Photomask defect detection method, device, storage medium and electronic equipment

Abstract

The present application discloses a photomask defect detection method, device, storage medium and electronic device, wherein the photomask defect detection method comprises obtaining a projection intensity image and a reflection intensity image of a photomask; obtaining a first edge distribution image of the photomask based on the projection intensity image and the reflection intensity image; comparing the first edge distribution image with a preset edge distribution image to obtain a difference image; calculating the root mean square error of the second edge distribution image of the difference image; and determining that the photomask is qualified when the root mean square error is less than a preset threshold value. This solution can quickly and accurately detect defects in photomasks of online chip production lines.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of image processing technology, and specifically to a method, device, storage medium and electronic device for detecting photomask defects. Background Art

[0002] The rapid development of integrated circuits has put forward higher requirements on photolithography methods. Exposure is carried out through high-precision photomasks, and the pattern on the photomask is transferred to the photoresist on the wafer surface. The chip is then processed and manufactured through processes such as development, etching, and deposition. Finally, high-end chips that meet design requirements are produced through packaging and testing.

[0003] During the exposure stage, the surface or interior of the photomask will suffer from exposure loss, particle contamination, and internal electrical property degradation as the use time increases, causing the quality of the exposure image in the photoresist on the wafer surface to deteriorate, seriously affecting the chip yield. Especially in the chip manufacturing process below 7nm, the distortion of the exposure image will seriously affect the chip performance and cause immeasurable losses.

[0004] Regular inspection, identification and performance evaluation of photomasks in the chip production process is an effective means to ensure chip yield. Due to the high operating frequency of the lithography machine (operating wavelength DUV 193nm, EUV 13.5nm), particle contamination on the photomask surface, pattern edge loss, changes in the absorption of light energy by internal materials, etc. will affect the exposure quality of the wafer surface. Therefore, regular inspection and evaluation of photomasks to ensure chip yield is one of the indispensable key technical links in the current high-end inheritance circuit production process.

[0005] With the rapid development of high-end integrated circuits, how to quickly and accurately detect defects in photomasks of online chip production lines has gradually become one of the core technical means to determine chip yield. Summary of the invention

[0006] The embodiments of the present application provide a photomask defect detection method, device, storage medium and electronic device, which can quickly and accurately detect defects in photomasks of an online chip production line.

[0007] In a first aspect, an embodiment of the present application provides a photomask defect detection method, comprising:

[0008] acquiring a projection intensity image and a reflection intensity image of the photomask;

[0009] acquiring a first edge distribution image of the photomask based on the projection intensity image and the reflection intensity image;

[0010] Comparing the first edge distribution image with a preset edge distribution image to obtain a difference image;

[0011] Calculating a root mean square error of a second marginal distribution image of the difference image;

[0012] When the root mean square error is less than a preset threshold value, it is determined that the photomask is qualified.

[0013] In the photomask defect detection method provided in an embodiment of the present application, the step of acquiring a first edge distribution image of the photomask based on the projection intensity image and the reflection intensity image includes:

[0014] superimposing the projection intensity image and the reflection intensity image to obtain an intensity distribution image of the photomask;

[0015] A first edge distribution image of the photomask is acquired based on the intensity distribution image.

[0016] In the photomask defect detection method provided in the embodiment of the present application, the step of calculating the root mean square error of the second edge distribution image of the difference image includes:

[0017] Inputting the difference image into a target edge detection model to obtain a second edge distribution image of the difference image;

[0018] The root mean square error of the second marginal distribution image is calculated according to a preset threshold.

[0019] In the photomask defect detection method provided in the embodiment of the present application, before acquiring the projection intensity image and the reflection intensity image of the photomask, the method further includes:

[0020] Build an object edge detection model.

[0021] In the photomask defect detection method provided in the embodiment of the present application, the building of the target edge detection model includes:

[0022] Calculating a simulated projection intensity image and a simulated reflection intensity image of the photomask according to the pattern distribution simulation of the photomask;

[0023] superimposing the simulated projection intensity image and the simulated reflection intensity image to obtain a simulated intensity distribution image of the photomask;

[0024] Using the simulated intensity distribution image to train a preset neural network to obtain a primary edge detection model;

[0025] The primary edge detection model is transferred to the chip platform for further training to generate a target edge detection model.

