KR102449421B1 - Inspection method for EUV mask - Google Patents

Abstract

The present invention relates to an extreme ultraviolet (EUV) mask inspection method. An EUV mask inspection method according to an embodiment of the present invention for inspecting whether an EUV mask used in a semiconductor exposure process is defective includes the steps of: obtaining first image data by photographing, by an image capturing unit, a mask pattern in which a plurality of sample patterns representing a defective state of the EUV mask are displayed; inputting, by a controlling unit, the first image data for the mask pattern to a deep learning-based model, and storing a plurality of sample patterns classified by a pattern analyzing unit in a memory unit; obtaining second image data by photographing the surface of the EUV mask by the image capturing unit; and determining, by the controlling unit, a defective state of the EUV mask by comparing the plurality of sample patterns extracted from the memory unit with the second image data. The EUV mask inspection process can be performed more quickly and accurately.

Description

EUV mask inspection method {Inspection method for EUV mask}

The present invention relates to an EUV mask inspection method, and more particularly, to an EUV mask inspection method capable of more rapidly and accurately performing an EUV mask inspection process.

Recently, circuit patterns formed on EUV (Extreme Ultra Violet) masks for transferring circuits to semiconductor devices are becoming more and more miniaturized in accordance with high performance and high integration of semiconductor devices. Accordingly, there is a problem in that fine defects that may occur on the surface of the EUV mask cause defects in the semiconductor device. Therefore, various devices and methods for inspecting the EUV mask with high precision and efficiency are being studied.

For example, in Korean Patent Publication No. 10-2009-0120121 (examination method of extreme ultraviolet mask and apparatus for performing the same) (published on November 24, 2009), a femtosecond laser is irradiated to a neon gas to interfere with extreme ultraviolet rays. After generation, a technique of inspecting the EUV mask by irradiating the interference extreme ultraviolet to the extreme ultraviolet mask to detect the scattered light is disclosed.

However, the conventional EUV mask inspection apparatus and method disclosed in the above patent documents have problems in that the structure of the EUV mask inspection apparatus is complicated, which increases the cost, and the control for performing the inspection process is not easy. In addition, since the conventional EUV mask inspection apparatus and method do not detect various defect states that may occur on the surface of the EUV mask, there is a problem in that the inspection result for the EUV mask is not accurate.

Accordingly, there is a need for an EUV mask inspection method capable of more rapidly and accurately performing an EUV mask inspection process.

Domestic Patent Publication No. 10-2009-0120121 (Extreme UV mask inspection method and apparatus for performing the same) (published on November 24, 2009)

The present invention was invented to improve the above problems, and the problem to be solved by the present invention is to use artificial intelligence to pattern image data obtained from a mask pattern in which a plurality of sample patterns representing a defective state of an EUV mask are displayed. An object of the present invention is to provide an EUV mask inspection method that can perform an EUV mask inspection process more quickly and accurately by classifying a plurality of sample patterns by input to an analysis unit and determining whether the EUV mask is defective by using them.

The technical problem of the present invention is not limited to those mentioned above, and another technical problem not mentioned will be clearly understood by those skilled in the art from the following description.

In order to achieve the above object, an EUV mask inspection method according to an embodiment of the present invention is an EUV mask inspection method for inspecting whether an EUV (Extreme Ultra Violet) mask used in a semiconductor exposure process is defective. acquiring first image data by photographing a mask pattern in which a plurality of sample patterns representing a defective state of an EUV mask are displayed; inputting, by a controller, the first image data for the mask pattern into a deep learning-based model, and storing a plurality of sample patterns classified by the pattern analyzer in a memory unit; obtaining, by the image capturing unit, second image data by photographing the surface of the EUV mask; and determining, by the controller, a defective state of the EUV mask by comparing a plurality of sample patterns extracted from the memory unit with the second image data.

