Mask plate defect detection method, device and equipment

Abstract

The invention provides a mask plate defect detection method, device and equipment. The method comprises the following steps: acquiring at least one piece of initial defect detection information of a mask plate and design layout information of the mask plate; performing secondary defect detection on the initial defect detection information based on the design layout information to obtain secondary defect detection information of the mask plate; carrying out photoetching simulation on the secondary defect detection information by utilizing a photoetching optical model to obtain a photoetching simulation analysis result; and based on the photoetching simulation analysis result and the secondary defect detection information, obtaining defect classification information of the mask plate. Therefore, more accurate defect classification information is obtained, and the defect classification precision and the defect classification accuracy are improved.

Description

Technical Field

[0001] The present application relates to the field of semiconductor detection technology, and in particular to a mask defect detection method, device and equipment. Background Art

[0002] In the chip manufacturing process, masks play a vital role. Masks, also known as photomasks or masks, are key tools used to transfer circuit patterns onto silicon wafers during semiconductor manufacturing. Their quality directly affects the performance and pass rate of the final chip. Therefore, defect detection of masks is an indispensable part of the chip manufacturing process.

[0003] At present, for the defect detection of mask plates, the chip-to-chip comparison detection method is generally used. The detection results of this method generally contain more false detection defects. A large number of false detections will affect the correct evaluation of the actual quality of the mask plate, and it is impossible to accurately determine whether the mask plate needs to be cleaned or trimmed. In this case, the detection results of the mask plate will be reclassified. At present, the detection results are generally reclassified manually. This manual classification requires a considerable amount of human resources, and manual classification depends on personal experience and ability. Classifiers of different levels will produce different classification results, and certain uncontrollable factors will be introduced into the classification results; in addition, the manual classification results can generally only give qualitative defect type classification results, and cannot give quantitative results for the accurate defect size, defect severity, etc. At present, there is also a technology for reclassification using classification software. Although it can solve the problem of inconsistent manual classification standards, the information source for reclassification is still the detection results obtained by comparing chips with chips. The reclassification based on such detection results with high false detection rates still has the problems of high false detection rates and insufficient classification.

[0004] Therefore, how to improve the classification accuracy of mask defect detection has become a top priority. Summary of the invention

[0005] In view of this, the present application provides a mask defect detection method, device, electronic device, computer-readable storage medium and computer program product, which are used to solve the problems of high false positive rate and low precision of existing defect classification results.

[0006] In a first aspect, an embodiment of the present application provides a mask defect detection method, comprising: obtaining at least one initial defect detection information of the mask and design layout information of the mask; performing secondary defect detection on the initial defect detection information based on the design layout information to obtain secondary defect detection information of the mask; performing lithography simulation on the secondary defect detection information using a lithography optical model to obtain a lithography simulation analysis result; and obtaining defect classification information of the mask based on the lithography simulation analysis result and the secondary defect detection information.

[0007] In a second aspect, an embodiment of the present application provides a mask defect detection device, comprising: an information acquisition module, used to acquire at least one initial defect detection information of the mask and design layout information of the mask; a secondary detection module, used to perform secondary defect detection on the initial defect detection information based on the design layout information, and obtain secondary defect detection information of the mask; a lithography simulation module, used to perform lithography simulation on the secondary defect detection information using a lithography optical model, and obtain a lithography simulation analysis result; a defect classification module, which obtains defect classification information of the mask based on the lithography simulation analysis result and the secondary defect detection information.

[0008] In a third aspect, an embodiment of the present application provides an electronic device, comprising a processor and a memory storing computer program instructions, wherein the computer program instructions, when executed by the processor, implement the steps of the mask defect detection method described in any embodiment of the present application.

[0009] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a mask defect detection program is stored, wherein when the mask defect detection program is executed by a processor, the steps of the mask defect detection method described in any embodiment of the present application are implemented.

[0010] In a fifth aspect, an embodiment of the present application provides a computer program product, which includes computer executable instructions, wherein when the computer executable instructions are executed by a processor, the steps of the mask defect detection method described in any embodiment of the present application are implemented.