[0026] In the photomask defect detection method provided in the embodiment of the present application, the use of the simulated intensity distribution image to train a preset neural network to obtain a primary edge detection model includes:

[0027] Randomly adding a Gaussian white noise signal to the simulated intensity distribution image;

[0028] The simulated intensity distribution image after adding the Gaussian white noise signal is input into a preset neural network for training to obtain a primary edge detection model.

[0029] In the photomask defect detection method provided in the embodiment of the present application, the primary edge detection model is transferred to the target chip platform for further training to generate a target edge detection model, including:

[0030] Measuring an initial intensity distribution image of an initialized photomask on the chip base;

[0031] The initial intensity distribution image is input into the primary edge detection model for training, a target edge detection model is generated, and a preset edge distribution image is output.

[0032] In a second aspect, an embodiment of the present application provides a photomask defect detection device, comprising:

[0033] A first acquisition unit, used for acquiring a projection intensity image and a reflection intensity image of the photomask;

[0034] A second acquisition unit, configured to acquire a first edge distribution image of the photomask based on the projection intensity image and the reflection intensity image;

[0035] An image comparison unit, configured to compare the first edge distribution image with a preset edge distribution image to obtain a difference image;

[0036] an error calculation unit, used for calculating a root mean square error of the second edge distribution image of the difference image;

[0037] The yield determination unit is used to determine that the photomask is qualified when the root mean square error is less than a preset threshold value.

[0038] In a third aspect, an embodiment of the present application provides a storage medium, wherein the storage medium stores a plurality of instructions, wherein the instructions are suitable for loading by a processor to execute any of the above-mentioned photomask defect detection methods.

[0039] In a fourth aspect, an embodiment of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements any of the above-described photomask defect detection methods when executing the computer program.

[0040] In summary, the photomask defect detection method provided by the embodiment of the present application can obtain the projection intensity image and the reflection intensity image of the photomask; obtain the first edge distribution image of the photomask based on the projection intensity image and the reflection intensity image; compare the first edge distribution image with the preset edge distribution image to obtain a difference image; calculate the root mean square error of the second edge distribution image of the difference image; when the root mean square error is less than the preset threshold value, determine that the photomask is qualified. This solution can quickly and accurately detect defects in photomasks of online chip production lines. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0042] Figure 1 It is a flow chart of the photomask defect detection method provided in an embodiment of the present application.

[0043] Figure 2 It is a flow chart of a method for constructing a target edge detection model provided in an embodiment of the present application.

[0044] Figure 3 It is a preset neural network provided in the embodiment of the present application.

[0045] Figure 4 It is a schematic diagram of the structure of the photomask defect detection device provided in an embodiment of the present application.

[0046] Figure 5 It is a structural schematic diagram of a device for constructing a target edge detection model provided in an embodiment of the present application.

[0047] Figure 6 It is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0048] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0049] It should be noted that, in this article, the terms "include", "comprises" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element. In addition, components, features, and elements with the same name in different embodiments of the present application may have the same meaning or different meanings, and their specific meanings need to be determined by their explanation in the specific embodiment or further combined with the context of the specific embodiment.

[0050] It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0051] In the subsequent description, the suffixes such as "module", "component" or "unit" used to represent elements are only used to facilitate the description of the present application, and have no specific meanings. Therefore, "module", "component" or "unit" can be used in a mixed manner.

[0052] In the description of the present application, it should be noted that the terms "upper", "lower", "left", "right", "inner", "outer", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present application. In addition, terms such as "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0053] Regular inspection, identification and performance evaluation of photomasks in the chip production process is an effective means to ensure chip yield. Due to the high operating frequency of the lithography machine (operating wavelength DUV 193nm, EUV 13.5nm), particle contamination on the photomask surface, pattern edge loss, changes in the absorption of light energy by internal materials, etc. will affect the exposure quality of the wafer surface. Therefore, regular inspection and evaluation of photomasks to ensure chip yield is one of the indispensable key technical links in the current high-end inheritance circuit production process.

[0054] With the rapid development of high-end integrated circuits, how to quickly and accurately detect defects in photomasks of online chip production lines has gradually become one of the core technical means to determine chip yield.

