JP2019086590A - Pattern inspection method and pattern inspection device - Google Patents

Abstract

To provide a pattern inspection method and a pattern inspection device, capable of accurately calculating a proper filter coefficient in a reference image creation process in a short time.SOLUTION: The pattern inspection method includes: a process for obtaining an optical image of a first inspection target; a process for calculating a filter coefficient and creating a reference image corresponding to the optical image in drawing pattern data as a base of pattern formation of the first inspection target; and a process for comparing the optical image and the reference image and detecting defects of the first inspection target, and the creation process of the reference image includes: a process for including at least one or more second areas of the first inspection target corresponding to an area selected in past inspection normally completed in at least one or more first areas selected for calculating the filter coefficient in the drawing pattern; and a process for calculating the filter coefficient so that the optical image and the drawing pattern almost coincide with each other in the first and second areas respectively.SELECTED DRAWING: Figure 2

Description

  The present embodiment relates to a pattern inspection method and a pattern inspection apparatus.

  Conventionally, a pattern inspection apparatus may perform D-DB (Die to DataBase) inspection for inspecting a defect of a mask by comparing an optical image of a pattern of a mask or a template with a reference image obtained based on design data. . In the D-DB inspection, a reference image generation step of processing a drawing pattern obtained from design data using filter coefficients to generate a reference image is performed. The filter coefficient is calculated from the rounding amount of the corner of the drawing pattern having the optical proximity correction (OPC), the blurring amount of the outer edge of the drawing pattern, and the like in order to bring the reference image closer to the optical image (learning step).

JP, 2016-021004, A

  However, the feature points of the drawing pattern used to calculate the filter coefficients have been selected for each mask by the operator. In this case, the filter coefficient changes depending on the selected feature point, and the accuracy of the reference image generation process also changes. If the accuracy of the reference image generation process is low, the reference image is not properly generated, the reference image generation process needs to be performed again, and the test setup time is prolonged. In addition, when the reference image is not appropriate, many false defects occur, which leads to an inspection error.

  Therefore, an object of the present invention is to provide a pattern inspection method and a pattern inspection apparatus capable of calculating an appropriate filter coefficient in a reference image generation process accurately and in a short time.

The pattern inspection method according to the present embodiment generates a reference image corresponding to an optical image based on an optical system that acquires an optical image of a first inspection target and drawing pattern data that is a basis of pattern formation of the first inspection target. A pattern inspection apparatus using a pattern inspection apparatus including a reference system for detecting the defects of the first inspection object using the optical image, and obtaining an optical image of the first inspection object; The process of generating a reference image corresponding to an optical image in a reference system by calculating filter coefficients for drawing pattern data that is the basis of the pattern formation of the first inspection object, comparing the optical image with the reference image, 1) detecting the defect to be inspected in the control system;
The step of generating the reference image is a process corresponding to the first area corresponding to the area selected in the past inspection that has been completed normally in at least one or more first areas selected for calculating the filter coefficient in the drawing pattern. The method includes the steps of including at least one or more second regions to be inspected, and calculating filter coefficients such that the optical image and the drawing pattern substantially match in each of the first and second regions.

BRIEF DESCRIPTION OF THE DRAWINGS Schematic which shows an example of the pattern inspection apparatus of 1st Embodiment. FIG. 2 is a flow chart showing an example of a mask inspection method according to the first embodiment. The flowchart which shows the method of selecting a normal past presumed point automatically. The flowchart which shows the use order of the past presumed point used for search of the point with high correlation. The conceptual diagram which shows an example of the search method of the point highly correlated with the past presumed point. The conceptual diagram which shows an example of the search method of the point highly correlated with the past presumed point. The conceptual diagram which shows an example of the search method of the point highly correlated with the past presumed point. The figure which shows the point selected as a point highly correlated with the past presumed point.

  Hereinafter, embodiments according to the present invention will be described with reference to the drawings. This embodiment does not limit the present invention.

  The drawings are schematic or conceptual, and the proportions of parts are not necessarily the same as the real ones. In the specification and the drawings, the same components as those described above with reference to the drawings are denoted by the same reference numerals, and the detailed description will be appropriately omitted.

First Embodiment
FIG. 1 is a schematic view showing an example of a pattern inspection apparatus according to the first embodiment. The pattern inspection apparatus 100 is used, for example, to inspect a defect in a pattern of a mask or template used in a semiconductor manufacturing process. In the following embodiments, an apparatus for inspecting a defect in a pattern of a mask will be described.

(Configuration of pattern inspection device)
The pattern inspection apparatus 100 includes an XYθ table 2, a light source 3, a polarization beam splitter 4, an optical system 5, a photodiode array 7, a sensor circuit 8, an autoloader 9, an X axis motor 10A, and a Y axis motor 10B. And the θ-axis motor 10 C, and the laser length measuring system 12.

  The XYθ table 2 can mount the mask 1 as an inspection target thereon, and can move in, for example, the X direction, the Y direction, and the θ direction in a horizontal plane. The mask 1 is a photomask used in a photolithography process of a semiconductor manufacturing process, and has a pattern to be transferred to a wafer or an overlying layer. Since the drawing pattern drawn on the mask 1 is a pattern including optical proximity correction (OPC (Optical Proximity Correction)), it may be different from the transfer pattern transferred to the wafer or the like.

