SRAF development defect detection method

Abstract

The invention provides a method for detecting SRAF developing defects. According to the SRAF development defect detection method, the principle that electron beams emitted by key size measurement equipment in the scanning and focusing process can enlarge the size of a development pattern caused by a pattern groove in patterned photoresist and SRAF is utilized, and SRAF development defect noise is effectively eliminated. Therefore, accurate grabbing and efficient detection of defect features in the patterned photoresist layer are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of semiconductor preparation, and in particular to a method for detecting SRAF development defects. Background Art

[0002] As the critical dimensions (CD) of semiconductor devices shrink, advanced processes require higher and higher resolution of lithography systems (LT), which results in a smaller and smaller process window (Depth of Focus, DOF) of LT. Figure 1 As shown, in order to improve the process window, the existing process adds a sub-resolution assist feature (SRAF) 101 around the target pattern 100 to improve its surrounding environment, enhance process stability, improve the accuracy of pattern transfer, and reduce potential process defects.

[0003] However, in the actual photolithography process, the SRAF pattern will be printed out on the photoresist layer after exposure and development due to factors such as exposure dose, focus position, SRAF pattern size, and the distance between the SRAF pattern and the target pattern. That is, the SRAF pattern is transferred to the photoresist layer 200 on the semiconductor structure to form Figure 2 The defect feature M shown in the figure. In this regard, in the graphic development stage, it is necessary to focus on detecting the distribution of the defect feature M caused by the SRAF graphic to avoid the problem of SRAF graphic development in the actual application stage. However, the current detection of defect feature M mainly relies on human judgment, which is not only inefficient, but also prone to omissions and misjudgments. Especially when the SRAF graphic is close to the critical development threshold, the defect feature M formed is not obvious and difficult to identify.

[0004] Therefore, a new SRAF development defect detection method is urgently needed to solve the above technical problems. Summary of the invention

[0005] The object of the present invention is to provide a method for detecting SRAF development defects, so as to solve the problem of how to improve the detection efficiency and detection accuracy of lithography defects caused by SRAF.

[0006] In order to solve the above technical problems, the present invention provides a method for detecting SRAF development defects, comprising:

[0007] Scanning the patterned photoresist layer using a critical dimension measurement device to obtain a plurality of first inspection images;

[0008] Screening out the first detection image having defect features from the plurality of first detection images;

[0009] Acquire position information of the first detection image having defect features;

[0010] Scanning the area corresponding to the position information in the patterned photoresist layer at least once using the critical dimension measurement device to obtain a plurality of second detection images;

[0011] The first detection image and the second detection image having the same position information are compared, and the second detection image having defect features is screened out.

[0012] Optionally, in the SRAF development defect detection method, the defect feature includes a development pattern of SRAF in the patterned photoresist layer.

[0013] Optionally, in the SRAF development defect detection method, the process of selecting the first detection image having defect characteristics from the plurality of first detection images includes:

[0014] Providing a database, the database comprising a plurality of reference images having defect features;

[0015] The plurality of first detection images are compared with the plurality of reference images having defect features to screen out the first detection images having defect features.

[0016] Optionally, in the SRAF development defect detection method, the process of comparing the plurality of first detection images with the plurality of reference images having defect features to screen out the first detection images having defect features includes:

[0017] Respectively obtaining characteristic value distribution diagrams of the plurality of first detection images and the plurality of reference images having defect characteristics;

[0018] Each of the first detection images is compared with the characteristic value distribution diagram of multiple reference images; and when the difference between the characteristic value of the SRAF corresponding position in the first detection image and the characteristic value of the SRAF corresponding position in the reference image is within a threshold range, the first detection image has the defect feature.

[0019] Optionally, in the SRAF development defect detection method, the process of comparing the first detection image and the second detection image having the same position information and screening out the second detection image having defect characteristics includes:

[0020] Respectively obtaining characteristic value distribution diagrams of the first detection image and the second detection image having defect characteristics;

[0021] Compare the feature value distribution diagrams of the first detection image and the second detection image of the same position information; and when the feature value of the SRAF corresponding position in the second detection image is less than or equal to the feature value of the SRAF corresponding position in the first detection image, the second detection image has the defect feature.

[0022] Optionally, in the SRAF development defect detection method, the characteristic value includes a grayscale value or a grayscale mean.

