: when the size relationship between the third accumulated value and the fourth accumulated value is not consistent with the size relationship between the first accumulated value and the second accumulated value, relatively moving the plurality of patterns on the image of the second region by one pitch with respect to the plurality of second CAD patterns and performing the second matching again.

[0023] In one aspect, the absolute value of the difference between the first cumulative value and the second cumulative value and the absolute value of the difference between the third cumulative value and the fourth cumulative value are larger than a predetermined value.

[0024] Effects of the Invention

[0025] According to the present invention, the accurate pattern matching in the second region can be ensured with reference to the size relationship of the pattern widths in the first region. In particular, according to the present invention, the measured values of the pattern widths can be used for the optimization of process parameters. Furthermore, the monitoring of the pattern widths in a region with a small process margin can be achieved. BRIEF DESCRIPTION OF DRAWINGS

[0026] Figure 1 A schematic diagram showing an embodiment of an image generation apparatus.

[0027] Figure 2 A schematic diagram showing a pattern region formed on a wafer.

[0028] Figure 3 A schematic diagram showing an example of an image of a first region.

[0029] Figure 4 A graph illustrating a first matching of a plurality of reference patterns on an image of a first region with a corresponding plurality of CAD patterns.

[0030] Figure 5 A graph illustrating a process of classifying a plurality of reference patterns on an SEM image into a first group and a second group.

[0031] Figure 6 A graph showing the relationship between the number of widths of the cumulative reference patterns and first and second cumulative values.

[0032] Figure 7 A schematic diagram showing another example of a pattern in a first region.

[0033] Figure 8 A schematic diagram showing an example of an image of a second region.

[0034] Figure 9 A graph illustrating a second matching of a plurality of patterns on an image of a second region with a corresponding plurality of CAD patterns.

[0035] Figure 10 A graph illustrating a process of classifying a plurality of patterns on an SEM image into a first group and a second group.

[0036] Figure 11 A flowchart illustrating an embodiment of a pattern matching method.

[0037] Figure 12This is a schematic diagram illustrating another example of an image representing region 1.

[0038] Figure 13 This is a schematic diagram illustrating another example of an image representing region 2.

[0039] Figure 14 A diagram illustrating an example of the brightness curve of a pattern on an image.

[0040] Figure 15 This is a flowchart illustrating one implementation of the pattern matching method.

[0041] Figure 16 This diagram illustrates potential problems in previous pattern matching processes. Detailed Implementation

[0042] The embodiments of the present invention will now be described with reference to the accompanying drawings.

[0043] Figure 1 This is a schematic diagram illustrating one embodiment of an image generation apparatus. (For example...) Figure 1 As shown, the image generation apparatus includes a scanning electron microscope 50 and a computing system 150. The scanning electron microscope 50 is connected to the computing system 150, and the operation of the scanning electron microscope 50 is controlled by the computing system 150.

[0044] The computing system 150 includes a storage device 162 storing a database 161 and programs, a processing device 163 executing calculations according to commands contained in the programs, and a display screen 165 displaying images and a GUI (Graphical User Interface). The processing device 163 includes a CPU (Central Processing Unit) or GPU (Graphics Processing Unit) that performs calculations according to commands contained in the programs stored in the storage device 162. The storage device 162 includes a main storage device (e.g., random access memory) accessible to the processing device 163 and an auxiliary storage device (e.g., a hard disk drive or solid-state drive) for storing data and programs.

[0045] The computing system 150 includes at least one computer. For example, the computing system 150 may be an edge server connected to the scanning electron microscope 50 via a communication line, a cloud server connected to the scanning electron microscope 50 via a communication network such as the Internet or a local area network, or a fog computing device (gateway, fog server, router, etc.) connected within the network of the scanning electron microscope 50. The computing system 150 may also be a combination of multiple servers. For example, the computing system 150 may be a combination of an edge server and a cloud server interconnected via a communication network such as the Internet or a local area network. In other examples, the computing system 150 may also include multiple servers (computers) not connected via a network.

[0046] The scanning electron microscope 50 has an electron gun 111 which emits an electron beam composed of primary electrons (charged particles), a condenser lens 112 which converges the electron beam emitted from the electron gun 111, an X deflector 113 which deflects the electron beam in the X direction, a Y deflector 114 which deflects the electron beam in the Y direction, and an objective lens 115 which focuses the electron beam onto a wafer 124 which is an example of a workpiece. The configuration of the electron gun 111 is not particularly limited. For example, a field emission type electron gun or a semiconductor photocathode type electron gun or the like can be used as the electron gun 111.

[0047] The condenser lens 112 and the objective lens 115 are connected to a lens control device 116, and the operation of the condenser lens 112 and the objective lens 115 is controlled by the lens control device 116. The lens control device 116 is connected to a computing system 150. The X deflector 113 and the Y deflector 114 are connected to a deflection control device 117, and the deflection operation of the X deflector 113 and the Y deflector 114 is controlled by the deflection control device 117. The deflection control device 117 is also connected to the computing system 150. The secondary electron detector 130 and the reflected electron detector 131 are connected to an image acquisition device 118. The image acquisition device 118 is configured to convert the output signals of the secondary electron detector 130 and the reflected electron detector 131 into images. The image acquisition device 118 is also connected to the computing system 150.