In this case, the plurality of sample patterns may include: a first sample pattern having a rectangular shape representing a foreign material formed on a surface of the EUV mask among the defective states of the EUV mask; a second sample pattern having a linear shape representing a scratch formed on a surface of the EUV mask among the defective states of the EUV mask; and a third sample pattern having a circle or oval shape representing a bubble formed on a surface of the EUV mask among the defective states of the EUV mask.

In addition, each of the first sample pattern, the second sample pattern, and the third sample pattern is characterized in that a plurality of them are disposed at different positions of the mask pattern to have different sizes and angles.

Also, the mask pattern is characterized in that a plurality of edge patterns having different shapes are formed along the edges to have a predetermined interval.

The storing of the plurality of sample patterns in the memory unit by the control unit may include: inputting, by the control unit, the first image data to the pattern analysis unit; receiving, by the controller, a tag for each of the plurality of sample patterns displayed on the mask pattern; classifying, by the pattern analysis unit, the plurality of sample patterns based on a shape and a tag of each of the plurality of sample patterns; and the control unit stores the plurality of sample patterns classified by the pattern analysis unit in the memory unit.

On the other hand, the step of the image capturing unit acquiring the first image data or the second image data, the control unit of the area of the mask pattern or the EUV mask and the area of the field of view (FOV) of the image capturing unit comparing sizes; When the area of the mask pattern or the EUV mask is wider than the area of the viewing angle, the control unit sets the area of the mask pattern or the EUV mask to the size of the area of the viewing angle along the longitudinal and lateral directions of the mask pattern or the EUV mask. dividing into a plurality of corresponding divided regions; generating, by the image capturing unit, a plurality of divided images by sequentially photographing the plurality of divided regions; and generating, by the controller, the first image data or the second image data by sequentially combining the plurality of divided images.

In this case, the plurality of divided regions included in the mask pattern may include: at least one first divided region indicating a good state of the EUV mask; and at least one second division region indicating a defective state of the EUV mask and in which the plurality of sample patterns are formed, wherein the plurality of sample patterns are formed on a surface of the EUV mask among the defective states of the EUV mask. a first sample pattern having a rectangular shape representing a particle; a second sample pattern having a linear shape representing a scratch formed on a surface of the EUV mask among the defective states of the EUV mask; and a third sample pattern having a circle or oval shape representing a bubble formed on a surface of the EUV mask among the defective states of the EUV mask.

In addition, each of the at least one second division region included in the mask pattern is characterized in that a plurality of edge patterns having different shapes are formed to have a predetermined interval along the edge.

The details of other embodiments are included in the detailed description and drawings.

According to the EUV mask inspection method according to an embodiment of the present invention, image data obtained from a mask pattern on which a plurality of sample patterns representing a defective state of an EUV mask are displayed is input to a pattern analysis unit using artificial intelligence to input a plurality of samples By classifying the patterns and determining whether the EUV mask is defective using the classification, the EUV mask inspection process can be performed more quickly and accurately.

The effects of the present invention are not limited to the above-mentioned effects, and other effects not mentioned will be clearly understood by those skilled in the art from the description of the claims.

1 is a block diagram schematically showing the configuration of an EUV mask inspection apparatus according to an embodiment of the present invention.
2 is a flowchart illustrating an EUV mask inspection method according to an embodiment of the present invention.
3 is a diagram illustrating an example of a sample pattern displayed on a mask pattern in an EUV mask inspection method according to an embodiment of the present invention.
4 is a diagram illustrating an example of an edge pattern displayed on a mask pattern in an EUV mask inspection method according to an embodiment of the present invention.
5 is a flowchart illustrating a method of classifying and storing a plurality of sample patterns by a pattern analyzer in an EUV mask inspection method according to an embodiment of the present invention.
6 is a flowchart illustrating a method of acquiring first image data by an image capturing unit capturing a mask pattern in an EUV mask inspection method according to an embodiment of the present invention.
7 is a diagram illustrating a process in which the image capturing unit captures the mask pattern when the area of the mask pattern is wider than the area of the viewing angle of the image capturing unit in the EUV mask inspection method according to an embodiment of the present invention.
8 is a diagram illustrating an example in which the mask pattern is divided into a plurality of divided regions when the region of the mask pattern is wider than the region of the viewing angle of the image capturing unit in the EUV mask inspection method according to an embodiment of the present invention.
9 is a diagram illustrating an example of a sample pattern displayed on a mask pattern when the area of the mask pattern is wider than the area of the viewing angle of the image capturing unit in the EUV mask inspection method according to an embodiment of the present invention.

