Defect detection device and method, photoetching machine, equipment, medium and program product

Abstract

The embodiment of the invention provides a defect detection device and method, a photoetching machine, equipment, a medium and a program product, the defect detection device is used for detecting defects of a thin film arranged on a photomask, and the defect detection device comprises a detection module used for bearing the photomask and scanning the thin film on the photomask, the defect information comprises the number of defects and coordinate values corresponding to the defects; the light source is used for providing illumination light for the photomask; the acquisition module is arranged along a transmission path of the illumination light through the photomask or a reflection path of the thin film, and is used for acquiring a sampling image at least comprising the thin film when the photomask is irradiated; and the processor is electrically connected with the detection module and the acquisition module and is used for determining the type of the defect according to the defect information and the sampling image. By adopting the technical scheme, the determination precision of the defect type can be improved.

Description

Technical Field

[0001] Embodiments of the present invention relate to the field of data processing technology, and in particular, to a defect detection device, method, lithography machine, equipment, medium and program product. Background Art

[0002] The exposure principle of the photolithography machine is to print the pattern on the mask onto the wafer surface through a photochemical reaction. The quality of the pattern on the mask directly affects the quality of the wafer product.

[0003] In order to prevent dust and other particles from falling onto the side of the mask where the pattern exists, a transparent film (pellicle) is usually applied. Since the mask is transferred between different machines or is not used for a long time, the film is inevitably contaminated or damaged, and different types of defects have different degrees of contamination on the mask. Therefore, how to provide a technical solution to determine the type of defect has become a technical problem that needs to be solved urgently. Summary of the invention

[0004] In view of this, embodiments of the present invention provide a defect detection device, method, lithography machine, equipment, medium and program product, which can improve the accuracy of determining the defect type.

[0005] An embodiment of the present invention provides a defect detection device for detecting defects of a thin film disposed on a mask, the defect detection device comprising:

[0006] A detection module, used for carrying the photomask and scanning the film on the photomask to obtain defect information on the film, wherein the defect information includes the number of defects and the coordinate value corresponding to each defect;

[0007] A light source, used to provide illumination light to the light shield;

[0008] A collection module, arranged along a path along which the illumination light is transmitted through the mask or reflected by the film, and configured to obtain a sample image at least including the film when the mask is illuminated;

[0009] The processor is electrically connected to the detection module and the acquisition module respectively, and is used to determine the type of the defect according to the defect information and the sampling image.

[0010] Optionally, the light source and the collection module are both located on the side where the film is located.

[0011] Optionally, the light source and the collection module are located on opposite sides of the light shield, and the collection module is located on the side where the film is located, and the light source is located on the side facing away from the film.

[0012] Optionally, the processor is used to segment the sampled image using a preset segmentation pattern according to the coordinate values ​​corresponding to each defect to obtain multiple sub-patterns, wherein one sub-pattern corresponds to one segmentation pattern; and determine the type of the defect according to each sub-pattern and the segmentation pattern corresponding to the sub-pattern, wherein the segmentation pattern is set based on the type of the defect.

[0013] The processor is used to determine the type of the defect according to the similarity between the contour of each sub-graphic and the contour of the segmented graphic corresponding to the sub-graphic.

[0014] Optionally, there is a corresponding relationship between the coordinate information of the sampling image acquired by the acquisition module and the coordinate value corresponding to the defect scanned by the detection module;

[0015] The processor is used to determine the position of each defect in the sampled image according to the corresponding relationship and the coordinate value corresponding to each defect; determine the grayscale value and / or brightness value of each defect according to the position of each defect in the sampled image; determine the type of the defect according to the grayscale value difference between the grayscale value of each defect and the preset grayscale value and / or the brightness value difference between the brightness value of each defect and the preset brightness value.

[0016] Optionally, the processor is used to determine the type of the defect according to a preset correspondence between the gray value difference and the defect type and the gray value difference;

[0017] And / or, the processor is used to determine the type of the defect based on a preset correspondence between the brightness value and the defect type, and the brightness value difference.

[0018] Optionally, the defect detection device also includes: a driving mechanism, electrically connected to the processor and respectively connected to the acquisition module and the light source, for responding to a motion control signal from the processor to drive the acquisition module and the light source to move so that the illuminated portion of the film is within the sampling field of view of the acquisition module.

[0019] Optionally, the driving mechanism comprises: a first slide rail extending along a first direction and driving the acquisition module and the light source to move along the first direction;

[0020] A second slide rail extending along a second direction and slidingly cooperating with the first slide rail to drive the acquisition module and the light source to move along the second direction;

[0021] The driver is connected to the first slide rail and the second slide rail, and is used to generate a first driving force along the first direction and a second driving force along the second direction respectively in response to the motion control signal.

[0022] Accordingly, an embodiment of the present invention further provides a defect detection method for detecting defects of a thin film disposed on a photomask, the defect detection method comprising:

[0023] Acquiring defect information on the film, the defect information including the number of defects and coordinate values ​​corresponding to each defect, wherein the defect information is determined based on scanning the film;

[0024] Acquiring a sample image at least including the film, the sample image being obtained when the mask is irradiated;

[0025] The type of the defect is determined according to the defect information and the sample image.

[0026] Optionally, the step of determining the type of the defect according to the defect information and the sampled image includes:

[0027] According to the coordinate values ​​corresponding to each defect, the sampled image is segmented using a preset segmentation pattern to obtain a plurality of sub-patterns, wherein one sub-pattern corresponds to one segmentation pattern;

[0028] The type of the defect is determined according to each sub-pattern and a segmentation pattern corresponding to the sub-pattern, wherein the segmentation pattern is set based on the type of the defect.

[0029] Optionally, there is a corresponding relationship between the coordinate information of the sampled image and the coordinate values ​​corresponding to each defect;

[0030] The step of determining the type of the defect according to the defect information and the sample image comprises:

[0031] Determine the position of each defect in the sampled image according to the corresponding relationship and the coordinate value corresponding to each defect;

[0032] Determine the grayscale value and / or brightness value of each defect according to the position of each defect in the sampled image;

[0033] The type of the defect is determined according to the grayscale value difference between the grayscale value of each defect and the preset grayscale value and / or the brightness value difference between the brightness value of each defect and the preset brightness value.