[0055] Based on this, the embodiments of the present application provide a photomask defect detection method, device, storage medium and electronic device. Specifically, the photomask defect detection device can be integrated in an electronic device, which can be a server or a terminal; wherein the terminal can include mobile phones, wearable smart devices, tablet computers, laptops, and personal computers (PC) and other terminal devices; the server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms.

[0056] The technical solutions shown in the present application will be described in detail below through specific embodiments. It should be noted that the description order of the following embodiments is not intended to limit the priority order of the embodiments.

[0057] See also Figure 1 , Figure 1 : is a schematic diagram of the process of the photomask defect detection method provided by the embodiment of the present application. The specific process of the photomask defect detection method can be as follows:

[0058] 101. Acquire a projection intensity image and a reflection intensity image of a photomask.

[0059] Among them, the projection intensity image carefully depicts the brightness distribution and transmittance of the photomask under light projection, while the reflection intensity image reveals the reflection ability and characteristics of the photomask surface for light.

[0060] It should be noted that the photomask refers to the photomask currently being used on the base.

[0061] 102. Acquire a first edge distribution image of the photomask based on the projection intensity image and the reflection intensity image.

[0062] Specifically, the projection intensity image and the reflection intensity image may be superimposed first to obtain an intensity distribution image of the photomask; and then a first edge distribution image of the photomask may be acquired based on the intensity distribution image.

[0063] In some embodiments, the projection intensity image and the reflection intensity image may be superimposed by weighted averaging or other image fusion techniques to ensure that the information of the two images can be fully combined. Through superposition processing, a comprehensive photomask intensity distribution image may be obtained, which can more comprehensively reflect the overall intensity characteristics of the photomask.

[0064] Afterwards, the intensity distribution image of the photomask is input into a pre-constructed target edge detection model to obtain a first edge distribution image of the photomask.

[0065] 103. Compare the first edge distribution image with a preset edge distribution image to obtain a difference image.

[0066] The preset edge distribution image refers to a high-precision edge distribution image that meets the requirements of the current base, that is, an edge distribution image of an initial qualified photomask.

[0067] Specifically, a pixel-by-pixel subtraction operation may be performed between the first edge distribution image and the preset edge distribution image, so that a difference image can be generated, and the difference image can clearly show the difference between the two.

[0068] 104. Calculate a root mean square error of the second marginal distribution image of the difference image.

[0069] Specifically, the difference image can be input into the target edge detection model to obtain a second edge distribution image of the difference image, and then the root mean square error of the second edge distribution image is calculated according to a preset threshold, wherein the second edge distribution image refers to the distribution of edge features identified in the difference image.

[0070] The preset threshold is used to determine the significance of the edge feature considered when calculating the root mean square error. The root mean square error is a statistical method to measure the difference between images. The root mean square error of the second edge distribution image can reflect the degree of difference between the first edge distribution image and the preset edge distribution image. Specifically, the root mean square error is obtained by calculating the square value of the pixel value of each pixel point of the second edge distribution image, then averaging these square values, and finally performing a square root operation on the average.

[0071] For example, when the preset threshold is When , the root mean square error err of the second marginal distribution image can be shown as follows:

[0072]

[0073] In the above formula, N is the number of pixels, p i is the pixel value of the i-th second edge distribution image.

[0074] 105. When the root mean square error is less than a preset threshold value, it is determined that the photomask is qualified.

[0075] The preset threshold value may be set according to the actual situation of the current base station, and is not limited in the embodiment of the present application.

[0076] It can be understood that when the RMS error is less than the preset threshold value, it means that the loss on the current photomask is small and the impact of pollutants on the mask performance is weak, and it can continue to be used for production. In other words, the current photomask is qualified. When the RMS error is greater than or equal to the preset threshold value, it means that the performance of the photomask is greatly different from that of the original design, and it cannot meet the requirements for continued production. The mask needs to be updated to ensure the yield requirements. In other words, the current photomask is unqualified.

[0077] In actual application, the photomask on the current substrate may be regularly inspected for defects using the photomask defect inspection method provided in the embodiment of the present application, thereby ensuring the yield requirements of the product.