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings to the extent that those of ordinary skill in the art to which the present invention pertains can easily practice the present invention.

In describing the embodiments, descriptions of technical contents that are well known in the technical field to which the present invention pertains and are not directly related to the present invention will be omitted. This is to more clearly convey the gist of the present invention by omitting unnecessary description.

For the same reason, some components are exaggerated, omitted, or schematically illustrated in the accompanying drawings. In addition, the size of each component does not fully reflect the actual size. In each figure, the same or corresponding elements are assigned the same reference numerals.

Also, device or element orientation (eg "front", "back", "up", "down", "top", "bottom") The expressions and predicates used herein with respect to terms such as ", "left", "right", "lateral", etc. are used merely to simplify the description of the invention, and the associated apparatus Or it will be appreciated that it does not simply indicate or imply that an element must have a particular orientation.

Hereinafter, the present invention will be described with reference to the drawings for explaining an EUV mask inspection method according to an embodiment of the present invention.

1 is a block diagram schematically showing the configuration of an EUV mask inspection apparatus according to an embodiment of the present invention.

As shown in FIG. 1 , an EUV mask inspection apparatus 100 according to an embodiment of the present invention includes a mask support unit 110 , an image capturing unit 120 , a pattern analysis unit 130 , a memory unit 140 , and The controller 150 may be included. The EUV mask inspection apparatus 100 uses a mask pattern 200 in which a plurality of sample patterns 210 representing a defective state of an EUV (Extreme Ultra Violet) mask 10 used in a semiconductor exposure process are displayed. (10) can be inspected for defects.

The mask support unit 110 may support the mask pattern 200 or the EUV mask 10 in a state in which the mask pattern 200 on which the plurality of sample patterns 210 are displayed or the EUV mask 10 to be inspected is seated. Although not shown in detail, the mask support unit 110 includes a mask driver (not shown) for moving the mask pattern 200 or the EUV mask 10 in a horizontal direction (X direction, Y direction) or a vertical direction (Z direction). not) can be provided. In addition, the mask support unit 110 includes a lighting unit (not shown) for adjusting the brightness when the image capturing unit 120 acquires the first image data or the second image data from the mask pattern 200 or the EUV mask 10 . not) can be provided.

The image capturing unit 120 may acquire image data by photographing the surface of the EUV mask 10 . As will be described later, the image capturing unit 120 acquires first image data by photographing the mask pattern 200 on which the plurality of sample patterns 210 are displayed, or by photographing the surface of the EUV mask 10 to be inspected to obtain a second image. data can be obtained. Preferably, the image capturing unit 120 may use a vision camera.

The pattern analysis unit 130 receives the first image data obtained by the image capturing unit 120 photographing the mask pattern 200 , and collects a plurality of sample patterns 210 according to the defective state of the EUV mask 10 . can classify the sample pattern 210 of .

Preferably, the pattern analysis unit 130 receives the first image data obtained by photographing the mask pattern 200 by the image capturing unit 120 and performs deep learning on the plurality of sample patterns 210 based on artificial intelligence. A plurality of sample patterns 210 may be classified according to the defective state of the EUV mask 10 by learning in a deep learning method. In particular, the pattern analyzer 130 may classify the plurality of sample patterns 210 based on a shape of each of the plurality of sample patterns 210 and a tag input for each of the plurality of sample patterns 210 .

