Overlay error measurement method, device, storage medium and computer equipment

Abstract

The application discloses a kind of overlay error measurement method, device, storage medium and computer equipment, involve semiconductor laser measurement technical field, wherein method includes: first, in multiple focal point positions, the scanning image of the scanning to be measured overlay mark is obtained along vertical direction, then scanning image is arranged to construct three-dimensional space model, after that, the center line of the two sides of the three-dimensional space of to-be-measured overlay mark is respectively vertically profiled, obtain two overfocus scanning images and carry out differential processing, obtain differential image, finally, differential image is input into the preset deep learning training model, the structural feature information of overlay mark is extracted, and the overlay error of overlay mark is obtained.The above-mentioned method utilizes differential overfocus scanning method to measure the scanning structural information of overlay mark relative to the two sides of center line, quantitative analysis and solution are carried out by image processing and deep learning method, realize the high-precision nondestructive measurement of overlay error with symmetrical structural characteristics.

Description

Technical Field

[0001] This invention relates to the field of semiconductor laser measurement technology, and in particular to a method, apparatus, storage medium, and computer equipment for measuring overlay error. Background Technology

[0002] Photolithography is an indispensable key technology in integrated circuit manufacturing. With the continuous development of photolithography, higher requirements are being placed on the accuracy of measuring overlay error. Overlay error specifically refers to the deviation of the pattern on the wafer from the standard reference pattern in the X and Y axes during integrated circuit manufacturing. This deviation needs to be less than 1 / 3 to 1 / 5 of the wafer feature size to ensure reliable circuit connection as much as possible.

[0003] In existing technologies, there are two main methods for measuring overlay errors: imaging-based overlay (IBO) and diffraction-based overlay (DBO). DBO has become the mainstream method due to its non-contact, non-destructive, and rapid characteristics. In DBO, the overlay marker is a specially designed nanograting structure. The overlay error is extracted by measuring the diffraction signal of the marker, such as its spectrum or angle-resolved spectrum. However, DBO cannot be applied to measuring large overlay errors, and it is primarily designed for periodic grating structures, making it unsuitable for measuring overlay errors in other symmetrical structures. Summary of the Invention

[0004] To address the shortcomings of the existing technology, the present invention provides a method and apparatus for measuring overlay error. The main purpose is to solve the technical problems of limited applicability of overlay error measurement technology and low accuracy of measurement results for symmetrical structures and when there is a large overlay error.

[0005] According to a first aspect of the present invention, a method for measuring overlay error is provided, the method comprising:

[0006] The mark to be tested is identified, and the mark is scanned at multiple focal positions along the vertical direction to obtain multiple scan images;

[0007] Multiple scanned images are arranged sequentially according to the setting order of the multiple focal positions, and a three-dimensional spatial model is constructed based on the arranged multiple scanned images, wherein the three-dimensional spatial model includes the light intensity information of the overlay mark to be tested;

[0008] Vertical cross-sectional processing is performed on both sides of the center line of the mark to be tested in the three-dimensional space to obtain a first overfocus scan image and a second overfocus scan image. The first overfocus scan image and the second scan image are then differentially processed to obtain a differential image.

[0009] The difference image is input into a preset deep learning training model to extract the structural feature information of the overlay mark, and the overlay error of the overlay mark is obtained based on the structural feature information.

[0010] According to a second aspect of the present invention, an overlay error measuring device is provided, the device comprising:

[0011] The image scanning module is used to determine the overlay mark to be tested, and to scan the overlay mark to be tested at multiple focal positions along the vertical direction to obtain multiple scan images;

[0012] The model building module is used to arrange multiple scanned images sequentially according to the setting order of the multiple focal positions, and to construct a three-dimensional spatial model based on the arranged multiple scanned images, wherein the three-dimensional spatial model includes the light intensity information of the overlay mark to be tested;

[0013] The differential processing module is used to perform vertical cross-sectional processing on both sides of the center line of the overlay mark to be tested, to obtain a first overfocus scan image and a second overfocus scan image, and to perform differential processing on the first overfocus scan image and the second scan image to obtain a differential image.