[0048] A sample stage 121 disposed in the sample chamber 120 is connected to a sample stage control device 122, and the position of the sample stage 121 is controlled by the sample stage control device 122. The sample stage control device 122 is connected to the computing system 150. A transport device 140 for placing the wafer 124 on the sample stage 121 in the sample chamber 120 is also connected to the computing system 150.

[0049] The electron beam emitted from the electron gun 111 is converged by the condenser lens 112 and is deflected by the X deflector 113 and the Y deflector 114 and is converged by the objective lens 115 to be irradiated onto the surface of the wafer 124. When the primary electrons of the electron beam are irradiated onto the wafer 124, secondary electrons and reflected electrons are emitted from the wafer 124. The secondary electrons are detected by the secondary electron detector 130, and the reflected electrons are detected by the reflected electron detector 131. The signals of the detected secondary electrons and the signals of the detected reflected electrons are input to the image acquisition device 118 and are converted into images. The images are transmitted to the computing system 150.

[0050] Design data of the pattern formed on the wafer 124 is stored in advance in the storage device 162. The design data contains coordinates of vertices of the pattern formed on the wafer 124, a position, a shape, and a size of the pattern, a layer number of a layer to which the pattern belongs, and the like, design information of the pattern. A database 161 is constructed in the storage device 162. The design data of the pattern is stored in advance in the database 161. The operation system 150 can read out the design data of the pattern from the database 161 stored in the storage device 162.

[0051] Next, an embodiment of a method of performing matching of a pattern on an image generated by the scanning electron microscope 50 with a corresponding CAD pattern on design data will be described. In the following description, the image generated by the scanning electron microscope 50 will be sometimes referred to as an SEM image. The pattern of the wafer 124 is formed in accordance with design data (also referred to as CAD data). CAD is an abbreviation of computer-aided design.

[0052] The design data is data containing design information of the pattern formed on the wafer 124, specifically, design information of the pattern, such as coordinates of vertices of the pattern, a position, a shape, and a size of the pattern, a layer number of a layer to which the pattern belongs. The CAD pattern on the design data is an imaginary pattern defined by the design information of the pattern contained in the design data. In the following description, the pattern already formed on the wafer 124 will be sometimes referred to as an actual pattern.

[0053] Figure 2 A schematic view of a pattern region 200 formed on the wafer 124 is shown. The pattern formed in the pattern region 200 is an actual pattern formed by multiple patterning, such as double patterning, quadruple patterning, or the like. In the present embodiment, the actual pattern formed in the pattern region 200 is a line-and-space pattern as an example of a repetitive pattern. As an example of the pattern region 200, a memory cell can be cited.

[0054] First, an image of a first region 201 in the pattern region 200 containing the pattern formed by the multiple patterning is generated by the scanning electron microscope 50. After the image of the first region 201 is generated, an image of a second region 202 different from the first region 201 is generated by the scanning electron microscope 50. Like the first region 201, the second region 202 is a region in the pattern region 200. The size of the first region 201 and the second region 202 is not particularly limited, and is, for example, the size of a field of view (FOV) of the scanning electron microscope 50 or the size of a combination of a plurality of FOVs of the scanning electron microscope 50.

[0055] To ensure the correct implementation of subsequent first and second matches, the first region 201 is a region containing a characteristic pattern whose position can be determined. In this embodiment, the first region 201 is an edge region containing the edge of the pattern region 200. The second region 202 is within the pattern region 200 and is located inside the first region 201.

[0056] Figure 3 This is a schematic diagram illustrating an example of image 205 representing region 201. For example... Figure 3 As shown, the pattern within the first region 201 includes two sets of patterns 210A and 210B, formed through double patterning. These patterns 210A and 210B are arranged alternately. The patterns 210A and 210B within the first region 201 are repeating patterns that include the edges of the patterned region 200. That is, the outermost pattern 210A is a characteristic pattern whose position can be determined. Therefore, although these patterns 210A and 210B are repeating patterns, accurate pattern matching is guaranteed. In the following description, the patterns 210A and 210B on the image 205 of the first region 201 are referred to as reference patterns. The number of reference patterns 210A and 210B included within the first region 201 is not limited to [specific number missing]. Figure 3 The example shown.

[0057] like Figure 3 As shown, reference patterns 210A and 210B do not include the pattern ends. That is, reference patterns 210A and 210B each traverse the entire first region 201. The reason why reference patterns 210A and 210B do not include the pattern ends is that pattern shrinkage is prone to occur at the pattern ends, and sometimes it is impossible to measure the correct pattern width. The size and position of the first region 201 can be set manually or automatically. Multiple first regions 201 can also be set.

[0058] Next, as Figure 4 As shown, the computing system 150 performs a first match between multiple reference patterns 210A and 210B on the image 205 of the first region 201 and corresponding multiple CAD patterns 215A and 215B. The CAD patterns 215A and 215B are pre-classified into group 1 and group 2, respectively, according to the layer information of these CAD patterns. Figure 4 In the example shown, CAD pattern 215A is classified as Group 1, and CAD pattern 215B is classified as Group 2. CAD patterns 215A and 215B are created by the computing system 150 based on the design data of reference patterns 210A and 210B.

[0059] The first matching is performed as follows: The calculation system 150 aligns the SEM image 205 with the CAD patterns 215A and 215B created according to the design data. A grayscale curve of the SEM image 205 is generated within a range where the edges of the CAD patterns 215A and 215B are set as starting points. The edges of the reference patterns 210A and 210B on the SEM image 205 are determined based on the grayscale curve, and the matching position where the offset between the determined edge and the corresponding edge of the CAD pattern 215A and 215B is minimized is determined. The offset is an index value representing the offset (distance) between the edge determined based on the grayscale curve and the edge of the corresponding CAD pattern 215A and 215B. The offset is calculated for all edges within the SEM image 205.