As will be described later, the plurality of sample patterns 210 displayed on the mask pattern 200 include a first sample pattern 211 of a rectangular shape representing a particle formed on the surface of the EUV mask 10 , and the EUV mask 10 . A second sample pattern 212 in a linear shape representing a scratch formed on the surface of the EUV mask 10, a third sample pattern 213 in a circle or oval shape representing a bubble formed on the surface of the EUV mask 10 may include

The memory unit 140 may temporarily or permanently store various programs necessary for driving the EUV mask inspection apparatus 100 and various data processed by the control unit 150 . That is, the memory unit 140 may store the first image data and the second image data captured by the image capturing unit 120 , and a plurality of sample patterns 210 classified by the pattern analyzing unit 130 . The memory unit 140 may use a volatile memory or a non-volatile memory, and may use various storage media capable of input/output and storage of information, such as a hard disk and a secure digital card (SD card).

The control unit 150 controls operations of various components constituting the vehicle EUV mask inspection apparatus 100 , that is, the mask support unit 110 , the image capturing unit 120 , the pattern analysis unit 130 , and the memory unit 140 . can be controlled That is, the controller 150 inputs the first image data captured by the image capturing unit 120 to the pattern analyzing unit 130 , and the plurality of samples classified by the first image data and the pattern analyzing unit 130 . The pattern 210 is stored in the memory unit 140 , and the defective state of the EUV mask 10 may be determined by comparing the plurality of sample patterns 210 extracted from the memory unit 140 with the second image data. .

As mentioned above, the term '~ unit' used in this embodiment means software or hardware components such as FPGA or ASIC, and '~ unit' performs certain roles. However, '-part' is not limited to software or hardware. '~unit' may be configured to reside on an addressable storage medium or may be configured to refresh one or more processors. Thus, as an example, '~' denotes components such as software components, object-oriented software components, class components, and task components, and processes, functions, properties, and procedures. , subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays, and variables. The functions provided in the components and '~ units' may be combined into a smaller number of components and '~ units' or further separated into additional components and '~ units'. In addition, components and '~ units' may be implemented to play one or more CPUs in a device or secure multimedia card.

Hereinafter, an EUV mask inspection method according to an embodiment of the present invention using the EUV mask inspection apparatus 100 according to an embodiment of the present invention configured as described above will be described with reference to FIGS. 2 to 9 . same.

2 is a flowchart illustrating an EUV mask inspection method according to an embodiment of the present invention.

As shown in FIG. 2 , the image capturing unit 120 acquires first image data by photographing a mask pattern 200 in which a plurality of sample patterns 210 representing a defective state of the EUV mask 10 are displayed. can be (S110).

3 is a diagram illustrating an example of a sample pattern displayed on a mask pattern in an EUV mask inspection method according to an embodiment of the present invention.

As shown in FIG. 3 , a plurality of sample patterns 210 representing the defective state of the EUV mask 10 are displayed on the mask pattern 200 , and the plurality of sample patterns 210 are the EUV mask 10 . ) can be formed by classifying into various shapes expressing the defective state.

Preferably, the plurality of sample patterns 210 include a first sample pattern 211 of a rectangular shape representing a foreign material formed on the surface of the EUV mask 10 among the defective states of the EUV mask 10 ; Among the defective states of the EUV mask 10 , a linear second sample pattern 212 representing scratches formed on the surface of the EUV mask 10 , and the EUV mask among the defective states of the EUV mask 10 . The third sample pattern 213 in the shape of a circle or an ellipse representing a bubble formed on the surface of (10) may be configured.

In particular, each of the first sample pattern 211 , the second sample pattern 212 , and the third sample pattern 213 may have a different size and may be disposed in plurality at different positions of the mask pattern 200 .