[0014] The result output module is used to input the difference image into a preset deep learning training model, extract the structural feature information of the overlay mark, and obtain the overlay error of the overlay mark based on the structural feature information.

[0015] According to a third aspect of the present invention, a storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the above-described overlay error measurement method.

[0016] According to a fourth aspect of the present invention, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-described overlay error measurement method.

[0017] This invention provides a method, apparatus, storage medium, and computer device for measuring overlay error. First, the overlay mark to be measured is determined. The overlay mark is scanned along a vertical direction at multiple focal points to obtain multiple scan images. Then, the multiple scan images are arranged sequentially according to the set order of the multiple focal points. A three-dimensional spatial model is constructed based on the arranged multiple scan images, wherein the three-dimensional spatial model includes the light intensity information of the overlay mark to be measured. Next, vertical cross-sectional processing is performed on both sides of the centerline of the overlay mark to obtain a first overfocus scan image and a second overfocus scan image. The first overfocus scan image and the second scan image are then differentially processed to obtain a difference image. Finally, the difference image is input into a preset deep learning training model to extract the structural feature information of the overlay mark, and the overlay error of the overlay mark is obtained based on the structural feature information.

[0018] In the above method, differential overfocus scanning is specifically used to measure and analyze the overlay marks under test. This eliminates the need for contact with the marks, reducing potential physical damage or impact. Scanning both sides of the center line of the overlay marks at different focal positions allows for the acquisition of information about the symmetrical structural features of the overlay marks from different perspectives, providing a more comprehensive understanding of the mark's structure. Furthermore, constructing a three-dimensional spatial model based on the arranged scanned images allows for a more accurate reconstruction of the overlay marks' morphology and structure, facilitating precise analysis and evaluation of their characteristics and errors. Utilizing deep learning models enables the learning and recognition of complex patterns and features, further facilitating the analysis of details and differences within the overlay marks. Finally, the overlay error is calculated to evaluate the quality and accuracy of the marks. This method utilizes differential overfocus scanning to measure the scanned structural information of the overlay marks relative to both sides of the center line, and employs image processing and deep learning methods for quantitative analysis and calculation, achieving high-precision, non-destructive measurement of overlay errors with symmetrical structural features.

[0019] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings.

[0020] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0021] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0022] Figure 1A flowchart illustrating a method for measuring overlay error provided in an embodiment of the present invention is shown;

[0023] Figure 2 A flowchart illustrating a method for measuring overlay error provided in an embodiment of the present invention is shown;

[0024] Figure 3 The diagram illustrates the overlay marks for three symmetrical structural features in an overlay error measurement method provided by an embodiment of the present invention.

[0025] Figure 4 A flowchart illustrating the overlay error measurement method provided by an embodiment of the present invention is shown.

[0026] Figure 5 This diagram illustrates the structure of an overlay error measuring device provided in an embodiment of the present invention.

[0027] Figure 6 This diagram illustrates the structure of an overlay error measuring device provided in an embodiment of the present invention.

[0028] Figure 7 A schematic diagram of the device structure of a computer device provided in an embodiment of the present invention is shown. Detailed Implementation

[0029] Exemplary embodiments of the present application will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this application will be thorough and complete, and will fully convey the scope of the present application to those skilled in the art.

[0030] This application provides a method for measuring overlay error, such as... Figure 1 As shown, the method includes the following steps:

[0031] 101. Determine the overlay mark to be tested, and scan the overlay mark to be tested at multiple focal positions along the vertical direction to obtain multiple scan images.

[0032] Specifically, overlay marks are crucial components used in semiconductor manufacturing, flat panel display manufacturing, and other micro / nano fabrication fields. An overlay mark is a specific pattern or structure whose primary function is to provide accurate positioning references, enabling higher alignment precision between different layers during micro / nano fabrication. They are often placed on the edges of chips or wafers, or distributed at critical locations on the chip surface. The design and structure of overlay marks vary depending on the application and manufacturing process. Common types include cross-shaped, square, circular, and linear structures, typically composed of lines or structures along the edges of multiple shapes to provide more information for precise positioning and alignment. Overlay marks play a vital role in micro / nano manufacturing, helping manufacturers improve product quality, increase production efficiency, and ensure precise alignment at each manufacturing step.