[0060] like Figure 5 As shown, the computing system 150 classifies multiple reference patterns 210A and 210B on the SEM image 205 into group 1 and group 2 according to the layer classification of CAD patterns 215A and 215B. The layer classification of CAD patterns 215A and 215B is predetermined based on the pattern formation order of multiple patterning (double patterning in this embodiment). Figure 5 In the example shown, the computing system 150 classifies the reference pattern 210A at odd-numbered positions into group 1 and the reference pattern 210B at even-numbered positions into group 2. Depending on the layer classification of CAD patterns 215A and 215B, the computing system 150 can also classify the reference pattern 210B at even-numbered positions into group 1 and the reference pattern 210A at odd-numbered positions into group 2.

[0061] Next, the computing system 150 measures the widths of multiple reference patterns 210A belonging to the first group and the widths of multiple reference patterns 210B belonging to the second group. The measured width values ​​of each reference pattern 210A, 210B are the Critical Dimension (CD) values ​​of each reference pattern 210A, 210B. The method for measuring the widths of reference patterns 210A, 210B is not particularly limited. In one example, the computing system 150 can calculate the sum of the average bias values ​​of each reference pattern and the width of the corresponding CAD pattern, using the calculated sum as the measured width value (i.e., the CD value). In other examples, the computing system 150 can also determine the distance between the peaks of the brightness curves of each reference pattern. Typically, the brightness of the edges of a pattern in an image is sometimes higher than that of other parts of the pattern. Therefore, the distance between the peaks of the brightness curves can be used as a measurement of the width of the reference pattern.

[0062] The arithmetic system 150 accumulates the measured values of the widths of the plurality of reference patterns 210A belonging to the first group to determine a first accumulated value, and accumulates the measured values of the widths of the plurality of reference patterns 210B belonging to the second group to determine a second accumulated value. Further, the arithmetic system 150 compares the first accumulated value and the second accumulated value, and determines the magnitude relation of the first accumulated value and the second accumulated value. That is, the arithmetic system 150 determines which of the first accumulated value and the second accumulated value is larger. The arithmetic system 150 stores the determined magnitude relation of the first accumulated value and the second accumulated value in the storage device 162.

[0063] The reference patterns 210A belonging to the first group and the reference patterns 210B belonging to the second group are actual patterns formed by the respective processes in the multiple patterning. Generally, the widths of the actual patterns formed by the respective processes in the multiple patterning have some difference. Thus, the widths of the reference patterns 210A belonging to the first group and the widths of the reference patterns 210B belonging to the second group also have some difference. The first accumulated value calculated for the first group and the second accumulated value calculated for the second group reflect the widths of the reference patterns 210A, 210B belonging to the two groups.

[0064] Figure 6 A graph showing the relation of the number of the widths of the accumulated reference patterns and the first accumulated value and the second accumulated value. According to the graph, the more the number of the widths of the accumulated reference patterns, the larger the difference between the first accumulated value and the second accumulated value. Figure 6 It is known that the more the number of the widths of the accumulated reference patterns 210A, 210B, the larger the difference between the first accumulated value and the second accumulated value. The widths of the reference patterns 210A belonging to the first group and the widths of the reference patterns 210B belonging to the second group have a very small difference, but there is a significant difference between the first accumulated value and the second accumulated value. Thus, the arithmetic system 150 can determine which of the widths of the reference patterns 210A belonging to the first group and the widths of the reference patterns 210B belonging to the second group is larger than the other according to the difference between the first accumulated value and the second accumulated value. In the example shown in FIG. 8, the magnitude relation of the first accumulated value and the second accumulated value is that the first accumulated value is larger than the second accumulated value. Figure 6

[0065] To determine the magnitude relation of the first accumulated value and the second accumulated value, it is desirable that there is a significant difference between the first accumulated value and the second accumulated value. From such a viewpoint, the number of the accumulated reference patterns can be set to be more than a predetermined number. Alternatively, the arithmetic system 150 can accumulate the measured values of the widths of the plurality of reference patterns 210A belonging to the first group and accumulate the measured values of the widths of the plurality of reference patterns 210B belonging to the second group until the absolute value of the difference between the first accumulated value and the second accumulated value exceeds a predetermined value. To increase the number of the reference patterns existing in the first region 201, the field of view (FOV) can be increased or a plurality of fields of view can be combined. ​

[0066] Typically, the outermost pattern image within the pattern area 200 is... Figure 7 As shown, due to effects such as optical proximity, the reference patterns sometimes have widths significantly different from other patterns. Therefore, the calculation system 150 may also exclude at least one reference pattern located on the outermost side of the first region 201 to calculate the first cumulative value and the second cumulative value. In one embodiment, the calculation system 150 may exclude reference patterns with widths exceeding a threshold from a plurality of reference patterns 210A, 210B within the first region 201 to calculate the first cumulative value and the second cumulative value.

[0067] for Figure 2 The cumulative value of the pattern in the second region 202 shown is calculated by the calculation system 150 in the same way as the reference patterns 210A and 210B in the first region 201. Figure 8 This is a schematic diagram illustrating an example of image 225 representing region 202. For example... Figure 8 As shown, the pattern in the second region 202 is the same as the reference patterns 210A and 210B in the first region 201, containing two sets of patterns 220A and 220B formed by double patterning. These patterns 220A and 220B are arranged alternately.