In the example of FIG. 3 , 16 (4×4) squares (5um, 10um, 15um, 20um, 25um) having different sizes are shown for the first sample pattern 211 representing the foreign material formed on the surface of the EUV mask 10 . , 30um, 35um, 40um, 45um, 50um, 55um, 60um, 5um, 10um, 20um, 30um) at different positions in the central part of the mask pattern 200, and scratches formed on the surface of the EUV mask 10 10 (5×2) linear (10umХ300um, 10umХ500um, 20umХ500um (Tilt 20°), 20umХ1000um, 30umХ500mu, 30umХ700um (Tilt 30°), 30umХ1000um, 40umХ300um, 40umХ500um, 40umХ1000um) are displayed at different positions, and a third sample pattern 213 expressing the bubbles formed on the surface of the EUV mask 10 is displayed in different sizes ( Diameter) of 10 (5×2) circles (Ф10um, Ф20um, Ф30um, Ф40um, Ф50um, Ф70um, Ф100um, Ф200um, Ф400um, Ф500um) marked at different positions in the lower part of the mask pattern 200. An example is shown.

However, in FIG. 3 , 16 first sample patterns 211 have the same size, 10 second sample patterns 212 have the same length, and 10 third sample patterns 213 have the same diameter. Although shown to have a , this does not reflect the actual size, but is expressed in the same way for convenience.

Meanwhile, in the mask pattern 200 , a plurality of edge patterns 220 may be formed along the edges in addition to the plurality of sample patterns 210 representing the defective state of the EUV mask 10 on the surface.

4 is a diagram illustrating an example of an edge pattern displayed on a mask pattern in an EUV mask inspection method according to an embodiment of the present invention.

As shown in FIG. 4 , the plurality of edge patterns 220 have different shapes at predetermined intervals along the edges surrounding the plurality of sample patterns 210 representing the defective state of the EUV mask 10 . can be formed to The plurality of edge patterns 220 may accurately specify the positions of the plurality of sample patterns 210 formed to have different sizes and angles.

Preferably, the plurality of edge patterns 220 have a predetermined interval along a first direction (horizontal direction in the example of FIG. 4 ) among the edges of the mask pattern 200 and include a plurality of edges formed to have different shapes. One edge pattern 221 and a plurality of second edge patterns 222 formed to have different shapes at predetermined intervals along the second direction (in the example of FIG. 4 , the vertical direction) among the edges of the mask pattern 200 . ), and four third edge patterns 223 formed at respective positions of four corners among the edges of the mask pattern 200 .

In FIG. 4 , in the example of FIG. 3 , a plurality of sample patterns 210 include 16 (4×4) first sample patterns 211, 10 (5×2) second sample patterns 212, and 10 samples. In the case of having the (5×2) third pattern, four first edge patterns 221 and four second An example in which the edge pattern 222 is formed is shown.

Referring back to FIG. 2 , after the image capturing unit 120 acquires the first image data ( S110 ), the controller 150 uses the first image data for the mask pattern 200 based on deep learning. A plurality of sample patterns 210 that are input to the model and classified by the pattern analysis unit 130 may be stored in the memory unit 140 ( S120 ).

5 is a flowchart illustrating a method of classifying and storing a plurality of sample patterns by a pattern analyzer in an EUV mask inspection method according to an embodiment of the present invention.

First, as shown in FIG. 5 , the control unit 150 inputs the first image data acquired by the image capturing unit 120 to the pattern analysis unit 130 ( S121 ), and displays the first image data displayed on the mask pattern 200 . A tag for each of the plurality of sample patterns 210 may be input (S122). At this time, the tag is a type (ie, foreign material, scratch, bubble), shape (square shape, linear shape, circle or oval shape), size (length, width, diameter, etc.) for each of the plurality of sample patterns 210 and It means information about an angle, etc., and brightness information of illumination when photographing the mask pattern 200 .

Then, the pattern analysis unit 130 classifies the plurality of sample patterns 210 based on the shape and tag of each of the plurality of sample patterns 210 ( S123 ), and then the control unit 150 controls the pattern analysis unit 130 . A plurality of sample patterns 210 classified by may be stored in the memory unit 140 .