[0034] An embodiment of the present invention further provides a lithography machine, comprising: a defect detection device as described in any of the aforementioned examples.

[0035] An embodiment of the present invention further provides a data processing device, comprising a memory and a processor, wherein the memory is suitable for storing one or more computer instructions, and when the processor runs the computer instructions, it executes the steps of the defect detection method as described in any example.

[0036] An embodiment of the present invention further provides a storage medium, wherein the storage medium stores one or more computer instructions, and the one or more computer instructions are used to implement the steps of the defect detection method as described in any example.

[0037] An embodiment of the present invention further provides a computer program product, characterized in that it includes computer instructions, and when the computer instructions are executed by a processor, they are used to implement the steps of the defect detection method as described in any example.

[0038] Compared with the prior art, the technical solution of the embodiment of the present invention has the following advantages:

[0039] In the defect detection solution provided by the embodiment of the present invention, the coordinate values ​​corresponding to each defect represent the location of the defect, and the sampling image represents the actual morphology of the film surface. Therefore, based on the coordinate values ​​corresponding to each defect, the actual position of each defect in the sampling image can be determined, and then the area corresponding to the actual position is processed, which narrows the processing range and realizes focusing processing, thereby improving the accuracy of determining the defect type. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments of the present invention or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0041] Figure 1 A schematic diagram of the structure of a defect detection device in an embodiment of the present invention is shown;

[0042] Figure 2 A schematic diagram showing the relative position relationship between the light source and the collection module in the first embodiment of the present invention is shown;

[0043] Figure 3 A schematic diagram showing the relative position relationship between the light source and the collection module in the second embodiment of the present invention is shown;

[0044] Figure 4 A flow chart of a defect detection method according to an embodiment of the present invention is shown;

[0045] Figure 5 A flowchart for determining the type of defect in the first embodiment of the present invention is shown;

[0046] Figure 6 A flowchart for determining the type of defect in the second embodiment of the present invention is shown;

[0047] Figure 7 A flowchart for determining the type of defect in the third embodiment of the present invention is shown. DETAILED DESCRIPTION

[0048] As described in the background art, different types of defects on a thin film have different degrees of contamination on the mask, so determining the type of defect is crucial to improving the cleanliness of the mask.

[0049] At present, the main detection method is to scan the upper and lower surfaces of the mask through the Integrated Reticle Inspection System (IRIS) to detect the location of the defect on the film, but it cannot determine the specific type of defect. At this time, it is generally necessary to manually open the mask carrier box and flip the mask for visual inspection, which increases the risk of damage to the mask and has low detection efficiency.

[0050] In order to solve the above technical problems, an embodiment of the present invention provides a defect detection device for detecting defects of a thin film arranged on a photomask, the defect detection device comprising: a detection module, for carrying the photomask and scanning the thin film on the photomask to obtain defect information on the thin film, the defect information comprising the number of defects and coordinate values ​​corresponding to each defect; a light source, for providing illumination light to the photomask; an acquisition module, arranged along a path through which the illumination light is transmitted by the photomask or a path through which the thin film is reflected, for acquiring a sampling image containing at least the thin film when the photomask is irradiated; a processor, electrically connected to the detection module and the acquisition module, respectively, for determining the type of the defect according to the defect information and the sampling image.

[0051] Using the above-mentioned defect detection scheme, the coordinate values ​​corresponding to each defect represent the location of the defect, and the sampling image represents the actual morphology of the film surface. Therefore, based on the coordinate values ​​corresponding to each defect, the actual position of each defect in the sampling image can be determined, and then the area corresponding to the actual position is processed, which narrows the processing range and realizes focused processing, thereby improving the accuracy of determining the defect type.

[0052] In order to make the above-mentioned purposes, features and advantages of the embodiments of the present invention more obvious and understandable, the embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0053] See also Figure 1 A structural diagram of a defect detection device in an embodiment of the present invention is shown in FIG. Figure 1 As shown, the defect detection device can be used to detect defects in the film Pe set on the mask M, so as to determine the type of defects and provide decisions for subsequent operations. For example, when it is determined that there are defects on the film Pe, a re-filming operation is performed; for example, when it is determined that there are no defects on the film Pe, no other operations need to be performed.

[0054] like Figure 1 As shown, the defect detection device may include:

[0055] A detection module 110, which is used to carry the mask M and scan the film Pe on the mask M to obtain defect information on the film Pe, wherein the defect information includes the number of defects and the coordinate value corresponding to each defect;

[0056] A light source 120, used for providing illumination light L1 to the light mask M;

[0057] A collection module 130, which is arranged along the path of the illumination light L transmitted through the mask M or the path of the film Pe reflected, and is used to obtain a sample image containing at least the film Pe when the mask is illuminated;

[0058] The processor 140 is electrically connected to the detection module 110 and the acquisition module 130 respectively, and is used to determine the type of the defect according to the defect information and the sampling image.

[0059] Specifically, the film Pe is attached to the side of the mask M having a pattern (wherein the side having a pattern is recorded as the first side, the side without a pattern is recorded as the second side, and the first side and the second side are arranged oppositely), and the mask M is placed on the detection module 110.

[0060] When defect detection needs to be performed, the detection module 110 can traverse the surface of the film Pe, determine the number of defects existing on the surface of the film Pe (as a non-limiting example, defects may include: scratches, breakages, stains, and combinations of the above defects, etc.), and the areas where each defect is located, so as to determine the coordinate values ​​used to characterize the location of the defect and transmit information about the defect to the processor 140.