[0011] The embodiments of the present application provide a mask defect detection method, device, electronic device, computer-readable storage medium, and computer program product, which have at least the following beneficial effects:

[0012] After performing an initial defect detection on the mask and obtaining the initial defect detection information, a secondary defect detection is performed on the initial defect detection information using the design layout information, which can eliminate the interference of false defects in the initial defect detection information and improve the recognition rate of real defects. The present application further simulates the secondary defect detection information through a lithography optical model to obtain the actual performance of real defects in the lithography process, which helps to provide a basis for judging the severity of the defects. At the same time, combining the lithography simulation analysis results with the secondary defect detection information can comprehensively evaluate the defects on the mask, obtain more accurate defect classification results, and improve the precision and accuracy of defect classification. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the specific implementation methods of the present application or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without paying any creative work.

[0014] Figure 1 A schematic diagram showing a process flow of a mask defect detection method proposed in an embodiment of the present application;

[0015] Figure 2 A partial schematic diagram of a mask transmission image provided by an embodiment of the present application is shown;

[0016] Figure 3 A partial schematic diagram of a mask plate reflection image provided by an embodiment of the present application is shown;

[0017] Figure 4 A schematic diagram showing a design pattern provided in an embodiment of the present application;

[0018] Figure 5 A schematic diagram showing secondary defect detection results of a mask reflection image provided by an embodiment of the present application is shown;

[0019] Figure 6 A schematic structural diagram of a mask defect detection device provided in an embodiment of the present application is shown;

[0020] Figure 7 A schematic diagram showing the structure of an electronic device provided in an embodiment of the present application is shown;

[0021] Figure 8 A schematic diagram showing the structure of a computer-readable storage medium provided in an embodiment of the present application is shown;

[0022] Fig. 9 A schematic diagram showing the structure of a computer program product provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0023] In order to make the above and other features and advantages of the present application more clear, the present application is further described below in conjunction with the accompanying drawings. It should be understood that the specific embodiments given herein are for the purpose of explaining to those skilled in the art and are only exemplary and not restrictive.

[0024] In the following description, many specific details are set forth to provide a thorough understanding of the present application. However, it is apparent to those skilled in the art that specific details need not be adopted to practice the present application. In other cases, well-known steps or operations are not described in detail to avoid blurring the present application.

[0025] During the research, the inventors found that the existing defect detection methods for masks have the following problems:

[0026] 1. The mask plate inspection equipment uses a chip-to-chip comparison inspection method, which results in a large number of false detections. In addition, since the inspection results lack relevant information about the layout location of the defect, it is difficult to determine the severity of the defect.

[0027] 2. It is impossible to judge whether the defects detected on the mask will form printable defects on the silicon wafer after the photolithography process based on the existing defect detection results, and thus there is a lack of basis for judging the importance of the defects.

[0028] In view of this, an embodiment of the present application provides a method for detecting mask defects. Figure 1 A schematic diagram of a process of a mask defect detection method proposed in an embodiment of the present application is shown, such as Figure 1 As shown, the mask defect detection method may include the following steps.

[0029] S11, obtaining at least one initial defect detection information of the mask plate and design layout information of the mask plate.

[0030] The "at least one initial defect detection information of the mask" involved in the embodiments of the present application may refer to defect detection information obtained by the mask detection device through different illumination modes to obtain one or more mask images for the mask, and performing image defect detection on each mask image. Among them, different illumination modes may include transmission and reflection. It should be noted that each mask image corresponds to one initial defect detection information.

[0031] The initial defect detection information may include at least one initial defect detection image and basic information of the initial defect detection image. The basic information of the initial defect detection image may include at least one of the following information: size information of the initial defect area, type information of the initial defect, pixel information of the initial defect area, and position information of the initial defect area.

[0032] In one implementation manner, the method for performing initial defect detection on the mask may be: the mask detection equipment performs detection by comparing chips with each other.

[0033] The design layout information may be graphical information of chip design, including design graphics of all levels on the chip and their position information.

[0034] S12, performing secondary defect detection on the initial defect detection information based on the design layout information to obtain secondary defect detection information of the mask.

[0035] The "secondary defect detection" involved in the embodiments of the present application may refer to re-evaluating the initial defects corresponding to the initial defect detection information using the design layout information. The secondary defect detection information includes the secondary defect detection image and the basic information of the secondary defect detection image, including: defect size information, defect location information, and defect type information, etc. The basic information may also include surrounding design layout information, wherein the surrounding design layout information refers to the graphic information adjacent to the secondary defect detection image on the design layout.

