Mask plate glass area surface particle detection method

Abstract

The invention discloses a method for detecting particles on the surface of a glass area of a mask plate. The method comprises the following steps: S01, initializing a defect result matrix C and acquiring an image T obtained by irradiating the mask plate with transmission light; s02, identifying a chromium area in the image T, and performing corresponding neglect identification on pixel points representing the chromium area in the defect result matrix C; s03, identifying an edge area in the image T without the chromium area, and performing corresponding neglect identification on pixel points representing the edge area in the defect result matrix C; s04, filtering out the chromium area and the edge area marked in the steps S2 and S3 from the image T, and remaining as a glass area; carrying out defect identification on the glass area, and carrying out corresponding defect identification on pixel points representing defects in the defect result matrix C; and S05, performing closed operation on the defect result matrix C, then counting defect positions, and performing defect filtering according to length and / or width parameters of defects to obtain final defect data. The defect capturing sensitivity of the glass area is improved, and the interference of edges on defect judgment can be effectively avoided.

Description

Technical Field

[0001] The invention relates to a method for detecting particles on the surface of a mask glass region, belonging to the technical field of semiconductor detection. Background Art

[0002] Currently, once a defect occurs in the mask, the defect will be repeatedly transmitted to each chip or wafer, directly affecting the yield of the photolithography process. Semiconductor masks generally use transmitted light detection for hard defects. The glass layer is a light-transmitting material, and the chrome layer is opaque. In photolithography, particles on the glass area are more serious than particles on the chrome layer, which can cause fatal defects in the chip. How to effectively detect these particle defects on the glass is particularly important. Summary of the invention

[0003] The technical problem to be solved by the present invention is to overcome the defects of the prior art and provide a method for detecting particles on the surface of a mask glass area, which improves the capture sensitivity of defects in the glass area and can effectively avoid the interference of edges on defect judgment.

[0004] In order to solve the above technical problems, the technical solution of the present invention is: a method for detecting particles on the surface of a mask glass area, the method comprising the following steps:

[0005] S01: Initialize the defect result matrix C and obtain the image T obtained by irradiating the mask with transmitted light;

[0006] S02: Identify the chrome area in the image T, and mark the pixel points representing the chrome area as being ignored in the defect result matrix C;

[0007] S03: identifying edge areas in the image T from which the chrome area is removed, and marking corresponding ignored pixels representing the edge areas in the defect result matrix C;

[0008] S04: filtering out the chrome area and edge area marked in steps S2 and S3 from the image T, leaving the glass area; identifying defects in the glass area, and marking corresponding defects for the pixels representing the defects in the defect result matrix C;

[0009] S05: Perform a closing operation on the defect result matrix C, then count the defect locations, filter the defects according to the length and / or width parameters of the defects, and obtain the final defect data.

[0010] Further, step S02 is specifically as follows:

[0011] A grayscale threshold Thrd is set, and each pixel point [x, y] in the image T is traversed; when the grayscale value of a pixel point [x, y] is less than the grayscale threshold Thrd, the pixel point [x, y] is considered to be a chrome area, and the value of the pixel point [x, y] in the defect result matrix C is set to an ignore mark value representing ignore.

[0012] Further, step S03 is specifically as follows:

[0013] An edge distance threshold range is set, and each pixel point [x, y] in the image T is traversed except the pixel point representing the chrome area in step S02; wherein, when the grayscale value of the neighboring pixel point [x1, y1] of a certain pixel point [x, y] is less than the grayscale threshold Thrd, the pixel point [x1, y1] is considered to be an edge area, and the value of the pixel point [x1, y1] in the defect result matrix C is set to an ignore mark value representing ignore; wherein, x-range≤x1≤x+range, y-range≤y1≤y+range.

[0014] Furthermore, defects are identified in the glass area, and corresponding defect identification is performed on the pixel points representing defects in the defect result matrix C. Specifically, a grayscale change threshold GrayThrd is set, and each pixel point in the glass area and the adjacent pixel points are taken as a pixel set. When the difference between the maximum and minimum grayscale values ​​of each pixel point in the pixel set is less than the grayscale change threshold GrayThrd, the value at the pixel point in the defect result matrix C is set to an ignore identification value representing ignore; otherwise, the value at the pixel point in the defect result matrix C is set to a defect identification value representing the defect.

[0015] Furthermore, the ignore flag value is 0.