[0023] Based on the same inventive concept, the present invention also provides a method for detecting SRAF development defects, comprising:

[0024] Determine the coordinates of the target sampling points in the patterned photoresist layer according to the coordinate system of the chip;

[0025] Performing a first focusing on one side of the sampling area corresponding to the target sampling point coordinates using a key dimension measurement device;

[0026] Performing a second focusing at the target sampling point coordinate position using the critical dimension measurement device;

[0027] Scanning the sampling area at least once using the key dimension measurement device to obtain a plurality of third detection images;

[0028] The third detection image having defect features is screened out from the multiple third detection images.

[0029] Optionally, in the SRAF development defect detection method, the defect feature includes a development pattern of SRAF in the patterned photoresist layer.

[0030] Optionally, in the SRAF development defect detection method, the process of selecting the third detection image having defect characteristics from the plurality of third detection images includes:

[0031] Providing a database, the database comprising a plurality of reference images having defect features;

[0032] The plurality of third detection images are compared with the plurality of reference images having defect features to screen out the third detection images having defect features.

[0033] Optionally, in the SRAF development defect detection method, the process of comparing the plurality of third detection images with the plurality of reference images having defect characteristics to screen out the third detection images having defect characteristics includes:

[0034] Respectively obtaining characteristic value distribution diagrams of the plurality of third detection images and the plurality of reference images having defect characteristics;

[0035] Compare each of the third detection images with the characteristic value distribution diagrams of the plurality of reference images; and when the difference between the characteristic value of the SRAF corresponding position in the third detection image and the characteristic value of the SRAF corresponding position in the reference image is within a threshold range, the third detection image has the defect feature;

[0036] The characteristic value includes a gray value or a gray mean.

[0037] In summary, the present invention provides a method for detecting SRAF development defects. Compared with the prior art, the method for detecting SRAF development defects utilizes the principle that the electron beam emitted by the key dimension measurement device during the scanning and focusing process can enlarge the size of the pattern groove in the patterned photoresist and the development pattern caused by SRAF, thereby effectively eliminating noise, and further realizing accurate capture and efficient detection of defect features in the patterned photoresist layer.

[0038] Among them, in a detection method of the SRAF development defect, a key dimension measurement device is first used to scan the patterned photoresist layer to obtain a first detection image; then, the first detection image with defect features is screened out from the first detection image, and the position information of the first detection image with defect features is obtained. Then, the key dimension measurement device is used to scan again once or several times to obtain a second detection image with the same position information. Finally, the first detection image and the second detection image with the same position information are compared to obtain a second detection image with defect features. Based on the above principle, it can be seen that compared with the first detection image, the size of the corresponding pattern groove and the development pattern caused by SRAF in the second detection image is enlarged by electron beam scanning, so that the defect features can be captured more clearly and clearly in the defect feature comparison process, thereby improving the detection accuracy.

[0039] Based on the same concept, in another detection method of the SRAF development defect, the first focusing is first performed on one side of the sampling area to achieve the lens focusing of the key dimension measurement device. Then, the second focusing is performed at the coordinate position of the target sampling point so that the sampling area is scanned by the electron beam, and the size of the pattern grooves and the development pattern caused by SRAF in the sampling area increases. Secondly, the key dimension measurement device is used to scan again once or several times to obtain the third detection image. Finally, the third detection image with defect features is screened out. Since the corresponding pattern grooves and the development pattern caused by SRAF in the third detection image are both of enlarged size, it is conducive to efficiently eliminating defect noise and realizing accurate detection of the defect features. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Those skilled in the art will appreciate that the drawings are provided for a better understanding of the present invention, but do not constitute any limitation on the scope of the present invention.

[0041] Figure 1 It is a schematic diagram of a target graphic and a SRAF graphic in the prior art.

[0042] Figure 2 It is a schematic diagram of defects formed by SRAF patterns in photoresist in the prior art.

[0043] Figure 3 It is a flow chart of the method for detecting SRAF development defects in the first embodiment of the present invention.

[0044] Figure 4 It is a schematic diagram of the first detection image in the first embodiment of the present invention.

[0045] Figure 5 It is a schematic diagram of the second detection image in the first embodiment of the present invention.

[0046] Figure 6 It is a flow chart of the method for detecting SRAF development defects in the second embodiment of the present invention.

[0047] Figure 7 Schematic diagram of the positional relationship between the first focusing point and the target sampling point in the chip coordinate system in the second embodiment of the present invention.

[0048] Figure 8 Schematic diagram of the positional relationship between the first focusing point and the target sampling point in the second embodiment of the present invention.