[0068] Patterns 220A and 220B within region 202 do not include pattern region 200 (see reference). Figure 2 A repeating pattern along the edge of a region. In one example, region 202 is located... Figure 2 The center of the pattern area 200 shown. According to Figure 8 It is understood that patterns 220A and 220B within region 202 traverse the entire region 202 and do not possess a characteristic shape that can define their position. The size and position of region 202 can be set manually or automatically. The number of patterns 220A and 220B contained within region 202 is not limited to [specific number missing]. Figure 8 The example shown.

[0069] like Figure 9 As shown, the computing system 150 performs a second matching of multiple patterns 220A and 220B on the image 225 of the second region 202 with corresponding multiple CAD patterns 230A and 230B. The CAD patterns 230A and 230B are pre-classified into group 1 and group 2, respectively, according to the layer information of these CAD patterns. Figure 9 In the example shown, CAD pattern 230A is classified into group 1, and CAD pattern 230B is classified into group 2. The second match is implemented in the same manner as the first match described above, so its repetition is omitted.

[0070] like Figure 10As shown, the operation system 150 classifies the plurality of patterns 220A, 220B on the SEM image 225 into the first group and the second group according to the layer classification of the CAD patterns 230A, 230B. In the present embodiment, the operation system 150 classifies the patterns 220A at odd positions into the first group and the patterns 220B at even positions into the second group. Next, the operation system 150 measures the widths of the plurality of patterns 220A belonging to the first group and the widths of the plurality of patterns 220B belonging to the second group.

[0071] The operation system 150 accumulates the measured values of the widths of the plurality of patterns 220A belonging to the first group to determine a third accumulated value and accumulates the measured values of the widths of the plurality of patterns 220B belonging to the second group to determine a fourth accumulated value. Further, the operation system 150 compares the third accumulated value and the fourth accumulated value to determine the magnitude relation of the third accumulated value and the fourth accumulated value. That is, the operation system 150 determines which one of the third accumulated value and the fourth accumulated value is larger. The operation system 150 stores the determined magnitude relation of the third accumulated value and the fourth accumulated value in the storage device 162.

[0072] As with the first accumulated value and the second accumulated value, the number of patterns to be accumulated can be set to be more than a predetermined number. Alternatively, the operation system 150 accumulates the measured values of the widths of the plurality of patterns 220A belonging to the first group and accumulates the measured values of the widths of the plurality of patterns 220B belonging to the second group until the absolute value of the difference between the third accumulated value and the fourth accumulated value exceeds a predetermined value. To increase the number of patterns existing in the second region 202, the field of view (FOV) can be increased or a plurality of fields of view can be combined.

[0073] Next, when the magnitude relation of the third accumulated value and the fourth accumulated value is identical to the above-described magnitude relation of the first accumulated value and the second accumulated value, the operation system 150 determines that the second matching is correctly performed. In the present embodiment, the operation system 150 determines that the second matching is correctly performed when the third accumulated value is larger than the fourth accumulated value. Figure 6 In the example shown, the first accumulated value is larger than the second accumulated value, so when the third accumulated value is larger than the fourth accumulated value, the operation system 150 determines that the magnitude relation of the third accumulated value and the fourth accumulated value is identical to the magnitude relation of the first accumulated value and the second accumulated value.

[0074] The operation system 150 displays the first accumulated value, the second accumulated value, the third accumulated value, the fourth accumulated value, the difference between the first accumulated value and the second accumulated value, the difference between the third accumulated value and the fourth accumulated value, and the like on the display screen 165. An operator can visually confirm the results displayed on the display screen 165.

[0075] When the size relationship between the 3rd cumulative value and the 4th cumulative value is not consistent with the above size relationship between the 1st cumulative value and the 2nd cumulative value (i.e., the 3rd cumulative value is smaller than the 4th cumulative value), it is inferred that the 3rd cumulative value is the cumulative value of the widths of the plurality of patterns belonging to the 2nd group, and the 4th cumulative value is the cumulative value of the widths of the plurality of patterns belonging to the 1st group. Thus, in this case, the computing system 150 relatively shifts the plurality of patterns 220A, 220B on the image 225 of the 2nd region 202 by 1 pitch with respect to the corresponding plurality of CAD patterns 230A, 230B, and performs the 2nd matching again.

[0076] According to the present embodiment, it is possible to ensure accurate pattern matching in the 2nd region 202 with reference to the size relationship of the pattern widths in the 1st region 201. In particular, according to the present embodiment, it is possible to use the measured values of the pattern widths for optimization of process parameters. Further, it is possible to realize monitoring of the pattern widths in a region with a small process margin.

[0077] Figure 11 A flowchart for explaining an embodiment of the pattern matching method.

[0078] In Step 1, the scanning electron microscope 50 generates an image 205 of a 1st region 201 in a pattern region 200 containing a repetitive pattern (refer to FIG. 2A). Figure 3 The image 205 of the 1st region 201 is sent to the computing system 150.