[0061] When acquiring defect information, the light source 120 may provide illumination light L1 to the mask M to illuminate the surface of the film Pe, thereby obtaining defect information based on the reflected light (eg, Figure 1 The illumination light L1 shown in FIG. 1 is reflected light L2 after being reflected by the film Pe, or the illumination light L1 is transmitted (for example, Figure 3The illustrated illumination light L1 is transmitted through the transmitted light L3), the acquisition module 130 can perform an acquisition operation to obtain a sampling image that at least includes the film Pe, and can transmit the sampling image to the processor 140 (as a non-limiting example, the sampling image can be transmitted to the processor 140 in a signal stream or other transmission methods).

[0062] After receiving the defect information and sampling image on the film Pe, the processor 140 can determine the actual position of each defect in the sampling image based on the coordinate values ​​corresponding to each defect according to the pre-set processing logic, and then only process the area corresponding to the actual position, thereby realizing the focusing processing of the processor 140, thereby improving the accuracy of determining the defect type.

[0063] It should be noted that, first, Figure 1 The relative positions of the various components illustrated in the figure are only illustrative and a simplified description, which is only used to illustrate that the detection of defects on the surface of the film is achieved through the coordination relationship between the above-mentioned components, and should not be understood as a limitation of the present invention; secondly, in this scheme, the sampling image and the defect information on the film can be acquired synchronously to improve the matching degree between the two. In some other embodiments, the sampling image and the defect information on the film can also be acquired asynchronously to reduce the difficulty of acquiring the two.

[0064] In this embodiment, the detection module can be implemented by integrating a mask detection system.

[0065] Specifically, the integrated mask inspection system is an integrated system in the lithography machine that is used to accurately scan and determine the number and location of defects. By setting scanning parameters, such as scanning speed, resolution, and detection sensitivity, the integrated mask inspection system can analyze and process the scanned data to generate a detailed defect information report, including the location and number of defects, to provide a reference for subsequent processing and decision-making.

[0066] It is understandable that the detection module may also be implemented by other devices or equipment. For example, the detection module may include a carrier for carrying the photomask, a detector disposed above the carrier, and a detector that cooperates with the detector.

[0067] Specifically, the detection light emitted by the detector can be irradiated to the surface of the film, and the reflected light reflected by the surface of the film can be detected by the detector, so that based on the detection result of the detector, it can be determined whether there is a defect at the incident position of the current detection light.

[0068] By changing the emission angle of the detection light, the position of the detection light on the film surface can be changed, thereby achieving measurement of the entire surface of the film.

[0069] In this embodiment, based on the cooperation between the light source and the acquisition module, a sampling image at least containing the film can be acquired.

[0070] See also Figure 2 The relative position relationship diagram of the light source and the collection module in the first embodiment of the present invention is shown in FIG. Figure 2 As shown, the light source 120 and the collection module 130 are both located on the side where the film Pe is located.

[0071] like Figure 2 As shown, when the illumination light L1 emitted by the light source 210 irradiates the surface of the film Pe, the reflected light L2 can be transmitted to the acquisition module 130, and then the acquisition module 130 can perform an acquisition operation to obtain a sample image at least including the film Pe.

[0072] By locating the light source 120 and the acquisition module 130 on the side where the film Pe is located, the transmission paths of the illumination light L1 and the reflected light L2 can be shortened, the complexity of light reflection and refraction is reduced, and the authenticity of the sampled image can be improved.

[0073] See also Figure 3 The relative position relationship diagram of the light source and the collection module in the second embodiment of the present invention is shown in FIG. Figure 3 As shown, the light source 120 and the collection module 130 are located on the side opposite to the mask M, and the collection module 130 is located on the side where the film Pe is located, and the light source 120 is located on the side facing away from the film Pe.

[0074] like Figure 3 As shown, when the illumination light L1 emitted by the light source 210 irradiates the second side of the mask M, it penetrates the mask M and the film Pe in sequence, and the formed transmitted light L3 is transmitted to the acquisition module 130, and then the acquisition module 130 can perform an acquisition operation to obtain a sampling image containing at least the film Pe.

[0075] By placing the light source 120 and the acquisition module 130 on the opposite side of the mask M, a stronger stereoscopic effect and sense of depth can be created, so that the acquired sampling image can more realistically reflect the current morphology of the film Pe, thereby improving the accuracy of subsequent defect type determination.

[0076] It should be noted that, first, Figure 2 and Figure 3 The transmission paths of the illustrated illumination light L1, transmitted light L3 and reflected light L2 are for illustrative purposes only and are used to represent a mode of light transmission under different placement conditions. Second, by setting optical components, the illumination light L1 can be incident on the position of the light mask M, and the transmitted light L3 and reflected light L2 can be received by the sampling module, thereby reducing the requirements for the position setting of the light source and the sampling module.

[0077] In this embodiment, the light source may be realized by a halogen light source, and the light emitted by the halogen light source may be used as illumination light to illuminate the surface of the film.

[0078] In some other embodiments, the light source can also be an LED, an infrared light source, a device that emits X-rays (for example, an X-ray tube and an X-ray machine), etc. The type of light source used is not limited in the embodiments of the present invention, as long as it can provide illumination light for the film.

[0079] In this embodiment, the acquisition module can be a charge coupled device (CCD). The CCD-based acquisition module can provide images with high resolution and high dynamic range, so that the collected sampling images can more realistically display the morphology of the film, thereby avoiding misjudgment problems caused by the acquisition module itself.

[0080] In a specific embodiment, the detection accuracy of the charge coupled device is between 0.005 mm and 0.1 mm.

[0081] In some other embodiments, the acquisition module may also be other types of devices or equipment with imaging functions, such as cameras.

[0082] When the sample image and defect information are acquired, the processor can perform further processing to determine the type of defect.

[0083] In this embodiment, the processor is used to segment the sampled image using a preset segmentation pattern according to the coordinate values ​​corresponding to each defect to obtain multiple sub-patterns, wherein one sub-pattern corresponds to one segmentation pattern; and determine the type of the defect according to each sub-pattern and the segmentation pattern corresponding to the sub-pattern, wherein the segmentation pattern is set based on the type of the defect.

