A method and system for detecting defects in a photomask

Abstract

The application provides a mask defect detection method and a detection system, which are suitable for defect detection of three-tone phase shift masks. The defect detection method can accurately apply the gray values of different material layers to the corresponding regions by combining design pattern information to identify material boundaries, so as to more accurately locate the defect positions. Through twice light intensity calculation and gray control, the gray difference between different material layers can be optimized at the same time, the defect detection capability for all material layers is improved, and the defect omission caused by insufficient gray difference is avoided. For defect detection of three-tone masks, the method can not only ensure the defect capture capability of the molybdenum-silicon material layer and the quartz material layer region, but also ensure the defect capture capability of the molybdenum-silicon material layer and the chromium material layer region, so as to maximize the defect capture capability among the three materials, effectively solve the problem of multi-material gray balance, eliminate the blind area of defect detection, and significantly improve the precision of defect detection.

Description

Technical Field

[0001] This application relates to the field of semiconductor manufacturing technology, and in particular to a method and system for detecting photomask defects. Background Technology

[0002] Phase-shift masks (PSMs) are a technique that uses the phase-shift principle to reverse the phase of light, improving image contrast and enhancing image exposure resolution. When the minimum linewidth of a semiconductor is less than 130nm, traditional binary masks exhibit light interference, resulting in situations where the shaded areas are still exposed and the transmitted areas have insufficient light intensity during wafer exposure, leading to a decrease in overall contrast. Therefore, PSMs are needed to eliminate interference in the exposure beam.

[0003] Currently, all inspection machines use photomask inspection methods that set grayscale values ​​for different materials in the photomask. By calculating whether the numerical difference between grayscale values ​​of the same material exceeds the theoretical threshold, defects can be detected at the corresponding locations.

[0004] For a two-tone mask, the grayscale values ​​of the two materials, one black and one white, are used to increase the grayscale difference between the two materials through the natural mapping of light intensity and parameter adjustment, so as to capture the location information of defects in the mask.

[0005] For a three-tone mask, when calculating the grayscale values ​​of the three materials (black, white, and gray), regardless of which two materials are selected for grayscale value calculation, existing methods can only optimize the grayscale targets of two materials (such as quartz and chromium) in a single light intensity calibration. The grayscale value of the third material (such as molybdenum silicon) depends only on natural mapping and cannot be actively controlled, thus sacrificing the grayscale difference between this material and other materials and reducing the defect detection capability of the machine.

[0006] Therefore, there is a need to provide an improved technical solution to address the above-mentioned shortcomings in order to further enhance the ability to detect defects in photomasks. Summary of the Invention

[0007] In view of the defects and shortcomings of the prior art in photomask detection, this application provides a photomask defect detection method and detection system to improve the defect detection accuracy of photomasks, especially three-color phase-shift photomasks.

[0008] In a first aspect, this application provides a method for detecting defects in a photomask, comprising at least the following steps:

[0009] Obtain the design pattern information and material partitioning information of the photomask;

[0010] The photomask is subjected to a first light intensity calculation, and the initial grayscale values ​​G of the three materials—the first material layer, the second material layer, and the third material layer—are obtained through a first grayscale adjustment. 11 G 21 and G 31 ;

[0011] Based on the material partitioning information, a second light intensity calculation is performed on the second and third material layers, and the grayscale values ​​G of the second and third material layers are obtained through a second grayscale adjustment. 22 and G 32 And the grayscale difference ΔG between the two;

[0012] By combining the design pattern information to identify the material boundaries, the first light intensity calculation result is applied to the first material layer and the second material layer region, and the second light intensity calculation result is applied to the first material layer and the third material layer region.

[0013] Defect detection is performed based on the grayscale mapping results of material partitioning, and the defect location information is output.