[0079] In Step 2, the computing system 150 performs a 1st matching of a plurality of reference patterns 210A, 210B on the image 205 of the 1st region 201 with a plurality of CAD patterns 215A, 215B classified into a 1st group and a 2nd group in advance based on layer information (refer to FIG. 2B). Figure 4

[0080] In Step 3, the computing system 150 classifies the plurality of reference patterns 210A, 210B into the 1st group and the 2nd group according to the layer classification of the CAD patterns 215A, 215B (refer to FIG. 2C). Figure 5

[0081] In Step 4, the computing system 150 measures the widths of the plurality of reference patterns 210A belonging to the 1st group and the widths of the plurality of reference patterns 210B belonging to the 2nd group.

[0082] In Step 5, the computing system 150 cumulatively adds the measured values of the widths of the plurality of reference patterns 210A belonging to the 1st group to determine a 1st cumulative value, and cumulatively adds the measured values of the widths of the plurality of reference patterns 210B belonging to the 2nd group to determine a 2nd cumulative value.

[0083] ​​In step 6, the operation system 150 determines the magnitude relation between the 1st cumulative value and the 2nd cumulative value. That is, the operation system 150 determines which one of the 1st cumulative value and the 2nd cumulative value is larger than the other.

[0084] In step 7, the scanning electron microscope 50 generates an image 225 of the 2nd region 202 within the pattern region 200 (refer to FIG. 2B). The image 225 of the 2nd region 202 is sent to the operation system 150. Figure 8 ). The image 225 of the 2nd region 202 is sent to the operation system 150.

[0085] In step 8, the operation system 150 performs the 2nd matching of the plurality of patterns 220A, 220B on the image 225 of the 2nd region 202 with the plurality of CAD patterns 230A, 230B classified into the 1st group and the 2nd group in advance from the layer information (refer to FIG. 2C). Figure 9

[0086] In step 9, the operation system 150 classifies the plurality of patterns 220A, 220B on the image 225 of the 2nd region 202 into the 1st group and the 2nd group in accordance with the layer classification of the CAD patterns 230A, 230B (refer to FIG. 2D). Figure 10

[0087] In step 10, the operation system 150 measures the widths of the plurality of patterns 220A belonging to the 1st group and the widths of the plurality of patterns 220B belonging to the 2nd group.

[0088] In step 11, the operation system 150 accumulates the measured values of the widths of the plurality of patterns 220A belonging to the 1st group to determine a 3rd cumulative value and accumulates the measured values of the widths of the plurality of patterns 220B belonging to the 2nd group to determine a 4th cumulative value.

[0089] In step 12, the operation system 150 determines the magnitude relation between the 3rd cumulative value and the 4th cumulative value.

[0090] In step 13, the operation system 150 compares the magnitude relation between the 3rd cumulative value and the 4th cumulative value with the magnitude relation between the 1st cumulative value and the 2nd cumulative value and determines whether the 2nd matching has been performed correctly based on the comparison result. Specifically, when the magnitude relation between the 3rd cumulative value and the 4th cumulative value is identical to the magnitude relation between the 1st cumulative value and the 2nd cumulative value, the operation system 150 determines that the 2nd matching has been performed correctly. When the magnitude relation between the 3rd cumulative value and the 4th cumulative value is not identical to the magnitude relation between the 1st cumulative value and the 2nd cumulative value, the operation system 150 relatively shifts the plurality of patterns 220A, 220B on the image 225 of the 2nd region 202 by 1 pitch with respect to the corresponding plurality of CAD patterns 230A, 230B and performs the 2nd matching again.

[0091] ​​In the above embodiment, the repetitive pattern is formed by double patterning, but the present application is not limited to the above embodiment, and can be applied to a repetitive pattern formed by quadruple patterning or other multiple patterning. For example, in the present application, a pattern formed by quadruple patterning is classified into 4 groups according to layers of a corresponding CAD pattern, 4 cumulative values are calculated for the 4 groups, and the size relationship of the 4 cumulative values is determined.

[0092] Further, the present application can be applied to a repetitive pattern other than a line and gap pattern. Figure 12 A schematic view showing a hole pattern on an image 305 of the 1st region 201. The hole pattern is an example of a repetitive pattern formed by double patterning. In the above embodiment, the hole pattern is formed by double patterning, but the present application is not limited to the above embodiment, and can be applied to a repetitive pattern formed by quadruple patterning or other multiple patterning. For example, in the present application, a pattern formed by quadruple patterning is classified into 4 groups according to layers of a corresponding CAD pattern, 4 cumulative values are calculated for the 4 groups, and the size relationship of the 4 cumulative values is determined. Figure 12 In the example shown, the hole pattern on the image 305 includes 2 groups of hole patterns 310A, 310B formed by double patterning, respectively. These patterns 310A, 310B are arranged alternately in the X direction and the Y direction. In the following description, these hole patterns 310A, 310B are referred to as reference patterns. In order to determine the positions of the reference patterns 310A, 310B in the X direction and the Y direction, the 1st region 201 is a Figure 2 edge region of the pattern region 200 shown and is a corner region.

[0093] The computing system 150 classifies the reference patterns 310A, 310B into a 1st group and a 2nd group according to layers of a corresponding CAD pattern. In one embodiment, the computing system 150 classifies the 1st reference pattern 310A into the 1st group and classifies the 2nd reference pattern 310B into the 2nd group.

[0094] Figure 13 A schematic view showing a hole pattern on an image 325 of the 2nd region 205. As shown in Figure 13 the 2nd region 202, the pattern composed of hole patterns includes 2 groups of patterns 320A, 320B formed by double patterning, respectively. These patterns 320A, 320B are arranged alternately in the X direction and the Y direction. The computing system 150 classifies the hole patterns 320A, 320B into a 1st group and a 2nd group according to layers of a corresponding CAD pattern. In one embodiment, the computing system 150 classifies the hole pattern 320A into the 1st group and classifies the hole pattern 320B into the 2nd group.

