Pattern matching method
By dividing workpiece surfaces into density groups and adjusting matching search distances or stage positions, the method addresses charging-induced image shifts, enhancing pattern matching accuracy and throughput in SEM image analysis.
Patent Information
- Authority / Receiving Office
- JP · JP
- Patent Type
- Patents
- Current Assignee / Owner
- TORAY ENG CO LTD
- Filing Date
- 2022-06-29
- Publication Date
- 2026-05-25
AI Technical Summary
Conventional pattern matching methods fail to accurately align CAD patterns with SEM images due to significant image shifts caused by uneven charging on workpiece surfaces with varying pattern densities, leading to increased error rates and reduced throughput.
A pattern matching method that divides the workpiece surface into multiple inspection areas, calculates pattern density groups, generates SEM images, and adjusts matching search distances or stage positions based on correlation data between density groups and image shift amounts to correct for charging-induced deviations.
Improves pattern matching accuracy and reduces completion time by accounting for pattern density-related charge effects, ensuring precise alignment and efficient image processing.
Smart Images
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Abstract
Description
Technical Field
[0001] The present invention relates to a method for performing pattern matching between a pattern on an image of a workpiece such as a wafer or a glass substrate and a CAD pattern created from design data of the pattern, and particularly relates to a technique for reducing the influence of image shift caused by charging on the surface of the workpiece on pattern matching.
Background Art
[0002] The die-to-database method is a pattern matching method for matching a pattern on an image of a workpiece (for example, a wafer or a glass substrate) on which a pattern is formed with a CAD pattern created from design data of the pattern. More specifically, the die-to-database method acquires the coordinates of the area to be inspected from the design data, moves the stage on which the workpiece is placed to those coordinates, generates a SEM image of the pattern on the workpiece by electron beam irradiation, overlays the SEM image and the CAD pattern created from the design data, creates a profile of the gray level of the SEM image within a range set starting from the edge of the CAD pattern, determines the edge of the pattern on the SEM image from the gray level profile, and determines the matching position where the bias value between the position of the determined edge and the edge of the corresponding CAD pattern is minimized.
[0003] During image generation using the die-to-database method, the electron beam may be bent due to the effect of static charge on the workpiece surface, resulting in a misalignment between the imaging target position and the actual imaging position. Figure 10 is a schematic diagram showing an example of misalignment between the CAD pattern and the pattern on the SEM image. If the misalignment between the CAD pattern 501 and the pattern 502 on the SEM image is large, matching between the CAD pattern 501 and the pattern 502 on the SEM image may fail. Therefore, the shift amount of the SEM image is determined from the amount of misalignment when the CAD pattern 501 and the pattern 502 on the SEM image are matched, and the stage position is moved by the shift amount during the next image generation to minimize the difference between the imaging target position and the actual imaging position. Alternatively, if a large effect of static charge is expected, the matching search range is set to be larger in advance to prevent pattern matching failure. [Prior art documents] [Patent Documents]
[0004] [Patent Document 1] Japanese Patent Application Publication No. 5-324836 [Overview of the project] [Problems that the invention aims to solve]
[0005] In high-density, simple patterns such as those found within memory cells, the charge distribution across the workpiece surface is uniform, and therefore the effect of charge is not significant. Consequently, conventional correction techniques can correctly match the pattern on the SEM image with the CAD pattern. However, in workpieces with significantly different pattern densities, such as logic devices, the effect of charge can be substantial locally.
[0006] Figure 11 is a schematic diagram showing the charging of a workpiece surface with a high pattern density, and Figure 12 is a schematic diagram showing the charging of a workpiece surface with a low pattern density. The pattern surface 601 of the workpiece has an insulating layer 603 (e.g., an interlayer insulating layer) made of an insulating material and wiring 605 made of a metal such as copper. When an image of the pattern surface 601 is generated, the electron beam is irradiated onto the pattern surface 601, causing the insulating layer 603 to become charged. As can be seen from comparing Figure 11 and Figure 12, on the pattern surface 601 with a low pattern density, the exposed area of the insulating layer 603 is large, and the amount of charge is large.
[0007] Thus, if a large local charge occurs within the pattern surface 601, the conventional technique described above may result in a significant deviation of the actual imaging position from the imaging target position, leading to a matching failure. To avoid this, widening the search range for the matching position increases the likelihood of errors in determining the matching position and the time required to find a suitable matching position, resulting in a decrease in throughput.