[0014] By adopting the above technical solution, two light intensity calculations and grayscale adjustment are performed to optimize the grayscale difference between different material layers. The second light intensity calculation further optimizes the grayscale values ​​of the second and third material layers, increasing the grayscale difference between them, thereby improving the sensitivity of defect detection. This technical solution can improve the detection capability of defects in all material layers and avoid missed defects due to low grayscale difference.

[0015] In an optional embodiment, the photomask is a three-tone phase-shifting photomask; wherein,

[0016] The first material layer is a fully transparent material layer; the second material layer is a partially transparent material layer; and the third material layer is a light-shielding material layer.

[0017] In an optional embodiment, the first material layer is a quartz material layer; the second material layer is a molybdenum-silicon material layer; and the third material layer is a chromium material layer. The quartz fully transparent material layer, the molybdenum-silicon partially transparent material layer, and the chromium light-blocking material layer possess different optical properties during photolithography. For three-tone phase-shifting masks, quartz glass exhibits extremely high optical transmittance, good chemical stability, and high hardness and wear resistance, ensuring efficient light transmission and improving the precision and efficiency of photolithography as a substrate material. The chromium layer precisely blocks areas that do not require exposure, ensuring the accuracy of the photolithographic pattern. The molybdenum-silicon material has partial light transmittance, allowing light to pass through and change its phase, while also possessing a high melting point and good resistance to high-temperature creep, maintaining stable structure and performance under high-temperature environments and resisting deformation or damage.

[0018] In an optional embodiment, the first light intensity calculation method includes: obtaining the grayscale values ​​of the first and second material layers by naturally mapping the light intensity of the photomask; and adjusting the grayscale values ​​of the two layers by parameter adjustment so that the grayscale value of the first material layer approaches 0 and the grayscale value of the second material layer approaches 255; wherein the above description of grayscale values ​​is based on the presentation under reflected light, and the description of grayscale values ​​under transmitted light is the opposite. This method ensures that the grayscale difference between the two materials is maximized, providing a good foundation for subsequent defect detection in the most important material areas of the photomask.

[0019] In an optional embodiment, the second light intensity calculation method includes: obtaining the grayscale values ​​of the second and third material layers by naturally mapping the light intensity of the photomask, and adjusting the grayscale values ​​of the two layers by parameter control to increase the difference in grayscale values ​​between the second and third material layers. This second light intensity calculation further increases the grayscale difference between the second and third material layers, improving the detection capability for defects in these two material layers, optimizing the grayscale control effect, and increasing the sensitivity of defect detection, enabling more accurate detection of minute defects.

[0020] In an optional embodiment, the grayscale value of the second material layer is between 100 and 200, and the difference between the grayscale value of the second material layer and the grayscale value of the third material layer is greater than 50. Ensuring that the grayscale value of the second material layer is within a reasonable range and that the grayscale difference between the two materials is sufficiently large improves the accuracy of defect detection and enables more accurate defect detection.

[0021] In an optional implementation, the parameters controlled in the light intensity calculation include the gain and offset of the grayscale value. By adjusting the gain and offset, the grayscale value can be flexibly adjusted to meet the needs of different material layers and detection requirements, thereby improving the adaptability and flexibility of the detection method.

[0022] In an optional implementation, obtaining the design pattern information and material partitioning information of the photomask includes: identifying material boundaries by combining the design pattern information; parsing the binary boundaries of the design pattern using an algorithm; mapping the boundary coordinates to the actual captured image of the photomask; and dividing adjacent regions of the first, second, and third material layers. Accurately dividing adjacent regions of different material layers provides accurate regional information for grayscale adjustment and defect detection, thereby improving the accuracy of defect detection and reducing the false detection rate.

[0023] In an optional implementation, if the grayscale difference ΔG in a region exceeds a preset threshold ΔGt, the corresponding region is marked as an out-of-standard defect and highlighted, and the defect location information is output. ΔGt provides a clear standard for defect judgment. By highlighting out-of-standard defects, the defect location can be displayed intuitively, facilitating quick identification and handling of defects by operators, thereby improving detection efficiency and operational convenience.