[0095] The pattern matching of the present embodiment is performed in the same manner as the embodiment described above. Figure 11

[0096] In step 1, the scanning electron microscope 50 generates an image 305 of the 1st region 201 in the pattern region 200 including a repetitive pattern (refer to FIG. 3A). Figure 12 ​). The image 305 of the 1st region 201 is sent to the arithmetic system 150.

[0097] In step 2, the arithmetic system 150 performs a 1st matching of the plurality of reference patterns 310A, 310B on the image 305 of the 1st region 201 with the plurality of CAD patterns classified into the 1st and 2nd groups in advance according to the layer information.

[0098] In step 3, the arithmetic system 150 classifies the plurality of reference patterns 310A, 310B into the 1st and 2nd groups in accordance with the layer classification of the corresponding CAD patterns.

[0099] In step 4, the arithmetic system 150 measures the widths of the plurality of reference patterns 310A belonging to the 1st group and the widths of the plurality of reference patterns 310B belonging to the 2nd group.

[0100] In step 5, the arithmetic system 150 accumulates the measured values of the widths of the plurality of reference patterns 310A belonging to the 1st group to determine a 1st accumulated value and accumulates the measured values of the widths of the plurality of reference patterns 310B belonging to the 2nd group to determine a 2nd accumulated value.

[0101] In step 6, the arithmetic system 150 determines the size relationship of the 1st accumulated value and the 2nd accumulated value. That is, the arithmetic system 150 determines which one of the 1st accumulated value and the 2nd accumulated value is larger than the other.

[0102] In step 7, the scanning electron microscope 50 generates an image 325 of a 2nd region 202 within the pattern region 200 (refer to FIG. 3B). Figure 13 ) The image 325 of the 2nd region 202 is sent to the arithmetic system 150.

[0103] In step 8, the arithmetic system 150 performs a 2nd matching of the plurality of patterns 320A, 320B on the image 325 of the 2nd region 202 with the plurality of CAD patterns classified into the 1st and 2nd groups in advance according to the layer information.

[0104] In step 9, the arithmetic system 150 classifies the plurality of patterns 320A, 320B on the image 325 of the 2nd region 202 into the 1st and 2nd groups in accordance with the layer classification of the corresponding CAD patterns.

[0105] In step 10, the arithmetic system 150 measures the widths of the plurality of patterns 320A belonging to the 1st group and the widths of the plurality of patterns 320B belonging to the 2nd group.

[0106] In step 11, the arithmetic system 150 accumulates the measured values of the widths of the plurality of patterns 320A belonging to the 1st group to determine a 3rd accumulated value and accumulates the measured values of the widths of the plurality of patterns 320B belonging to the 2nd group to determine a 4th accumulated value.

[0107] In step 12, the operation system 150 determines the magnitude relation between the 3rd cumulative value and the 4th cumulative value.

[0108] In step 13, the operation system 150 compares the magnitude relation between the 3rd cumulative value and the 4th cumulative value with the magnitude relation between the 1st cumulative value and the 2nd cumulative value, and determines whether the 2nd matching has been performed correctly based on the comparison result. Specifically, when the magnitude relation between the 3rd cumulative value and the 4th cumulative value is identical to the magnitude relation between the 1st cumulative value and the 2nd cumulative value, the operation system 150 determines that the 2nd matching has been performed correctly. When the magnitude relation between the 3rd cumulative value and the 4th cumulative value is not identical to the magnitude relation between the 1st cumulative value and the 2nd cumulative value, the operation system 150 relatively moves the plurality of patterns on the image 325 of the 2nd region 202 by 1 pitch with respect to the corresponding plurality of CAD patterns and performs the 2nd matching again.

[0109] In the embodiment described above, the cumulative value of the width of the pattern belonging to each group is calculated, but instead of the cumulative value of the width of the pattern, the cumulative value of the slope of the luminance curve of the edge of the pattern on the image can be calculated. Generally, the slope of the edge of the actual pattern formed by each process in the multiple patterning sometimes differs slightly due to the etching process. Thus, the slope of the luminance curve of the edge of the pattern appearing on the image also differs slightly between the groups.

[0110] Figure 14 A graph showing an example of the luminance curve of the pattern on the image. The luminance curve of the pattern is the distribution of the luminance along the direction crossing the pattern. The luminance is expressed by a value following the gradation, for example, 0 to 255. According to the example shown in FIG. 3, the luminance curve of the pattern is expressed by a straight line. The slope of the luminance curve of the pattern is calculated from the luminance value and the distance (number of pixels). Figure 14 As is known, the slope S of the luminance curve of the edge of the pattern can be calculated from the luminance value and the distance (number of pixels).

[0111] Next, an embodiment in which the slope of the luminance curve of the edge of the pattern is used instead of the width of the pattern in the example shown in FIG. 2 will be described. The details of the following embodiment not specifically described are the same as those of the embodiment described with reference to FIG. 2, so the repeated description thereof is omitted. Figures 2 to 5 As is known, the slope S of the luminance curve of the edge of the pattern can be calculated from the luminance value and the distance (number of pixels). Figures 2 to 13 As is known, the slope S of the luminance curve of the edge of the pattern can be calculated from the luminance value and the distance (number of pixels).