[0008] Therefore, the present invention provides a technique for reducing the influence of uneven charging on the surface of a workpiece on pattern matching. [Means for solving the problem]
[0009] In one embodiment, a pattern matching method is provided, which involves setting a plurality of inspection areas within the pattern surface of a workpiece, calculating the pattern density of each of the plurality of inspection areas from the design data of the workpiece pattern, dividing the pattern density into a plurality of density groups according to the numerical value, generating an image of each of the plurality of inspection areas using a scanning electron microscope, performing pattern matching between the pattern on the image and the corresponding CAD pattern, calculating a plurality of image shift amounts corresponding to each of the plurality of inspection areas based on the result of the pattern matching, dividing the plurality of inspection areas into a plurality of area groups corresponding to the plurality of density groups, calculating a plurality of correction amounts corresponding to each of the plurality of area groups based on the image shift amount corresponding to the inspection area belonging to each area group, and generating correlation data showing the correlation between the plurality of density groups and the plurality of correction amounts.
[0010] In one embodiment, the pattern matching method further includes calculating the pattern density of a target inspection area on a workpiece to be inspected, which has a pattern surface having the same structure as the pattern surface of the workpiece, from the design data of the pattern in the target inspection area; selecting a density group to which the pattern density of the target inspection area belongs from a plurality of density groups; correcting the matching search distance using a correction amount corresponding to the selected density group; generating an image of the target inspection area using a scanning electron microscope; and performing pattern matching between the pattern on the image of the target inspection area and the corresponding CAD pattern using the corrected matching search distance.
[0011] In one embodiment, the step of correcting the matching search distance is to correct the matching search distance by adding a correction amount corresponding to the selected density group to the reference search distance. In one embodiment, the step of calculating the multiple correction amounts corresponding to each of the multiple area groups is to calculate the average value of the image shift amount and 3σ corresponding to the inspection area belonging to each area group, and to calculate a correction amount for each area group which is the sum of the average value and 3σ, thereby determining the multiple correction amounts corresponding to each of the multiple area groups.
[0012] In one embodiment, a pattern matching method is provided, which involves setting a plurality of inspection areas within the pattern surface of a workpiece, calculating the pattern density of each of the plurality of inspection areas from the design data of the workpiece pattern, dividing the pattern density into a plurality of density groups according to the numerical value, generating an image of each of the plurality of inspection areas using a scanning electron microscope, performing pattern matching between the pattern on the image and the corresponding CAD pattern, calculating a plurality of X-direction image shift amounts and a plurality of Y-direction image shift amounts corresponding to each of the plurality of inspection areas based on the results of the pattern matching, dividing the plurality of inspection areas into a plurality of area groups corresponding to the plurality of density groups, calculating a plurality of X-direction correction amounts corresponding to each of the plurality of area groups based on the X-direction image shift amount corresponding to the inspection area belonging to each area group, calculating a plurality of Y-direction correction amounts corresponding to each of the plurality of area groups based on the Y-direction image shift amount corresponding to the inspection area belonging to each area group, generating X-direction correlation data showing the correlation between the plurality of density groups and the plurality of X-direction correction amounts, and generating Y-direction correlation data showing the correlation between the plurality of density groups and the plurality of Y-direction correction amounts.
[0013] In one embodiment, the pattern matching method further includes calculating the pattern density of a target inspection area on a workpiece to be inspected that has a pattern surface having the same structure as the pattern surface of the workpiece from the design data of the pattern in the target inspection area, selecting a density group to which the pattern density of the target inspection area belongs from a plurality of density groups, determining an X-direction correction amount and a Y-direction correction amount corresponding to the selected density group, correcting the position of the workpiece stage of a scanning electron microscope supporting the workpiece to be inspected by moving it in the X-direction by the determined X-direction correction amount and in the Y-direction by the determined Y-direction correction amount, generating an image of the target inspection area of the workpiece to be inspected on the corrected workpiece stage using the scanning electron microscope, and performing pattern matching between the pattern on the image of the target inspection area and the corresponding CAD pattern.