  The light source 3 emits laser light toward the polarization beam splitter 4. The light used for pattern defect inspection, that is, inspection light may be laser light. The polarization beam splitter 4 reflects the light from the light source 3 toward the optical system 5.

  The optical system 5 irradiates a laser beam toward the XYθ table 2 via an objective lens. The mask 1 placed on the XYθ table 2 reflects the light from the optical system 5. Reflected light from the mask 1 enters the photodiode array 7 via the optical system 5. The optical system 5 causes the incident reflected light of the mask 1 to form an image of the mask 1 on the photodiode array 7. The photodiode array 7 photoelectrically converts the optical image of the mask 1. Defects of the mask 1 are inspected based on the photoelectrically converted optical image of the mask 1.

  The sensor circuit 8 captures an optical image photoelectrically converted by the photodiode array 7 and A / D converts the captured optical image. Then, the sensor circuit 8 outputs the A / D converted optical image to the comparison circuit 25. The sensor circuit 8 may be, for example, a circuit of a TDI (Time Delay Integration) sensor. By using the TDI sensor, the pattern of the mask 1 can be imaged with high accuracy.

  The autoloader 9 automatically conveys the mask 1 onto the XYθ table 2 according to a command from the autoloader control circuit 15, or automatically recovers the mask 1 on the XYθ table 2. The X-axis motor 10A, the Y-axis motor 10B and the θ-axis motor 10C move the XYθ table 2 in the X direction, Y direction and θ direction (rotational direction in the XY plane (substantially horizontal plane)), respectively. Thereby, the light of the light source 3 is scanned with respect to the mask 1 on the XYθ table 2. The laser length measuring system 12 detects the position of the XYθ table 2 in the X direction and the Y direction.

The pattern inspection apparatus 100 further includes a control computer 30, an autoloader control circuit 15, a table control circuit 17, an autofocus control circuit 18, a position circuit 22, a comparison circuit 25, an expansion circuit 26, and a reference circuit 27. A first determination circuit 31, a second determination circuit 32, a correlation determination circuit 33, a pattern storage circuit 34, a storage unit 35, a monitor 41, and a printer 42 are provided. The position circuit 22, the comparison circuit 25, the control computer 30, the autoloader control circuit 15, the table control circuit 17, and the autofocus control circuit 18 are provided as a control system. The expansion circuit 26, the reference circuit 27, the first determination circuit 31, the second determination circuit 32, the correlation determination circuit 33, and the pattern storage circuit 34 are provided as a reference system. The control system and the reference system may be configured of one or more CPUs.
The reference system generates a reference image corresponding to the optical image of the mask 1 based on the drawing pattern data that is the basis of the pattern formation of the mask 1. The control system compares the optical image of the mask 1 with the reference image to detect defects in the mask 1.

  The control computer 30 is connected to the above circuit via the bus 20 and executes various controls related to the defect inspection of the mask 1.

  The autoloader control circuit 15 controls the autoloader 9. The table control circuit 17 controls driving of the motors 10A to 10C. The motors 10A to 10C move the XYθ table 2 so that the light of the light source 3 scans the mask 1.

  The autofocus control circuit 18 controls the XYθ table 2 to perform focusing. For example, the autofocus control circuit 18 moves the XYθ table 2 in the Z direction based on the focus signal corresponding to the height of the sensor surface detected by the Z sensor (not shown).

  The laser length measuring system 12 detects the movement position of the XYθ table 2 and outputs the detected movement position to the position circuit 22. The position circuit 22 detects the position of the mask 1 on the XYθ table 2 based on the movement position input from the laser measurement system 12. The position circuit 22 outputs the detected position of the mask 1 to the comparison circuit 25.

  The expansion circuit 26 converts (expands) data of a drawing pattern used for drawing the mask 1 into binary or multilevel image data. The data of the drawing pattern may be information such as coordinates of a figure representing the mask 1, side lengths, and types, and is design data including optical proximity correction (OPC) in consideration of optical proximity effects. The drawing pattern may be stored in advance in the storage unit 35 or may be generated from design data in consideration of optical proximity correction (OPC). The expansion circuit 26 outputs the expanded image data to the reference circuit 27.

  The reference circuit 27 performs appropriate filter processing on the drawing data input from the expansion circuit 26 to generate a reference image used for defect inspection of the pattern of the mask 1. The reference image is image data obtained by simulating the transfer pattern to the wafer from the drawing data by using the exposure condition at the time of transferring the pattern of the mask 1 to the wafer. That is, the reference image is an image obtained by emulating the exposure process from the drawing data of the transfer pattern. The reference circuit 27 outputs the generated reference image to the comparison circuit 25.

  Here, the filter processing is to calculate filter coefficients for data of a drawing pattern, and a reference image corresponding to an optical image is obtained by the filter processing. The filter coefficients are calculated in the control calculator 30 such that the pattern of one or more estimated points substantially matches in the optical image and the drawing pattern. The estimation point is a feature point of the drawing pattern used to calculate the filter coefficient, and is, for example, an image area of 1024 pixels × 1024 pixels. The characteristic points of the drawing pattern are, for example, a relatively large area of the optical proximity effect, such as a line pattern having a minimum line width, a space pattern, a hole pattern, a corner, a tip, and the like. Such estimated points (feature points) may be arbitrarily selected by the operator, or may be determined by the first determination circuit 31, the second determination circuit 32, and the correlation determination circuit 33.