[0036] In an embodiment of the present application, based on the initial defect detection information, a reference graphic area corresponding to the initial defect can be searched on the design layout, and then the initial defect can be evaluated again based on the comparison result between the initial defect area and the reference graphic area in the mask template image, so as to generate secondary defect detection information of the mask template based on the evaluation result.

[0037] S13, using a lithography optical model to perform lithography simulation on the secondary defect detection information to obtain a lithography simulation analysis result.

[0038] The "lithography optical model" involved in the embodiments of the present application refers to a physical model for lithography simulation of the light source and the lithography machine system, which can be used to simulate the image effect generated by the pattern on the mask on the silicon wafer and the photoresist. The results of lithography simulation analysis can be used to evaluate the impact of defects on the lithography image.

[0039] In one embodiment of the present application, secondary defect detection information is input into a lithography optical model, the lithography process of the mask is simulated using the lithography optical model, and the impact of defects on the lithography pattern is evaluated, thereby obtaining a lithography simulation analysis result of the mask.

[0040] S14, obtaining defect classification information of the mask based on the lithography simulation analysis result and the secondary defect detection information.

[0041] The defect classification information involved in this application is comprehensive information about each defect in the mask, which includes not only the category to which the defect belongs, but also the defect location information, location importance, defect size information, defect type, defect level, defect printability, etc.

[0042] In one embodiment of the present application, the secondary defect detection information is associated with the lithography simulation analysis results to obtain comprehensive classification information of each defect, thereby obtaining defect classification information of the mask, that is, determining the category to which the defect belongs.

[0043] In the above embodiment, after performing initial defect detection on the mask and obtaining initial defect detection information, secondary defect detection is performed in combination with design layout information, which can eliminate the interference of false defects, improve the recognition rate of real defects, and further refine the basic information of defects. And the simulation of the secondary defect detection information by the lithography optical model can obtain the actual performance of the real defects in the lithography process, which helps to provide a basis for judging the severity of the defects. At the same time, combining the lithography simulation analysis results with the secondary defect detection information can comprehensively evaluate the defects on the mask, obtain more accurate defect classification results, and improve the precision and accuracy of defect classification.

[0044] In some embodiments, the method of performing secondary defect detection on the initial defect detection information based on the design layout information to obtain secondary defect detection information of the mask plate in step S12 includes:

[0045] Based on the position information of the initial defect area, the design graphic at the corresponding position of the initial defect area in the design layout information is determined; by comparing the design graphic at the corresponding position with the initial defect detection image, the secondary defect detection information of the initial defect detection image is obtained; that is, using the defect area position information provided in the initial defect detection information, the design graphics corresponding to these positions are found in the design layout through coordinate matching, and for each defect area, the corresponding design graphic is compared with each initial defect detection image, so as to obtain the secondary defect detection information.

[0046] The secondary defect detection information of all initial defect detection images is summarized to obtain the secondary defect detection information of the mask. Based on the comparison result between the design layout and each initial defect detection image, the secondary defect detection information of each initial defect detection image is generated. Finally, the secondary defect detection information of all initial defect detection images is summarized to form the complete secondary defect detection information of the mask.

[0047] In the above embodiment, by combining the initial defect detection information with the design layout information, defects can be more accurately located and analyzed. At the same time, since different types of initial defect detection images may be acquired based on different lighting technologies (such as transmission and reflection), they can each capture different types of defect features. Therefore, merging the secondary defect detection information of these images can ensure comprehensive coverage of various types of defects that may exist on the mask, as well as cross-verification and complementarity, thereby reducing the occurrence of false positives and false negatives and improving the integrity and accuracy of detection.

[0048] In some embodiments, obtaining the secondary defect detection information of the initial defect detection image by comparing the design graphics at corresponding positions with the initial defect detection image includes: correcting the basic information of the initial defect detection image by comparing the design graphics at corresponding positions with the initial defect detection image to obtain the secondary defect detection information of the initial defect detection image.

[0049] In one embodiment of the present application, it is possible to determine whether the initial defect actually exists and the real basic information corresponding to the actually existing defect based on the comparison analysis results of the design pattern at the corresponding position and the initial defect detection image, compare the real basic information with the basic information, and when the comparison results are inconsistent, correct the basic information of the initial defect detection image based on the real basic information, and generate secondary defect detection information based on the corrected basic information. The real basic information may include: defect location information, defect type information, defect size information, etc.