As described above, the plurality of sample patterns 210 classified by the pattern analysis unit 130 and stored in the memory unit 140 are second image data acquired from the EUV mask 10 to be inspected by the image capturing unit 120 . It can be compared with and used to check whether the EUV mask 10 is defective.

Referring back to FIG. 2 , after the control unit 150 stores the plurality of sample patterns 210 classified by the pattern analysis unit 130 in the memory unit 140 ( S120 ), the image capturing unit 120 is Second image data is acquired by photographing the surface of the EUV mask 10 ( S130 ), and the control unit 150 compares the plurality of sample patterns 210 extracted from the memory unit 140 with the second image data to obtain EUV A defective state of the mask 10 may be determined (S140).

Meanwhile, when the image capturing unit 120 acquires the first image data or the second image data from the mask pattern 200 or the EUV mask 10 in steps S110 and S130 of FIG. 2 , the image capturing unit 120 is The entire area of the mask pattern 200 or the EUV mask 10 may be photographed at once, but in some cases, the image capturing unit 120 may divide the entire area of the mask pattern 200 or the EUV mask 10 and divide the photographing. have.

6 is a flowchart illustrating a method in which an image capturing unit acquires first image data by photographing a mask pattern in an EUV mask inspection method according to an embodiment of the present invention, and FIG. 7 is an EUV mask according to an embodiment of the present invention. In the inspection method, when the area of the mask pattern is wider than the area of the viewing angle of the image capturing unit, it is a diagram illustrating a process in which the image capturing unit captures the mask pattern.

6 , before the image capturing unit 120 acquires the first image data from the mask pattern 200 , the controller 150 controls the area of the mask pattern 200 and the viewing angle of the image capturing unit 120 . By comparing the size of the (Field of View, FOV) area, it may be determined whether the area of the mask pattern 200 is wider than the area of the viewing angle of the image capturing unit 120 (S111),

As a result of the determination in step S111 , when the area of the mask pattern 200 is wider than the area of the viewing angle of the image capturing unit 120 (Yes in step S112 ), the controller 150 selects the area of the mask pattern 200 as the mask pattern. It can be divided into a plurality of divided regions corresponding to the size of the viewing angle region along the longitudinal and lateral directions of 200 ( S113 ). In FIG. 7 , the controller 150 determines the size of the viewing angle area along the vertical and horizontal directions (horizontal and vertical directions) of the mask pattern 200 in consideration of the area of the viewing angle of the image capturing unit 120 for the entire area of the mask pattern 200 . An example of division into nine (3×3) divided areas M1 to M9 corresponding to .

Then, the image capturing unit 120 generates a plurality of divided images by sequentially photographing a plurality of divided regions ( S114 ), and the control unit 150 sequentially scans the plurality of divided images generated by the image capturing unit 120 . By combining them, one piece of first image data may be generated (S115). 7 shows an example in which the image capturing unit 120 photographs nine (3×3) divided regions M1 to M9 in the order of M1, M2, M3, M6, M5, M4, M7, M8, and M9. are doing Although not shown in detail, in order for the image capturing unit 120 to sequentially photograph a plurality of divided regions, the image capturing unit 120 may be moved to a corresponding position, and a mask driving unit ( (not shown) may move the mask pattern 200 to the corresponding position.

Conversely, as a result of the determination in step S111, when the area of the mask pattern 200 is narrower than the area of the viewing angle of the image capturing unit 120 (NO in step S112), the controller 150 controls the image capturing unit 120 to First image data may be obtained by photographing the entire area of the mask pattern 200 once.

Meanwhile, in FIG. 6 , the process in which the image capturing unit 120 acquires the first image data from the mask pattern 200 is described as an example in step S110 of FIG. 2 , but in step S130 the image capturing unit 120 is EUV Even when acquiring the second image data from the mask 10 , the controller 150 may perform substantially the same process as in FIG. 6 for the EUV mask 10 .