[0084] Specifically, the types of common or existing defects on the film can be collected in advance, and then based on the morphology of various types of defects, a segmentation pattern that matches the morphology of the defects can be set, so that the segmentation pattern can more completely extract an image that at least includes the defects, thereby improving the segmentation accuracy.

[0085] Specifically, the coordinate values ​​corresponding to each defect define the location of the defect, so that when a segmentation pattern is used for segmentation processing, a sub-pattern containing at least the defect can be obtained, thereby obtaining a sub-pattern consistent with the number of defects.

[0086] Since the segmentation pattern is set based on the type of defect, the morphology of the sub-image obtained based on the segmentation pattern can more realistically reflect the morphology of the defect. In this way, when matching operations are performed based on each sub-pattern and the segmentation pattern corresponding to the sub-pattern, the accuracy of the determined defect type can be improved.

[0087] In this embodiment, the processor may be configured to determine the type of the defect according to the similarity between the contour of each sub-graphic and the contour of the segmentation graphic corresponding to the sub-graphic.

[0088] Specifically, the outline of the sub-graph defines the actual morphology of the defect obtained by segmenting the segmentation graph, while the outline of the segmentation graph shows the standard morphology of the defect, so the type of the defect can be determined based on the similarity between the two.

[0089] For example, when the similarity between the contour of each sub-graphic and the contour of the segmentation graphic corresponding to the sub-graphic meets the set conditions, it is determined that the detected defect is the defect type corresponding to the segmentation graphic. For another example, when the similarity between the contour of each sub-graphic and the contour of the segmentation graphic corresponding to the sub-graphic does not meet the set conditions, it is determined that the detected defect is not the defect type corresponding to the segmentation graphic. In this case, other types of segmentation graphics can be used to segment the sampled image again, and then the similarity between the contours can be judged.

[0090] In this embodiment, the sampled image can be segmented in the following manner: using the segmentation figure as a template, searching for an area in the sampled image that is similar to the template; gradually expanding the area from one of the positions of the segmentation figure as a base point until the similarity between the image and the segmentation figure meets the requirements; using a watershed algorithm, threshold segmentation, and edge detection method, and combining the features of the segmentation figure for segmentation.

[0091] It can be understood that, first, the segmentation methods listed above are only for illustrative purposes, and the specific implementation methods can be adjusted according to actual application requirements and the characteristics of the sampled images; second, the above methods can be used alone or in combination to improve the accuracy and robustness of segmentation.

[0092] In this embodiment, the contour of the image can be determined in the following ways: 1) using edge detection algorithms, such as Canny edge detection, Sobel operator, Laplacian operator, etc., to determine the contour of the image; 2) threshold segmentation; 3) contour extraction algorithm.

[0093] It can be understood that the above-listed methods for determining the contour of an image are only for illustrative purposes, and the specific implementation method can be adjusted according to actual application requirements and characteristics of the sampled image.

[0094] In this embodiment, after determining the contours of each sub-graphic and the contours of the segmented graphics corresponding to the sub-graphic, the similarity can be determined in the following ways: 1) contour matching algorithm (as a non-restrictive form, the contour matching algorithm can include Hu moment, shape context, etc.); 2) Hausdorff distance, that is, extracting contour point sets of two images, and determining the maximum distance from each contour point in one contour point set to the other contour point set, where the smaller the value, the more similar the contours are.

[0095] It can be understood that the similarity determination methods listed above are only for illustrative purposes, and the specific implementation methods can be adjusted according to actual application requirements and characteristics of the sampled images.

[0096] It should be noted that, first, the coordinate value in this embodiment can refer to a specific coordinate value or a set of multiple coordinate values, which can be set specifically according to the size of the defect; second, when the segmentation graphics are used for image segmentation, in one segmentation operation, one type of segmentation graphics can be used to segment the image, or multiple types of segmentation graphics can be used to segment the image. This scheme does not impose any restrictions on the segmentation process of the sampled image, as long as a segmented image including the defect can be obtained.

[0097] In this embodiment, there is a corresponding relationship between the coordinate information of the sampling image collected by the acquisition module and the coordinate value corresponding to the defect scanned by the detection module. By establishing this corresponding relationship, the position matching between the sampling image and the defect can be achieved.

[0098] Specifically, the acquisition module is set at a position that satisfies the acquisition module to acquire a sampling image containing all surface morphologies of the film (for example, a sampling image containing all surface morphologies of the film is acquired in one acquisition process; for example, a sampling image containing all surface morphologies of the film is acquired in multiple sampling processes). Therefore, the position information of the acquisition module and the adopted sampling coordinate system are known, and the position of the mask is relatively fixed. After setting the detection parameters of the detection module, the coordinate information of the sampling image and the coordinate value corresponding to the defect can be calibrated based on the detection coordinate system of the detection parameters and the determined coordinates, so that the position of the defect and the sampling image matches.

[0099] In this way, the processor can be used to determine the position of each defect in the sampling image based on the corresponding relationship and the coordinate values ​​corresponding to each defect; determine the grayscale value of each defect based on the position of each defect in the sampling image; determine the type of the defect based on the grayscale value difference between the grayscale value of each defect and the preset grayscale value.

[0100] Among them, the preset grayscale value can be determined in the following way: for the same film, when other factors (light brightness, environmental conditions, etc.) are the same, the grayscale values ​​of each area of ​​the sampled image should be the same or the grayscale difference is very small. In this way, a sampled image of the film without any defects can be collected, and the grayscale value of the surface of the sampled image can be obtained. The maximum grayscale value, the minimum grayscale value, or the weighted average of multiple grayscale values, etc., can be used as the preset grayscale value.

[0101] Specifically, after determining the coordinate values ​​corresponding to each defect, the coordinate values ​​of the defect can be mapped to the sampling image according to the corresponding relationship, so that the position (or range) of the defect in the sampling image can be determined. In this way, by processing the sampling image, the grayscale value of the defect can be obtained, wherein the grayscale value of the defect can refer to a grayscale value, or a set of multiple grayscale values. Since there are differences in the grayscale values ​​of different types of defects, the type of defect can be determined by calculating the grayscale value of the defect and the grayscale value difference between the preset grayscale value.