[0050] For example, when it is determined based on the comparison results that a defect actually exists, but its size is inconsistent with the initial defect detection size and its type is inconsistent with the initial defect detection type, the size in the basic information of the initial defect detection image is modified to the actual size indicated in the comparison analysis result, and the defect type is modified to the actual defect type indicated in the comparison analysis result.

[0051] In the above embodiment, by accurately comparing the initial defect detection image with the design pattern, the difference between the actual defect and the normal design can be more accurately identified, and the basic information such as the type, size, and location of the defect can be further refined and corrected, thereby reducing the possibility of false alarms and missed alarms and improving the accuracy of detection.

[0052] In some embodiments, the initial defect detection image may include a mask transmission image and a mask reflection image. Figure 2 A partial schematic diagram of a mask transmission image provided by the present application is shown. Figure 3 FIG. 1 is a partial schematic diagram of a mask plate reflection image provided by an embodiment of the present application. Figure 2 and Figure 3As shown, different types of initial defect detection images may show different features. The secondary defect detection information of the initial defect detection image may include: secondary defect detection information of the mask plate reflection image and secondary defect detection information of the mask plate transmission image.

[0053] Obtaining secondary defect detection information of the initial defect detection image by comparing the design pattern at the corresponding position with the initial defect detection image can include: obtaining secondary defect detection information of the mask template reflection image by comparing the design pattern at the corresponding position with the mask template reflection image, and obtaining secondary defect detection information of the mask template transmission image by comparing the design pattern at the corresponding position with the mask template transmission image.

[0054] To further understand the comparison process between the design pattern and the initial defect detection image, the mask template reflection image is used as an example. Figure 4 A schematic diagram showing a design pattern provided in an embodiment of the present application is shown. Figure 4 and Figure 3 Compare and check whether the initial defect area is a real defect. If it is a real defect, mark the defect area as "Defect". Figure 5 As shown, the comparison process between the design pattern at the corresponding position and the initial defect detection image is completed.

[0055] Summarizing the secondary defect detection information of all initial defect detection images to obtain the secondary defect detection information of the mask may include: summarizing the secondary defect detection information of the mask reflection image and the secondary defect detection information of the mask transmission image to obtain the secondary defect detection information of the mask.

[0056] In one embodiment of the present application, the secondary defect detection information of the mask plate reflection image and the secondary defect detection information of the mask plate transmission image can be summarized according to a preset summary strategy, thereby generating the secondary defect detection information of the mask plate. The preset summary strategy may include determining whether the basic information of the defect is based on the secondary defect detection information of the mask plate reflection image or the mask plate transmission image according to the defect type. For example, for the same defect, if the defect is a scratch defect, the secondary defect detection information of the mask plate reflection image shall prevail.

[0057] In the above embodiment, by considering the secondary defect detection results of the reflection image and the transmission image at the same time, various defects that may exist in the mask can be more comprehensively covered, thereby improving the comprehensiveness of the detection. In addition, since the reflection image and the transmission image may reveal different aspects of the same defect from different angles, by summarizing the secondary defect detection information of the two, the nature, location and severity of the defect can be more accurately determined, thereby enhancing the accuracy of the detection.

[0058] In some embodiments, the method described in step S13 of using a lithography optical model to perform lithography simulation on the secondary defect detection information to obtain a lithography simulation analysis result includes: using a lithography optical model to perform lithography simulation on the secondary defect detection image in the secondary defect detection information to obtain a lithography simulation analysis result.

[0059] The lithography simulation analysis results involved in the embodiments of the present application include the defect printability corresponding to the secondary defect detection image. The secondary defect detection image is a mask plate image with defect areas marked. In other words, the secondary defect detection image is an image generated by marking defects on the original image of the mask plate.

[0060] In one embodiment of the present application, for each defect in the secondary defect detection image, basic information of each defect is obtained from the secondary defect detection information, and the secondary defect detection image and the basic information are used as inputs of a lithography optical model. The lithography optical model is used to obtain simulation results of each defect on the mask after the lithography process on the silicon wafer, and the printing effect of each defect on the silicon wafer is analyzed based on the simulation results, thereby generating a lithography simulation analysis result.

[0061] In the above embodiment, through simulation, the impact of defects on the mask during the photolithography process on the final silicon wafer product can be predicted before actual manufacturing, which is helpful to distinguish problem areas from normal areas. And by accurately evaluating the printability of each defect, that is, its actual performance on the silicon wafer, those defects that may have a significant impact on the performance of the final product can be screened out.