[0049] Fig. 9 It is a schematic diagram of the third detection image in the second embodiment of the present invention.

[0050] And, in the attached drawings:

[0051] 100-target pattern; 101-SRAF pattern;

[0052] 200-photoresist layer;

[0053] 300-photoresist; 301-pattern groove;

[0054] 400-photoresist; 401-pattern groove;

[0055] M-defect feature; N-noise; W-wafer; S-sampling area; O0-origin of chip coordinate system; O1-first focusing point coordinates; O2-target sampling point coordinates; P1-first detection image; P2-second detection image; P3-third detection image. DETAILED DESCRIPTION

[0056] In order to make the purpose, advantages and features of the present invention clearer, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the drawings are all in a very simplified form and are not drawn to scale, and are only used to conveniently and clearly assist in explaining the purpose of the embodiments of the present invention. In addition, the structure shown in the drawings is often a part of the actual structure. In particular, the emphasis of each drawing is different, and sometimes different proportions are used. It should also be understood that, unless otherwise specified or indicated, the terms "first", "second", "third", etc. in the specification are only used to distinguish between the various components, elements, steps, etc. in the specification, and are not used to represent the logical relationship or sequential relationship between the various components, elements, steps, etc.

[0057] <Example 1>

[0058] See also Figure 3 This embodiment provides a method for detecting SRAF development defects, including:

[0059] Step 1 S10: Scanning the patterned photoresist layer using a key dimension measurement device to obtain a plurality of first detection images;

[0060] Step 2 S11: Screening out the first detection image having defect features from the plurality of first detection images;

[0061] Step three S12: obtaining position information of the first detection image having defect features;

[0062] Step 4 S13: using the critical dimension measurement device to scan the area corresponding to the position information in the patterned photoresist layer at least once to obtain a plurality of second detection images;

[0063] Step five S14: compare the first detection image and the second detection image having the same position information, and select the second detection image having defect features.

[0064] Based on this, the SRAF development defect detection method provided in this embodiment is to use a critical dimension measurement device to scan the patterned photoresist layer, so as to utilize the principle that the electron beam emitted by the critical dimension measurement device can expand the size of the pattern groove and the development pattern, thereby achieving effective elimination of noise, and accurately capturing and efficiently detecting the defect features in the patterned photoresist layer.

[0065] The following is combined with Figure 3 to Figure 5 The SRAF development defect detection method provided in this embodiment is specifically described.

[0066] Step 1 S10: Please refer to Figure 4 , a key dimension measurement device is used to scan the patterned photoresist layer to obtain multiple first detection images.

[0067] It should be noted that the critical dimension measurement device, also known as a scanning electron microscope (Critical Dimension Scanning Electron Microscope, CDSEM) for characteristic dimension measurement, is a scanning electron microscope that determines the boundary of a graphic based on the grayscale of the image and then calculates the line width. Also, in the process of scanning the patterned photoresist layer using the critical dimension measurement device, the critical dimension measurement device will emit an electron beam, and the electron beam will have a certain impact on the patterned photoresist layer after being irradiated to the patterned photoresist layer. Among them, based on the image acquisition characteristics of the critical dimension measurement device, the first detection image obtained by scanning with the critical dimension measurement device is an image of the patterned photoresist layer before it is affected by the electron beam. After the first detection image is obtained, the patterned photoresist layer will be affected by the electron beam and change.

[0068] Specifically, Figure 4 As shown, the patterned photoresist layer includes a photoresist 300 and a pattern groove 301 formed in the photoresist 300. After being irradiated by an electron beam, the photoresist 300 will shrink, resulting in an increase in the size of the pattern groove 301. Based on this, since the defect feature M detected by the SRAF development defect detection method provided in this embodiment is a development pattern formed by SRAF in the patterned photoresist layer. Therefore, when the development pattern is irradiated by an electron beam, the size of the development pattern will also expand, making it easier to identify and capture.

[0069] Furthermore, since different pattern grooves 301 are formed in each area of ​​the patterned photoresist layer, in order to take into account both detection accuracy and detection efficiency, during the execution of step 1 S10, images of each area corresponding to different position information in the patterned photoresist layer can be sampled to obtain multiple first detection images P1.

[0070] Preferably, before executing the step S10, a focusing is performed outside the area to be collected to achieve lens focusing of the critical dimension measurement device, so as to obtain a clearer first detection image P1. And, after executing the step S10, the critical dimension measurement device can obtain and output the critical dimension (CD) of the patterned photoresist layer according to the obtained first detection image P1 while detecting defects.