[0014] In one embodiment, the step of calculating the multiple X-direction correction amounts corresponding to each of the multiple area groups is to calculate the average value of the X-direction image shift amount and 3σ corresponding to the inspection area belonging to each area group, and to calculate the X-direction correction amount for each area group, which is the sum of the average value of the X-direction image shift amount and 3σ, thereby determining the multiple X-direction correction amounts corresponding to each of the multiple area groups. In one embodiment, the step of calculating the multiple Y-direction correction amounts corresponding to each of the multiple area groups is to calculate the average value of the Y-direction image shift amount and 3σ corresponding to the inspection area belonging to each area group, and to calculate a Y-direction correction amount for each area group which is the sum of the average value of the Y-direction image shift amount and 3σ, thereby determining the multiple Y-direction correction amounts corresponding to each of the multiple area groups. [Effects of the Invention]
[0015] Based on the relationship between the pattern density of the target inspection area obtained from the pattern design data and the statistical image shift amount of the SEM image caused by charging, a correction amount corresponding to the pattern density of the target inspection area is determined. Furthermore, by correcting the matching search distance and / or the stage position according to the pattern density, the accuracy of pattern matching can be improved, and pattern matching can be completed in the shortest matching search time.
Brief Description of Drawings
[0016] [Figure 1] It is a schematic diagram showing an embodiment of an image generation system. [Figure 2] It is a schematic diagram showing examples of a plurality of inspection areas. [Figure 3] It is a graph showing a plurality of inspection areas and an example of the pattern density of these inspection areas. [Figure 4] It is a graph showing a plurality of inspection areas and an example of the image shift amount corresponding to each of these inspection areas. [Figure 5] It is a graph obtained by overlapping the graph of the pattern density shown in FIG. 3 and the graph of the image shift amount shown in FIG. 4. [Figure 6] It is a flowchart for explaining an embodiment of calculating the correction amount of the matching search distance. [Figure 7] It is a flowchart for explaining an embodiment of performing pattern matching using the correction amount of the matching search distance. [Figure 8] It is a flowchart for explaining an embodiment of calculating the correction amount of the position of the workpiece stage. [Figure 9] It is a flowchart for explaining an embodiment of performing pattern matching using the X-direction correction amount and the Y-direction correction amount of the workpiece stage. [Figure 10] It is a schematic diagram showing an example of the positional deviation between the CAD pattern and the pattern on the SEM image. [Figure 11] It is a schematic diagram showing charging within the surface of the workpiece where the pattern density is high. [Figure 12] It is a schematic diagram showing charging within the surface of a workpiece with a low pattern density.
Embodiments for Carrying Out the Invention
[0017] [[ID=ID=10]]Hereinafter, embodiments of the present invention will be described with reference to the drawings. FIG. 1 is a schematic diagram showing an embodiment of an image generation system. The image generation system includes a scanning electron microscope 1 that generates an image of a workpiece W, and a processing system 5 that processes the image generated by the scanning electron microscope 1. Examples of the workpiece W include wafers, masks, panels, substrates, etc. used in the manufacture of semiconductor devices.
[0018] The processing system 5 is composed of at least one computer. The processing system 5 includes a storage device 5a in which a program is stored, and an arithmetic device 5b that executes arithmetic operations according to instructions included in the program. 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 a solid state drive (SSD). Examples of the arithmetic device 5b include a CPU (central processing unit) and a GPU (graphics processing unit). However, the specific configuration of the processing system 5 is not limited to this embodiment.
[0019] The processing system 5 may be an edge server connected to the scanning electron microscope 1 via a communication line, 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 within the network connected to the scanning electron microscope 1. The processing system 5 may be a combination of multiple servers. For example, the processing system 5 may be a combination of edge servers and cloud servers connected to each other via a communication network such as the internet or a local network. In another example, the processing system 5 may consist of multiple servers (computers) that are not connected by a network.
[0020] The scanning electron microscope 1 includes an electron gun 15 that emits an electron beam, a focusing lens 16 that focuses the electron beam emitted from the electron gun 15, an X-deflector 17 that deflects the electron beam in the X direction, a Y-deflector 18 that deflects the electron beam in the Y direction, an objective lens 20 that focuses the electron beam onto a workpiece W, which is an example of a sample, a workpiece stage 31 that supports the workpiece W, and a stage moving device 35 that moves the workpiece stage 31 in translation. The configuration of the electron gun 15 is not particularly limited. For example, a field emitter type electron gun or a semiconductor photocathode type electron gun can be used as the electron gun 15.
[0021] The electron beam emitted from the electron gun 15 is focused by the focusing lens 16, then deflected by the X-deflector 17 and Y-deflector 18, and finally focused by the objective lens 20 to irradiate the surface of the workpiece W. When the primary electrons of the electron beam irradiate the workpiece W, secondary electrons and backscattered electrons are emitted from the workpiece W. The electrons emitted from the workpiece W are detected by the electron detector 26. The electron detection signal from the electron detector 26 is input to the image acquisition device 28 and converted into an image. In this way, the scanning electron microscope 1 generates an image of the surface of the workpiece W. The image acquisition device 28 is connected to the processing system 5.