[0078] In summary, the photomask defect detection method provided in the embodiment of the present application can be performed by obtaining the projection intensity image and the reflection intensity image of the photomask; obtaining the first edge distribution image of the photomask based on the projection intensity image and the reflection intensity image; comparing the first edge distribution image with the preset edge distribution image to obtain a difference image; calculating the root mean square error of the second edge distribution image of the difference image; when the root mean square error is less than the preset threshold value, determining that the photomask is qualified. This scheme can perform defect detection on the photomask by measuring the projection intensity image and the reflection intensity image of the photomask, thereby realizing fast and accurate defect detection of the photomask of the online chip production line. In addition, by comparing the first edge distribution image with the preset edge distribution image, the tiny defects of the photomask can be more accurately identified, thereby ensuring that the quality of the photomask meets the production requirements.

[0079] In practical applications, the method of the embodiment of the present application can be integrated into the chip production line to realize automated defect detection. By setting an automated image acquisition device, the photomask on the base can be regularly inspected, thereby improving the yield of the product. At the same time, by setting the corresponding software algorithm, the acquisition of the image, the generation of the edge distribution image, the calculation of the difference image and the calculation of the root mean square error can be automatically completed, thereby reducing manual intervention and reducing production costs.

[0080] The present application also provides a method for constructing a target edge detection model. The following will describe in detail the process of constructing a target edge detection model through a specific embodiment. It is understandable that the construction of the target edge detection model needs to be performed before step 101. The meanings of the terms are the same as those in the above-mentioned photomask defect detection method. For specific implementation details, please refer to the description in the photomask defect detection method embodiment. Figure 2 As shown, the specific process of the method for constructing the target edge detection model can be as follows:

[0081] 201. Calculate a simulated projection intensity image and a simulated reflection intensity image of the photomask according to the pattern distribution simulation of the photomask.

[0082] Among them, simulation calculation is not the focus of the embodiments of the present application, and will not be described in detail here.

[0083] 202. Superimpose the simulated projection intensity image and the simulated reflection intensity image to obtain a simulated intensity distribution image of the photomask.

[0084] In some embodiments, the simulated projection intensity image and the simulated reflection intensity image may be superimposed using weighted averaging or other image fusion techniques.

[0085] 203. Use the simulated intensity distribution image to train the preset neural network to obtain a primary edge detection model.

[0086] In some embodiments, a Gaussian white noise signal may be randomly added to the simulated intensity distribution image; and then the simulated intensity distribution image after the Gaussian white noise signal is added is input into a preset neural network for training to obtain a primary edge detection model.

[0087] It is understandable that randomly introducing Gaussian white noise signals on the generated simulated intensity distribution images can simulate image noise in the real world, thereby improving the robustness and generalization ability of the model. After adding the Gaussian white noise signal, these noise-processed simulated intensity distribution images are input into a pre-set neural network model for training. Through this training process, the neural network can learn how to extract useful features from noisy images and finally obtain a primary edge detection model. By continuously optimizing the network parameters and structure, the model's ability to recognize and detect image edges can be gradually improved. After a long period of training and debugging, the model has initially acquired the ability to accurately detect image edges.

[0088] Among them, the preset neural network can be as follows Figure 3 shown.

[0089] It should be noted that, in the process of model training, in order to keep the size of the simulated intensity distribution image unchanged, this embodiment fills 0 in the periphery of the simulated intensity distribution image, and uses a multi-layer convolutional neural network to extract vertical and horizontal directions to achieve edge extraction. In addition, for the diffraction effect of lithography, the extraction effect of a single vertical convolution layer or horizontal convolution is poor, and the edge features have problems such as blur and distortion. This embodiment takes the normalized mask pattern as the target and conducts network parameter training. The error function is a pixel-based error function, as follows:

[0090]

[0091] In the above formula, N is the number of pixels, is the pixel value of the preset edge distribution image, The pixel value of the marginal distribution image output by the model.

[0092] 204. Migrate the primary edge detection model to the chip platform for further training to generate a target edge detection model.

[0093] Specifically, an initial intensity distribution image of an initialized photomask on a chip substrate may be measured; the initial intensity distribution image may be input into a primary edge detection model for training, a target edge detection model may be generated, and a preset edge distribution image may be output.