  In the present embodiment, the estimated point of the mask 1 selected to calculate the filter coefficient is selected at least in the past inspection completed normally, in addition to the point (first area) arbitrarily selected by the operator or the like. The points (the second area) of Mask 1 corresponding to the estimated points in the past are automatically included. The completion of the normal inspection means that the defect of the mask pattern can be extracted almost accurately, and the inspection is completed with few false defects. The filter coefficients used for such a normal inspection in the past are considered to be calculated at appropriate estimation points of the drawing pattern. Therefore, the control computer 30 arbitrarily selects the point (second region) of the mask 1 corresponding to the estimated point (hereinafter also referred to as a normal past estimated point) selected at the time of calculation of such filter coefficients. Included in the estimated point (first area) As a result, an appropriate filter coefficient can be calculated for the mask 1 to be inspected this time.

  The first determination circuit 31 is a circuit that determines whether or not the same mask as the mask 1 as the first inspection target has been inspected in the past in order to calculate an appropriate filter coefficient. The mask similar to the mask 1 includes, for example, a pattern which is substantially the same as the minimum line width of the pattern of the mask 1 among the masks as the second inspection target inspected in the past (hereinafter, also referred to as a past mask). A mask, a mask whose material is substantially the same as that of the mask 1, or a mask used for manufacturing the same semiconductor device as the mask 1 may be used.

  For example, the first determination circuit 31 may compare the minimum line widths (i.e., technology nodes) of the pattern of the mask 1 and the pattern of the past mask. When the technology nodes of the mask 1 and the past mask are close, it is considered that their optical proximity effect is also approximate. For this reason, it is considered that the filter coefficient of the mask 1 can also be calculated using the estimated points of the past mask. In addition, when the technology nodes of the mask 1 and the past mask are close, the correlation determination circuit 33 described later can relatively easily find a point highly correlated with the past estimated point from the inspection area of the mask 1.

  The material of the mask 1 and the past mask may be compared. The material of the mask is related to the reflectance and transmittance of light. Therefore, it is preferable that the material of the mask 1 and the mask of the past be the same.

  It is determined whether the mask 1 and the past mask are masks used for manufacturing the same semiconductor device. That is, the series numbers of the mask 1 and the past mask may be compared. When the mask 1 and the past mask are used to manufacture the same semiconductor device, the shapes of the respective patterns of the mask 1 and the past mask are often approximately equal or similar. Therefore, it is considered that the filter coefficient of the mask 1 can also be calculated using the estimated points of the past mask. In addition, when the mask 1 and the past mask are used for manufacturing the same semiconductor device, the correlation determination circuit 33 can relatively easily find a point highly correlated with the past estimated point from the inspection area of the mask 1.

  Thus, the first determination circuit 31 can determine whether or not the same mask as the mask 1 has been inspected in the past.

  The second determination circuit 32 is a circuit that determines whether or not the past inspection of the mask is normally completed for the past mask determined to be the same as the mask 1 by the first determination circuit 31. In pattern inspection, if the estimated points selected to calculate the filter coefficients are not appropriate, it is not possible to obtain an appropriate reference image even if the filter coefficients are calculated over the entire drawing pattern. In this case, even if the comparison circuit 25 compares the optical image with the reference image in pattern inspection, it is not possible to accurately detect a pattern defect. That is, if the filter coefficients are not appropriate, false defects will occur frequently and the inspection will not be completed normally. The pseudo defects are to be detected as defects due to the defect of the reference image although there is no defect in the mask 1 actually.

  On the other hand, if the estimated points for calculating filter coefficients are appropriate, appropriate filter coefficients can be obtained. In this case, the comparison circuit 25 can accurately detect pattern defects. That is, if the filter coefficients are appropriate, there will be less false defects and the inspection will complete successfully. In order to calculate an appropriate filter coefficient in the inspection of the mask 1, the second determination circuit 32 selects a mask which has been successfully inspected in the past masks.

  The correlation determination circuit 33 searches the drawing pattern of the mask 1 for a point with high correlation in the shape, size, and / or position of the past estimated points selected in the past mask selected by the second determination circuit 32. It is a circuit. The point (second region) on the drawing pattern of the mask 1 determined to have a high correlation with the past estimated point is included in the estimated point for calculating the filter coefficient. Note that "high correlation" may be reworded as "similar" or "close".

  The high correlation in the shape indicates that, for example, types of shapes such as a line pattern, a space pattern, a hole pattern and the like are substantially the same. When a plurality of types of shapes are included in the past estimated points, having a high correlation in shapes may mean that all the plurality of types of shapes are included in the points of the mask 1.

  The high correlation in dimension indicates that the dimension difference between the dimension of the pattern in the past estimated point and the dimension of the pattern in the point of the mask 1 is within a predetermined threshold. The predetermined threshold may be set in advance and stored in the storage unit 35. Further, the pattern to be compared in the image area of the estimation point may be substantially at the center of the estimation point (image area) or may be an arbitrarily designated portion.