[0062] Obtaining the defect classification information of the mask based on the lithography simulation analysis results and the secondary defect detection information in step S14 includes: determining the defect classification information of the mask based on defect printability combined with at least one of defect size information, defect type information and defect position information.

[0063] In one embodiment of the present application, based on the results of lithography simulation analysis, it can be determined whether a defect is a printable defect or an unprintable defect, and the printability of a defect is further associated with at least one of the defect size information, defect type information and defect location information. According to preset classification rules, each defect on the mask template is comprehensively classified to obtain the defect classification information of the mask plate (i.e., the category to which the defect belongs).

[0064] Among them, the preset classification rules can be determined according to specific production requirements and product specifications. For example, the printability of the defect is combined with the defect position for classification, and the printable defects are classified into printable defects in important positions and printable defects in unimportant positions according to the importance of the position. It can be understood that the division of important positions and unimportant positions can be determined according to actual conditions. Or the printability of the defect is combined with the defect type for classification, and the printable defects are classified into broken line type printable defects and bridge type printable defects according to the defect type. Or the printability of the defect is combined with the defect size information, and the printable defects are classified into printable large defects, printable medium defects and printable small defects. The printability of the defect can also be combined with multiple basic information, for example, the printability of the defect is combined with the defect position, defect type and defect size information, and the printable defects are divided into: medium-sized defects with broken lines at important positions, large defects with bridges at important positions, small defects with broken lines at unimportant positions, and so on.

[0065] The embodiment of the present application classifies defects more finely by combining defect printability with at least one of defect size information, defect type information and defect location information, so that the impact of each detected defect on the subsequent process can be quickly identified, and a matching treatment plan can be adopted, thereby avoiding wasting time and resources on a large number of non-critical defects, improving overall production efficiency, and helping to optimize the repair process.

[0066] In addition, based on the detailed defect classification information of the above-mentioned embodiment of the present application, the defect repair position of the mask and the corresponding repair strategy can be determined.

[0067] In one embodiment of the present application, defects to be repaired and defects that do not need to be repaired are screened out based on defect classification information. For example, non-printable defects are determined as defects that do not need to be repaired; small printable defects in non-important locations are determined as defects that do not need to be repaired; printable defects in important locations are determined as defects to be repaired; or large printable defects are determined as defects to be repaired, etc. It can be seen that the embodiment of the present application can formulate different defect repair strategies for defects in different categories corresponding to different defect classification rules.

[0068] Among them, the defect repair position can be further obtained for the defect to be repaired. Since the position information of each defect has been obtained in the secondary defect detection, when the defect is determined to be a defect to be repaired, the position information of the defect can be further obtained. At the same time, the defect type and size information can be obtained. Matching repair strategies can be formulated for different defect types and sizes. Common repair strategies include physical repair, electron beam repair, optical repair, etc.

[0069] In the above embodiment, whether defects of the mask need to be repaired can be determined based on accurate defect classification information, and if repair is required, the repair location and corresponding repair strategy can be determined, thereby ensuring that defects are handled in a timely and effective manner and improving product quality and production efficiency.

[0070] Another aspect of the present application provides a mask defect detection device, Figure 6 A schematic diagram of the structure of a mask defect detection device provided in an embodiment of the present application is shown. Figure 6 As shown, the mask defect detection device 60 may include the following modules.

[0071] The information acquisition module 61 is used to acquire at least one initial defect detection information of the mask plate and the design layout information of the mask plate.

[0072] The secondary detection module 62 is used to perform secondary defect detection on the initial defect detection information based on the design layout information to obtain secondary defect detection information of the mask.

[0073] The photolithography simulation module 63 is used to perform photolithography simulation on the secondary defect detection information using a photolithography optical model to obtain a photolithography simulation analysis result.

[0074] The defect classification module 64 obtains defect classification information of the mask based on the photolithography simulation analysis result and the secondary defect detection information.

[0075] In some embodiments, the secondary detection module 62 may include the following sub-modules.

[0076] The image determination submodule is used to determine the design pattern of the corresponding position of the initial defect area in the design layout information based on the position information of the initial defect area.

[0077] An image detection submodule, used to obtain secondary defect detection information of the initial defect detection image by comparing the design pattern at the corresponding position with the initial defect detection image;

[0078] The mask template detection submodule is used to summarize the secondary defect detection information of all initial defect detection images to obtain the secondary defect detection information of the mask template.