[0024] Secondly, this application provides a photomask defect detection system for detecting defects in a three-color phase-shifting photomask, comprising:

[0025] The optical capture module is used to acquire the design pattern information and material partition information of the photomask;

[0026] The light intensity calculation module is used to calculate the light intensity of a photomask with three material layers and to adjust the grayscale of the light intensity data.

[0027] The defect inspection module generates inspection images based on grayscale adjustment results and outputs defect location information. This technical solution achieves systematization and automation of photomask defect detection, improving detection efficiency and accuracy. Through automated system operation, it enhances detection efficiency, reduces errors and labor intensity from manual operation, and improves detection reliability and repeatability.

[0028] Compared with the prior art, the photomask defect detection method and system provided in this application have at least the following advantages:

[0029] The photomask defect detection method provided in this application can identify material boundaries by combining design pattern information. It can accurately apply the grayscale values ​​of different material layers to the corresponding areas, thereby more accurately locating the defect position. Through two light intensity calculations and grayscale adjustment, it can simultaneously optimize the grayscale differences between different material layers, improve the detection capability of defects in all material layers, and avoid missed defects due to insufficient grayscale differences. For defect detection of three-tone photomasks, this method can not only ensure the defect capture capability in the areas of molybdenum-silicon material layers and quartz material layers, but also ensure the defect capture capability in the areas of molybdenum-silicon material layers and chromium material layers, thereby maximizing the defect capture capability among the three materials. The problem of multi-material grayscale balance is fundamentally solved, eliminating the defect detection blind zone and improving the defect detection accuracy. Attached Figure Description

[0030] Figure 1 This is a flowchart of the photomask defect detection method provided in this application;

[0031] Figure 2 A logical diagram illustrating the two light intensity calculations provided in this application. Detailed Implementation

[0032] To make the technical objectives, technical solutions, and technical effects of this application clearer, the technical solutions in this application will be clearly and completely described below in conjunction with embodiments. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0033] Therefore, the detailed description of the embodiments of this application below is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance. It should be noted that the descriptions using terms such as "an embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in conjunction with implementation methods or examples that are included in at least one implementation method or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same implementation method or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any suitable manner in one or more implementation methods or examples. The following will provide a detailed description through specific embodiments.

[0034] First, it should be understood that the following methods are commonly used for photomask defect detection:

[0035] For Two-Tone BIN products, the photomask is composed of quartz and chromium materials. Using a two-tone algorithm can fully guarantee that the quartz and chromium materials can achieve the best ability to capture defects in the photomask pattern under the premise of light intensity mapping and grayscale control.

[0036] For three-tone phase-shift PSM products, the photomask is composed of three materials: quartz, molybdenum silicon, and chromium. By intervening and selecting two of these materials, the optimal target grayscale value can be achieved through light intensity mapping and grayscale control, while sacrificing the grayscale value of the unselected materials.

[0037] Both Two-Tone and Tri-Tone products, based on the current methods of calculating the grayscale values ​​of photomask materials, involve sacrificing the grayscale value of one of the materials. Sacrificing the grayscale value of molybdenum-silicon material reduces the grayscale difference between molybdenum-silicon material and quartz, resulting in insufficient detection capability for soft-defect type defects on molybdenum-silicon material. Sacrificing the grayscale value of chromium material reduces the grayscale difference between chromium and molybdenum-silicon material, resulting in insufficient detection capability for chromium damage type defects on molybdenum-silicon material.

[0038] Example 1:

[0039] See Figure 1 and Figure 2 This embodiment provides a method for detecting defects in a photomask, including the following steps:

[0040] S1: Obtain the design pattern information and material partition information of the photomask. The algorithm combines the photomask pattern to perform initial identification of different materials when capturing the two-dimensional boundary.