  Furthermore, high correlation at the position indicates that the position of the estimated point in the past is within the inspection area of the mask 1. More specifically, it is determined that the correlation is high when the coordinates of the estimated point in the past are within the inspection area of the mask 1 to be inspected this time. Even if the shape or size of the past estimated point is similar to the shape or size of a certain point of the mask 1, if the position of the past estimated point is outside the inspection area of the mask 1, appropriateness of the inspection area Filter coefficients can not be calculated.

  In this manner, the correlation determination circuit 33 searches the drawing pattern of the mask 1 having high correlation in the shape, size, and / or position of the past estimated points, and includes the points having high correlation in the drawing pattern of the mask 1 as estimation points. . The first determination circuit 31, the second determination circuit 32, and the correlation determination circuit 33 may be configured by individual CPUs, or may be configured by one CPU. Further, the first determination circuit 31, the second determination circuit 32 and the correlation determination circuit 33 may be incorporated in the control computer 30. A more detailed method of selecting estimated points will be described later with reference to FIGS.

  The control computer 30 calculates the filter coefficients so that the optical image and the drawing pattern substantially match at the estimated points selected by the first determination circuit 31, the second determination circuit 32, and the correlation determination circuit 33. The reference circuit 27 generates a reference image by calculating filter coefficients on the drawing data from the expansion circuit 26. The reference circuit 27 outputs the generated reference image to the comparison circuit 25. As described above, the reference system calculates the filter coefficient by including the points on the drawing pattern of the mask 1 having high correlation with the normal past estimation points in the arbitrarily selected estimation point, and the drawing pattern of the mask 1 The filter coefficient is calculated on the data of to generate a reference image.

  The comparison circuit 25 measures the line width and the like of each position of the optical image obtained from the sensor circuit 8 while using the position information inputted from the position circuit 22. The comparison circuit 25 compares the line width and the gradation value (brightness) of both the measured optical image and the reference image input from the reference circuit 27. Then, the comparison circuit 25 detects, for example, an error between the pattern of the optical image and the pattern of the reference image as a defect of the mask 1.

  The pattern storage circuit 34 receives, from the comparison circuit 25, the image of the estimated point and the position information used for the calculation of the filter coefficient when the pattern inspection is normally completed, and stores the image in the storage unit 35. The images of these estimated points are used as the images of past estimated points in the calculation of the filter coefficients in the subsequent pattern inspection.

  The control computer 30 executes various controls and processes related to the defect inspection of the mask 1 with respect to each component connected to the bus 20. The storage unit 35 stores various types of information related to defect inspection. The monitor 41 displays various images related to defect inspection. The printer 42 prints various information related to defect inspection.

(Mask inspection method)
Next, a mask inspection method using the mask inspection apparatus 100 will be described.
FIG. 2 is a flowchart showing an example of the mask inspection method according to the first embodiment. In the present embodiment, the mask inspection apparatus 100 performs a D-DB (Die to DataBase) inspection that inspects a defect of the mask 1 by comparing the transmission image and the reference image.

  First, the autoloader 9 loads the mask 1 onto the XYθ table 2, and the XYθ table 2 aligns the mask 1 (S10).

  Next, an optical image of the mask 1 is captured (S20). For example, the mask inspection apparatus 100 virtually divides the inspection area of the mask 1 into stripes, and scans light from the optical system along the stripes. Reflected light from the mask 1 is photoelectrically converted by the photodiode array 7, and an optical image of the mask 1 is acquired by the sensor circuit 8. The optical image is an image close to the transfer pattern transferred to the wafer by the exposure process. That is, the optical image is an image in which the outer edge of the drawing pattern is somewhat blurred and the corners are rounded so as to be close to the actual transfer pattern.

  On the other hand, filter coefficients for generating a reference image are calculated in parallel with or before or after steps S10 and S20.

  In the calculation of the filter coefficient, first, the first determination circuit 31 receives the drawing pattern of the mask 1 and the pattern of the estimated points in the past from the storage unit 35 and inspects the same (similar) mask as the mask 1 in the past. It is judged whether there is any (S30). As described above, the first determination circuit 31 determines whether the pattern of the mask 1 and the pattern of the past mask are substantially the same at the minimum line width, and whether the materials of the mask 1 and the past mask are substantially the same. Alternatively, it is determined whether the mask 1 and the past mask are masks used for manufacturing the same semiconductor device.

  For example, when the technology node, material and / or series number of the mask 1 and the past mask are different (NO in S30), the first judgment circuit 31 judges that the past mask is not the same as the mask 1. The estimation points of the past mask not similar to the mask 1 are not used for extracting the estimation points of the mask 1 and are not used for calculation of the filter coefficient (S40).

  On the other hand, if the technology node, material and / or series number of the mask 1 and the past mask are the same (YES in S30), the first judgment circuit 31 judges that the past mask is the same as the mask 1 Do. The first judgment circuit 31 may judge that the past mask is the same as the mask 1 when all of the technology node, the material and the series number are the same. Alternatively, the first judgment circuit 31 may judge that the past mask is the same as the mask 1 when any one or two of the technology node, the material and the series number are the same.