[0022] The stage moving device 35 is configured to move the workpiece stage 31 in a direction parallel to the surface of the workpiece W. More specifically, the stage moving device 35 is configured to move the workpiece stage 31 in the X direction and in the Y direction perpendicular to the X direction. Both the X and Y directions are horizontal directions parallel to the surface of the workpiece W. The operation of the stage moving device 35 is controlled by the processing system 5.
[0023] Next, we will describe one embodiment of a pattern matching method performed by an image generation system. The processing system 5 sets up multiple inspection areas within the patterned surface (the surface on which the pattern is formed) of the workpiece W. The size and shape of each inspection area are not particularly limited. For example, each inspection area may be an area corresponding to the field of view (FOV) of the scanning electron microscope 1, or it may be multiple areas within the field of view (FOV) of the scanning electron microscope 1.
[0024] The processing system 5 calculates the pattern density in each of the multiple inspection areas from the design data of the patterns on the workpiece W. The patterns on the workpiece W are created according to the design data (also called CAD data). CAD is an abbreviation for computer-aided design. The design data is pre-stored in the storage device 5a of the processing system 5. Pattern density is defined as the ratio of the total area of patterns within an inspection area to the total area of the inspection area. Pattern density (%) = Total area of patterns within the inspection area / Total area of the inspection area × 100
[0025] FIG. 2 is a schematic diagram showing examples of a plurality of inspection areas. In inspection areas A1, A2, and A3, there are patterns and non-pattern areas. The patterns are composed of metal, and the non-pattern areas are composed of insulating materials. The shape and area of the patterns are usually different for each inspection area. Therefore, the pattern density is also different for each inspection area. In FIG. 2, three inspection areas A1, A2, and A3 are shown, but more inspection areas are set within the pattern plane of one workpiece W.
[0026] The processing system 5 divides the plurality of pattern densities of the plurality of inspection areas into a plurality of density groups according to their numerical values. In the present embodiment, the plurality of pattern densities are classified using a plurality of threshold values. For example, the processing system 5 divides the plurality of pattern densities into a first density group smaller than a first threshold value, a second density group between the first threshold value and a second threshold value (first threshold value < second threshold value), and a third density group larger than the second threshold value. The plurality of threshold values can be a plurality of discrete values within the range from 0% to 100%, or continuous values from 0% to 100%.
[0027] FIG. 3 is a graph showing a plurality of inspection areas and an example of the pattern density of these inspection areas. In FIG. 3, the vertical axis represents the pattern density (%), and the horizontal axis represents the number of the inspection areas. In the example of FIG. 3, by a first threshold value T1 and a second threshold value T2 (T1 < T2), the plurality of pattern densities are divided into three density groups DG1, DG2, and DG3.
[0028] Next, the processing system 5 gives an instruction to the scanning electron microscope 1 to cause the scanning electron microscope 1 to generate an image of each of the plurality of inspection areas. The plurality of images of the plurality of inspection areas are stored in the storage device 5a of the processing system 5. The processing system 5 performs pattern matching between the pattern on each image and the corresponding CAD pattern. The CAD pattern is a virtual pattern created by the processing system 5 based on the pattern design information contained in the design data of the pattern formed on the pattern surface of the workpiece W. Pattern matching is performed according to the die-to-database method.
[0029] The processing system 5 calculates multiple image shift amounts corresponding to multiple inspection areas based on the pattern matching results. The image shift amount is represented by a vector of the displacement between the pattern on the image and the corresponding CAD pattern. That is, the image shift amount is represented by the square root of the sum of the squares of the shift amount in the X direction, which is the X component of the vector, and the shift amount in the Y direction, which is the Y component of the vector. Depending on the shape of the pattern, the image shift amount may be represented by either the shift amount in the X direction or the shift amount in the Y direction.
[0030] Figure 4 is a graph showing an example of multiple inspection areas and the corresponding image shift amounts for each of these inspection areas. In Figure 4, the vertical axis represents the image shift amount, and the horizontal axis represents the inspection area number. The processing system 5 may calculate a moving average of the image shift amounts along the direction in which the inspection areas are arranged. In one embodiment, the image shift amount may be the moving average of the image shift amounts.