[0094] Specifically, measuring the initial intensity distribution image of the initialized photomask on the chip base comprises: measuring the projection intensity image and the reflection intensity image of the initialized photomask on the chip base, superimposing the projection intensity image and the reflection intensity image of the initialized photomask, and obtaining the initial intensity distribution image of the initialized photomask.

[0095] It is understandable that this embodiment can further improve the adaptability and practicality of the model by migrating the primary edge detection model to the chip platform for further training and optimization. In the specific hardware environment of the chip platform, the model performance is fully tested and adjusted to ensure that it can complete the edge detection task efficiently and accurately.

[0096] In order to better implement the photomask defect detection method provided in the embodiment of the present application, the embodiment of the present application also provides a photomask defect detection device, wherein the meanings of the terms are the same as those in the above-mentioned photomask defect detection method, and the specific implementation details can refer to the description in the method embodiment.

[0097] See also Figure 4 , Figure 4 3 is a schematic diagram of the structure of a photomask defect detection device provided in an embodiment of the present application. The photomask defect detection device may include a first acquisition unit 301, a second acquisition unit 302, an image comparison unit 303, an error calculation unit 304 and a yield determination unit 305.

[0098] A first acquisition unit 301 is used to acquire a projection intensity image and a reflection intensity image of a photomask;

[0099] A second acquisition unit 302 is used to acquire a first edge distribution image of the photomask based on the projection intensity image and the reflection intensity image;

[0100] An image comparison unit 303, configured to compare the first edge distribution image with a preset edge distribution image to obtain a difference image;

[0101] An error calculation unit 304 is used to calculate a root mean square error of the second edge distribution image of the difference image;

[0102] The yield determination unit 305 is used to determine that the photomask is qualified when the root mean square error is less than a preset threshold value.

[0103] The specific implementation of each of the above units can refer to the above-mentioned embodiment of the photomask defect detection method, which will not be described one by one here.

[0104] In summary, the photomask defect detection device provided in the embodiment of the present application acquires the projection intensity image and the reflection intensity image of the photomask by the first acquisition unit 301; the second acquisition unit 302 acquires the first edge distribution image of the photomask based on the projection intensity image and the reflection intensity image; the image comparison unit 303 compares the first edge distribution image with the preset edge distribution image to obtain a difference image; the error calculation unit 304 calculates the root mean square error of the second edge distribution image of the difference image; the yield determination unit 305 determines that the photomask is qualified when the root mean square error is less than the preset threshold value. This solution can perform defect detection on the photomask by measuring the projection intensity image and the reflection intensity image of the photomask, thereby realizing fast and accurate defect detection of the photomask of the online chip production line. In addition, by comparing the first edge distribution image with the preset edge distribution image, the tiny defects of the photomask can be more accurately identified, thereby ensuring that the quality of the photomask meets the production requirements.

[0105] In order to better implement the method for constructing the target edge detection model provided in the embodiment of the present application, the embodiment of the present application also provides a device for constructing the target edge detection model. The meanings of the terms are the same as those in the method for constructing the target edge detection model, and the specific implementation details can refer to the description in the embodiment of the method for constructing the target edge detection model.

[0106] See also Figure 5 , Figure 5 4 is a schematic diagram of the structure of the target edge detection model construction device provided in the embodiment of the present application. The target edge detection model construction device may include a simulation unit 401, a superposition unit 402, a first training unit 403 and a second training unit 404. Among them,

[0107] A simulation unit 401 is used to simulate and calculate a simulated projection intensity image and a simulated reflection intensity image of the photomask according to the pattern distribution of the photomask;

[0108] A superposition unit 402 is used to superimpose the simulated projection intensity image and the simulated reflection intensity image to obtain a simulated intensity distribution image of the photomask;

[0109] A first training unit 403 is used to train a preset neural network using a simulated intensity distribution image to obtain a primary edge detection model;

[0110] The second training unit 404 is used to transfer the primary edge detection model to the chip platform for further training to generate a target edge detection model.

[0111] The specific implementation of each of the above units can refer to the embodiment of the method for constructing the target edge detection model mentioned above, and will not be described in detail here.