Meanwhile, in the examples of FIGS. 6 and 7 , the area of the mask pattern 200 is wider than the area of the viewing angle of the image capturing unit 120 , so the controller 150 divides the entire area of the mask pattern 200 into a plurality of divided areas. In the case of division, each divided area photographed by the image capturing unit 120 may indicate a good state or a bad state depending on whether a plurality of sample patterns 210 are formed.

8 is a view showing an example in which the mask pattern is divided into a plurality of divided regions when the region of the mask pattern is wider than the region of the viewing angle of the image capturing unit in the EUV mask inspection method according to an embodiment of the present invention. A diagram illustrating an example of a sample pattern displayed on the mask pattern when the area of the mask pattern is wider than the area of the viewing angle of the image capturing unit in the EUV mask inspection method according to an embodiment of the present invention.

In FIG. 8 , as in FIG. 7 , a case in which the mask pattern 200 has nine (3×3) divided regions M1 to M9 is illustrated as an example.

As shown in FIG. 8 , the plurality of divided regions included in the mask pattern 200 includes at least one first divided region indicating a good state of the EUV mask 10 and a bad state of the EUV mask 10 . , may be divided into at least one second division area in which the plurality of sample patterns 210 are formed. In FIG. 8 , of the nine (3×3) divided regions M1 to M9, three M4, M5, and M6 are the first divided regions indicating the good state of the EUV mask 10, and six M1, M2, An example in which M3, M7, M8, and M9 is set as the second division region indicating the defective state of the EUV mask 10 is shown.

As shown in FIG. 9 , a plurality of sample patterns 210 are formed in the second divided region indicating a defective state of the EUV mask 10 , and the plurality of sample patterns 210 are formed on the surface of the EUV mask 10 . A first sample pattern 211 in a rectangular shape representing a foreign material formed, a second sample pattern 212 in a linear shape representing a scratch formed on the surface of the EUV mask 10 , and a bubble formed on the surface of the EUV mask 10 . It may be composed of a third sample pattern 213 in the shape of a circle or an ellipse representing

In addition, as shown in FIG. 9 , in the second divided region indicating the defective state of the EUV mask 10, a plurality of edge patterns 220 having different shapes are formed so as to have a predetermined interval along the edge, The plurality of edge patterns 220 are a plurality of first edge patterns ( 221), a plurality of second edge patterns 222 formed to have different shapes at predetermined intervals along the second direction (in the example of FIG. 9, the vertical direction in the example of FIG. 9) among the edges of the mask pattern 200, the mask pattern It may be composed of four third edge patterns 223 formed at respective positions of the four corners among the edges of 200 .

For convenience of explanation, in the example of FIG. 9 , the plurality of sample patterns 210 and the plurality of edge patterns 220 formed in the second divided region are substantially the same as the examples shown in FIGS. 3 and 4 , but are not limited thereto. does not In addition, in the second divided regions M1, M2, M3, M7, M8, and M9 of the divided regions of the mask pattern 200, the plurality of sample patterns 210 and the plurality of edge patterns 220 may be formed to be identical to each other. and may be formed differently.

As described above, in the EUV mask inspection method according to an embodiment of the present invention, image data obtained from the mask pattern 200 in which a plurality of sample patterns 210 representing the defective state of the EUV mask 10 are displayed are artificially intelligent. The EUV mask 10 inspection process can be performed more quickly and accurately by inputting it into the pattern analysis unit 130 using can

On the other hand, in the present specification and drawings, preferred embodiments of the present invention have been disclosed, and although specific terms are used, these are only used in a general sense to easily explain the technical content of the present invention and help the understanding of the present invention, It is not intended to limit the scope of the invention. It will be apparent to those of ordinary skill in the art to which the present invention pertains that other modifications based on the technical spirit of the present invention can be implemented in addition to the embodiments disclosed herein.