  If it is determined that the past mask is the same as the mask 1 (YES in S30), the second determination circuit 32 determines whether the inspection for the past mask is normally completed (S60). If the inspection for the past mask is not completed normally (NO in S60), the estimated point of the past mask is not used for extracting the estimated point of mask 1 and is not used for calculation of the filter coefficient ( S40).

  On the other hand, when the inspection has been completed normally for the past mask (YES in S60), the correlation determination circuit 33 determines the estimated points (normal past estimated points) selected in the past mask and the shape and size Alternatively, a point with high correlation at the position is searched in the drawing pattern of the mask 1 (S70).

  Here, in order to search for a point with high correlation in the drawing pattern of the mask 1, an estimated point serving as a reference from among normal estimated points in the past in the normal past mask selected in steps S30 to S60. You need to choose Hereinafter, a method of selecting a normal past estimated point (reference point) serving as a reference will be described.

  First, the reference point may be arbitrarily selected from past estimated points in the normal past mask by the operator. In this case, an image of a normal past estimated point may be displayed on the monitor 41, and the operator may select from the points displayed on the monitor 41. Alternatively, the control computer 30 may automatically select a reference point from normal past estimated points. Only one reference point may be selected, but a plurality of reference points may be selected.

  For example, when the reference point is automatically selected, the control calculator 30 estimates that the condition (1): the (latest) estimated point whose inspection date and time is relatively new, the condition (2): a relatively large number of pattern shapes. Point, Condition (3): Estimated point having relatively small pattern size, Condition (4): Estimated point whose position coordinate is in the inspection area of Mask 1, and / or Condition (5): Specified earlier Select the estimated point (the one with the smaller image number of the estimated point).

  FIG. 3 is a flow chart showing a method of automatically selecting a reference point. The conditions (1) to (5) are used for automatic selection in this order (priority). For example, the control computer 30 first selects a normal estimated past point satisfying the condition (1) as a reference point (S100). If the number of selected reference points is less than or equal to a preset threshold (NO in S110), the selection of the reference points ends.

  On the other hand, if the number of selected reference points is larger than the preset threshold (YES in S110), then the control computer 30 selects one of the reference points selected under the condition (1). Is selected as a reference point (S130). When the number of reference points selected in the condition (2) is equal to or less than a preset threshold (NO in S140), the selection of the reference points is ended.

  On the other hand, when the number of reference points selected in the condition (2) is larger than the preset threshold (YES in S140), the control computer 30 next selects the criterion selected using the condition (2). Among the points, a normal past estimated point satisfying the condition (3) is selected as a reference point (S150). When the number of reference points selected in the condition (3) is equal to or less than a preset threshold (NO in S160), the selection of the reference points is ended (S120).

  On the other hand, when the number of reference points selected in the condition (3) is larger than the preset threshold (YES in S160), the control computer 30 next selects the criterion selected using the condition (3). Among the points, a normal past estimated point satisfying the condition (4) is selected as a reference point (S170). When the number of reference points selected in the condition (4) is equal to or less than a preset threshold (NO in S180), the selection of the reference points is ended (S120).

  On the other hand, when the number of reference points selected in the condition (4) is larger than the preset threshold (YES in S180), the control computer 30 next selects the criterion selected using the condition (4). Among the points, a normal past estimated point satisfying the condition (5) is selected as a reference point (S190). The reference points selected in the condition (5) are selected up to the threshold in ascending order of the image number. Thus, one or more reference points are selected.

  Next, the correlation determination circuit 33 uses the reference point selected as described above to select a point having a high correlation with the reference point (hereinafter, also simply referred to as a point having a high correlation) as an inspection area of the mask 1. Search within. The search for highly correlated points will be described later with reference to FIGS.

  When there are a plurality of reference points, the correlation determination circuit 33 uses the reference points for searching for highly correlated points in the order of priority of the conditions (1) to (5). FIG. 4 is a flow diagram showing the priority of reference points used to search for highly correlated points. For example, the correlation determination circuit 33 first searches for a point having a high correlation with the reference point selected in the condition (1) in the inspection area of the mask 1 (S200). As a result of the search, when the number of highly correlated points reaches the threshold (YES in S210), the process of searching for highly correlated points ends. The threshold value of the number of highly correlated points is set so that the sum of the estimated number of points arbitrarily selected by the operator and the number of highly correlated points does not exceed a predetermined value.

  On the other hand, when the number of high correlation points is smaller than the threshold as a result of the search (NO in S210), next, the correlation determination circuit 33 determines the high correlation points with respect to the reference point selected in condition (2). Are searched in the inspection area of the mask 1 (S220). As a result of the search, when the number of highly correlated points reaches the threshold (YES in S230), the processing for searching highly correlated points is ended.

  On the other hand, when the high correlation point is less than the threshold as a result of the search (NO in S230), the correlation determination circuit 33 next selects the high correlation point with respect to the reference point selected in the condition (3). A search is made in the inspection area of the mask 1 (S240). As a result of the search, when the number of highly correlated points reaches the threshold (YES in S250), the process of searching for highly correlated points is ended.

