Defect self-classification method and system

Abstract

The present invention provides a defect self-classification method and system for self-classifying defects on a wafer, wherein the defect self-classification method comprises: obtaining image information of the defect location; converting the image information into a digital grayscale image; performing graphic level recognition on the digital grayscale image; and judging the lethality of the defect according to the result of the graphic level recognition, and classifying the defect. The present invention converts the image information of the defect location into a digital grayscale image for graphic level recognition, thereby judging the lethality of the defect without introducing a chip design file (GDS file), thereby achieving defect self-classification.

Description

Technical Field

[0001] The present invention relates to the technical field of integrated circuit manufacturing, and in particular to a defect self-classification method and system. Background Art

[0002] With the rapid development of semiconductor integrated circuit manufacturing technology, the characteristic size of wafers continues to decrease. Defects on the wafer surface have become the main obstacle affecting the yield, and defect detection technology has become a key technology in the integrated circuit industry.

[0003] A wafer usually includes multiple chips (dies), and defects at different locations in the chips have different impacts on the chip yield. Figure 1 If the defect is a graphic defect (i.e., the defect spans at least two graphic areas), it means that this type of defect has a high lethality; refer to Figure 2. If the defect is a non-graphic defect (i.e., the defect only exists in one graphic area), it means that this type of defect has a low lethality. In the current wafer preparation process, in order to accurately distinguish the lethal defects, it is necessary to use a defect observation machine (review machine) to collect images of the defect location, and then manually screen and classify the killer defects. However, the above-mentioned wafer defect classification method has the problems of long time consumption, low efficiency, and high classification cost.

[0004] In view of this, a method and system are needed to automatically classify defects with different degrees of lethality, provide a more accurate basis for judging wafer yield loss, and improve work efficiency. Summary of the invention

[0005] The purpose of the present invention is to provide a defect self-classification method and system, which converts the image information of the defect location into a digital grayscale image to perform graphic hierarchy recognition, thereby judging the lethality of the defect and realizing defect self-classification.

[0006] In order to achieve the above object, the present invention provides a defect self-classification method for self-classifying defects on a wafer, comprising:

[0007] Obtain image information of the defect location;

[0008] Converting the image information into a digital grayscale image;

[0009] Performing graphic level recognition on the digital grayscale image; and,

[0010] The lethality of the defect is determined according to the result of the graphic level recognition, and the defect is classified.

[0011] Optionally, the process of converting the image information into a digital grayscale image includes:

[0012] The image information is processed into small pixel grids to obtain the grayscale value in each grid, thereby obtaining the digital grayscale image.

[0013] Optionally, the process of performing layer recognition on the digital grayscale image includes:

[0014] The defect graphic layer where the defect is located and two background graphic layers contained in the digital grayscale image are obtained.

[0015] Optionally, the process of obtaining the defect graphic level and the background graphic level includes:

[0016] Counting the number of grids corresponding to each grayscale value in the digital grayscale image, and drawing a change curve of the number of grids corresponding to the grayscale value, wherein the change curve includes three peaks;

[0017] The minimum value and the maximum value of the three grayscale values ​​corresponding to the three peak values ​​are respectively set as the first center value and the second center value;

[0018] A plurality of defect grids are set in the digital grayscale pattern, the absolute value of the difference between the grayscale value corresponding to the defect grid and the first central value is less than or equal to a first set threshold, and all the defect grids constitute the defect pattern hierarchy;

[0019] A plurality of first background grids are set in the digital grayscale image, the absolute value of the difference between the grayscale value corresponding to the first background grid and the second center value is less than or equal to a second set threshold, and all the first background grids constitute a first background graphic layer; and,

[0020] The remaining grids in the digital grayscale image are set as second background grids, and all the second background grids constitute a second background graphic layer.

[0021] Optionally, the process of judging the lethality of the defect according to the result of the graphic level recognition includes:

[0022] It is determined whether the defective graphic layer spans across different background graphic layers. If so, the defect corresponding to the defective graphic layer has a greater lethality; if not, the defect corresponding to the defective graphic layer has a smaller lethality.

[0023] Optionally, the process of classifying the defects includes:

[0024] It is determined whether the defective graphic layer spans across different background graphic layers. If so, the defect corresponding to the defective graphic layer is a graphic defect; if not, the defect corresponding to the defective graphic layer is a non-graphic defect.