[0112] The present application also provides an electronic device, in which the photomask defect detection device of the present application can be integrated, such as Figure 6 As shown, it shows a schematic diagram of the structure of the electronic device involved in the embodiment of the present application, specifically:

[0113] The electronic device may include a radio frequency (RF) circuit 601, a memory 602 including one or more computer-readable storage media, an input unit 603, a display unit 604, a sensor 605, an audio circuit 606, a wireless fidelity (WiFi) module 607, a processor 608 including one or more processing cores, and a power supply 609. Those skilled in the art will appreciate that Figure 6 The electronic device structure shown in the figure does not constitute a limitation on the electronic device, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0114] The RF circuit 601 can be used for receiving and sending signals during information transmission or calls. In particular, after receiving the downlink information of the base station, it is handed over to one or more processors 608 for processing; in addition, the data related to the uplink is sent to the base station. Generally, the RF circuit 601 includes but is not limited to an antenna, at least one amplifier, a tuner, one or more oscillators, a subscriber identity module (Subscriber Identity Module, SIM) card, a transceiver, a coupler, a low noise amplifier (LowNoiseAmplifier, LNA), a duplexer, etc. In addition, the RF circuit 601 can also communicate with the network and other devices through wireless communication. Wireless communication can use any communication standard or protocol, including but not limited to Global System of Mobile communication (Global System of Mobile communication, GSM), General Packet Radio Service (General PacketRadio Service, GPRS), Code Division Multiple Access (Code Division Multiple Access, CDMA), Wideband Code Division Multiple Access (Wideband Code Division Multiple Access, WCDMA), Long Term Evolution (Long Term Evolution, LTE), email, Short Messaging Service (SMS), etc.

[0115] The memory 602 can be used to store software programs and modules. The processor 608 executes various functional applications and information processing by running the software programs and modules stored in the memory 602. The memory 602 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area may store data created according to the use of the electronic device (such as audio data, a phone book, etc.), etc. In addition, the memory 602 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices. Accordingly, the memory 602 may also include a memory controller to provide the processor 608 and the input unit 603 with access to the memory 602.

[0116] The input unit 603 can be used to receive input digital or character information, and generate keyboard, mouse, joystick, optical or trackball signal input related to user settings and function control. Specifically, in a specific embodiment, the input unit 603 may include a touch-sensitive surface and other input devices. The touch-sensitive surface, also known as a touch display screen or a touch pad, can collect the user's touch operations on or near it (such as the user's operation on or near the touch-sensitive surface using any suitable object or accessory such as a finger, a stylus, etc.), and drive the corresponding connection device according to a pre-set program. Optionally, the touch-sensitive surface may include a touch detection device and a touch controller. Among them, the touch detection device detects the user's touch orientation, detects the signal brought by the touch operation, and transmits the signal to the touch controller; the touch controller receives the touch information from the touch detection device, converts it into the touch point coordinates, and then sends it to the processor 608, and can receive and execute the command sent by the processor 608. In addition, the touch-sensitive surface can be implemented using multiple types such as resistive, capacitive, infrared and surface acoustic wave. In addition to the touch-sensitive surface, the input unit 603 may also include other input devices. Specifically, other input devices may include, but are not limited to, one or more of a physical keyboard, function keys (such as a volume control key, a switch key, etc.), a trackball, a mouse, a joystick, and the like.

[0117] The display unit 604 can be used to display information input by the user or information provided to the user, as well as various graphical user interfaces of the electronic device, which can be composed of graphics, text, icons, videos, and any combination thereof. The display unit 604 may include a display panel. Optionally, the display panel may be configured in the form of a liquid crystal display (LCD), an organic light-emitting diode (OLED), or the like. Furthermore, the touch-sensitive surface may cover the display panel, and when the touch-sensitive surface detects a touch operation on or near it, it is transmitted to the processor 608 to determine the type of touch event, and then the processor 608 provides a corresponding visual output on the display panel according to the type of touch event. Although in Figure 6 In the embodiment, the touch-sensitive surface and the display panel are used as two independent components to realize input and output functions, but in some embodiments, the touch-sensitive surface and the display panel can be integrated to realize input and output functions.