  On the other hand, when the high correlation point is less than the threshold as a result of the search (NO in S250), the correlation determination circuit 33 next selects the high correlation point with respect to the reference point selected in the condition (4). A search is made in the inspection area of the mask 1 (S260). As a result of the search, when the number of highly correlated points reaches the threshold (YES in S270), the process of searching for highly correlated points ends.

  On the other hand, when the high correlation point is less than the threshold as a result of the search (NO in S270), the correlation determination circuit 33 next selects the high correlation point with respect to the reference point selected in the condition (5). A search is made in the inspection area of the mask 1 (S280). As a result of the search, when the number of highly correlated points reaches the threshold (YES in S290), the process of searching for highly correlated points ends.

If the number of highly correlated points is smaller than the threshold (NO in S290), it is determined whether there is another reference point selected in FIG. 3 (S292). If there is another reference point (YES in S292), the reference point is changed (S293), and the search is similarly performed on the reference point.
If there is no other reference point (NO in S292), the control computer 30 adds the point of Mask 1 that has already been determined to be highly correlated to the estimated point to calculate the filter coefficient (S295). If there is no point determined to be highly correlated, the control computer 30 calculates the filter coefficient using the estimated point selected by the operator without adding the estimated point.
When the number of points with high correlation exceeds the upper limit, the control computer 30 may add the points with high correlation to the estimated points in the descending order of the correlation to the upper limit.

  The search for highly correlated points will be described in more detail with reference to FIGS. 5 to 7.

  5 to 7 are conceptual diagrams showing an example of a method of searching for points that are highly correlated with past estimated points. FIG. 5 is an image of reference points selected from normal past estimated points. The reference points include, for example, the vertical line pattern PL1, the horizontal line pattern PL2, and the hole pattern PH. The correlation determination circuit 33 acquires the image of the reference point selected as described above, and searches for the point of the drawing pattern of the mask 1 based on the image of the reference point.

  The correlation determination circuit 33 searches the drawing pattern of the mask 1 for points including, for example, all types of shapes (vertical line pattern PL1, horizontal line pattern PL2, and hole pattern PH) included in the reference point. It is assumed that there are five points within the search range of the mask 1 including points of all types included in the reference point. For example, FIGS. 6A to 6E show points of the mask 1 including all of the vertical line pattern PL1, the horizontal line pattern PL2, and the hole pattern PH. These points of the mask 1 are points highly correlated with the reference point in the shape.

  At this time, the correlation determination circuit 33 may randomly check the points of the drawing pattern of the mask 1 using, for example, random numbers. The search range is within the inspection area of the drawing pattern. The number of searches may have an upper limit. For example, the number of searches may be 100 points (ie, 100 image areas).

  If there is no point highly correlated with the reference point in the shape, the control computer 30 does not add the point to the estimated point, and calculates the filter coefficient using the estimated point (first area) selected by the operator ((1) S45).

  Next, the correlation determination circuit 33 searches for points with high correlation in size from five points with high correlation in shape. For example, the correlation determination circuit 33 determines the dimensional difference between the patterns in the center frame C of each of the five points (the pattern in the center frame C of the reference point and the pattern of the points in the center frame C of the points of the mask 1). Search for points whose dimensional difference is within the threshold.

  The dimension of the pattern of the center frame C in FIGS. 6B, 6D and 6E is largely different from the dimension of the pattern of the center frame C in FIG. 5, and the dimension difference between them is at least the threshold value. Suppose that there is. For example, the dimension of the pattern in the center frame C of FIG. 6B is considerably smaller than that of FIG. The dimension of the pattern in the center frame C of FIG. 6D is considerably larger than that of FIG. 5, and the dimension difference between them is equal to or more than the threshold. There is no pattern in the center frame C of FIG. 6 (E). In this case, the correlation determination circuit 33 does not select the points shown in FIG. 6 (B), FIG. 6 (D) and FIG. 6 (E).

  On the other hand, it is assumed that the dimension of the pattern of central frame C in FIGS. 6A and 6C is close to the dimension of the pattern of central frame C in FIG. 5, and their dimensional difference is less than the threshold. For example, the dimensions of the patterns in the central frame C of FIGS. 6A and 6C are close to that of FIG. 5, and their dimensional difference is less than the threshold. In this case, two points shown in FIG. 6 (A) and FIG. 6 (A) and FIG. 6 (C) are selected.

  Next, the correlation determination circuit 33 searches for a point with high correlation in position from two points with high correlation in size. For example, the correlation determination circuit 33 searches for a point whose position coordinate is within the range of the inspection area of the mask 1 among the two points in FIG. 7A and FIG. 7B.

  It is assumed that the point in FIG. 7B is outside the inspection area of the mask 1. In this case, the correlation determination circuit 33 does not select the points shown in FIG. 7 (B). On the other hand, it is assumed that the point in FIG. 7A is within the inspection area of the mask 1. In this case, the correlation determination circuit 33 selects a point shown in FIG. 7 (A). That is, the point shown in FIG. 8 is selected as a point highly correlated with the reference point.

  FIG. 8 shows points selected as points highly correlated with the reference point. Of the drawing patterns of the inspection area of the mask 1, the points shown in FIG. 8 selected by the correlation determination circuit 33 are included in the estimated points used for calculation of the filter coefficient.