[0102] In a specific implementation example, the processor may be configured to determine the type of the defect according to a preset correspondence between the grayscale value difference and the defect type and the grayscale value difference.

[0103] In other words, the processor pre-stores the correspondence between the grayscale value difference and the defect type. When the above method is used to determine the grayscale value of each defect and the grayscale value difference between the preset grayscale value, the type of defect can be determined based on the correspondence. In this way, the entire process does not require manual processing, which can improve the computing efficiency and the accuracy of the determined defect type.

[0104] In this embodiment, the corresponding relationship between the gray value difference and the defect type can be obtained in the following manner:

[0105] Example 1: A film is selected and a type of defect is set on the film. Then, a sampling image containing the defect is obtained, and the grayscale value of the defect at the location of the sampling image and the grayscale value of the non-defect area within the preset range of the defect are obtained respectively to determine a grayscale value difference; then, under the same test conditions, multiple grayscale value differences are obtained again, so that a grayscale value difference range value corresponding to a defect can be determined, that is, the correspondence between the grayscale value difference and the defect type.

[0106] Example 2: For the same type of film (i.e., films with the same parameters such as size, properties and materials), select any two of them, where there is no defect on the first film and a type of defect is set on the second film, and determine the position of the defect; then, under the same test conditions, obtain an image containing the first film and an image containing the second film respectively, and obtain the grayscale value of the position corresponding to the defect on the image of the first film and the grayscale value of the position where the defect is located in the second film respectively, so as to determine a grayscale value difference; then, change the position of the defect, and under the same test conditions, obtain multiple grayscale value differences again, so as to determine a grayscale value difference range value corresponding to a defect, that is, the correspondence between the grayscale value difference and the defect type.

[0107] In this embodiment, calibration is implemented between the coordinate information of the sampling image and the coordinate values ​​corresponding to the defects so that the positions of the defects and the sampling image match. The processor can be used to determine the positions of each defect in the sampling image based on the corresponding relationship and the coordinate values ​​corresponding to each defect; determine the brightness value of each defect based on the position of each defect in the sampling image; and determine the type of the defect based on the brightness value difference between the brightness value of each defect and a preset brightness value.

[0108] Among them, the preset brightness value can be determined in the following way: for the same film, when other factors (light brightness, environmental conditions, etc.) are the same, the brightness values ​​of each area of ​​the sampled image should be the same or the grayscale difference should be very small. In this way, a sampled image of the film without any defects can be collected, and the brightness value of the surface of the sampled image can be obtained. The maximum brightness value, the minimum brightness value, or the weighted average of multiple brightness values, etc., can be used as the preset brightness value.

[0109] Specifically, after determining the coordinate values ​​corresponding to each defect, the coordinate values ​​of the defect can be mapped to the sampling image according to the corresponding relationship, so that the position (or range) of the defect in the sampling image can be determined. In this way, by processing the sampling image, the brightness value of the defect can be obtained, wherein the brightness value of the defect can refer to a brightness value, or a set of multiple brightness values. Since there are differences in the brightness values ​​of different types of defects, the type of defect can be determined by calculating the brightness value of the defect and the brightness value difference between the preset grayscale value.

[0110] In a specific implementation example, the processor may be configured to determine the type of the defect according to a preset correspondence between the brightness value difference and the defect type and the brightness value difference.

[0111] In other words, the processor pre-stores the correspondence between the brightness value difference and the defect type. When the above method is used to determine the brightness value of each defect and the brightness value difference between the preset brightness value, the type of defect can be determined based on the correspondence. In this way, the entire process does not require manual processing, which can improve the computing efficiency and the accuracy of the determined defect type.

[0112] In this embodiment, the corresponding relationship between the brightness value difference and the defect type can be obtained in the following manner:

[0113] Example 1: A film is selected and a type of defect is set on the film. Then, a sample image containing the defect is obtained, and the brightness value of the defect at the location of the sample image and the brightness value of the non-defect area within the preset range of the defect are obtained respectively to determine a brightness value difference. Then, under the same test conditions, multiple brightness value differences are obtained again, so that a brightness value difference range value corresponding to a defect can be determined, that is, the correspondence between the brightness value difference and the defect type.

[0114] Example 2: For the same type of film (i.e., films with the same parameters such as size, properties, and materials), select any two of them, where there is no defect on the first film and a type of defect is set on the second film, and determine the position of the defect; then, under the same test conditions, obtain an image containing the first film and an image containing the second film respectively, and obtain the brightness value of the position corresponding to the defect on the image of the first film and the grayscale value of the position where the defect is located in the second film respectively, so that a brightness value difference can be determined; then, change the position of the defect, and under the same test conditions, obtain multiple brightness value differences again, so that a brightness value difference range corresponding to a defect can be determined, that is, the corresponding relationship between the brightness value difference and the defect type.

[0115] It should be noted that, first, in order to further improve the accuracy of the determined defect type, at least two different types of defects can be set on the same film, or defects of the same type at different positions can be set to determine the above-mentioned correspondence; second, the type of defect can also be determined based on both the grayscale value and the brightness value, and the embodiments of the present invention do not impose any restrictions on this.

[0116] In this embodiment, considering various factors such as the illumination diameter of the illumination light, the sampling field of view of the sampling module, and the size of the mask, it may not be possible to fully acquire an image containing all film surfaces at one time, and therefore multiple image acquisition operations need to be performed. In this case, the film can be illuminated by moving the position of the acquisition module and the light source so that different surfaces are within the sampling field of view of the acquisition module.

[0117] As an optional example, the defect detection device may also include: a driving mechanism, electrically connected to the processor and respectively connected to the acquisition module and the light source, for responding to a motion control signal from the processor to drive the acquisition module and the light source to move so that the illuminated portion of the film is within the sampling field of view of the acquisition module.