[0071] Step 2 S11: Please refer to Figure 4 , screening out the first detection image having the defect feature M from the multiple first detection images.

[0072] Specifically, the step 2 S11 includes:

[0073] Sub-step 1: Provide a database, wherein the database includes a plurality of reference images having defect features M.

[0074] Preferably, the collection process of the reference image in the database is: in the actual process, using key dimension measurement equipment to collect images of various patterned photoresist layers, and using the partial image with the typical defect feature M in each acquired image as the reference image.

[0075] Sub-step 2: Compare the plurality of first detection images P1 with the plurality of reference images having defect features to select the first detection images P1 having the defect feature M.

[0076] Specifically, in the comparison process, it is necessary to first obtain the characteristic value distribution diagrams of the multiple first detection images P1 and the multiple reference images with defect feature M respectively. Then, each of the first detection images P1 is compared with the characteristic value distribution diagrams of the multiple reference images one by one. Among them, when the difference between the characteristic value of the corresponding position of SRAF in the first detection image P1 and the characteristic value of the corresponding position of SRAF in the reference image is within the threshold range, the first detection image P1 has the defect feature M. And, since the images acquired by the key dimension measurement equipment are all grayscale images, the characteristic values ​​in this embodiment can be grayscale values, or grayscale mean values. Among them, the grayscale mean value is the average grayscale value within a certain range of the corresponding position. For example, the average grayscale value within the range where the photoresist shrinks after electron beam irradiation.

[0077] It can be understood that since the grayscale value includes 0 to 255 graduation values, and the larger the value, the lighter the color; the smaller the value, the darker the color. And in the image scanned and acquired by the key dimension measurement equipment, the pattern groove 301 and the developed pattern are darker, while the photoresist 300 is lighter. Exemplarily, assuming that the grayscale value or grayscale mean of the defect feature M at the position corresponding to SRAF in the reference image ranges from 0 to 150, and the threshold range is from 0 to 10, when the grayscale value or grayscale mean at the position corresponding to SRAF in the first detection image P1 is between [0,160], it can be determined that the first detection image P1 has the defect feature M, otherwise, the first detection image P1 is a normal image and does not have the defect feature M. Among them, the defect feature M in the first detection image P1 screened out with the defect feature M may indeed be the developed pattern caused by SRAF, or it may be noise. Therefore, it is necessary to perform subsequent processes to further accurately determine the authenticity of the defect feature M.

[0078] Preferably, before executing the step 2 S11, image processing operations such as contrast enhancement and filtering may be performed on the acquired first detection image P1 to enhance the clarity of the first detection image P1.

[0079] Step three S12: obtaining the position information of the first detection image P1 having the defect feature M.

[0080] As can be seen from the above, the first detection image P1 scanned and obtained by the critical dimension measurement device has corresponding position information in the coordinate system of the patterned photoresist layer. Therefore, after screening, the position information of the first detection image P1 with defect feature M can be obtained. Among them, the position information of the image referred to in this embodiment is the coordinate of the image in the coordinate system relative to the patterned photoresist layer.

[0081] Step 4 S13: Please refer to Figure 5 , use the key dimension measurement device to scan the area corresponding to the position information in the patterned photoresist layer at least once to obtain a plurality of second detection images P2.

[0082] From the above, it can be seen that the first detection image P1 is an image of the patterned photoresist layer that has not been affected by the electron beam, and when a scan is performed again at the same position, the second detection image P2 obtained is an image after being affected by the electron beam scanning in step 1 S10. That is, the size of the pattern groove 301 and the development pattern caused by SRAF have been enlarged. Based on this, if the defect feature M in the first detection image P1 also appears in the second detection image P2 at the same position, and the size increases, it can be determined that the defect feature M is a real defect. On the contrary, if the defect feature M disappears or shrinks in size, it indicates that the defect feature M is noise and should be excluded.

[0083] Further, in the process of executing the step four S13, the area corresponding to the position information in the patterned photoresist layer can be scanned multiple times, and after multiple electron beam irradiations, the size of the development pattern caused by the pattern groove 301 and SRAF can be further expanded, which is more conducive to clearly and clearly capturing the defect feature M and improving the detection accuracy. For example, in the process of executing the step four S13, the area corresponding to the position information in the patterned photoresist layer is first scanned once, and the second detection image P2 of the first batch is obtained; and then the area corresponding to the position information in the patterned photoresist layer is scanned for the second time, and the second detection image P2 of the second batch is obtained. It can be understood that the second detection image P2 of the first batch is an image obtained after one electron beam irradiation, and the second detection image P2 of the second batch is an image obtained after two electron beam irradiations. The size of the development pattern caused by the pattern groove 301 and SRAF in the second detection image P2 of the second batch is larger than the size of the corresponding pattern in the second detection image P2 of the first batch, which is more conducive to accurately capturing the defect feature M and improving the detection effect. In this embodiment, the number of scans in step 4 S13 is not specifically limited, and can be one, two, three, etc.