[0016] Furthermore, the defect identification value is 255.

[0017] Furthermore, in the pixel set, the neighboring pixel points are the pixel points adjacent to the right, adjacent to the bottom, and diagonally to the lower right of the corresponding pixel point.

[0018] Furthermore, the defect result matrix C is initialized to be an all-0 matrix.

[0019] Furthermore, in step S05, the defect positions in the defect result matrix C are mapped to the original graph for marking.

[0020] After adopting the above technical scheme, when detecting by the method of the present invention, it is not necessary to provide design files, but only the imaging data of the transmitted light, that is, the image T obtained by irradiating the mask with the transmitted light, is required. The image T can be divided into three parts, namely: glass area, chrome area, and edge area. The edge area is the transition area between the glass area and the chrome area. The chrome area is a completely opaque area, and its grayscale value is lower than the grayscale values ​​of the glass area, the edge area, and the particle defect. The present invention separates the glass area, the chrome area, and the edge area by appropriate methods, and then identifies the particle defects in the glass area; during the exposure process of the photolithography process, the particle defects will change the transmission of light, resulting in insufficient energy at the position that needs to be exposed. The present invention utilizes the characteristics of insufficient light energy during particle defect exposure and the opaque characteristics of chromium, a light-shielding material, to detect glass dust particles and detached chrome, which is very convenient and highly sensitive. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a transmitted light detection diagram of the present invention;

[0022] FIG. 2(A) is a transmission light detection diagram of the present invention;

[0023] FIG2(B) is a graph showing the local grayscale value of the chrome area detected by transmitted light according to the present invention;

[0024] FIG3(A) is an edge region of a transmitted light detection diagram of the present invention;

[0025] FIG3(B) is a local gray value at a corner of a transmitted light detection image of the present invention;

[0026] FIG4(A) is a transmitted light detection diagram of the glass area of ​​the present invention;

[0027] FIG4(B) is a local gray value of particles in the glass region of the transmitted light detection diagram of the present invention;

[0028] Figure 5 is a schematic diagram of the defect result matrix C of the present invention;

[0029] Figure 6 It is the defect location diagram of the present invention. DETAILED DESCRIPTION

[0030] In order to make the contents of the present invention more clearly understood, the present invention is further described in detail below based on specific embodiments in conjunction with the accompanying drawings.

[0031] like Figures 1 to 6 As shown, a method for detecting particles on the surface of a mask glass area includes the following steps:

[0032] S01: Initialize the defect result matrix C and obtain the image T obtained by irradiating the mask with transmitted light, such as Figure 1 As shown; wherein the defect result matrix C can be initialized as an all-0 matrix;

[0033] S02: Identify the chrome area in the image T, and assign a corresponding ignore mark to the pixel points representing the chrome area in the defect result matrix C; specifically, set a grayscale threshold Thrd, and traverse each pixel point [x, y] in the image T; wherein, when the grayscale value of a certain pixel point [x, y] is less than the grayscale threshold Thrd, the pixel point [x, y] is considered to be the chrome area, and the value at the pixel point [x, y] in the defect result matrix C is set to the ignore mark value representing ignore, and finally separate the black area shown in FIG2(A); wherein the ignore mark value can be 0;

[0034] S03: Identify the edge area in the image T with the chrome area removed, and assign corresponding ignore marks to the pixels representing the edge area in the defect result matrix C; specifically: set an edge distance threshold range, and traverse each pixel [x, y] in the image T except the pixel representing the chrome area in step S02; wherein, when the gray value of the neighboring pixel [x1, y1] of a certain pixel [x, y] is less than the gray value threshold Thrd, the pixel [x1, y1] is considered to be the edge area, and the value at the pixel [x1, y1] in the defect result matrix C is set to the ignore mark value representing ignore, and finally separate the non-white area shown in FIG3 (A); wherein, x-range≤x1≤x+range, y-range≤y1≤y+range; the ignore mark value can also be 0;

[0035] S04: Filter out the chrome area and edge area marked in steps S2 and S3 from image T, leaving the glass area (the gray area in Figure 4 (A)); identify defects in the glass area, and mark the corresponding defects for the pixels representing defects in the defect result matrix C; specifically: set a grayscale change threshold GrayThrd, and use each pixel in the glass area and the adjacent pixels as a pixel set. When the difference between the maximum and minimum grayscale values ​​of each pixel in the pixel set is less than the grayscale change threshold GrayThrd, the value at the pixel in the defect result matrix C is set to the ignore mark value representing the ignore mark, otherwise the value at the pixel in the defect result matrix C is set to the defect mark value representing the defect. The ignore mark value can also be 0, and the defect mark value can be 255;

[0036] S05: Perform a closed operation on the defect result matrix C, then count the defect locations, filter the defects according to the length (preset parameter) and / or width (preset parameter) parameters of the defects, and obtain the final defect data, such as Figure 5 As shown; then the non-zero positions in the defect result matrix C are mapped to the original graph for marking, and the data finally presented to the user is Figure 6 .