[0112] Figure 15 A flowchart for describing an embodiment of the pattern matching method using the slope of the luminance curve of the edge of the pattern.

[0113] In step 2-1, the scanning electron microscope 50 generates an image 205 of the 1st region 201 within the pattern region 200 containing the repetitive pattern (refer to FIG. 2). The image 205 of the 1st region 201 is sent to the operation system 150. Figure 3

[0114] ​In step 2-2, the operation system 150 performs a first matching of the plurality of reference patterns 210A, 210B on the image 205 of the first region 201 with the plurality of CAD patterns 215A, 215B pre-classified into the first and second groups according to the layer information (refer to Figure 4 ).

[0115] In step 2-3, the operation system 150 classifies the plurality of reference patterns 210A, 210B into the first and second groups according to the layer classification of the CAD patterns 215A, 215B (refer to Figure 5 ).

[0116] In step 2-4, the operation system 150 calculates the slopes of the brightness curves of the edges of the plurality of reference patterns 210A belonging to the first group and the slopes of the brightness curves of the edges of the plurality of reference patterns 210B belonging to the second group.

[0117] In step 2-5, the operation system 150 accumulates the calculated values of the slopes of the brightness curves of the edges of the plurality of reference patterns 210A belonging to the first group to determine a first accumulated value and accumulates the calculated values of the slopes of the brightness curves of the edges of the plurality of reference patterns 210B belonging to the second group to determine a second accumulated value.

[0118] The slopes of the brightness curves of the edges of the individual reference patterns 210A belonging to the first group and the slopes of the brightness curves of the edges of the individual reference patterns 210B belonging to the second group are extremely small, but there is a significant difference between the first accumulated value and the second accumulated value. In step 2-6, the operation system 150 determines the magnitude relationship of the first accumulated value and the second accumulated value. That is, the operation system 150 determines which one of the first accumulated value and the second accumulated value is larger.

[0119] In step 2-7, the scanning electron microscope 50 generates an image 225 of a second region 202 within the pattern region 200 (refer to Figure 8 ). The image 225 of the second region 202 is sent to the operation system 150.

[0120] In step 2-8, the operation system 150 performs a second matching of the plurality of patterns 220A, 220B on the image 225 of the second region 202 with the plurality of CAD patterns 230A, 230B pre-classified into the first and second groups according to the layer information (refer to Figure 9 ).

[0121] In step 2-9, the operation system 150 classifies the plurality of patterns 220A, 220B on the image 225 of the second region 202 into the first and second groups according to the layer classification of the CAD patterns 230A, 230B (refer to Figure 10 ).

[0122] In step 2-10, the operation system 150 calculates the slopes of the luminance curves of the edges of the plurality of patterns 220A belonging to the first group and the slopes of the luminance curves of the edges of the plurality of patterns 220B belonging to the second group.

[0123] In step 2-11, the operation system 150 accumulates the calculated values of the slopes of the luminance curves of the edges of the plurality of patterns 220A belonging to the first group to determine a third accumulated value and accumulates the calculated values of the slopes of the luminance curves of the edges of the plurality of patterns 220B belonging to the second group to determine a fourth accumulated value.

[0124] In step 2-12, the operation system 150 determines the magnitude relationship between the third accumulated value and the fourth accumulated value.

[0125] In step 2-13, the operation system 150 compares the magnitude relationship between the third accumulated value and the fourth accumulated value with the magnitude relationship between the first accumulated value and the second accumulated value and determines whether the second matching has been performed correctly based on the comparison result. Specifically, when the magnitude relationship between the third accumulated value and the fourth accumulated value is identical to the magnitude relationship between the first accumulated value and the second accumulated value, the operation system 150 determines that the second matching has been performed correctly. When the magnitude relationship between the third accumulated value and the fourth accumulated value is not identical to the magnitude relationship between the first accumulated value and the second accumulated value, the operation system 150 relatively moves the plurality of patterns 220A, 220B on the image 225 of the second region 202 by one pitch with respect to the corresponding plurality of CAD patterns 230A, 230B and performs the second matching again.

[0126] Although detailed description is omitted, the embodiment described with reference to Figure 15 can also be applied to Figure 12 and Figure 13 the hole patterns shown in FIGS. 17A and 17B.

[0127] The above-described embodiments are described for the purpose of enabling a person having ordinary knowledge in the technical field to which the present application pertains to carry out the present application. As long as a person skilled in the art, of course, can complete various modifications of the above-described embodiments, the technical idea of the present application can also be applied to other embodiments. Thus, the present application is not limited to the described embodiments, but is to be interpreted in the broadest scope possible in accordance with the technical idea defined by the claims.

[0128] Industrial Applicability

[0129] The present application can be used for a method of matching a pattern formed on a surface of a work such as a wafer or a glass substrate used in manufacturing a semiconductor device with a CAD pattern produced based on design data of the pattern.