[0033] In this embodiment, by scanning at multiple focal positions, image information of the overlay mark under test from multiple perspectives can be obtained, acquiring more comprehensive and detailed visual data. This enables more accurate analysis and evaluation of the morphology and structure of the overlay mark. Scanned images at different focal positions can reveal the details and changes of the overlay mark at different locations, facilitating a more comprehensive understanding and analysis of the structural features of the overlay mark. During the scanning process, the overlay mark under test is scanned non-contactly, without the need for physical contact or interference, which helps reduce the risk of interference and damage to the object under test and maintains the integrity of the object. Furthermore, setting multiple focal positions for scanning in the vertical direction enables high-precision positioning and quantitative analysis of the overlay mark under test, and the scanned image at each focal position provides information about the specific location, making the judgment of the overlay mark's position more accurate and reliable. Richer data information is obtained through multiple scanned images, and by arranging and analyzing these images, more data references can be obtained, thereby improving the accuracy of the analysis of the structure and features of the overlay mark.

[0034] 102. Arrange multiple scanned images sequentially according to the setting order of multiple focal positions, and construct a three-dimensional spatial model based on the arranged multiple scanned images. The three-dimensional spatial model includes the light intensity information of the overlay mark to be measured.

[0035] In this embodiment, arranging and constructing a three-dimensional spatial model based on scanned images at multiple focal positions can improve the accuracy of the data. By considering the light intensity information at different focal positions, the shape and structure of the overlay mark to be tested can be restored more accurately. Specifically, the multiple scanned images after arrangement provide information from different angles and focal positions. By constructing a three-dimensional spatial model, this information can be merged into a comprehensive model that includes all-round features of the overlay mark to be tested, thereby obtaining richer and more comprehensive data, which helps to better understand and analyze the overlay mark. The three-dimensional spatial model can visualize the spatial relationship of the overlay mark to be tested. Compared with a single two-dimensional image, the three-dimensional model can more accurately represent the shape, size, and positional relationship of the overlay mark, making the analysis of the structural features and overlay error of the mark more intuitive and accurate. It also helps to check the morphological consistency of the mark and qualitatively and quantitatively analyze the deviation or error of the mark. At the same time, by integrating the light intensity information into the three-dimensional spatial model, the structural feature information of the overlay mark can be extracted more accurately.

[0036] 103. Perform vertical cross-sectional processing on both sides of the center line of the mark to be measured in three dimensions to obtain the first overfocus scan image and the second overfocus scan image, and perform differential processing on the first overfocus scan image and the second scan image to obtain the differential image.

[0037] Specifically, this application is mainly used to measure the error of overlay markings on symmetrical structural features, and the overlay markings on symmetrical structural features typically include bar-in-bar, frame-in-frame, and box-in-box markings; such as Figure 3 As described above, a bar-in-bar is a structure consisting of a large rectangular or linear frame surrounding a smaller rectangular or linear bar, typically used for positioning and alignment calibration, and widely used in micro / nano fabrication. A frame-in-frame is a structure consisting of a large frame surrounding a smaller frame; this structure can be used for positioning, alignment calibration, and the detection of accuracy and shape. A box-in-box is an overlay mark consisting of a large rectangular frame surrounding a smaller rectangular frame, commonly used for positioning, alignment calibration, and the evaluation of shape changes during etching or photolithography. These common symmetrical overlay mark structures, by providing features for positioning, alignment calibration, and the evaluation and analysis of overlay errors, are frequently applied in semiconductor chip manufacturing, MEMS device manufacturing, and microelectronic packaging.