[0031] Figure 5 is a graph that overlays the pattern density graph shown in Figure 3 and the image shift amount graph shown in Figure 4. In Figure 5, the vertical axis on the right represents the image shift amount, the vertical axis on the left represents the pattern density, and the horizontal axis represents the inspection area number. In the example shown in Figure 5, the processing system 5 divides the multiple inspection areas into multiple area groups AG1, AG2, and AG3, which correspond to multiple density groups DG1, DG2, and DG3. In the example shown in Figure 5, inspection areas A1, A5, and A6 belonging to density group DG1 are classified into area group AG1, inspection areas A2, A7, and A8 belonging to density group DG2 are classified into area group AG2, and inspection areas A3, A4, A9, and A10 belonging to density group DG3 are classified into area group AG3.
[0032] The processing system 5 calculates multiple correction amounts corresponding to multiple area groups based on the image shift amounts corresponding to the inspection areas belonging to each area group. In the example shown in Figure 5, the processing system 5 calculates the correction amount C1 corresponding to area group AG1 based on the image shift amounts corresponding to inspection areas A1, A5, and A6 belonging to area group AG1. The image shift amount may be a moving average along the direction of arrangement of the inspection areas. The correction amount C1 is the value obtained by adding 3σ to the average value Av1 of the image shift amounts corresponding to inspection areas A1, A5, and A6 (i.e., C1 = Av1 + 3σ). The symbol σ represents the standard deviation.
[0033] Similarly, the processing system 5 calculates a correction amount C2 (i.e., C2 = Av2 + 3σ) corresponding to area group AG2 based on the image shift amounts corresponding to inspection areas A2, A7, and A8 belonging to area group AG2, and calculates a correction amount C3 (i.e., C3 = Av3 + 3σ) corresponding to area group AG3 based on the image shift amounts corresponding to inspection areas A3, A4, A9, and A10 belonging to area group AG3.
[0034] As described above, the correction amounts C1, C2, and C3 are determined for area groups AG1, AG2, and AG3, respectively. As can be seen from Figure 5, multiple area groups AG1, AG2, and AG3 correspond to multiple density groups DG1, DG2, and DG3. Therefore, the correction amounts C1, C2, and C3 correspond to density groups DG1, DG2, and DG3, respectively. The processing system 5 generates correlation data showing the correlation between multiple density groups DG1, DG2, and DG3 and multiple correction amounts C1, C2, and C3, and stores this correlation data in the storage device 5a. The correlation data may be a table showing the correlation between multiple density groups and multiple correction amounts, or it may be a relational expression.
[0035] Next, we will describe one embodiment in which a matching pattern is executed using the correction amounts C1, C2, and C3 obtained as described above. The processing system 5 calculates the pattern density of the target inspection area on the workpiece to be inspected from the design data of the patterns within the target inspection area. The design data of the patterns within the target inspection area is pre-stored in the storage device 5a of the processing system 5.
[0036] The workpiece to be inspected is a workpiece having a patterned surface with the same structure as the workpiece W used to generate correlation data showing the correlation between multiple density groups DG1, DG2, DG3 and multiple correction amounts C1, C2, C3. For example, if the patterned surface of the workpiece W used to generate the correlation data has a gate structure, then the patterned surface of the workpiece to be inspected also has a gate structure. Workpiece W and the workpiece to be inspected have the same surface structure and the same surface material. In this specification, "same" means not only being completely identical, but also being substantially the same in light of common technical knowledge.
[0037] The processing system 5 selects the density group to which the pattern density of the target inspection area belongs from the above-mentioned density groups DG1, DG2, and DG3. Furthermore, the processing system 5 corrects the matching search distance using a correction amount corresponding to the selected density group. For example, if density group DG2 is selected as the density group to which the pattern density of the target inspection area belongs, the processing system 5 corrects the matching search distance using a correction amount C2 corresponding to density group DG2.
[0038] The matching search distance is the distance (or range) from the edges of the CAD pattern created from the design data of the pattern within the target inspection area, and the edges of the pattern on the image are searched within this matching search distance. According to this embodiment, the matching search distance is corrected according to the pattern density of the target inspection area. Since the pattern density affects the charge amount, in other words, the matching search distance is corrected according to the charge amount of the target inspection area.
[0039] In one embodiment, the processing system 5 corrects the matching search distance by adding a correction amount corresponding to the selected density group to the reference search distance. An example of the reference search distance is half the maximum pitch of the pattern within the target inspection area. Half the maximum pitch of the pattern is the minimum distance (range) at which pattern matching can be successfully performed. In one example, if the selected density group is DG2, the correction amount is C2, and the maximum pitch of the pattern within the target inspection area is PT, then the corrected matching search distance is expressed as PT / 2 + C2.