[0118] Specifically, by driving the acquisition module and the light source to move, the relative position relationship between the acquisition module and the light source can be maintained, so that at any time, the illuminated part of the film is located within the sampling field of view of the acquisition module, so that a sampling image including the film surface can be obtained.

[0119] More specifically, the driving mechanism may include: a first slide rail extending along a first direction to drive the acquisition module and the light source to move along the first direction; a second slide rail extending along a second direction to slide with the first slide rail to drive the acquisition module and the light source to move along the second direction; a driver connected to the first slide rail and the second slide rail, for generating a first driving force along the first direction and a second driving force along the second direction in response to the motion control signal.

[0120] In short, the acquisition module and the light source can move synchronously, so that a sampling image containing the morphological features of all surfaces of the film can be obtained.

[0121] It is understandable that the above-mentioned embodiments provide a variety of implementation schemes, and the various implementation schemes can be combined and cross-referenced with each other without conflict, thereby extending a variety of possible implementation schemes, which can all be considered as embodiment schemes disclosed and open in the embodiments of this application.

[0122] An embodiment of the present invention further provides a defect detection method corresponding to the above-mentioned defect detection device, which is described in detail below through specific embodiments with reference to the accompanying drawings.

[0123] Combination Figures 1 to 3 , see Figure 4 A flow chart of a defect detection method in an embodiment of the present invention is shown in FIG. Figure 4 As shown, the defect detection method can be used to detect defects in the film Pe set on the mask M, so as to determine the type of defects and provide decisions for subsequent operations. For example, when it is determined that there are defects on the film Pe, a re-filming operation is performed; for example, when it is determined that there are no defects on the film Pe, no other operations need to be performed.

[0124] like Figure 4 As shown, the defect detection method may include:

[0125] A. Obtaining defect information on the film, wherein the defect information includes the number of defects and coordinate values ​​corresponding to each defect, wherein the defect information is determined based on scanning the film.

[0126] Specifically, when defect detection needs to be performed, the entire surface of the film Pe can be traversed to determine the number of defects on the surface of the film Pe (as a non-limiting example, defects may include: scratches, breakages, stains, and combinations of the above defects, etc.), as well as the areas where each defect is located, so that the coordinate value used to characterize the location of the defect can be determined.

[0127] The method of obtaining defect information on the film can refer to the description in the above example.

[0128] B. Acquire a sample image at least including the film, wherein the sample image is obtained when the mask is irradiated.

[0129] Specifically, when acquiring defect information, when the mask M is irradiated, the surface of the film Pe can be illuminated, and then the reflected light (eg Figure 1 The illumination light L1 shown in FIG. 1 is reflected light L2 after being reflected by the film Pe, or the illumination light L1 is transmitted (for example, Figure 3 The schematic illumination light L1 is transmitted through the transmitted light L3 , and a collection operation can be performed to obtain a sample image containing at least the thin film Pe.

[0130] In this embodiment, steps A and B may be executed simultaneously, or asynchronously, and this solution does not impose any limitation on this.

[0131] C. Determine the type of the defect according to the defect information and the sampled image.

[0132] Specifically, when the sampled image and defect information are acquired, further processing may be performed to determine the type of defect.

[0133] See also Figure 5 The flowchart of determining the type of defect in the first embodiment of the present invention is shown in FIG. Figure 5 As shown, the following steps can be performed:

[0134] C11, according to the coordinate values ​​corresponding to each defect, using a preset segmentation pattern, segment the sampled image to obtain a plurality of sub-patterns, wherein one sub-pattern corresponds to one segmentation pattern.

[0135] Specifically, the types of common or existing defects on the film can be collected in advance, and then based on the morphology of various types of defects, a segmentation pattern that matches the morphology of the defects can be set, so that the segmentation pattern can more completely extract an image that at least includes the defects, thereby improving the segmentation accuracy.

[0136] Specifically, the coordinate values ​​corresponding to each defect define the location of the defect, so that when a segmentation pattern is used for segmentation processing, a sub-pattern containing at least the defect can be obtained, thereby obtaining a sub-pattern consistent with the number of defects.

[0137] C12, determining the type of the defect according to each sub-graphic and a segmentation graphic corresponding to the sub-graphic, wherein the segmentation graphic is set based on the type of the defect.

[0138] Specifically, since the segmentation pattern is set based on the type of defect, the morphology of the sub-image obtained based on the segmentation pattern can more realistically reflect the morphology of the defect. In this way, when matching operations are performed based on each sub-pattern and the segmentation pattern corresponding to the sub-pattern, the accuracy of the determined defect type can be improved.

[0139] In a specific implementation example, the type of the defect is determined based on the similarity between the contour of each sub-graphic and the contour of the segmentation graphic corresponding to the sub-graphic.

[0140] Specifically, the outline of the sub-graph defines the actual morphology of the defect obtained by segmenting the segmentation graph, while the outline of the segmentation graph shows the standard morphology of the defect, so the type of the defect can be determined based on the similarity between the two.

[0141] For example, when the similarity between the contour of each sub-graphic and the contour of the segmentation graphic corresponding to the sub-graphic meets the set conditions, it is determined that the detected defect is the defect type corresponding to the segmentation graphic. For another example, when the similarity between the contour of each sub-graphic and the contour of the segmentation graphic corresponding to the sub-graphic does not meet the set conditions, it is determined that the detected defect is not the defect type corresponding to the segmentation graphic. In this case, other types of segmentation graphics can be used to segment the sampled image again, and then the similarity between the contours can be judged.

[0142] The image segmentation method, the image contour determination method, and the contour similarity determination method can refer to the above examples.

[0143] In this embodiment, there is a corresponding relationship between the coordinate information of the sampling image and the coordinate value corresponding to the defect. By establishing this corresponding relationship, the position matching between the sampling image and the defect can be achieved.