[0038] In this embodiment, differential processing is performed on the overfocus scan images on both sides of the center line of the overlay mark to be tested. This can highlight the subtle differences and features on both sides of the center line in the symmetrical structure of the overlay mark to be tested, and make the changes and features of the overlay mark more easily observed and analyzed. The differential image obtained after differential processing can provide more clear and accurate structural information, specifically including information on structural changes such as the offset and deformation of the overlay mark. This can further analyze the morphology, positioning and deviation of the overlay mark. By performing differential processing, normal and abnormal features can be clearly distinguished, which can improve the accuracy of overlay mark detection and screening.

[0039] 104. Input the difference image into the preset deep learning training model, extract the structural feature information of the overlay mark, and obtain the overlay error of the overlay mark based on the structural feature information.

[0040] In this embodiment, the deep learning model can automatically learn representational features from the difference image, enabling more efficient and accurate extraction of structural feature information of the overlay markings. The difference image highlights subtle differences in the overlay markings, facilitating more accurate detection and screening of defects, flaws, or anomalies. The deep learning model can utilize global information from the difference image to analyze and extract the structural features of the overlay markings. Compared to local features, global features provide more comprehensive and integrated information, better describing the shape, size, and symmetry of the overlay markings. The deep learning model, trained with a large number of training samples, can automatically learn and adapt to the structural features of various overlay markings. Regardless of changes in the shape, size, or symmetry of the overlay markings, the deep learning model can effectively extract features and estimate overlay errors, saving significant time and manpower compared to traditional manual analysis and measurement methods. In summary, inputting the difference image into a pre-set deep learning training model allows for efficient and accurate extraction of the structural feature information of the overlay markings, and the estimation of overlay errors can be obtained based on this information.

[0041] This invention provides a method for measuring overlay error. First, the overlay mark to be tested is determined. The overlay mark to be tested is scanned at multiple focal positions along the vertical direction to obtain multiple scan images. Then, the multiple scan images are arranged sequentially according to the setting order of the multiple focal positions. A three-dimensional spatial model is constructed based on the arranged multiple scan images. The three-dimensional spatial model includes the light intensity information of the overlay mark to be tested. Then, vertical cross-sectional processing is performed on both sides of the center line of the overlay mark to obtain a first overfocus scan image and a second overfocus scan image. The first overfocus scan image and the second scan image are then differentially processed to obtain a difference image. Finally, the difference image is input into a preset deep learning training model to extract the structural feature information of the overlay mark, and the overlay error of the overlay mark is obtained based on the structural feature information. In the above method, differential overfocus scanning is specifically used to measure and analyze the overlay marks under test. This eliminates the need for contact with the marks, reducing potential physical damage or impact. Scanning both sides of the center line of the overlay marks at different focal positions allows for the acquisition of information about the symmetrical structural features of the overlay marks from different perspectives, providing a more comprehensive understanding of the mark's structure. Furthermore, constructing a three-dimensional spatial model based on the arranged scanned images allows for a more accurate reconstruction of the overlay marks' morphology and structure, facilitating precise analysis and evaluation of their characteristics and errors. Utilizing deep learning models enables the learning and recognition of complex patterns and features, further facilitating the analysis of details and differences within the overlay marks. Finally, the overlay error is calculated to evaluate the quality and accuracy of the marks. This method utilizes differential overfocus scanning to measure the scanned structural information of the overlay marks relative to both sides of the center line, and employs image processing and deep learning methods for quantitative analysis and calculation, achieving high-precision, non-destructive measurement of overlay errors with symmetrical structural features.

[0042] This application provides a method for measuring overlay error, such as... Figure 2 As shown, the method includes the following steps:

[0043] 201. Scan the markings to be tested at multiple focal points along the vertical direction to obtain multiple scan images.

[0044] Specifically, firstly, the mark to be tested is determined, and the structure type of the mark to be tested is obtained. The structure type includes at least one of Bar-in-Bar, Frame-in-Frame, and Box-in-Box. Then, according to the structure type of the mark to be tested, multiple different focal positions are selected in the vertical direction, and the scanning parameters at each focal position are determined. The scanning parameters include scanning speed and scanning range. Finally, the mark to be tested is scanned in the vertical direction at each focal position according to a preset scanning order to obtain the scanning image corresponding to each focal position.