[0118] The electronic device may also include at least one sensor 605, such as a light sensor, a motion sensor, and other sensors. Specifically, the light sensor may include an ambient light sensor and a proximity sensor, wherein the ambient light sensor may adjust the brightness of the display panel according to the brightness of the ambient light, and the proximity sensor may turn off the display panel and / or backlight when the electronic device is moved to the ear. As a type of motion sensor, the gravity acceleration sensor can detect the magnitude of acceleration in each direction (generally three axes), and can detect the magnitude and direction of gravity when stationary. It can be used for applications that identify the posture of the mobile phone (such as horizontal and vertical screen switching, related games, magnetometer posture calibration), vibration recognition related functions (such as pedometer, tapping), etc.; As for other sensors that can be configured in electronic devices, such as gyroscopes, barometers, hygrometers, thermometers, infrared sensors, etc., they will not be repeated here.

[0119] The audio circuit 606, the speaker, and the microphone can provide an audio interface between the user and the electronic device. The audio circuit 606 can transmit the electrical signal converted from the received audio data to the speaker, which is converted into a sound signal for output; on the other hand, the microphone converts the collected sound signal into an electrical signal, which is received by the audio circuit 606 and converted into audio data, and then the audio data is output to the processor 608 for processing, and then sent to another electronic device through the RF circuit 601, or the audio data is output to the memory 602 for further processing. The audio circuit 606 may also include an earplug jack to provide communication between an external headset and the electronic device.

[0120] WiFi is a short-range wireless transmission technology. Electronic devices can help users send and receive emails, browse web pages, and access streaming media through WiFi module 607. It provides users with wireless broadband Internet access. Figure 6 , a WiFi module 607 is shown, but it is understandable that it is not a necessary component of the electronic device and can be omitted as needed without changing the essence of the invention.

[0121] The processor 608 is the control center of the electronic device. It uses various interfaces and lines to connect various parts of the entire mobile phone. By running or executing software programs and / or modules stored in the memory 602, and calling data stored in the memory 602, it executes various functions of the electronic device and processes data, thereby monitoring the mobile phone as a whole. Optionally, the processor 608 may include one or more processing cores; preferably, the processor 608 may integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user interface, and application programs, and the modem processor mainly processes wireless communications. It is understandable that the above-mentioned modem processor may not be integrated into the processor 608.

[0122] The electronic device also includes a power supply 609 (such as a battery) for supplying power to each component. Preferably, the power supply can be logically connected to the processor 608 through a power management system, so that the power management system can manage charging, discharging, and power consumption management. The power supply 609 can also include one or more DC or AC power supplies, recharging systems, power failure detection circuits, power converters or inverters, power status indicators, and other arbitrary components.

[0123] Although not shown, the electronic device may also include a camera, a Bluetooth module, etc., which will not be described in detail herein. Specifically in this embodiment, the processor 608 in the electronic device will load the executable files corresponding to the processes of one or more application programs into the memory 602 according to the following instructions, and the processor 608 will run the application programs stored in the memory 602, thereby realizing various functions, such as:

[0124] acquiring a projection intensity image and a reflection intensity image of the photomask;

[0125] Acquire a first edge distribution image of the photomask based on the projection intensity image and the reflection intensity image;

[0126] Comparing the first edge distribution image with a preset edge distribution image to obtain a difference image;

[0127] Calculating a root mean square error of a second marginal distribution image of the difference image;

[0128] When the RMS error is less than a preset threshold value, the photomask is determined to be qualified.

[0129] In summary, the electronic device provided in the embodiment of the present application can obtain the projection intensity image and the reflection intensity image of the photomask; obtain the first edge distribution image of the photomask based on the projection intensity image and the reflection intensity image; compare the first edge distribution image with the preset edge distribution image to obtain a difference image; calculate the root mean square error of the second edge distribution image of the difference image; when the root mean square error is less than the preset threshold value, determine that the photomask is qualified. This solution can perform defect detection on the photomask by measuring the projection intensity image and the reflection intensity image of the photomask, thereby realizing fast and accurate defect detection of the photomask of the online chip production line. In addition, by comparing the first edge distribution image with the preset edge distribution image, the tiny defects of the photomask can be more accurately identified, thereby ensuring that the quality of the photomask meets the production requirements.