[0025] Optionally, after determining the lethality of the defect and classifying the defect, the method further includes:

[0026] Establish a grid coordinate system and obtain the position coordinates and defect types of all defects on the wafer in sequence;

[0027] A distribution diagram of different types of defects on the wafer is drawn according to the position coordinates and defect types of all the defects.

[0028] Accordingly, the present invention also provides a defect self-classification system, comprising:

[0029] A data collection unit, used to obtain the position coordinates of all defects on the wafer and image information of the locations of the defects;

[0030] An image conversion and recognition unit, used for converting the image information into a digital grayscale image and performing graphic level recognition on the digital grayscale image;

[0031] The defect classification unit is used to determine the lethality of the defect and classify the defect.

[0032] Optionally, the defect self-classification system also includes a UI interface for outputting the image information, the digital grayscale image and distribution graphs of different types of defects on the wafer.

[0033] Optionally, an electron beam observation device is used to obtain image information of the defect location.

[0034] In summary, the present invention provides a defect self-classification method and system for self-classifying defects on a wafer, wherein the defect self-classification method includes: obtaining image information of the defect location; converting the image information into a digital grayscale image; performing graphic level recognition on the digital grayscale image; and judging the lethality of the defect according to the result of the graphic level recognition, and classifying the defect. The present invention converts the image information of the defect location into a digital grayscale image for graphic level recognition, thereby judging the lethality of the defect without introducing a chip design file (GDS file), thereby achieving defect self-classification. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 A flowchart of a defect self-classification method provided by an embodiment of the present invention;

[0036] Figure 2aand Figure 2b They are respectively image information of locations of different defects in the defect self-classification method provided by an embodiment of the present invention;

[0037] Figure 3a for Figure 2a A digital grayscale image converted from the image information;

[0038] Figure 3b for Figure 2b A digital grayscale image converted from the image information;

[0039] Figure 4 A curve diagram showing the change in the number of grids corresponding to the grayscale value in the defect self-classification method provided by an embodiment of the present invention;

[0040] Figure 5 A distribution diagram of different types of defects on a wafer in a defect self-classification method provided by an embodiment of the present invention;

[0041] The reference numerals are as follows:

[0042] 1-wafer; 11-graphic defect; 12-non-graphic defect;

[0043] A1, A2, A3-peak values; B1-first center value; B2-second center value. DETAILED DESCRIPTION

[0044] The specific implementation of the present invention will be described in more detail below in conjunction with the schematic diagram. The advantages and features of the present invention will become clearer based on the following description. It should be noted that the drawings are all in a very simplified form and are not in exact proportions, and are only used to facilitate and clearly assist in explaining the purpose of the embodiments of the present invention.

[0045] Figure 1 A flowchart of a defect self-classification method provided by an embodiment of the present invention. Figure 1 The defect self-classification method described in this embodiment is used to self-classify defects on a wafer, and includes:

[0046] Step S01: Acquire image information of the defect location;

[0047] Step S02: converting the image information into a digital grayscale image;

[0048] Step S03: performing graphic level recognition on the digital grayscale image; and,

[0049] Step S04: judging the lethality of the defect according to the result of the graphic level recognition, and classifying the defect.

[0050] Figure 2a and Figure 2b They are respectively image information of locations of different defects in the defect self-classification method provided in this embodiment; Figure 3a for Figure 2a A digital grayscale image converted from the image information; Figure 3b for Figure 2b A digital grayscale image converted from the image information; Figure 4 A curve diagram showing the change in the number of grids corresponding to the grayscale value in the defect self-classification method provided in this embodiment; Figure 5 The distribution diagram of different types of defects on the wafer in the defect self-classification method provided in this embodiment. Figure 2a-2b , Figure 3a-Figure 3b , Figure 4 and Figure 5 The defect self-classification method described in this embodiment is described in detail.

[0051] First, see Figure 2a and Figure 2b , execute step S01 to obtain image information of the positions of defects P1 and P2. In this embodiment, an electron beam observation machine is used to obtain image information of the positions of defects P1 and P2. In other embodiments of the present invention, other devices may also be used to obtain image information of the defects, and the present invention is not limited to this.