[0144] Specifically, the acquisition module is set at a position that satisfies the acquisition module to acquire a sampling image containing all surface morphologies of the film (for example, a sampling image containing all surface morphologies of the film is acquired in one acquisition process; for example, a sampling image containing all surface morphologies of the film is acquired in multiple sampling processes). Therefore, the position information of the acquisition module and the adopted sampling coordinate system are known, and the position of the mask is relatively fixed. After setting the detection parameters of the detection module, the coordinate information of the sampling image and the coordinate value corresponding to the defect can be calibrated based on the detection coordinate system of the detection parameters and the determined coordinates, so that the position of the defect and the sampling image matches.

[0145] Thus, see Figure 6 The flowchart of determining the type of defect in the second embodiment of the present invention is shown in FIG. Figure 6 As shown, the following steps can be performed:

[0146] C21, determining the position of each defect in the sampled image according to the corresponding relationship and the coordinate value corresponding to each defect.

[0147] Specifically, after determining the coordinate values ​​corresponding to each defect, the coordinate values ​​of the defect may be mapped to the sampled image according to the corresponding relationship, so that the position (or range) of the defect in the sampled image may be determined.

[0148] C22, determining the grayscale value of each defect according to the position of each defect in the sampled image.

[0149] C23, determining the type of the defect according to the grayscale value difference between the grayscale value of each defect and the preset grayscale value.

[0150] Specifically, by processing the sampled image, the grayscale value of the defect can be obtained, wherein the grayscale value of the defect may refer to a grayscale value, or a set of multiple grayscale values. Since different types of defects have different grayscale values, the type of defect can be determined by calculating the grayscale value of the defect and the grayscale value difference between the preset grayscale value.

[0151] In a specific embodiment, the type of the defect is determined based on a preset correspondence between the gray value difference and the defect type and the gray value difference.

[0152] In other words, by pre-storing the correspondence between the grayscale value difference and the defect type, when the above method is used to determine the grayscale value of each defect and the grayscale value difference between the preset grayscale value, the type of defect can be determined based on the correspondence. In this way, the entire process does not require manual processing, which can improve the computing efficiency and the accuracy of the determined defect type.

[0153] See also Figure 7The flowchart of determining the type of defect in the third embodiment of the present invention is shown in FIG. Figure 7 As shown, the following steps can be performed:

[0154] C31, determining the position of each defect in the sampled image according to the corresponding relationship and the coordinate value corresponding to each defect.

[0155] Specifically, after determining the coordinate values ​​corresponding to each defect, the coordinate values ​​of the defect may be mapped to the sampled image according to the corresponding relationship, so that the position (or range) of the defect in the sampled image may be determined.

[0156] C32, determining the brightness value of each defect according to the position of each defect in the sampled image.

[0157] C33, determining the type of the defect according to the brightness value of each defect and the brightness value difference between the preset brightness value.

[0158] Specifically, by processing the sampled image, the brightness value of the defect can be obtained, wherein the brightness value of the defect may refer to one brightness value, or a set of multiple brightness values. Since different types of defects have different brightness values, the type of defect can be determined by calculating the brightness value of the defect and the brightness value difference between the preset brightness value.

[0159] In a specific embodiment, the type of the defect is determined according to a preset correspondence between the brightness value difference and the defect type and the brightness value difference.

[0160] In other words, by pre-storing the correspondence between the brightness value difference and the defect type, when the above method is used to determine the brightness value of each defect and the brightness value difference between the preset brightness value, the type of defect can be determined based on the correspondence, so that the entire process does not require manual processing, which can improve the calculation efficiency and the accuracy of the determined defect type. An embodiment of the present invention provides a lithography machine, which can include the defect detection device described in any of the above examples.

[0161] Among them, the specific structure and working principle of the defect detection device can be found in the above examples.

[0162] By setting up a defect detection device in the lithography machine, the defect type on the film can be determined, which is helpful for judging whether the defects on the film will affect the exposure, so as to improve the lithography quality.

[0163] An embodiment of the present invention provides a data processing device, which may include a memory and a processor. The memory and the processor may communicate with each other through a communication bus. The memory stores one or more computer instructions that can be run on the processor. When the processor runs the computer instructions, the steps of the defect detection method described in any of the above embodiments may be executed. For details, please refer to the above-mentioned related content and will not be repeated here.

[0164] In some examples, computer instructions may include any suitable type of code, e.g., source code, compiled code, interpreted code, executable code, static code, dynamic code, encrypted code, etc., implemented using any suitable high-level, low-level, object-oriented, visual, compiled and / or interpreted programming language.

[0165] In some examples, the data processing device may further include a display interface and a display connected through the display interface. The display interface may communicate with the memory and the processor through a communication bus. The display may display various data during the defect detection process provided by the embodiment of the present invention executed by the processor.

[0166] In some embodiments, the data processing device may further include a data output interface. The data output interface may communicate with the memory and the processor via a communication bus to output various data in the defect detection process.

[0167] The present invention also provides a computer-readable storage medium on which computer instructions are stored. When the computer instructions are executed, the steps of the defect detection method described in any of the above embodiments of the present invention can be executed. For details, please refer to the above related content and will not be repeated here.

[0168] Among them, the computer-readable storage medium may include any suitable type of memory unit, memory device, memory article, memory medium, storage device, storage article, storage medium and / or storage unit, such as memory, removable or non-removable medium, erasable or non-erasable medium, writable or rewritable medium, digital or analog medium, hard disk, floppy disk, compact disk read-only memory (CDROM), compact disk recordable (CD-R), compact disk rewritable (CD-RW), optical disk, magnetic medium, magneto-optical medium, removable memory card or disk, various types of digital versatile disks (DVD), magnetic tape, cassette, etc.

[0169] Furthermore, the computer instructions may include any suitable type of code implemented using any suitable high-level, low-level, object-oriented, visual, compiled and / or interpreted programming language, such as source code, compiled code, interpreted code, executable code, static code, dynamic code, encrypted code, etc.

[0170] The present invention also provides a computer program product, which may include computer instructions. When the computer instructions are executed by a processor, the steps of the defect detection method described in any of the above embodiments of the present invention are implemented. For details, please refer to the above-mentioned related content and will not be repeated here.