[0130] Explanation of Symbols

[0131] 50 scanning electron microscope

[0132] 111 electron gun

[0133] 112 condenser lens

[0134] 113 X deflector

[0135] 114 Y deflector

[0136] 115 objective lens

[0137] 116 lens control device

[0138] 117 deflection control device

[0139] 118 image acquisition device

[0140] 120 sample chamber

[0141] 121 sample stage

[0142] 122 sample stage control device

[0143] 124 wafer

[0144] 130 secondary electron detector

[0145] 131 reflected electron detector

[0146] 140 transport device

[0147] 150 computing system

[0148] 161 database

[0149] 162 storage device

[0150] 163 processing device

[0151] 165 display screen

[0152] 200 pattern region

[0153] 201 first region

[0154] 202 second region

[0155] 205 image

[0156] 210A, 210B reference pattern

[0157] 220A, 220B pattern

[0158] 225 image

Claims

1. A pattern matching method, characterized in that, Generate an image of the first region within a patterned region containing a pattern formed through multiple patterning. The first matching is performed between multiple reference patterns on the image of the first region and multiple first CAD patterns pre-classified into group 1 and group 2 according to layer information. The multiple reference patterns are classified into Group 1 and Group 2 according to the layer classification of the first CAD pattern. Measure the widths of multiple reference patterns belonging to the first group and the widths of multiple reference patterns belonging to the second group. The first cumulative value is determined by accumulating the width measurements of the plurality of reference patterns belonging to the first group. The second cumulative value is determined by accumulating the width measurements of the plurality of reference patterns belonging to the second group. Determine the relationship between the first cumulative value and the second cumulative value. Generate an image of the second region within the pattern area. The second matching is performed on multiple patterns on the image of the second region and multiple second CAD patterns pre-classified into group 1 and group 2 according to layer information. According to the layer classification of the second CAD pattern, the multiple patterns on the image of the second region are classified into the first group and the second group. Measure the widths of multiple patterns belonging to the first group and the widths of multiple patterns belonging to the second group. The third cumulative value is determined by accumulating the width measurements of the plurality of patterns belonging to the first group. The fourth cumulative value is determined by accumulating the width measurements of the plurality of patterns belonging to the second group. Determine the relationship between the third cumulative value and the fourth cumulative value. When the relationship between the third and fourth cumulative values ​​is consistent with the relationship between the first and second cumulative values, it is determined that the second match was performed correctly.

2. The pattern matching method according to claim 1, characterized in that, The first region is the edge region that includes the edge of the pattern region. The second region is located within the pattern area and is situated inside the first region.

3. The pattern matching method according to claim 1 or 2, characterized in that, The pattern formed through the aforementioned multiple patterning is a repeating pattern.

4. The pattern matching method according to claim 1 or 2, characterized in that, It also includes the following processes: When the relationship between the third and fourth cumulative values ​​is inconsistent with the relationship between the first and second cumulative values, the multiple patterns on the image of the second region are shifted relative to the multiple second CAD patterns by a spacing of 1, and the second matching is performed again.

5. The pattern matching method according to claim 1 or 2, characterized in that, The absolute value of the difference between the first cumulative value and the second cumulative value, and the absolute value of the difference between the third cumulative value and the fourth cumulative value, are greater than predetermined values.

6. A pattern matching method, characterized in that, Generate an image of the first region within a patterned region containing a pattern formed through multiple patterning. The first matching is performed between multiple reference patterns on the image of the first region and multiple first CAD patterns pre-classified into group 1 and group 2 according to layer information. The multiple reference patterns are classified into Group 1 and Group 2 according to the layer classification of the first CAD pattern. Calculate the slope of the brightness curves of the edges of the multiple reference patterns belonging to the first group and the slope of the brightness curves of the edges of the multiple reference patterns belonging to the second group. The first cumulative value is determined by accumulating the calculated values ​​of the slopes of the brightness curves of the edges of the plurality of reference patterns belonging to the first group. The calculated values ​​of the slopes of the brightness curves of the edges of the plurality of reference patterns belonging to the second group are accumulated to determine the second accumulated value. Determine the relationship between the first cumulative value and the second cumulative value. Generate an image of the second region within the pattern area. The second matching is performed on multiple patterns on the image of the second region and multiple second CAD patterns pre-classified into group 1 and group 2 according to layer information. According to the layer classification of the second CAD pattern, the multiple patterns on the image of the second region are classified into the first group and the second group. Calculate the slope of the brightness curves of the edges of the multiple patterns belonging to the first group and the slope of the brightness curves of the edges of the multiple patterns belonging to the second group. The calculated values ​​of the slopes of the brightness curves of the edges of the plurality of patterns belonging to the first group are accumulated to determine the third cumulative value. The fourth cumulative value is determined by accumulating the calculated values ​​of the slopes of the brightness curves of the edges of the plurality of patterns belonging to the second group. Determine the relationship between the third cumulative value and the fourth cumulative value. When the relationship between the third and fourth cumulative values ​​is consistent with the relationship between the first and second cumulative values, it is determined that the second match was performed correctly.

7. The pattern matching method according to claim 6, characterized in that, The first region is the edge region that includes the edge of the pattern region. The second region is located within the pattern area and is situated inside the first region.

8. The pattern matching method according to claim 6 or 7, characterized in that, The pattern formed through the aforementioned multiple patterning is a repeating pattern.

9. The pattern matching method according to claim 6 or 7, characterized in that, This includes the following steps: When the relationship between the third and fourth cumulative values ​​is inconsistent with the relationship between the first and second cumulative values, the multiple patterns on the image of the second region are shifted relative to the multiple second CAD patterns by a spacing of 1, and the second matching is performed again.

10. The pattern matching method according to claim 6 or 7, characterized in that, The absolute value of the difference between the first cumulative value and the second cumulative value, and the absolute value of the difference between the third cumulative value and the fourth cumulative value, are greater than predetermined values.

Images