[0045] In this embodiment, by selecting multiple different focal positions in the vertical direction, scanned images of the overlay mark can be obtained at different depths, thus acquiring more comprehensive structural information. Depending on the structural type of the overlay mark to be tested, appropriate focal positions can be selected according to actual needs, and suitable scanning parameters can be set at each focal position to fully demonstrate the structural features of the overlay mark. Through scanned images from multiple focal positions, the structural features of the overlay mark can be observed and analyzed from different angles, which helps to comprehensively evaluate the shape, size, and symmetry of the overlay mark. At each focal position, scanning can be performed according to a preset scanning sequence, resulting in high-resolution images that more accurately reflect the details and morphological features of the overlay mark.

[0046] 202. Construct a three-dimensional spatial model based on multiple scanned images arranged in the order of their focal positions.

[0047] Specifically, firstly, the setting order of multiple focal positions is obtained, and the arrangement order of the scanned images is determined based on the setting order. The arrangement order corresponds to the vertical direction of the multiple focal positions. Then, the multiple scanned images are arranged sequentially according to the arrangement order of the scanned images, and the pixel value and light intensity information within the pixel value of each scanned image are extracted. Finally, a three-dimensional spatial framework is constructed based on the pixels, the light intensity information is embedded in the corresponding position in the three-dimensional spatial framework, and the interval in the three-dimensional spatial framework is interpolated to obtain the three-dimensional spatial model.

[0048] In this embodiment, the pixel values ​​and light intensity information within each pixel value of each scanned image are extracted, and the light intensity information is embedded into the corresponding position in the three-dimensional spatial frame to construct a three-dimensional spatial model of the overlay mark. During this process, interpolation operations are used to fill gaps in the three-dimensional spatial frame to obtain a complete three-dimensional representation. Specifically, based on the pixel values ​​and light intensity information of the multifocal scanned image, a three-dimensional spatial model of the overlay mark can be constructed, providing a more comprehensive perspective and facilitating the analysis and evaluation of features such as the shape, size, and symmetry of the overlay mark. By embedding the light intensity information into the corresponding position within the three-dimensional spatial frame, the detailed information of each pixel can be preserved, more accurately reflecting the internal structure and surface morphology of the overlay mark. Interpolation operations then fill the gaps in the three-dimensional spatial frame to obtain a complete three-dimensional spatial model. This model provides a more coherent and continuous visual display, allowing observers to more clearly understand and analyze the characteristics of the overlay mark. Finally, by constructing the three-dimensional spatial model, the information of the overlay mark can be displayed in a visual manner, facilitating observation, comparison, and decision-making by analysts. In summary, constructing a three-dimensional spatial model of overlay marks based on multifocal scanning images can provide a more comprehensive and accurate visual representation, facilitating feature analysis, evaluation, and visualization of overlay marks, and contributing to a more comprehensive and accurate understanding of the shape and structure of overlay marks.

[0049] 203. Perform vertical cross-sectional processing on the three-dimensional space to obtain two overfocus scan images, and preprocess the two overfocus scan images respectively.

[0050] Specifically, firstly, the first overfocus scan image and the second scan image are acquired and differentially processed. Then, the first overfocus scan image and the second scan image are sequentially denoised, smoothed, and contrast-enhanced. Next, the first overfocus scan image is processed to extract its contour. Based on the contour, the centerline of the first overfocus scan image is determined, and a straight line is fitted to the centerline of the first overfocus scan image to determine its equation. Finally, the second overfocus scan image is processed to extract its contour. Based on the contour, the centerline of the second overfocus scan image is determined, and a straight line is fitted to the centerline of the second overfocus scan image to determine its equation.