<Explanation of symbols for main parts of the drawing>
10: EUV mask
100: EUV mask inspection device
110: mask support unit 120: image capturing unit
130: pattern analysis unit 140: memory unit
150: control unit
200: mask pattern 210: sample pattern
211: first sample pattern 212: second sample pattern
213: third sample pattern 220: edge pattern
221: first edge pattern 222: second edge pattern
223: third edge pattern

Claims (8)

In the EUV mask inspection method for inspecting whether an EUV (Extreme Ultra Violet) mask used in a semiconductor exposure process is defective,
acquiring, by an image capturing unit, a mask pattern in which a plurality of sample patterns representing a defective state of the EUV mask are displayed to obtain first image data;
A control unit inputs the first image data for the mask pattern to a pattern analysis unit that is a deep learning-based model, and selects a plurality of sample patterns classified by the pattern analysis unit according to the defective state of the EUV mask. storing in a memory unit;
obtaining, by the image capturing unit, second image data by photographing the surface of the EUV mask; and
Comprising the step of determining, by the control unit, a defective state of the EUV mask by comparing a plurality of sample patterns extracted from the memory unit with the second image data;
The step of the control unit storing the plurality of sample patterns in the memory unit,
inputting, by the control unit, the first image data to the pattern analysis unit;
receiving, by the controller, a tag for each of the plurality of sample patterns displayed on the mask pattern;
classifying, by the pattern analysis unit, the plurality of sample patterns based on a shape and a tag of each of the plurality of sample patterns; and
and storing, by the control unit, the plurality of sample patterns classified by the pattern analysis unit in the memory unit,
The step of the image capturing unit acquiring the first image data or the second image data,
comparing, by the controller, the size of the area of the mask pattern or the EUV mask with the area of a field of view (FOV) of the image capturing unit;
When the area of the mask pattern or the EUV mask is wider than the area of the viewing angle, the control unit sets the area of the mask pattern or the EUV mask to the size of the area of the viewing angle along the longitudinal and lateral directions of the mask pattern or the EUV mask. dividing into a plurality of corresponding divided regions;
generating, by the image capturing unit, a plurality of divided images by sequentially photographing the plurality of divided regions; and
and generating, by the controller, the first image data or the second image data by sequentially combining the plurality of divided images. The method of claim 1,
The plurality of sample patterns,
a first sample pattern having a rectangular shape representing a particle formed on a surface of the EUV mask among the defective states of the EUV mask;
a second sample pattern having a linear shape representing a scratch formed on a surface of the EUV mask among the defective states of the EUV mask; and
and a third sample pattern having a circle or oval shape representing a bubble formed on a surface of the EUV mask, among the defective states of the EUV mask. 3. The method of claim 2,
Each of the first sample pattern, the second sample pattern and the third sample pattern,
EUV mask inspection method, characterized in that a plurality of different sizes are disposed at different positions of the mask pattern. The method of claim 1,
The mask pattern is
An EUV mask inspection method, characterized in that a plurality of edge patterns having different shapes are formed along the edges to have predetermined intervals. delete delete The method of claim 1,
The plurality of divided regions included in the mask pattern,
at least one first divided region indicating a good state of the EUV mask; and
indicates a defective state of the EUV mask, and includes at least one second divided region in which the plurality of sample patterns are formed;
The plurality of sample patterns,
a first sample pattern having a rectangular shape representing a particle formed on a surface of the EUV mask among the defective states of the EUV mask;
a second sample pattern having a linear shape representing a scratch formed on a surface of the EUV mask among the defective states of the EUV mask; and
and a third sample pattern having a circle or oval shape representing a bubble formed on a surface of the EUV mask, among the defective states of the EUV mask. 8. The method of claim 7,
Each of the at least one second division region included in the mask pattern,
An EUV mask inspection method, characterized in that a plurality of edge patterns having different shapes are formed along the edges to have predetermined intervals.

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