[0052] Next, see Figure 3a and Figure 3b , execute step S02 to convert the image information into a digital grayscale image. Specifically, the process of converting the image information into a digital grayscale image includes: performing small pixel gridding processing on the image information, obtaining the grayscale value in each grid, and thus obtaining the digital grayscale image. Optionally, before converting the image information into the digital grayscale image, the image information may be processed, such as adjusting the contrast, brightness or sharpening the image information, so as to highlight the defects in the image information and improve the efficiency and accuracy of subsequent graphic layer recognition.

[0053] Then, see Figure 3a and Figure 4, execute step S03, and perform graphic level recognition on the digital grayscale image. Optionally, the process of performing level recognition on the digital grayscale image includes: obtaining the defect graphic level where the defect is located and the two background graphic levels contained in the digital grayscale image. It should be noted that since the defect usually exists on the surface of the wafer, and the surface of the wafer is uneven due to the existence of top metal lines, isolation grooves and other structures, different structures have different brightness in the image information, so that the background part in the image information has two different areas with relatively high brightness and relatively low brightness, and the two areas correspond to the two background graphic levels in the digital grayscale image.

[0054] The following takes the defect P1 as an example to introduce the detailed process of obtaining the defect graphic level and the background graphic level:

[0055] First, see Figure 3a and Figure 4 , execute step 1, count the number of grids corresponding to each grayscale value in the digital grayscale image, and draw a change curve of the number of grids corresponding to the grayscale value (such as Figure 4 As shown), the variation curve includes three peaks A1, A2 and A3;

[0056] Next, step 2 is performed to set the minimum value and the maximum value of the three grayscale values ​​corresponding to the three peak values ​​A1, A2 and A3 as the first center value B1 and the second center value B2 respectively; Figure 4 It can be seen that the first center value B1 = 90, and the second center value B2 = 60;

[0057] Then, step three is performed to set a plurality of defect grids in the digital grayscale pattern, wherein the absolute value of the difference between the grayscale value corresponding to the defect grid and the first central value B1 is less than or equal to a first set threshold value, and all the defect grids constitute the defect pattern hierarchy; Figure 3a and Figure 4 , the first set threshold is set to 15, then Figure 3a All grids with medium grayscale values ​​between 75 and 105 are defective grids;

[0058] Next, step 4 is performed to set a plurality of first background grids in the digital grayscale image, wherein the absolute value of the difference between the grayscale value corresponding to the first background grid and the second center value B2 is less than or equal to the second set threshold value, and all the first background grids constitute a first background graphic layer; Figure 3a and Figure 4 , the second set threshold is set to 14, then Figure 3a All grids with medium grayscale values ​​between 46 and 69 are the first background grids;

[0059] Then, step five is performed to set the remaining grids in the digital grayscale image as second background grids, and all the second background grids constitute a second background graphic layer; Figure 3a and Figure 4 It can be seen that at this time Figure 3a The grayscale values ​​of the remaining grids are between 0 and 45, that is, the grids with grayscale values ​​between 0 and 45 are all the second background grids.

[0060] See also Figure 3b In the digital grayscale image of the defect P2, the defect graphic level includes all grids with grayscale values ​​between 71 and 79, the first background graphic level includes all grids with grayscale values ​​between 50 and 70, and the second background graphic level includes all grids with grayscale values ​​between 0 and 49. The specific graphic level recognition process is the same as the graphic level recognition process of the defect P1 (i.e., steps 1 to 5), which will not be repeated here.

[0061] It should be noted that, since the defects on the wafer are usually very small, the area of ​​the defect in the image information of the location of the defect accounts for a very small proportion of the total area of ​​the image information; accordingly, after the image information is converted into the digital grayscale image, the number of grids representing the defect is relatively small, while the number of background grids representing different areas is relatively large. Therefore, in step 2, the minimum value of the three grayscale values ​​corresponding to the three peaks A1, A2 and A3 can be used to represent the grayscale value of the grid of the defect, and the maximum value of the three grayscale values ​​corresponding to the three peaks A1, A2 and A3 can be used to represent the grayscale value of the grid of a background pattern.