[0037] In this embodiment, in the pixel set, the neighboring pixel points are the pixel points adjacent to the right, adjacent to the bottom, and diagonally to the lower right of the corresponding pixel point.

[0038] In this embodiment, the grayscale threshold Thrd can be specifically 60; the edge distance threshold range can be specifically 3; the grayscale change threshold GrayThrd can be set in the interval [25,35]. When the value is less than 25, false defects will be caused near the edge of the graphic, and when the value is greater than 35, defects will be missed.

[0039] In summary, this method has a very high sensitivity for capturing particles in the glass area of ​​the mask, and can simultaneously capture hard defects such as chromium oxide shedding, which can effectively avoid the interference of edges in defect judgment.

[0040] The specific embodiments described above further illustrate the technical problems, technical solutions and beneficial effects solved by the present invention. It should be understood that the above are only specific embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for detecting particles on the surface of a mask glass area, characterized in that: The steps of the method include: S01: Initialize the defect result matrix C and obtain the image T obtained by irradiating the mask with transmitted light; S02: Identify the chrome area in the image T, and mark the pixel points representing the chrome area as being ignored in the defect result matrix C; S03: identifying edge areas in the image T from which the chrome area is removed, and marking corresponding ignored pixels representing the edge areas in the defect result matrix C; S04: filtering out the chrome area and edge area marked in steps S2 and S3 from the image T, leaving the glass area; identifying defects in the glass area, and marking corresponding defects for the pixels representing the defects in the defect result matrix C; S05: Perform a closing operation on the defect result matrix C, then count the defect locations, filter the defects according to the length and / or width parameters of the defects, and obtain the final defect data.

2. The method according to claim 1, characterized in that Step S02 is specifically as follows: A grayscale threshold Thrd is set, and each pixel point [x, y] in the image T is traversed; when the grayscale value of a pixel point [x, y] is less than the grayscale threshold Thrd, the pixel point [x, y] is considered to be a chrome area, and the value of the pixel point [x, y] in the defect result matrix C is set to an ignore mark value representing ignore.

3. The method according to claim 1, characterized in that Step S03 is specifically as follows: An edge distance threshold range is set, and each pixel point [x, y] in the image T is traversed except the pixel point representing the chrome area in step S02; wherein, when the grayscale value of the neighboring pixel point [x1, y1] of a certain pixel point [x, y] is less than the grayscale threshold Thrd, the pixel point [x1, y1] is considered to be an edge area, and the value of the pixel point [x1, y1] in the defect result matrix C is set to an ignore mark value representing ignore; wherein, x-range≤x1≤x+range, y-range≤y1≤y+range.

4. The method according to claim 1, characterized in that Defects in the glass area are identified, and corresponding defect marks are made for the pixels representing defects in the defect result matrix C. Specifically, a grayscale change threshold GrayThrd is set, and each pixel in the glass area and the adjacent pixels are taken as a pixel set. When the difference between the maximum and minimum grayscale values ​​of each pixel in the pixel set is less than the grayscale change threshold GrayThrd, the value at the pixel in the defect result matrix C is set to an ignore mark value representing ignore; otherwise, the value at the pixel in the defect result matrix C is set to a defect mark value representing the defect.

5. The method according to any one of claims 2 to 4, characterized in that: The ignore flag value is 0.

6. The method according to claim 4, characterized in that The defect identification value is 255.

7. The method according to claim 4, characterized in that In the pixel set, the neighboring pixel points are the pixel points to the right, below, and lower right diagonally of the corresponding pixel point.

8. The method according to claim 1, characterized in that Initialize the defect result matrix C to be a matrix of all zeros.

9. The method according to claim 1, characterized in that: In step S05, the defect positions in the defect result matrix C are mapped to the original image for marking.

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