[0040] The processing system 5 generates an image of the target inspection area using the scanning electron microscope 1, acquires the image of the target inspection area from the scanning electron microscope 1, and performs pattern matching between the pattern on the image of the target inspection area and the corresponding CAD pattern using the corrected matching search distance. Since the processing system 5 searches for edges on the image within the corrected matching search distance, pattern matching can be performed accurately and in the shortest possible search time.
[0041] Figure 6 is a flowchart illustrating one embodiment for calculating the correction amount for the matching search distance described above. In step 101, the processing system 5 sets up multiple inspection areas within the pattern surface of the workpiece W. The number of inspection areas is not particularly limited, but more than 100 inspection areas may be set up. In step 102, the processing system 5 calculates the pattern density for each of the multiple inspection areas set in step 101 from the pattern design data on the workpiece W. In step 103, the processing system 5 divides the multiple pattern densities calculated in step 102 into multiple density groups according to their values. Specifically, the processing system 5 divides the multiple pattern densities into multiple density groups using multiple thresholds.
[0042] In step 104, the processing system 5 gives a command to the scanning electron microscope 1 to generate images of each of the multiple inspection areas set in step 101. In step 105, the processing system 5 performs pattern matching between the patterns on each image of multiple inspection areas and the corresponding CAD patterns. Pattern matching can be performed according to known methods. In step 106, the processing system 5 calculates multiple image shift amounts corresponding to multiple inspection areas based on the pattern matching results.
[0043] In step 107, the processing system 5 divides the multiple inspection areas into multiple area groups corresponding to the multiple density groups set in step 103 (see Figure 5). In step 108, the processing system 5 calculates multiple correction amounts corresponding to multiple area groups based on the image shift amount corresponding to the inspection area belonging to each area group. More specifically, the processing system 5 calculates the average value and 3σ of the image shift amount within each area group, and calculates a correction amount for each area group which is the sum of the average value and 3σ. The image shift amount may be a moving average of the image shift amount along the direction in which the inspection areas are arranged. In step 109, the processing system 5 generates correlation data showing the correlation between multiple density groups and multiple correction amounts, and stores it in the storage device 5a.
[0044] Figure 7 is a flowchart illustrating one embodiment of performing pattern matching using a correction amount for the matching search distance. In step 201, the processing system 5 calculates the pattern density of the target inspection area on the workpiece to be inspected, which has a pattern surface with the same structure as the pattern surface of the workpiece W, from the design data of the pattern within the target inspection area. In step 202, the processing system 5 selects a density group to which the pattern density of the target inspection area belongs from among the multiple density groups determined in step 103 above.
[0045] In step 203, the processing system 5 corrects the matching search distance using a correction amount corresponding to the density group selected in step 202. The correction amount corresponding to the selected density group is uniquely determined from the correlation data generated in step 109. In step 204, the processing system 5 gives a command to the scanning electron microscope 1 to generate an image of the target inspection area. In step 205, the processing system 5 acquires an image of the target inspection area from the scanning electron microscope 1 and performs pattern matching between the pattern on the image of the target inspection area and the corresponding CAD pattern using the matching search distance corrected in step 203.
[0046] Next, another embodiment of pattern matching will be described with reference to Figures 8 and 9. In this embodiment, instead of correcting the matching search distance, the processing system 5 corrects the position of the workpiece stage 31 shown in Figure 1, thereby reducing the image shift of the target inspection area itself. The configuration and operation of this embodiment, which are not specifically described, are the same as those of the embodiments described above with reference to Figures 1 to 7, so their redundant explanations will be omitted.
[0047] Figure 8 is a flowchart illustrating one embodiment for calculating the correction amount for the position of the workpiece stage 31. Steps 301 to 305 in Figure 8 are the same as steps 101 to 105 in Figure 6, so the redundant explanation is omitted. In step 306, the processing system 5 calculates multiple X-direction image shift amounts and multiple Y-direction image shift amounts corresponding to each of the multiple inspection areas, based on the pattern matching results in step 305. The X-direction image shift amount is the image shift amount in the X direction, and the Y-direction image shift amount is the image shift amount in the Y direction perpendicular to the X direction. In step 307, the processing system 5 divides the multiple inspection areas into multiple area groups corresponding to the multiple density groups set in step 303 (see Figure 5).