[0130] In the above embodiments, the description of each embodiment has its own emphasis. For the part that is not described in detail in a certain embodiment, please refer to the detailed description of the photomask defect detection method above, and will not be repeated here.

[0131] It should be noted that, for the photomask defect detection method in the embodiment of the present application, those skilled in the art can understand that all or part of the process of implementing the photomask defect detection method in the embodiment of the present application can be completed by controlling the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, such as stored in the memory of a terminal, and executed by at least one processor in the terminal. During the execution process, it can include the process of the embodiment of the photomask defect detection method.

[0132] For the photomask defect detection device of the embodiment of the present application, each functional module can be integrated into a processing chip, or each module can exist physically separately, or two or more modules can be integrated into one module. The above-mentioned integrated module can be implemented in the form of hardware or in the form of software functional modules. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0133] To this end, the present application provides a storage medium in which a plurality of instructions are stored, and the instructions can be loaded by a processor to execute the steps in any of the photomask defect detection methods provided in the present application. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), etc.

[0134] The above respectively introduces in detail the photomask defect detection method, device, storage medium and electronic device provided by the present application. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the core idea of ​​the present application; at the same time, for technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A method for detecting photomask defects, It is characterized in that include: acquiring a projection intensity image and a reflection intensity image of the photomask; superimposing the projection intensity image and the reflection intensity image to obtain an intensity distribution image of the photomask; acquiring a first edge distribution image of the photomask based on the intensity distribution image; Comparing the first edge distribution image with a preset edge distribution image to obtain a difference image; Inputting the difference image into a target edge detection model to obtain a second edge distribution image of the difference image; Calculating a root mean square error of the second edge distribution image according to a preset threshold; When the root mean square error is less than a preset threshold value, it is determined that the photomask is qualified.

2. The photomask defect detection method according to claim 1, It is characterized in that Before acquiring the projection intensity image and the reflection intensity image of the photomask, the method further includes: Build an object edge detection model.

3. The photomask defect detection method according to claim 2, It is characterized in that The constructing of the target edge detection model comprises: Calculating a simulated projection intensity image and a simulated reflection intensity image of the photomask according to the pattern distribution simulation of the photomask; superimposing the simulated projection intensity image and the simulated reflection intensity image to obtain a simulated intensity distribution image of the photomask; Using the simulated intensity distribution image to train a preset neural network to obtain a primary edge detection model; The primary edge detection model is transferred to the chip platform for further training to generate a target edge detection model.

4. The photomask defect detection method according to claim 3, It is characterized in that The method of using the simulated intensity distribution image to train a preset neural network to obtain a primary edge detection model includes: Randomly adding a Gaussian white noise signal to the simulated intensity distribution image; The simulated intensity distribution image after adding the Gaussian white noise signal is input into a preset neural network for training to obtain a primary edge detection model.

5. The photomask defect detection method according to claim 3, It is characterized in that The primary edge detection model is transferred to the target chip platform for further training to generate a target edge detection model, including: Measuring an initial intensity distribution image of an initialized photomask on the chip base; The initial intensity distribution image is input into the primary edge detection model for training, a target edge detection model is generated, and a preset edge distribution image is output.

6. A photomask defect detection device, It is characterized in that include: A first acquisition unit, used for acquiring a projection intensity image and a reflection intensity image of the photomask; A second acquisition unit is used to superimpose the projection intensity image and the reflection intensity image to obtain an intensity distribution image of the photomask; and to acquire a first edge distribution image of the photomask based on the intensity distribution image; An image comparison unit, configured to compare the first edge distribution image with a preset edge distribution image to obtain a difference image; an error calculation unit, used for inputting the difference image into a target edge detection model to obtain a second edge distribution image of the difference image; Calculating a root mean square error of the second edge distribution image according to a preset threshold; The yield determination unit is used to determine that the photomask is qualified when the root mean square error is less than a preset threshold value.

7. A storage medium, It is characterized in that The storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor to execute the photomask defect detection method according to any one of claims 1 to 5.

8. An electronic device, It is characterized in that The invention comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the photomask defect detection method according to any one of claims 1 to 5 is implemented.

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