[0079] In some embodiments, the image detection submodule is specifically used to correct the basic information of the initial defect detection image by comparing the design pattern at the corresponding position with the initial defect detection image to obtain the secondary defect detection information of the initial defect detection image.

[0080] In some embodiments, the secondary defect detection information of the initial defect detection image includes: the secondary defect detection information of the mask reflection image and the secondary defect detection information of the mask transmission image. The mask detection submodule is specifically used to aggregate the secondary defect detection information of the mask reflection image and the secondary defect detection information of the mask transmission image to obtain the secondary defect detection information of the mask.

[0081] In some embodiments, the secondary defect detection information of the mask plate includes: a secondary defect detection image. The lithography simulation module 63 is specifically used to perform lithography simulation on the secondary defect detection image in the secondary defect detection information using a lithography optical model to obtain a lithography simulation analysis result, wherein the lithography simulation analysis result includes the defect printability corresponding to the secondary defect detection image.

[0082] In some embodiments, the secondary defect detection information includes at least one of the following: defect size information, defect type information, and defect location information. The lithography simulation analysis result includes defect printability. The defect classification module 64 is specifically used to determine the defect classification information of the mask based on the defect printability combined with at least one of the defect size information, defect type information, and defect location information.

[0083] In some embodiments, the mask defect detection device 60 may further include a repair strategy determination module, which is used to determine the defect repair position of the mask and the corresponding repair strategy according to the defect classification information.

[0084] It should be understood that the specific features, operations and details described hereinabove with respect to the method of the present application may also be similarly applied to the device and system of the present application, or vice versa. In addition, each step of the method of the present application described above may be performed by a corresponding component or unit of the device or system of the present application.

[0085] It should be understood that each module / unit of the device of the present application can be implemented in whole or in part by software, hardware, firmware or a combination thereof. Each module / unit can be embedded in the processor of the electronic device or independent of the processor in the form of hardware or firmware, or can be stored in the memory of the electronic device in the form of software for the processor to call to perform the operation of each module / unit. Each module / unit can be implemented as an independent component or module, or two or more modules / units can be implemented as a single component or module.

[0086] According to another aspect of the embodiments of the present application, an electronic device is provided. Figure 7 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application is shown. Figure 7As shown, the electronic device 70 includes a processor 71 and a memory 72 storing computer program instructions. When the processor 71 executes the computer program instructions, each step of the above-mentioned mask defect detection method is implemented. The electronic device 70 can be generally a server, a terminal, or any other electronic device with necessary computing and / or processing capabilities.

[0087] In one embodiment, the electronic device 70 may include a processor, a memory, a network interface, a communication interface, etc. connected through a system bus. The processor of the electronic device 70 may be used to provide necessary computing, processing and / or control capabilities. The memory of the electronic device 70 may include a non-volatile storage medium and an internal memory. The non-volatile storage medium may store an operating system, a computer program, etc. The internal memory may provide an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface and the communication interface of the electronic device 70 may be used to connect and communicate with external devices through a network. When the computer program is executed by the processor, the steps of the mask defect detection method of the present application are executed.

[0088] Another aspect of the present application provides a computer-readable storage medium, Figure 8 A schematic diagram showing the structure of a computer-readable storage medium provided in an embodiment of the present application is shown as follows: Figure 8 As shown, a mask defect detection program 81 is stored on a computer-readable storage medium 80. When the mask defect detection method program 81 is executed by a processor, the steps of the mask defect detection method provided in any of the above embodiments are implemented.

[0089] Another aspect of the present application provides a computer program product, Fig. 9 A schematic diagram showing the structure of a computer program product provided by an embodiment of the present application is shown in FIG. Fig. 9 As shown, the computer program product 90 includes computer executable instructions 91, wherein when the computer executable instructions 91 are executed by a processor, the steps of the mask defect detection method provided in any of the above embodiments are implemented.

[0090] It will be appreciated by those skilled in the art that the method steps of the present application may be performed by instructing related hardware such as the electronic device 70 or a processor through a computer program, and the computer program may be stored in a non-temporary computer-readable storage medium, which causes the steps of the present application to be performed when the computer program is executed. Depending on the circumstances, any reference to memory, storage, or other media herein may include non-volatile or volatile memory. Examples of non-volatile memory include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, magnetic tape, floppy disk, magneto-optical data storage device, optical data storage device, hard disk, solid state disk, etc. Examples of volatile memory include random access memory (RAM), external cache memory, etc.