[0048] In step 308, the processing system 5 calculates multiple X-direction correction amounts corresponding to multiple area groups based on the X-direction image shift amount corresponding to the inspection area belonging to each area group. More specifically, the processing system 5 calculates the average value of the X-direction image shift amount and 3σ within each area group, and calculates an X-direction correction amount for each area group, which is the sum of the average value of the X-direction image shift amount and 3σ, thereby determining multiple X-direction correction amounts corresponding to multiple area groups. The X-direction image shift amount may be a moving average of the X-direction image shift amount along the direction of the arrangement of the inspection areas.
[0049] In step 309, the processing system 5 calculates multiple Y-direction correction amounts corresponding to multiple area groups based on the Y-direction image shift amount corresponding to the inspection area belonging to each area group. More specifically, the processing system 5 calculates the average value of the Y-direction image shift amount and 3σ within each area group, and calculates a Y-direction correction amount for each area group, which is the sum of the average value of the Y-direction image shift amount and 3σ, thereby determining multiple Y-direction correction amounts corresponding to multiple area groups. The Y-direction image shift amount may be a moving average of the Y-direction image shift amount along the direction of the arrangement of the inspection areas.
[0050] Step 308 may be performed after step 309, or it may be performed simultaneously with step 309. In step 310, the processing system 5 generates X-direction correlation data showing the correlation between multiple density groups and multiple X-direction correction amounts, generates Y-direction correlation data showing the correlation between multiple density groups and multiple Y-direction correction amounts, and stores the X-direction correlation data and Y-direction correlation data in the storage device 5a.
[0051] Figure 9 is a flowchart illustrating one embodiment of performing pattern matching using the X-direction correction amount and Y-direction correction amount of the workpiece stage 31. In step 401, the processing system 5 calculates the pattern density of the target inspection area on the workpiece to be inspected, which has a pattern surface with the same structure as the pattern surface of the workpiece W, from the design data of the pattern within the target inspection area. In step 402, the processing system 5 selects a density group to which the pattern density of the target inspection area belongs from among the multiple density groups determined in step 303.
[0052] In step 403, the processing system 5 determines the X-direction correction amount and the Y-direction correction amount corresponding to the density group selected in step 402. The X-direction correction amount is uniquely determined from the X-direction correlation data, and the Y-direction correction amount is uniquely determined from the Y-direction correlation data. In step 404, the processing system 5 instructs the scanning electron microscope 1 to correct the position of the workpiece stage 31 of the scanning electron microscope 1, which supports the workpiece to be inspected, by moving it in the X direction by a determined X direction correction amount and in the Y direction by a determined Y direction correction amount. In some cases, either the X direction correction amount or the Y direction correction amount may be 0.
[0053] In step 405, the processing system 5 instructs the scanning electron microscope 1 to generate an image of the target inspection area of the workpiece to be inspected on the position-corrected workpiece stage 31. In step 406, the processing system 5 performs pattern matching between the pattern on the image of the target inspection area and the corresponding CAD pattern. According to this embodiment, since the position of the workpiece stage 31 is corrected based on the pattern density, the image shift of the target inspection area itself is reduced. As a result, the accuracy of pattern matching is improved, and pattern matching is performed in the shortest possible search time.
[0054] The embodiments described with reference to the flowcharts in Figures 6 and 7 and the embodiments described with reference to the flowcharts in Figures 8 and 9 may be combined. That is, both the matching search distance and the position of the workpiece stage 31 may be corrected according to the pattern density of the target inspection area.
[0055] The embodiments described above are intended to enable persons with ordinary skill in the art to implement the present invention. Various modifications of the above embodiments can be made naturally by those skilled in the art, and the technical idea of the present invention can be applied to other embodiments as well. Therefore, the present invention is not limited to the embodiments described, but is to be interpreted in the broadest sense according to the technical idea defined by the claims. [Explanation of symbols]
[0056] 1. Scanning electron microscope 5 Processing System 15. Electron gun 16 Focusing lens 17X deflector 18 Y deflector 20 Objective lenses 26 Electron detectors 28 Image acquisition device 31 Workpiece Stage 35 Stage Moving Device
Claims
1. By setting multiple inspection areas within the pattern surface of the workpiece, The pattern density in each of the aforementioned multiple inspection areas is calculated from the design data of the workpiece pattern. The pattern density is divided into multiple density groups according to that value, Images of each of the aforementioned multiple inspection areas are generated using a scanning electron microscope. Pattern matching is performed between the pattern on the aforementioned image and the corresponding CAD pattern. Based on the results of the pattern matching, multiple image shift amounts corresponding to each of the multiple inspection areas are calculated. The aforementioned multiple inspection areas are divided into multiple area groups corresponding to the aforementioned multiple density groups, Based on the image shift amount corresponding to the inspection area belonging to each area group, multiple correction amounts corresponding to each of the multiple area groups are calculated. A pattern matching method for generating correlation data that shows the correlation between the plurality of density groups and the plurality of correction amounts.