[0084] Step 5 S14: Please refer to Figure 4 and Figure 5 , compare the first detection image P1 and the second detection image P2 with the same position information, and screen out the second detection image P2 with the defect feature M.

[0085] Consistent with the principle of screening the first detection image P1 having the defect feature M, the step five S14 includes:

[0086] Sub-step 1: respectively obtain the feature value distribution diagrams of the first detection image P1 and the second detection image P2 having the defect feature M1.

[0087] Sub-step 2: Compare the feature value distribution diagrams of the first detection image P1 and the second detection image P2 of the same position information; and when the feature value of the SRAF corresponding position in the second detection image P2 is less than or equal to the feature value of the SRAF corresponding position in the first detection image P1, the second detection image P2 has the defect feature M. The feature value includes a gray value or a gray mean.

[0088] In other words, if the defect feature M in the first detection image P1 of the same position information is the development pattern caused by SRAF, that is, a real defect, the size of the defect feature M will be enlarged after being irradiated by the electron beam, and the color of the partial image around the defect feature M will become darker, that is, the grayscale value or grayscale mean of the position corresponding to SRAF in the second detection image P2 at the same position information becomes smaller or equal to the grayscale value or grayscale mean corresponding to the defect feature M in the first detection image P1. Therefore, by comparing the grayscale values ​​of the first detection image P1 and the second detection image P2 at the same position information, or comparing the grayscale mean values ​​of the first detection image P1 and the second detection image P2 within a certain range at the same position, it can be clearly determined whether the defect feature M at the position corresponding to SRAF in the first detection image P1 is true. If the color of the defect feature M and the partial image around it does not change or becomes lighter after being irradiated by the electron beam, it indicates that the defect feature M found in the first detection image P1 is noise N and should be excluded. It should be noted that, in the above comparison process, the second detection image P2 compared with the first detection image P1 is an image obtained through one scan and / or an image obtained through several scans, which is not specifically limited in this embodiment.

[0089] After the above comparison process, the second detection image P2 having the defect feature M can be quickly screened out. Obviously, the defect features M in the screened out second detection image P2 having the defect feature M are all the development patterns caused by SRAF, that is, the real defects to be detected by the SRAF development defect detection method described in this embodiment.

[0090] In summary, the SRAF development defect detection method provided in this embodiment utilizes the principle that the electron beam emitted during the scanning process of the key dimension measurement device will increase the size of the groove pattern and the development pattern in the patterned photoresist layer, and combines the double-screen detection method to accurately detect the SRAF development defect. That is, the first detection image P1 obtained after the first scan is first compared with the reference image in the database for feature value comparison to initially screen out the first detection image P1 with the defect feature M. And, obtain the position information of the first detection image P1 with the defect feature M. Then, scan and obtain the second detection image P2 with the same position information, and compare the feature values ​​of the first detection image P1 and the second detection image P2 with the same position information to screen out the second detection image P with the defect feature M again. And because the patterned photoresist layer is irradiated by the electron beam, the size of the pattern groove and the development pattern therein will increase, so that the noise N can be effectively eliminated, and the defect feature M in the patterned photoresist layer can be accurately captured and efficiently detected.

[0091] <Example 2>

[0092] Based on the same idea, see Figure 6 This embodiment also provides a method for detecting SRAF development defects, including:

[0093] Step 1 S20: Determine the coordinates of the target sampling point in the patterned photoresist layer according to the coordinate system of the chip;

[0094] Step 2 S21: using a key dimension measurement device to perform a first focusing on one side of the sampling area corresponding to the target sampling point coordinates;

[0095] Step 3 S22: using the key dimension measurement device to perform a second focusing at the target sampling point coordinate position;

[0096] Step 4 S23: Scanning the sampling area at least once using the key dimension measurement device, and acquiring a plurality of third detection images;

[0097] Step five S24: Filter out the third detection images having defect features from the plurality of third detection images.