[0041] S2: Subsequently, the grayscale values ​​of different materials are converted according to the two light intensity calculations to achieve the optimal grayscale value calculation for the corresponding regions of different materials: the first light intensity calculation is performed on the photomask, and the initial grayscale values ​​G of the three materials of the first material layer, the second material layer, and the third material layer are obtained through the first grayscale adjustment. 11 G 21 and G 31 ;

[0042] S3: Based on the material partitioning information, a second light intensity calculation is performed on the second and third material layers, and the grayscale values ​​G of the second and third material layers are obtained through a second grayscale adjustment. 22 and G 32 And the grayscale difference ΔG between the two;

[0043] S4: Combine the design pattern information to identify the material boundaries, apply the first light intensity calculation result to the first material layer and the second material layer area, and apply the second light intensity calculation result to the first material layer and the third material layer area;

[0044] S5: Defect detection is performed based on the grayscale mapping results of material partitions. If the grayscale difference ΔG in a region exceeds the preset threshold ΔGt, the corresponding region is marked as an out-of-standard defect and highlighted, and the defect location information is output. ΔGt is a system preset standard that can be adjusted according to actual usage and detection accuracy requirements. The preset threshold ΔGt provides a clear standard for defect judgment. Highlighting out-of-standard defects allows for a direct visual display of the defect location, facilitating quick identification and handling by operators, and improving detection efficiency and operational convenience.

[0045] The following details each step of this photomask defect detection method:

[0046] In an optional embodiment, the first material layer of the three-tone phase-shifting mask is a fully transparent material layer, the second material layer is a partially transparent material layer, and the third material layer is a light-shielding material layer. Further, for a three-tone phase-shifting mask with a first material layer of quartz, a second material layer of molybdenum-silicon, and a third material layer of chromium, quartz glass possesses extremely high optical transmittance, good chemical stability, and high hardness and wear resistance, with a transmittance >99.5%. As a substrate material in the photolithography process, it can ensure efficient light transmission, improving the precision and efficiency of photolithography. The chromium layer can precisely block areas that do not need exposure, with a transmittance <0.1%, ensuring the accuracy of the photolithographic pattern. Molybdenum-silicon material has partially transparent characteristics, with a transmittance of 6% ± 0.5% and a phase shift of 180°, allowing light to pass through and change its phase. It also has a high melting point and good resistance to high-temperature creep, maintaining stable structure and performance under high-temperature environments and is not easily deformed or damaged. The process of obtaining the design pattern information and material partitioning information of the aforementioned photomask is as follows: Material boundaries are identified by combining the design pattern information; the binary boundaries of the design pattern are parsed using an algorithm; the boundary coordinates are mapped to the actual captured image of the photomask; and adjacent regions of the first, second, and third material layers are divided. Specifically, the photomask design pattern file is first parsed to extract the binary boundary coordinates; then optical image capture is performed; and finally, an affine transformation matrix is ​​used to convert the design coordinates to image coordinates. By accurately dividing the adjacent regions of the quartz, molybdenum-silicon, and chromium material layers, accurate regional information is provided for grayscale control and defect detection, thereby improving the accuracy of defect detection and reducing the false detection rate.