[0062] In other embodiments of the present invention, the specific values ​​of the first set threshold and the second set threshold need to be reasonably determined according to the distribution of all grayscale values ​​in the digital grayscale image, and the present invention does not limit this. When the grayscale values ​​of all grids in the digital grayscale image are not very different, and it is difficult to distinguish the defect image level, the first background graphic level, and the second background graphic level, the process can return to step S02 and re-process the image information to make the light and dark difference between the defect and different backgrounds in the image information more obvious.

[0063] Next, step S04 is executed to determine the lethality of the defect according to the result of the graphic level recognition, and to classify the defect. Specifically, the process of determining the lethality of the defect according to the result of the graphic level recognition includes: determining whether the defect graphic level spans across different background graphic levels (i.e., whether it covers the first background graphic level and the second background graphic level at the same time). If so, the lethality of the defect corresponding to the defect graphic level is large; if not, the lethality of the defect corresponding to the defect graphic level is small. Correspondingly, the process of classifying the defect includes: determining whether the defect graphic level spans across different background graphic levels. If so, the defect corresponding to the defect graphic level is a graphic defect; if not, the defect corresponding to the defect graphic level is a non-graphic defect.

[0064] See also Figure 3a and Figure 3b Combined with the graphic level recognition results of defects P1 and P2 in step S03, it can be seen that the defect graphic level of defect P1 spans the first background graphic level and the second background graphic level. Therefore, the defect P1 is a graphic defect with great lethality; the defect graphic level of defect P2 only covers the first background graphic level and does not cover the second background graphic level. Therefore, the defect P2 is a non-graphic defect with low lethality.

[0065] It should be noted that the above method of determining the lethality of the defects and classifying the defects is merely an example. In other embodiments of the present invention, a comprehensive judgment may be made based on the specific location of the defects in the chip or other factors, and the present invention is not limited to this.

[0066] In addition, see Figure 5 In this embodiment, after determining the lethality of the defects and classifying the defects, it also includes: establishing a grid coordinate system, obtaining the position coordinates and defect types of all defects on the wafer 1 (i.e., graphic defects 11 and non-graphic defects 12) in sequence; and drawing a distribution graph of different types of defects on the wafer 1 according to the position coordinates and defect types of all the defects, so as to analyze the wafer yield.

[0067] Accordingly, the present invention also provides a defect self-classification system, comprising:

[0068] A data collection unit, used to obtain the position coordinates of all defects on the wafer and image information of the locations of the defects;

[0069] An image conversion and recognition unit, used for converting the image information into a digital grayscale image and performing graphic level recognition on the digital grayscale image;

[0070] The defect classification unit is used to determine the lethality of the defect and classify the defect.

[0071] In this embodiment, the defect self-classification system further includes a UI interface for outputting the image information, the digital grayscale image and the distribution diagram of different types of defects on the wafer. Optionally, an electron beam observation machine is used to obtain image information of the defect location.

[0072] Compared with the existing manual screening methods, the defect self-classification method and system described in this embodiment does not need to introduce chip design files (GDS files). Defect self-classification can be achieved only through image information of the defect location on the wafer, which greatly improves the classification efficiency and the accuracy of the classification results, and provides a more accurate basis for judging wafer yield loss.

[0073] In summary, the present invention provides a defect self-classification method and system for self-classifying defects on a wafer, wherein the defect self-classification method includes: obtaining image information of the defect location; converting the image information into a digital grayscale image; performing graphic level recognition on the digital grayscale image; and judging the lethality of the defect according to the result of the graphic level recognition, and classifying the defect. The present invention converts the image information of the defect location into a digital grayscale image for graphic level recognition, thereby judging the lethality of the defect without introducing a chip design file (GDS file), thereby achieving defect self-classification.

[0074] The above is only a preferred embodiment of the present invention and does not limit the present invention in any way. Any technician in the relevant technical field, without departing from the scope of the technical solution of the present invention, makes any form of equivalent replacement or modification to the technical solution and technical content disclosed in the present invention, which does not depart from the content of the technical solution of the present invention and still falls within the protection scope of the present invention.