  As described above, the pattern inspection apparatus 100 according to the present embodiment reads the estimated points from the storage unit 35 from the inspection result of the past mask similar to the mask 1 and correlates with the normal past estimation points (reference points). The high point of is searched in the drawing pattern of the mask 1.

  In the above embodiment, the correlation determination circuit 33 searches for a pattern with high correlation in all of the shape, size, and position. However, the correlation determination circuit 33 may search for a pattern with high correlation in any part of the shape, size or position.

  In FIGS. 5 to 8, only one high correlation point is extracted. However, multiple points with high correlation may be extracted. Also, only one reference point is shown in FIG. However, the first determination circuit 31, the second determination circuit 32, and the correlation determination circuit 33 may execute step S70 for each of the plurality of reference points.

  The search in step S70 of FIG. 2 may be performed for all of the inspection areas of the mask 1. In this case, there may be a large number of highly correlated points. Therefore, when the total number of the number of estimated points (first area) arbitrarily selected by the operator and the number of highly correlated points (second area) with respect to normal past estimated points is equal to or more than a predetermined value, the correlation determination is made The circuit 33 may select some (for example, three) points in descending order of correlation from the point of high correlation so that the sum is smaller than a predetermined value, and include them in the estimation points. By including the points in the order of high correlation in this way, the control computer 30 can calculate more appropriate filter coefficients in a short time. If the total number of the optional estimated number of points and the number of highly correlated points is less than a predetermined value, the correlation determination circuit 33 may select all the highly correlated points as the estimated points. If a point with high correlation is not extracted, the control computer 30 may calculate the filter coefficient using only the estimated point arbitrarily selected by the operator.

  Alternatively, the search in step S70 may be ended when the total number of the optional estimated number of points and the number of highly correlated points reaches a predetermined value. In this case, the correlation determination circuit 33 may be sufficient to search for a part of the inspection area of the mask 1. Therefore, the search time in step S70 can be shortened while calculating an appropriate filter coefficient.

  Refer back to FIG. Next, a point highly correlated with the past estimated point is included in the estimated point of mask 1 (S80). The control computer 30 automatically includes the selected point shown in FIG. 8 as the estimated point of the mask 1 as a result of the search in step S70. Alternatively, the operator may determine whether to manually include the points shown in FIG. 8 in the estimated points of the mask 1 or not.

  When a plurality of highly correlated points are extracted, the control computer 30 may include all of the extracted plurality of points in the estimated points of the mask 1. Alternatively, the control computer 30 may include, among the extracted points, points selected under other conditions in the estimated points of the mask 1. Furthermore, the operator may include an arbitrarily selected point of the plurality of extracted points in the estimated point of the mask 1.

  Next, the control computer 30 calculates filter coefficients so that the optical image and the drawing pattern substantially match at each of the estimated points selected as described above (S85). The reference circuit 27 calculates the filter coefficient of the drawing pattern in the inspection area of the mask 1 to generate a reference image from the drawing pattern (S90). Thereby, it is possible to create a reference image emulating the exposure condition. Emulation may create a reference image of the spot in real time while the photodiode array 7 is imaging a certain stripe on the mask 1.

  The drawing pattern is a design pattern including an OPC, and is a pattern to be actually drawn on the mask 1. That is, the data of the drawing pattern is not design data (pre-OPC data) not including OPC, but design data (post-OPC data) including OPC. The pattern according to the pre-OPC data (pre-OPC pattern) is approximately equal to the transfer pattern to be transferred to the wafer in the exposure step because it does not include the OPC. On the other hand, the pattern (post-OPC pattern) according to the post-OPC data is a drawing pattern drawn on the mask 1 in consideration of the optical proximity effect in the exposure process. Therefore, the drawing pattern of the mask 1 is different from the transfer pattern to the wafer.

  Therefore, in order to emulate (simulate) the exposure conditions in the wafer exposure process, the expansion circuit 26 and the reference circuit 27 generate a reference image by processing the data of the drawing pattern with the above-mentioned filter coefficient. This filtering process is also referred to as a reference image learning process. By emulating the exposure conditions in the reference image learning step as described above, a reference image close to the transfer pattern (pre-OPC pattern) can be obtained.

  Thereafter, the comparison circuit 25 compares the optical image with the reference image to detect a defect of the mask 1 (S95).

  When the defect inspection of the mask 1 is completed normally, the image data and coordinates of the estimated point used to calculate the filter coefficient are stored in the storage unit 35 via the pattern storage circuit 34. The data of these estimated points can then be used as past estimated points (i.e., reference points) when examining other masks.

  In addition, the estimated point may be stored in the storage unit 35 as the image of the area. Alternatively, the estimated points may be stored in the storage unit 35 in a data format such as information and coordinates of the pattern of the area. The pattern information is information indicating the feature and type of the shape of the pattern, and for example, the line width and number of vertical line patterns at the estimation point, the line width and number of horizontal line patterns, the line width of vertical space patterns and The information may be information such as the number, the line width and the number of the horizontal space patterns, and the diameter and the number of the hole patterns.