2. The pattern density of the target inspection area on the workpiece to be inspected, which has a pattern surface having the same structure as the pattern surface of the aforementioned workpiece, is calculated from the design data of the pattern within the target inspection area. The density group to which the pattern density of the target inspection area belongs is selected from the plurality of density groups. The matching search distance is corrected using the correction amount corresponding to the selected density group. An image of the target inspection area is generated using a scanning electron microscope. The pattern matching method according to claim 1, further comprising performing pattern matching between a pattern on an image of the target inspection area and a corresponding CAD pattern using the corrected matching search distance.
3. The pattern matching method according to claim 2, wherein the step of correcting the matching search distance is a step of correcting the matching search distance by adding a correction amount corresponding to the selected density group to the reference search distance.
4. The process of calculating the multiple correction amounts corresponding to each of the multiple area groups is as follows: The average value of the image shift amount and 3σ corresponding to the inspection area belonging to each area group are calculated. The pattern matching method according to claim 1, comprising the step of determining a plurality of correction amounts corresponding to each of the plurality of area groups by calculating a correction amount which is the sum of the mean value and the 3σ for each area group.
5. By setting multiple inspection areas within the pattern surface of the workpiece, The pattern density in each of the aforementioned multiple inspection areas is calculated from the design data of the workpiece pattern. The pattern density is divided into multiple density groups according to that value, Images of each of the aforementioned multiple inspection areas are generated using a scanning electron microscope. Pattern matching is performed between the pattern on the aforementioned image and the corresponding CAD pattern. Based on the results of the pattern matching, multiple X-direction image shift amounts and multiple Y-direction image shift amounts corresponding to each of the multiple inspection areas are calculated. The aforementioned multiple inspection areas are divided into multiple area groups corresponding to the aforementioned multiple density groups, Based on the X-direction image shift amount corresponding to the inspection area belonging to each area group, multiple X-direction correction amounts corresponding to each of the multiple area groups are calculated. Based on the Y-direction image shift amount corresponding to the inspection area belonging to each area group, multiple Y-direction correction amounts corresponding to each of the multiple area groups are calculated. X-direction correlation data is generated that shows the correlation between the plurality of density groups and the plurality of X-direction correction amounts. A pattern matching method for generating Y-direction correlation data that shows the correlation between the plurality of density groups and the plurality of Y-direction correction amounts.
6. The pattern density of the target inspection area on the workpiece to be inspected, which has a pattern surface having the same structure as the pattern surface of the aforementioned workpiece, is calculated from the design data of the pattern within the target inspection area. The density group to which the pattern density of the target inspection area belongs is selected from the plurality of density groups. The X-direction correction amount and Y-direction correction amount corresponding to the selected density group are determined. The position of the workpiece stage of the scanning electron microscope supporting the workpiece to be inspected is corrected by moving it in the X direction by the determined X direction correction amount and in the Y direction by the determined Y direction correction amount. An image of the target inspection area of the workpiece to be inspected on the workpiece stage, whose position has been corrected, is generated by the scanning electron microscope. The pattern matching method according to claim 5, further comprising performing pattern matching between a pattern on an image of the target inspection area and a corresponding CAD pattern.
7. The step of calculating the multiple X-direction correction amounts corresponding to each of the multiple area groups is as follows: The average value of the X-direction image shift amount and 3σ corresponding to the inspection area belonging to each area group are calculated. The pattern matching method according to claim 5, comprising the step of determining a plurality of X-direction correction amounts corresponding to each of the plurality of area groups by calculating an X-direction correction amount for each area group, which is the sum of the average value of the X-direction image shift amount and 3σ.
8. The process of calculating the multiple Y-direction correction amounts corresponding to each of the multiple area groups is as follows: The average value of the Y-direction image shift amount and 3σ corresponding to the inspection area belonging to each area group are calculated. The pattern matching method according to claim 5, comprising the step of determining a plurality of Y-direction correction amounts corresponding to each of the plurality of area groups by calculating a Y-direction correction amount for each area group, which is the sum of the average value of the Y-direction image shift amount and 3σ.