Claims

1. A defect self-classification method for self-classifying defects on a wafer, characterized in that: include: Obtain image information of the defect location; Converting the image information into a digital grayscale image; Performing graphic level recognition on the digital grayscale image; and, Determine the lethality of the defect according to the result of the graphic level recognition, and classify the defect; The process of converting the image information into a digital grayscale image includes: Performing small pixel gridding processing on the image information to obtain the grayscale value in each grid, thereby obtaining the digital grayscale image; The process of performing layer recognition on the digital grayscale image includes: Acquire a defect graphic layer where the defect is located and two background graphic layers contained in the digital grayscale image; The process of obtaining the defect graphic level and the background graphic level includes: Counting the number of grids corresponding to each grayscale value in the digital grayscale image, and drawing a change curve of the number of grids corresponding to the grayscale value, wherein the change curve includes three peaks; The minimum value and the maximum value of the three grayscale values ​​corresponding to the three peak values ​​are respectively set as the first center value and the second center value; A plurality of defect grids are set in the digital grayscale image, wherein the absolute value of the difference between the grayscale value corresponding to the defect grid and the first central value is less than or equal to a first set threshold, and all the defect grids constitute the defect pattern hierarchy; A plurality of first background grids are set in the digital grayscale image, the absolute value of the difference between the grayscale value corresponding to the first background grid and the second center value is less than or equal to a second set threshold, and all the first background grids constitute a first background graphic layer; and, The remaining grids in the digital grayscale image are set as second background grids, and all the second background grids constitute a second background graphic layer.

2. The defect self-classification method according to claim 1, characterized in that: The process of judging the lethality of the defect according to the result of the graphic level recognition includes: It is determined whether the defective graphic layer spans across different background graphic layers. If so, the defect corresponding to the defective graphic layer has a greater lethality; if not, the defect corresponding to the defective graphic layer has a smaller lethality.

3. The defect self-classification method according to claim 2, characterized in that: The process of classifying the defects includes: It is determined whether the defective graphic layer spans across different background graphic layers. If so, the defect corresponding to the defective graphic layer is a graphic defect; if not, the defect corresponding to the defective graphic layer is a non-graphic defect.

4. The defect self-classification method according to any one of claims 1 to 3, characterized in that: After determining the lethality of the defect and classifying the defect, the method further includes: Establish a grid coordinate system and obtain the position coordinates and defect types of all defects on the wafer in sequence; A distribution diagram of different types of defects on the wafer is drawn according to the position coordinates and defect types of all the defects.

5. A defect self-classification system, characterized in that: include: A data collection unit, used to obtain the position coordinates of all defects on the wafer and image information of the locations of the defects; An image conversion and recognition unit, used for converting the image information into a digital grayscale image and performing graphic level recognition on the digital grayscale image; The process of converting the image information into a digital grayscale image includes: Performing small pixel gridding processing on the image information to obtain the grayscale value in each grid, thereby obtaining the digital grayscale image; The process of performing layer recognition on the digital grayscale image includes: Acquire a defect graphic layer where the defect is located and two background graphic layers contained in the digital grayscale image; The process of obtaining the defect graphic level and the background graphic level includes: Counting the number of grids corresponding to each grayscale value in the digital grayscale image, and drawing a change curve of the number of grids corresponding to the grayscale value, wherein the change curve includes three peaks; The minimum value and the maximum value of the three grayscale values ​​corresponding to the three peak values ​​are respectively set as the first center value and the second center value; A plurality of defect grids are set in the digital grayscale image, wherein the absolute value of the difference between the grayscale value corresponding to the defect grid and the first central value is less than or equal to a first set threshold, and all the defect grids constitute the defect pattern hierarchy; A plurality of first background grids are set in the digital grayscale image, the absolute value of the difference between the grayscale value corresponding to the first background grid and the second center value is less than or equal to a second set threshold, and all the first background grids constitute a first background graphic layer; and, The remaining grids in the digital grayscale image are set as second background grids, and all the second background grids constitute a second background graphic layer; The defect classification unit is used to determine the lethality of the defect and classify the defect.

6. The defect self-classification system according to claim 5, characterized in that: It also includes a UI interface for outputting the image information, the digital grayscale image and distribution graphs of different types of defects on the wafer.

7. The defect self-classification system according to claim 5, characterized in that: An electron beam observation machine is used to obtain image information of the defect location.

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