[0047] In an optional embodiment, a first light intensity calculation is performed on the photomask, and the initial grayscale values ​​of the three material layers (first, second, and third) are obtained through a first grayscale adjustment, i.e., the grayscale value of the quartz material layer is G. 11 The grayscale value of the molybdenum-silicon material layer is G. 21 The grayscale value of the chromium material layer is G. 31 The first light intensity calculation process is as follows: By performing natural mapping of the light intensity onto the photomask, the grayscale values ​​of the quartz, molybdenum-silicon, and chromium material layers are obtained. The grayscale values ​​of the quartz and molybdenum-silicon layers are then parameter-adjusted to make the grayscale value of the quartz layer approach 0 and the grayscale value of the molybdenum-silicon layer approach 255. The descriptions of these grayscale values ​​are based on the reflected light, while the descriptions of the grayscale values ​​under transmitted light are the opposite. The parameters adjusted in the light intensity calculation include the gain and offset of the material grayscale values, and the adjustment formula is: G out =(G in *K gain )+G offset G out That is, the initial grayscale value of the corresponding material obtained through calculation, K. gainG is the gain coefficient. offset The grayscale value offset is defined by the above-mentioned gain and offset adjustment formulas. This allows for flexible adjustment of the grayscale value to meet the needs of different material layers and detection requirements, improving the adaptability and flexibility of the detection method. Appropriate K values ​​are then assigned to the quartz and molybdenum-silicon material layers. gian and G offset The parameters are set such that the grayscale value of the quartz material layer approaches 0 and the grayscale value of the molybdenum-silicon material layer approaches 255. Similarly, the above description of grayscale values ​​is based on the presentation under reflected light, while the description of grayscale values ​​under transmitted light is the opposite. This method ensures that the grayscale difference between the two materials is maximized, providing a good foundation for the subsequent defect detection of the most important material area of ​​the photomask.

[0048] In an optional embodiment, a second light intensity calculation is performed on the photomask. These two light intensity calculations and grayscale adjustments optimize the grayscale differences between different material layers. The second light intensity calculation further optimizes the grayscale values ​​of the second and third material layers, i.e., increasing the grayscale difference between the molybdenum-silicon and chromium material layers, thereby improving the sensitivity of defect detection. This technical solution can improve the detection capability for defects in all material layers and avoid missed defects due to low grayscale differences. Further, the second light intensity calculation process is as follows: by performing natural mapping of the light intensity on the photomask, the grayscale values ​​of the second and third material layers are obtained, and their grayscale values ​​are parameter-adjusted, i.e., increasing the grayscale difference between the molybdenum-silicon and chromium material layers. The principle of the second light intensity calculation is similar to that of the first light intensity calculation, also using the adjustment formula: G out =(G in *K gain )+G offset To impart new suitable K to the molybdenum-silicon material layer and the chromium material layer. gian and G offset Parameters. Through a second light intensity calculation, addressing the insufficient grayscale difference between the second and third material layers, grayscale adjustment is used to maximize this difference, improving the defect signal-to-noise ratio. This further increases the grayscale difference between the molybdenum-silicon and chromium material layers, enhancing the detection capability for defects in these two material layers. The grayscale adjustment effect is optimized, improving defect detection sensitivity and enabling more accurate detection of minute defects. After two light intensity calculations, the defect detection capability in both the first and second material layer regions is guaranteed, as well as the defect detection capability in the first and third material layer regions, maximizing the defect detection capability among the three materials.

[0049] In the above embodiments, the grayscale value of the second material layer, i.e., the molybdenum-silicon material layer, after grayscale adjustment, ranges from 100 to 200, and the difference between the grayscale value of the second material layer and the third material layer is greater than 50. Ensuring that the grayscale value of the second material layer is within a reasonable range and that the grayscale difference between the two materials is sufficiently large improves the accuracy of defect detection, enabling more accurate defect detection. For example, if the grayscale value of the chromium material layer is between 245 and 255, then the grayscale value of the molybdenum-silicon material layer can be between 140 and 180 to provide clear defect display and defect location information.

[0050] Example 2:

[0051] This application provides a photomask defect detection system for detecting defects in a three-color phase-shifting photomask. The defect detection system includes an optical capture module, a light intensity calculation module, and a defect inspection module.

[0052] In an optional implementation, the optical capture module is used to acquire the design pattern information and material partitioning information of the photomask. First, a high-resolution optical imaging device is used to capture an actual image of the photomask, ensuring image clarity and accuracy. Then, pattern analysis is performed, for example, by analyzing the photomask's design pattern using computer-aided design (CAD) software, extracting material boundary information, and mapping the binarized boundary coordinates of the design pattern to the actual captured image of the photomask. This accurately delineates adjacent regions of the first, second, and third material layers. For example, an affine transformation matrix is ​​used to convert the design coordinates to image coordinates, ensuring precise alignment between the design pattern and the actual captured image.