[0051] In this embodiment, differential processing of two overfocus scan images can highlight the edge information of the overlay mark, and contour extraction can obtain the shape information of the overlay mark, which is helpful for subsequent analysis and measurement. By performing linear fitting on the center lines of the first and second overfocus scan images, the center line equation of the overlay mark can be obtained, which further describes the morphology and shape characteristics of the overlay mark. After obtaining the center line equation of the overlay mark, it can be used to locate the position and orientation of the overlay mark, as well as to perform other quantitative analyses based on the center line, such as size measurement and shape evaluation. This process can be realized through automated algorithms and image processing technology, which improves efficiency and accuracy and reduces the dependence on manual operation.

[0052] 204. Differential processing is performed on the two overfocus scan images to obtain a differential image.

[0053] Specifically, the first overfocus scan image is converted into a first grayscale image, the second overfocus scan image is converted into a second grayscale image, a preset difference operator is used to perform difference calculation on the first grayscale image and the second grayscale image to obtain a difference image, and the difference image is subjected to image enhancement processing, wherein the image enhancement processing includes thresholding, gradient enhancement and normalization.

[0054] In this embodiment, by converting the scanned image to a grayscale image and performing differential calculation, a differential image reflecting the structural features of the overlay markings can be obtained. Subsequently, image enhancement processing operations, such as thresholding, gradient enhancement, and normalization, are applied to the differential image to increase its contrast and clarity. Specifically, the differential image obtained through differential calculation highlights the structural features of the overlay markings, making it easier to analyze and detect their shape and edge information. Image enhancement processing, such as thresholding, gradient enhancement, and normalization, increases the contrast of the differential image, highlighting the details of the overlay markings and making them easier to observe and analyze. In practice, the process of enhancing the differential image is flexible; different enhancement algorithms and parameters can be selected according to actual needs to obtain better results. In summary, converting two overfocus scanned images to grayscale images, performing differential calculation, and enhancing the image can highlight the structural features of the overlay markings, improve image contrast and clarity, and facilitate further image analysis and quality assessment.

[0055] 205. Input the difference image into the preset deep learning training model, extract the structural feature information of the overlay mark, and obtain the overlay error of the overlay mark based on the structural feature information.

[0056] Specifically, the structural feature information of the overlay mark is first obtained, the structural feature information is preprocessed, and then multiple overlay error features are extracted from the preprocessed structural feature information. The error value of each overlay error feature is calculated, and finally the error values ​​of the overlay error features are statistically analyzed to obtain the overlay error measurement results.

[0057] Deep learning training models are either convolutional neural networks (CNNs) or recurrent neural networks (RNNs). CNNs are a type of deep learning model widely used in image processing and computer vision tasks. They feature convolutional layers, pooling layers, activation functions, fully connected layers, and deep structures, allowing them to progressively extract and combine abstract features from images, from low-level to high-level features. CNNs have achieved significant results in computer vision, widely applied in tasks such as image classification, object detection, image generation, and semantic segmentation. Through deep learning training, CNNs can learn feature representations and patterns from large amounts of data, exhibiting strong automatic learning capabilities. RNNs, on the other hand, are a type of neural network model capable of processing sequential data. They introduce recurrent connections, enabling information to be passed and persisted within the network. When processing sequential data, RNNs introduce hidden states to associate the current input with previous context information. At each time step, the RNN calculates the current hidden state and output based on the current input and the hidden state from the previous time step. This transmission and updating of hidden states allows RNNs to progressively model and learn sequential data.

[0058] In this embodiment, by acquiring the structural feature information of the overlay mark and preprocessing and extracting it, multiple overlay error features can be obtained. Then, by calculating the error value of each overlay error feature and performing statistical analysis, the measurement results of the overlay error, such as average error, maximum error, and error distribution, can be obtained. This application, through the calculation and analysis of overlay error features, can intuitively evaluate the quality and manufacturing precision of the overlay mark, helping to promptly identify and correct problems in the manufacturing process and improve the quality of the overlay mark. Furthermore, by measuring and analyzing the structural feature information, the overlay error measurement results are made objective and repeatable, reducing the influence of subjective factors and improving the reliability and repeatability of the measurement results.