[0091] The various technical features described above can be combined arbitrarily. Although all possible combinations of these technical features are not described, any combination of these technical features should be considered to be covered by this specification as long as there is no contradiction in such combination.

[0092] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for detecting defects in a mask, characterized in that: include: Acquire at least one initial defect detection information of the mask plate and design layout information of the mask plate; Performing secondary defect detection on the initial defect detection information based on the design layout information to obtain secondary defect detection information of the mask; Performing lithography simulation on the secondary defect detection information using a lithography optical model to obtain a lithography simulation analysis result; Based on the lithography simulation analysis result and the secondary defect detection information, defect classification information of the mask is obtained.

2. The method according to claim 1, characterized in that The initial defect detection information includes: at least one initial defect detection image and basic information of the initial defect detection image, and the basic information of the initial defect detection image includes: location information of the initial defect area; The performing secondary defect detection on the initial defect detection information based on the design layout information to obtain secondary defect detection information of the mask template includes: Determine, based on the position information of the initial defect area, a design pattern corresponding to the position of the initial defect area in the design layout information; Obtaining secondary defect detection information of the initial defect detection image by comparing the design pattern at the corresponding position with the initial defect detection image; The secondary defect detection information of all initial defect detection images is summarized to obtain the secondary defect detection information of the mask.

3. The method according to claim 2, characterized in that The basic information of the initial defect detection image also includes: initial defect detection type and / or initial defect detection size information; The obtaining of secondary defect detection information of the initial defect detection image by comparing the design pattern of the corresponding position with the initial defect detection image comprises: By comparing the design pattern at the corresponding position with the initial defect detection image, basic information of the initial defect detection image is corrected to obtain secondary defect detection information of the initial defect detection image.

4. The method according to claim 2, characterized in that: The secondary defect detection information of the initial defect detection image includes: the secondary defect detection information of the mask plate reflection image and the secondary defect detection information of the mask plate transmission image; The step of aggregating the secondary defect detection information of all initial defect detection images to obtain the secondary defect detection information of the mask template includes: The secondary defect detection information of the mask plate reflection image and the secondary defect detection information of the mask plate transmission image are summarized to obtain the secondary defect detection information of the mask plate.

5. The method according to claim 1, characterized in that The secondary defect detection information of the mask plate includes: a secondary defect detection image; The using of the lithography optical model to perform lithography simulation on the secondary defect detection information to obtain lithography simulation analysis results includes: A lithography simulation is performed on the secondary defect detection image in the secondary defect detection information using a lithography optical model to obtain a lithography simulation analysis result, wherein the lithography simulation analysis result includes defect printability corresponding to the secondary defect detection image.

6. The method according to any one of claims 1 to 5, characterized in that: The secondary defect detection information includes at least one of the following: defect size information, defect type information and defect location information; the photolithography simulation analysis result includes defect printability; The obtaining defect classification information of the mask based on the lithography simulation analysis result and the secondary defect detection information includes: Defect classification information of the mask is determined based on the defect printability combined with at least one of the defect size information, the defect type information, and the defect position information.

7. The method according to claim 6, characterized in that The method further comprises: Determine the defect repair position of the mask and the corresponding repair strategy according to the defect classification information.

8. A mask defect detection device, characterized in that: The device comprises: An information acquisition module, used to acquire at least one initial defect detection information of the mask plate and design layout information of the mask plate; A secondary detection module, configured to perform secondary defect detection on the initial defect detection information based on the design layout information to obtain secondary defect detection information of the mask; A lithography simulation module is used to perform lithography simulation on the secondary defect detection information using a lithography optical model to obtain a lithography simulation analysis result; The defect classification module obtains defect classification information of the mask based on the photolithography simulation analysis result and the secondary defect detection information.

9. An electronic device, characterized in that: The electronic device comprises a processor and a memory storing computer program instructions, wherein the computer program instructions, when executed by the processor, implement the steps of the mask defect detection method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer-executable program, wherein when the computer-executable program is executed by a processor, the steps of the mask defect detection method according to any one of claims 1 to 7 are implemented.

11. A computer program product, characterized in that The computer program product comprises computer executable instructions, wherein when the computer executable instructions are executed by a processor, the steps of the mask defect detection method according to any one of claims 1 to 7 are implemented.

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