[0053] In an optional implementation, the light intensity calculation module is used to calculate the light intensity of a photomask with three material layers and to adjust the grayscale of the light intensity data. The module execution process is as follows.

[0054] First light intensity calculation: Naturally map the light intensity onto the photomask to obtain the initial grayscale values ​​G of the first material layer (quartz material layer), the second material layer (molybdenum-silicon material layer), and the third material layer (chromium material layer). 11 G 21 and G 31 Subsequently, by adjusting the gain and offset parameters, the grayscale value of the first material layer is made close to 0, and the grayscale value of the second material layer is made close to 255. The adjustment formula is: G out =(G in *K gain )+G offset Among them, G out To calculate the initial grayscale value of the corresponding material, K gain G is the gain coefficient. offset This refers to the grayscale value offset. Specific parameter settings: The grayscale value G of the quartz material layer... 11The grayscale value G of the molybdenum-silicon material layer is adjusted to approach 0. 21 The grayscale value is adjusted to approximately 255 to maximize the difference in grayscale between the two materials. Similarly, the grayscale value descriptions above are based on the reflection under reflected light, while the descriptions of grayscale values ​​under transmitted light are the opposite.

[0055] Second light intensity calculation: Natural mapping of light intensity is performed on the second material layer (molybdenum-silicon material layer) and the third material layer (chromium material layer) to obtain their grayscale values ​​G. 22 and G 32 Subsequently, the grayscale difference between the second and third material layers is further increased by adjusting the gain and offset parameters. The adjustment formula is the same as the first light intensity calculation, but new gain coefficients and offsets are assigned. Specific parameter settings: Ensure that the grayscale value G of the second material layer is... 22 The range is between 100 and 200, and it is related to the gray value G of the third material layer. 32 The difference is greater than 50, for example, the gray value G of the chromium material layer. 32 The grayscale value G of the molybdenum-silicon material layer is 245–255. 22 The values ​​range from 140 to 180. Similar to the first light intensity calculation, the above description of grayscale values ​​is based on the presentation under reflected light. The description of grayscale values ​​under transmitted light is the opposite, which will not be elaborated here.

[0056] In an optional implementation, the defect inspection module generates an inspection image based on the grayscale adjustment results and outputs defect location information. During module execution, it first identifies material boundaries by combining design pattern information, applies the first light intensity calculation result to the first and second material layer regions, and then applies the second light intensity calculation result to the first and third material layer regions. Finally, defect detection is performed based on the grayscale mapping results of the material partitions. If the grayscale difference ΔG in a region exceeds a preset threshold ΔGt, the corresponding region is marked as an over-standard defect and highlighted, and the defect location information is output. The preset threshold ΔGt can be adjusted according to actual usage and detection accuracy requirements, providing a clear standard for defect judgment.

[0057] The photomask defect detection system provided in this embodiment optimizes the grayscale differences between different material layers by performing two light intensity calculations and grayscale adjustment. This improves the detection capability for defects in all material layers of a three-tone phase-shifting photomask. Through precise material partitioning and defect judgment criteria, the system can effectively avoid missed defects and improve the accuracy and efficiency of detection. In addition, the system provides flexible parameter adjustment functions to meet the needs of different material layers and detection requirements.

[0058] In an optional embodiment, the defect detection system further includes a grayscale enhancement unit, which performs multiple iterative adjustments on the selected material partition until the target grayscale difference is achieved.

[0059] In an optional embodiment, the defect detection system further includes a visualization output module, which outputs the defect coordinate mapping in the form of a heat map and marks the defects using different colors, such as red marking defects in the molybdenum-silicon material layer area and blue marking defects in the chromium material layer area.