[0059] The present invention provides a method for measuring overlay error, the principle of which is as follows: Figure 4As shown, the marking to be tested is first scanned at multiple focal positions along the vertical direction to obtain multiple scan images. Then, a three-dimensional spatial model is constructed based on the multiple scan images arranged in the order of the focal positions. After that, the three-dimensional space is processed by vertical cross-section to obtain two overfocus scan images. The two overfocus scan images are preprocessed separately, and then differential processing is performed on the two overfocus scan images to obtain differential images. Finally, the differential images are input into a preset deep learning training model to extract the structural feature information of the marking and obtain the marking error of the marking based on the structural feature information.

[0060] Furthermore, as Figure 1 To specifically implement the method, this application provides an overlay error measurement device, such as... Figure 5 As shown, the device includes: an image scanning module 301, a model building module 302, a difference processing module 303, and a result output module 304.

[0061] The image scanning module 301 is used to determine the overlay mark to be tested, and to scan the overlay mark to be tested at multiple focal positions along the vertical direction to obtain multiple scan images;

[0062] The model building module 302 is used to arrange multiple scanned images sequentially according to the setting order of multiple focal positions, and to construct a three-dimensional spatial model based on the arranged multiple scanned images. The three-dimensional spatial model includes the light intensity information of the overlay mark to be tested.

[0063] The differential processing module 303 is used to perform vertical cross-sectional processing on both sides of the center line of the overlay mark to be tested, to obtain a first overfocus scan image and a second overfocus scan image, and to perform differential processing on the first overfocus scan image and the second scan image to obtain a differential image.

[0064] The result output module 304 is used to input the difference image into a preset deep learning training model, extract the structural feature information of the overlay mark, and obtain the overlay error of the overlay mark based on the structural feature information.

[0065] In specific application scenarios, the image scanning module 301 can be used to determine the overlay mark to be tested, obtain the structural type of the overlay mark to be tested, wherein the structural type includes at least one of Bar-in-Bar, Frame-in-Frame, and Box-in-Box; select multiple different focal positions in the vertical direction according to the structural type of the overlay mark to be tested, and determine the scanning parameters at each focal position, wherein the scanning parameters include scanning speed and scanning range; scan the overlay mark to be tested at each focal position in the vertical direction according to a preset scanning sequence, and obtain the scanning image corresponding to each focal position.

[0066] In specific application scenarios, the model building module 302 can be used to obtain the setting order of multiple focal positions, determine the arrangement order of scanned images based on the setting order, and the arrangement order corresponds to the vertical direction of multiple focal positions; arrange multiple scanned images sequentially according to the arrangement order of the scanned images, and extract the pixel value and light intensity information within each scanned image; construct a three-dimensional spatial frame based on the pixels, embed the light intensity information into the corresponding position in the three-dimensional spatial frame, and perform interpolation operation on the intervals in the three-dimensional spatial frame to obtain a three-dimensional spatial model.

[0067] In specific application scenarios, such as Figure 6 As shown, the device also includes an image preprocessing module 305, which is specifically used to acquire a first overfocus scan image and a second scan image and perform differential processing, respectively performing noise reduction, smoothing, and contrast enhancement on the first overfocus scan image and the second scan image; performing image processing on the first overfocus scan image, extracting the contour of the first overfocus scan image, determining the center line of the first overfocus scan image based on the contour, and performing straight line fitting on the center line of the first overfocus scan image to determine the equation of the center line of the first overfocus scan image; performing image processing on the second overfocus scan image, extracting the contour of the second overfocus scan image, determining the center line of the second overfocus scan image based on the contour, and performing straight line fitting on the center line of the second overfocus scan image to determine the equation of the center line of the second overfocus scan image.

[0068] In specific application scenarios, the differential processing module 303 can be used to convert the first overfocus scan image into a first grayscale image and the second overfocus scan image into a second grayscale image; perform differential calculation on the first grayscale image and the second grayscale image using a preset differential operator to obtain a differential image, and perform image enhancement processing on the differential image, wherein the image enhancement processing includes thresholding, gradient enhancement and normalization.