[0098] Based on this, the SRAF development defect detection method provided in this embodiment is to use a key dimension measurement device to perform a second focusing and at least one scanning to realize electron beam irradiation of the sampling area S, so as to utilize the principle that the electron beam can expand the size of the pattern groove and the development pattern, thereby realizing effective elimination of noise, and accurately capturing and efficiently detecting defective features in the patterned photoresist layer.

[0099] The following is combined with Figure 6 to Figure 9 The SRAF development defect detection method provided in this embodiment is specifically described.

[0100] Step 1 S20: Please refer to Figure 7 , Figure 8 and Fig. 9 , according to the coordinate system of the chip, determine the coordinate O2 of the target sampling point in the patterned photoresist layer.

[0101] like Figure 7 As shown, a plurality of chips (Die) are formed in the wafer W. And illustratively, the lower left corner position of each chip is used as the corresponding origin O0 of the chip coordinate system. The coordinates of any point position in the chip can be determined based on the origin O0 of the chip coordinate system. Therefore, the preset target sampling point coordinates O2 can be accurately obtained in the chip coordinate system. Preferably, the target sampling point coordinates O2 are the center coordinates of the sampling area S. Among them, the X-axis direction and the Y-axis direction are two directions perpendicular to each other on the plane where the chip is located.

[0102] It should be noted that a patterned photoresist layer is formed on the surface of the chip, and the defect feature M detected in this embodiment is generally located in the sampling area S corresponding to the target sampling point coordinate O2. Figure 8 and Fig. 9 As shown, the defect feature M in this embodiment includes a development pattern formed by the SRAF pattern 101 in the patterned photoresist layer. The patterned photoresist layer includes a photoresist 400 and a pattern groove 401 formed in the photoresist.

[0103] Step 2 S21: Please refer to Figure 7 , Figure 8 and Fig. 9 , a key dimension measurement device is used to perform a first focusing on one side of the sampling area S corresponding to the target sampling point coordinate O2.

[0104] The critical dimension measurement equipment referred to in this embodiment is consistent with the critical dimension measurement equipment in Embodiment 1, and in the process of focusing the patterned photoresist layer using the critical dimension measurement equipment, an electron beam will also be emitted to the corresponding position of the patterned photoresist layer, which will also cause the photoresist 400 in the patterned photoresist layer to shrink and the size of the pattern groove 401 to expand, as well as the size of the development pattern caused by the SRAF pattern 101 to expand, thereby facilitating the accurate capture of the defect feature M and eliminating noise.

[0105] Furthermore, the purpose of performing the first focusing is to achieve the lens focusing of the critical dimension measurement device, so as to obtain a clear image later. Therefore, the first focusing point coordinate O1 corresponding to the first focusing can be selected as any point on one side of the sampling area S. In other words, the position of the first focusing point coordinate O1 needs to be separated from the sampling area S by a certain distance to avoid the electron beam emitted by the critical dimension measurement device during the first focusing process from affecting the sampling area S. And preferably, the position of the first focusing point coordinate O1 is located on other graphics outside the sampling area S, which is conducive to the accurate focusing and clear imaging of the critical dimension measurement device.

[0106] Step 3 S22: Please refer to Figure 8 and Fig. 9 , the key dimension measurement device is used to perform a second focusing at the target sampling point coordinate O2 position.

[0107] Based on the above records, it can be known that during the second focusing process at the target sampling point O2 coordinate position, the critical dimension measurement equipment emits an electron beam and irradiates the entire sampling area S. Then, the size of the pattern groove 401 and the development pattern covered by the entire sampling area S will become larger, which is conducive to accurately identifying the defect feature M.

[0108] Step 4 S23: Please refer to Figure 8 and Fig. 9 , use the key dimension measurement equipment to scan the sampling area S at least once, and obtain multiple third detection images P3.

[0109] Based on the image acquisition characteristics of the key dimension measurement device, the third detection image P3 obtained by performing a scan using the key dimension measurement device is the pattern of the sampling area S after the second focusing, in which the pattern groove 401 and the development pattern are enlarged once. Therefore, after the electron beam irradiation, the real defect feature M will inevitably be enlarged in size, darker in color, and easier to capture; while the defect noise may disappear or shrink in size and fade in color after electron beam irradiation, which makes it easier to be eliminated, thereby improving the accuracy of defect detection. It should be noted that when only one scan is performed on the sampling area S and multiple third detection images P3 are obtained, the detection efficiency of the SRAF development defect detection method provided in this embodiment is higher than that of the SRAF development defect detection method provided in Example 1. Specifically, in this embodiment, only one second focusing and one scan are required on the sampling area S, while at least two scans are required on the patterned photoresist layer in Example 1. Since the time consumed for focusing is much less than the time consumed for scanning, and both methods can emit an electron beam toward the patterned photoresist layer to increase the size of the pattern grooves in the patterned photoresist layer and the size of the development pattern caused by SRAF, the detection method provided in this embodiment is less time-consuming and has higher detection efficiency than Example 1.