[0060] The above technical solution realizes the systematization and automation of photomask defect detection, improves detection efficiency and accuracy. Through the automated operation of the system, the detection efficiency is improved, the error and labor intensity of manual operation are reduced, and the reliability and repeatability of detection are improved.

[0061] In summary, this application provides a method and system for detecting defects in photomasks, applicable to the defect detection of three-tone phase-shift photomasks. This method can identify material boundaries by combining design pattern information, accurately applying the grayscale values ​​of different material layers to their corresponding areas, thereby more accurately locating defects. Through two light intensity calculations and grayscale adjustment, it can simultaneously optimize the grayscale differences between different material layers, improving the detection capability for defects in all material layers and avoiding missed defects due to insufficient grayscale differences. For the defect detection of three-tone photomasks, it can ensure the defect capture capability in the areas of molybdenum-silicon and quartz material layers, as well as the defect capture capability in the areas of molybdenum-silicon and chromium material layers, maximizing the defect capture capability among the three materials. This fundamentally solves the problem of multi-material grayscale balance, eliminates blind spots in defect detection, and improves defect detection accuracy. The technical solution of this application has high industrial application value because it effectively overcomes the various shortcomings of existing technologies.

[0062] The above embodiments are merely illustrative of the principles and effects of this application and are not intended to limit this application. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in this application should still be covered by the claims of this application.

Claims

1. A method for detecting defects in a photomask, characterized in that, At least the following steps are included: Obtain the design pattern information and material partitioning information of the photomask; The photomask is subjected to a first light intensity calculation, and the initial grayscale values ​​G of the three materials—the first material layer, the second material layer, and the third material layer—are obtained through a first grayscale adjustment. 11 G 21 and G 31 The first light intensity calculation method includes: obtaining the gray values ​​of the first material layer and the second material layer by naturally mapping the light intensity of the photomask, and adjusting the gray values ​​of the two materials by parameter adjustment so that the gray value of the first material layer approaches 0 and the gray value of the second material layer approaches 255; wherein, the above description of gray values ​​is based on the presentation under reflected light, and the description of gray values ​​under transmitted light is the opposite. Based on the material partitioning information, a second light intensity calculation is performed on the second and third material layers, and the grayscale values ​​G of the second and third material layers are obtained through a second grayscale adjustment. 22 and G 32 The second light intensity calculation method includes: obtaining the gray values ​​of the second material layer and the third material layer by naturally mapping the light intensity of the photomask, and adjusting the gray values ​​of the two materials by parameter adjustment to increase the difference in gray values ​​between the second material layer and the third material layer. By combining the design pattern information to identify the material boundaries, the first light intensity calculation result is applied to the first material layer and the second material layer region, and the second light intensity calculation result is applied to the first material layer and the third material layer region. Defect detection is performed based on the grayscale mapping results of material partitioning, and the defect location information is output. The first material layer is a quartz material layer; the second material layer is a molybdenum-silicon material layer; and the third material layer is a chromium material layer.

2. The method for detecting photomask defects according to claim 1, characterized in that, The grayscale value of the second material layer is between 100 and 200, and the difference between the grayscale value of the second material layer and the grayscale value of the third material layer is greater than 50.

3. The method for detecting photomask defects according to claim 1, characterized in that, The parameters that can be adjusted in light intensity calculation include the gain and offset of grayscale values.

4. The method for detecting photomask defects according to claim 1, characterized in that, Obtaining the design pattern information and material partition information of the photomask includes: By combining the design pattern information to identify the material boundaries, the binary boundary of the design pattern is parsed through the algorithm, and the boundary coordinates are mapped to the actual captured image of the photomask to divide the adjacent regions of the first material layer, the second material layer and the third material layer.

5. The method for detecting photomask defects according to claim 1, characterized in that, If the grayscale difference ΔG in a region exceeds the preset threshold ΔGt, the corresponding region will be marked as an out-of-standard defect and highlighted, and the defect location information will be output.

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