  Thus, when generating the reference image of the mask 1, the pattern inspection apparatus 100 according to the present embodiment is normal to at least one estimated point (first area) selected to calculate the filter coefficient. Automatically include at least one or more points (second area) highly correlated with past estimated points. Thus, the reference circuit 27 can calculate an appropriate filter coefficient for the drawing pattern of the mask 1. The reference image obtained from the drawing pattern using such filter coefficients becomes an appropriate reference image with few false defects. As a result, the pattern inspection apparatus 100 according to the present embodiment can accurately calculate an appropriate filter coefficient in a short time, and can perform mask inspection with few pseudo defects.

  Also, the estimated points of the mask 1 are automatically selected based on the estimated points used in the past inspection completed normally. Such an estimation point can be said to be highly reliable because an appropriate reference image with higher accuracy can be obtained as compared to an estimation point arbitrarily selected by the operator. Therefore, inspection errors due to recalculation of filter coefficients and frequent occurrence of false defects are suppressed, leading to shortening of the inspection time.

  At least a part of the data processing method in the pattern inspection apparatus according to the present embodiment may be configured by hardware or software. When configured with software, a program for realizing at least a part of the functions of the data processing method may be stored in a recording medium such as a flexible disk or a CD-ROM, and read by a computer and executed. The recording medium is not limited to a removable medium such as a magnetic disk or an optical disk, and may be a fixed recording medium such as a hard disk drive or a memory. In addition, a program for realizing at least a part of the functions of the data processing method may be distributed via a communication line (including wireless communication) such as the Internet. Furthermore, the program may be encrypted, modulated, compressed, or stored in a recording medium via a wired line or a wireless line such as the Internet or may be distributed.

  While certain embodiments of the present invention have been described, these embodiments have been presented by way of example only, and are not intended to limit the scope of the invention. These embodiments can be implemented in other various forms, and various omissions, replacements, and modifications can be made without departing from the scope of the invention. These embodiments and modifications thereof are included in the invention described in the claims and the equivalents thereof as well as included in the scope and the gist of the invention.

100 pattern inspection apparatus, 2 XYθ table, 3 light source, 4 polarization beam splitter, 5 optical system, 7 photodiode array, 8 sensor circuit, 9 autoloader, 10A X axis motor, 10B Y axis motor, 10C θ axis motor, 12 laser Length measuring system, 30 control computer, 15 auto loader control circuit, 17 table control circuit, 18 auto focus control circuit, 22 position circuit, 25 comparison circuit, 26 expansion circuit, 27 reference circuit, 31 first judgment circuit, 32 second judgment Circuit, 33 correlation determination circuit, 34 pattern storage circuit, 35 storage unit, 41 monitor, 42 printer

Claims (5)

An optical system for acquiring an optical image of a first inspection object, a reference system for generating a reference image corresponding to the optical image based on drawing pattern data that is a basis of pattern formation of the first inspection object, and A pattern inspection method using a pattern inspection apparatus comprising: a control system that detects a defect of the first inspection target using an image;
Acquiring the optical image of the first inspection object;
Calculating filter coefficients for drawing pattern data that is the basis of the pattern formation of the first inspection target to generate a reference image corresponding to the optical image in the reference system;
Comparing the optical image with the reference image to detect a defect in the first inspection target in the control system;
The generation step of the reference image is
At least one of the first inspection objects corresponding to the region selected in the past inspection successfully completed in at least one or more first regions selected to calculate the filter coefficient in the drawing pattern Including one or more second regions,
Calculating the filter coefficient such that the optical image and the drawing pattern substantially match in each of the first and second regions. In the step of including the second region in the first region,
Whether the pattern of the first inspection object and the pattern of the second inspection object in the past inspection are substantially the same in the minimum line width, and whether the materials of the first and second inspection objects are about the same Or determining whether the first and second inspection targets are masks used for manufacturing the same semiconductor device;
Determining whether or not the previous test to be subjected to the second test is a test that is normally completed;
The pattern inspection method according to claim 1, comprising the step of including in the first region the second region highly correlated with the region to be inspected in the part, or all of the shape, size and position. The high correlation in the shape indicates that the shape is the same in the kind of shape such as a line pattern, a space pattern, and a hole pattern,
The high correlation in the dimensions indicates that the dimensional difference of the pattern is within a predetermined threshold,
The pattern inspection method according to claim 2, wherein the high correlation at the position indicates that the position coordinate of the second inspection target area is within the inspection area of the first inspection target.   When the total number of the number of the first regions and the number of the second regions having high correlation with the second inspection target region is equal to or greater than a predetermined value, the total number of the second regions is less than the predetermined value The pattern inspection method according to claim 2 or 3, wherein the first area is included in the descending order of correlation so as to be. An optical system for acquiring an optical image of a first inspection target;
A reference system that calculates a filter coefficient to drawing pattern data that is a basis of the pattern formation of the first inspection target, and generates a reference image corresponding to the optical image;
A control system that compares the optical image with the reference image to detect a defect in the first inspection target;
The reference system corresponds to the area corresponding to the area selected in the past inspection that has been completed normally in at least one or more first areas selected to calculate the filter coefficient in the drawing pattern. A pattern inspection apparatus that calculates the filter coefficient such that the optical image and the drawing pattern substantially match in each of the first and second regions, including at least one or more second regions to be inspected. .

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