[0110] In other examples, the critical dimension measurement device may also perform multiple scans so that the sampling area S is irradiated with electron beams multiple times. Then, compared with the third image P3 which is scanned only once, the pattern groove 401 and the development pattern size in the third image P3 obtained through multiple scans may be enlarged multiple times, which is more conducive to clearly and clearly detecting the defect feature M. In this embodiment, the number of scans in step 4 S23 is not specifically limited, and may be once, twice, or three times.

[0111] Step 5 S24: Please refer to Fig. 9 , the third detection image P3 having the defect feature M is screened out from the multiple third detection images P3.

[0112] Specifically, the step five S24 includes:

[0113] Sub-step 1: Provide a database, wherein the database includes a plurality of reference images having defect features M.

[0114] Preferably, the acquisition process of the reference image in the database is the same as that described in the first embodiment, and will not be described in detail in this embodiment.

[0115] Sub-step 2: Compare the plurality of third detection images P3 with the plurality of reference images having the defect feature M to screen out the third detection image P3 having the defect feature M.

[0116] Specifically, in the comparison process, it is necessary to first obtain the characteristic value distribution diagrams of the multiple third test images P3 and the multiple reference images with defect feature M respectively. Then compare each of the third test images P3 with the characteristic value distribution diagrams of the multiple reference images. Among them, when the difference between the characteristic value of the corresponding position of the SRAF graphic 101 in the third test image P3 and the characteristic value of the corresponding position of the SRAF graphic 101 in the reference image is within the threshold range, the third test image P3 has the defect feature M. And, since the images acquired by the key dimension measurement equipment are all grayscale images, the characteristic values ​​in this embodiment can be grayscale values, or grayscale mean values. Among them, the grayscale mean value is the average grayscale value within a certain range of the corresponding position. For example, the average grayscale value within the range where the photoresist shrinks after electron beam irradiation.

[0117] It can be understood that since the grayscale value includes 0 to 255 graduation values, and the larger the value, the lighter the color; the smaller the value, the darker the color. And in the image scanned and acquired by the key dimension measurement equipment, the pattern groove 401 and the developed pattern are darker, while the photoresist 400 is lighter. Exemplarily, assuming that the grayscale value or grayscale mean range of the defect feature M at the corresponding position of the SRAF graphic 101 in the reference image is: 0 to 150, and the threshold range is: 0 to 10, then when the grayscale value or grayscale mean at the corresponding position of the SRAF graphic 101 in the third detection image P3 is between [0, 160], it can be determined that the third detection image P3 has the defect feature M, otherwise, the third detection image P3 is a normal image and does not have the defect feature M.

[0118] It should be noted that, in the above comparison process, the third detection image P3 is an image obtained after one scan and / or an image obtained after several scans, and this embodiment does not specifically limit this. Also, since the third detection image P3 obtained in this embodiment is an image collected after electron beam irradiation, the size of the corresponding pattern groove 401 and the development pattern in the third detection image P3 will increase, which is conducive to efficiently eliminating noise and accurately capturing the defect feature M, thereby improving the defect detection accuracy and detection efficiency.

[0119] Preferably, before executing step five S24, image processing operations such as contrast enhancement and filtering may be performed on the acquired third detection image P3 to enhance the clarity of the third detection image P3.

[0120] In summary, the detection method of the SRAF development defect provided in this embodiment is to first perform the first focusing on one side of the sampling area S to achieve the lens focusing of the key dimension measurement equipment. Then, a second focusing is performed at the target sampling point coordinate O2 position so that the sampling area S is scanned by the electron beam, and the size of the pattern grooves in the sampling area S and the development pattern caused by SRAF increases. Secondly, the key dimension measurement equipment is used to scan again once or several times to obtain the third detection image P3. Finally, the third detection image P3 with the defect feature M is screened out. Since the corresponding pattern grooves and the development pattern caused by SRAF in the third detection image P3 are both of enlarged size, it is conducive to efficiently eliminating defect noise and realizing accurate detection of the defect feature M.