[0171] It should be noted that the “examples” or “embodiments” referred to in this specification refer to specific features, structures or characteristics that may be included in at least one implementation of an embodiment of the present invention. And in the description of this specification, the terms “first”, “second”, etc. are used only for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, features defined by terms such as “first”, “second”, etc. may explicitly or implicitly include one or more of the features. Moreover, the terms such as “first”, “second”, etc. are used to distinguish similar objects, and are not necessarily used to describe a specific order or to express importance. It is understood that the terms used in this way can be interchangeable where appropriate so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein.

[0172] Although the embodiments of the present invention are disclosed above, the present invention is not limited thereto. Any person skilled in the art may make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, the protection scope of this specification shall be subject to the scope defined by the claims.

Claims

1. A defect detection device, characterized in that: Used to detect defects of a thin film disposed on a photomask, the defect detection device comprises: A detection module, used for carrying the photomask and scanning the film on the photomask to obtain defect information on the film, wherein the defect information includes the number of defects and the coordinate value corresponding to each defect; A light source, used to provide illumination light to the light shield; A collection module, arranged along a path along which the illumination light is transmitted through the mask or reflected by the film, and configured to obtain a sample image at least including the film when the mask is illuminated; The processor is electrically connected to the detection module and the acquisition module respectively, and is used to determine the type of the defect according to the defect information and the sampling image.

2. The defect detection device according to claim 1, characterized in that: The light source and the collection module are both located on the side where the film is located.

3. The defect detection device according to claim 1, characterized in that: The light source and the collection module are located on the side opposite to the light shield, and the collection module is located on the side where the film is located, and the light source is located on the side facing away from the film.

4. The defect detection device according to claim 1, characterized in that: The processor is used to segment the sampled image using a preset segmentation pattern according to the coordinate values ​​corresponding to each defect to obtain a plurality of sub-graphs, wherein one sub-graph corresponds to one segmentation pattern; and determine the type of the defect according to each sub-graph and the segmentation pattern corresponding to the sub-graph, wherein the segmentation pattern is set based on the type of the defect.

5. The defect detection device according to claim 4, characterized in that: The processor is used to determine the type of the defect according to the similarity between the contour of each sub-graphic and the contour of the segmented graphic corresponding to the sub-graphic.

6. The defect detection device according to claim 1, characterized in that: There is a corresponding relationship between the coordinate information of the sampling image acquired by the acquisition module and the coordinate value corresponding to the defect scanned by the detection module; The processor is used to determine the position of each defect in the sampled image according to the corresponding relationship and the coordinate value corresponding to each defect; determine the grayscale value and / or brightness value of each defect according to the position of each defect in the sampled image; determine the type of the defect according to the grayscale value difference between the grayscale value of each defect and the preset grayscale value and / or the brightness value difference between the brightness value of each defect and the preset brightness value.

7. The defect detection device according to claim 6, characterized in that: The processor is used to determine the type of the defect according to the preset correspondence between the gray value difference and the defect type and the gray value difference; And / or, the processor is used to determine the type of the defect based on a preset correspondence between the brightness value and the defect type, and the brightness value difference.

8. The defect detection device according to claim 1, characterized in that: Also includes: The driving mechanism is electrically connected to the processor and is respectively connected to the acquisition module and the light source, and is used to drive the acquisition module and the light source to move in response to a motion control signal from the processor so that the illuminated portion of the film is located within the sampling field of view of the acquisition module.

9. The defect detection device according to claim 8, characterized in that: The driving mechanism comprises: A first slide rail extending along a first direction and driving the acquisition module and the light source to move along the first direction; A second slide rail extending along a second direction and slidingly cooperating with the first slide rail to drive the acquisition module and the light source to move along the second direction; The driver is connected to the first slide rail and the second slide rail, and is used to generate a first driving force along the first direction and a second driving force along the second direction respectively in response to the motion control signal.

10. A defect detection method, characterized in that: Used to detect defects of a thin film disposed on a photomask, the defect detection method comprises: Acquiring defect information on the film, the defect information including the number of defects and coordinate values ​​corresponding to each defect, wherein the defect information is determined based on scanning the film; Acquiring a sample image at least including the film, the sample image being obtained when the mask is irradiated; The type of the defect is determined according to the defect information and the sample image.

11. The defect detection method according to claim 10, characterized in that: The step of determining the type of the defect according to the defect information and the sample image comprises: According to the coordinate values ​​corresponding to each defect, the sampled image is segmented using a preset segmentation pattern to obtain a plurality of sub-patterns, wherein one sub-pattern corresponds to one segmentation pattern; The type of the defect is determined according to each sub-pattern and a segmentation pattern corresponding to the sub-pattern, wherein the segmentation pattern is set based on the type of the defect.

12. The defect detection method according to claim 10, characterized in that: There is a corresponding relationship between the coordinate information of the sampled image and the coordinate values ​​corresponding to each defect; The step of determining the type of the defect according to the defect information and the sample image comprises: Determine the position of each defect in the sampled image according to the corresponding relationship and the coordinate value corresponding to each defect; Determine the grayscale value and / or brightness value of each defect according to the position of each defect in the sampled image; The type of the defect is determined according to the grayscale value difference between the grayscale value of each defect and the preset grayscale value and / or the brightness value difference between the brightness value of each defect and the preset brightness value.

13. A photolithography machine, characterized in that: include: A defect detection device as claimed in any one of claims 1 to 9.

14. A data processing device, characterized in that: It comprises a memory and a processor, wherein the memory is suitable for storing one or more computer instructions, and when the processor runs the computer instructions, the steps of the defect detection method according to any one of claims 10 to 12 are executed.

15. A storage medium, characterized in that: The storage medium stores one or more computer instructions, and the one or more computer instructions are used to implement the steps of the defect detection method according to any one of claims 10 to 12.

16. A computer program product, characterized in that The method comprises computer instructions, which are used to implement the steps of the defect detection method according to any one of claims 10 to 12 when the computer instructions are executed by a processor.

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