[0121] It should be noted that the various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other. In addition, the different parts between the various embodiments can also be used in combination with each other, and the present invention is not limited to this.

[0122] In addition, it should be recognized that although the present invention has been disclosed as a preferred embodiment, the above embodiment is not intended to limit the present invention. For any technician familiar with the art, without departing from the scope of the technical solution of the present invention, the technical content disclosed above can be used to make many possible changes and modifications to the technical solution of the present invention, or modified into equivalent embodiments of equivalent changes. Therefore, any simple modification, equivalent change and modification made to the above embodiment according to the technical essence of the present invention without departing from the content of the technical solution of the present invention still belongs to the scope of protection of the technical solution of the present invention.

Claims

1. A method for detecting SRAF development defects, characterized in that: include: Scanning the patterned photoresist layer using a critical dimension measurement device to obtain a plurality of first inspection images; Screening out the first detection image having defect features from the plurality of first detection images; Acquire position information of the first detection image having defect features; Scanning the area corresponding to the position information in the patterned photoresist layer at least once using the critical dimension measurement device to obtain a plurality of second detection images; The first detection image and the second detection image having the same position information are compared, and the second detection image having defect features is screened out.

2. The method for detecting SRAF development defects according to claim 1, characterized in that: The defect feature includes a developed pattern of SRAF in the patterned photoresist layer.

3. The method for detecting SRAF development defects according to claim 2, characterized in that: The process of selecting the first detection image having defect features from the plurality of first detection images includes: Providing a database, the database comprising a plurality of reference images having defect features; The plurality of first detection images are compared with the plurality of reference images having defect features to screen out the first detection images having defect features.

4. The method for detecting SRAF development defects according to claim 3, characterized in that: The process of comparing the plurality of first detection images with the plurality of reference images having defect features to screen out the first detection images having defect features includes: Respectively obtaining characteristic value distribution diagrams of the plurality of first detection images and the plurality of reference images having defect characteristics; Each of the first detection images is compared with the characteristic value distribution diagram of multiple reference images; and when the difference between the characteristic value of the SRAF corresponding position in the first detection image and the characteristic value of the SRAF corresponding position in the reference image is within a threshold range, the first detection image has the defect feature.

5. The method for detecting SRAF development defects according to claim 2, characterized in that: The process of comparing the first detection image and the second detection image having the same position information and selecting the second detection image having defect features includes: Respectively obtaining characteristic value distribution diagrams of the first detection image and the second detection image having defect characteristics; Compare the feature value distribution diagrams of the first detection image and the second detection image of the same position information; and when the feature value of the SRAF corresponding position in the second detection image is less than or equal to the feature value of the SRAF corresponding position in the first detection image, the second detection image has the defect feature.

6. The method for detecting SRAF development defects according to claim 4 or 5, characterized in that: The characteristic value includes a grayscale value or a grayscale mean value.

7. A method for detecting SRAF development defects, characterized in that: include: Determine the coordinates of the target sampling points in the patterned photoresist layer according to the coordinate system of the chip; Performing a first focusing on one side of the sampling area corresponding to the target sampling point coordinates using a key dimension measurement device; Performing a second focusing at the target sampling point coordinate position using the critical dimension measurement device; Scanning the sampling area at least once using the key dimension measurement device to obtain a plurality of third detection images; The third detection image having defect features is screened out from the multiple third detection images.

8. The method for detecting SRAF development defects according to claim 7, characterized in that: The defect feature includes a developed pattern of SRAF in the patterned photoresist layer.

9. The method for detecting SRAF development defects according to claim 8, characterized in that: The process of selecting the third detection image having defect features from the plurality of third detection images includes: Providing a database, the database comprising a plurality of reference images having defect features; The plurality of third detection images are compared with the plurality of reference images having defect features to screen out the third detection images having defect features.

10. The method for detecting SRAF development defects according to claim 9, characterized in that: The process of comparing the plurality of third detection images with the plurality of reference images having defect features to screen out the third detection images having defect features includes: Respectively obtaining characteristic value distribution diagrams of the plurality of third detection images and the plurality of reference images having defect characteristics; Compare each of the third detection images with the characteristic value distribution diagrams of the plurality of reference images; and when the difference between the characteristic value of the SRAF corresponding position in the third detection image and the characteristic value of the SRAF corresponding position in the reference image is within a threshold range, the third detection image has the defect feature; The characteristic value includes a gray value or a gray mean.

Images