[0069] In specific application scenarios, the result output module 304 can also be used to obtain the structural feature information of the overlay mark, preprocess the structural feature information, extract multiple overlay error features from the preprocessed structural feature information, calculate the error value of each overlay error feature, and perform statistical analysis on the error values ​​of the overlay error features to obtain the overlay error measurement result.

[0070] It should be noted that other corresponding descriptions of the functional units involved in the overlay error measuring device provided in this embodiment can be found in [reference]. Figure 1 and Figure 2 The corresponding descriptions in [the document] will not be repeated here.

[0071] Based on the above, Figure 1Accordingly, this embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the above-described overlay error measurement method.

[0072] Based on this understanding, the technical solution of this application can be embodied in the form of a software product. The software product to be identified can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, mobile hard drive, etc.) and includes several instructions to enable a computer device (such as a personal computer, server, or network device, etc.) to execute the overlay error measurement method of various implementation scenarios of this application.

[0073] Based on the above, Figure 1 and Figure 2 The method shown, and Figure 5 and Figure 6 The embodiment of the overlay error measuring device shown is designed to achieve the above objectives, such as... Figure 7 As shown, this embodiment also provides a physical device for measuring overlay error. This device includes a communication bus, a processor, a memory, and a communication interface. It may also include input / output interfaces and a display device. The various functional units can communicate with each other via the bus. The memory stores a computer program, and the processor executes the program stored in the memory to perform the overlay error measurement method described in the above embodiment.

[0074] Optionally, the physical device may also include a user interface, a network interface, a camera, radio frequency (RF) circuitry, sensors, audio circuitry, a Wi-Fi module, etc. The user interface may include a display screen, input units such as a keyboard, etc., and optional user interfaces may also include USB interfaces, card reader interfaces, etc. The network interface may optionally include standard wired interfaces, wireless interfaces (such as Wi-Fi interfaces), etc.

[0075] Those skilled in the art will understand that the structure of the physical device for measuring overlay error provided in this embodiment does not constitute a limitation on the physical device, and may include more or fewer components, or combine certain components, or have different component arrangements.

[0076] The storage medium may also include an operating system and a network communication module. The operating system is a program that manages the hardware and software resources of the aforementioned physical device, supporting the operation of information processing programs and other software and / or programs to be identified. The network communication module is used to enable communication between the various components within the storage medium, as well as communication with other hardware and software in the information processing physical device.

[0077] Through the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware platform, or it can be implemented by hardware. By applying the technical solution of this application, firstly, the overlay mark to be tested is determined, and the overlay mark to be tested is scanned at multiple focal positions along the vertical direction to obtain multiple scan images. Then, the multiple scan images are arranged sequentially according to the setting order of the multiple focal positions. A three-dimensional spatial model is constructed based on the arranged multiple scan images. The three-dimensional spatial model includes the light intensity information of the overlay mark to be tested. Then, vertical cross-sectional processing is performed on both sides of the center line of the overlay mark to be tested to obtain a first overfocus scan image and a second overfocus scan image. The first overfocus scan image and the second scan image are then differentially processed to obtain a differential image. Finally, the differential image is input into a preset deep learning training model to extract the structural feature information of the overlay mark, and the overlay error of the overlay mark is obtained based on the structural feature information. In the above method, differential overfocus scanning is specifically used to measure and analyze the overlay marks under test. This eliminates the need for contact with the marks, reducing potential physical damage or impact. Scanning both sides of the center line of the overlay marks at different focal positions allows for the acquisition of information about the symmetrical structural features of the overlay marks from different perspectives, providing a more comprehensive understanding of the mark's structure. Furthermore, constructing a three-dimensional spatial model based on the arranged scanned images allows for a more accurate reconstruction of the overlay marks' morphology and structure, facilitating precise analysis and evaluation of their characteristics and errors. Utilizing deep learning models enables the learning and recognition of complex patterns and features, further facilitating the analysis of details and differences within the overlay marks. Finally, the overlay error is calculated to evaluate the quality and accuracy of the marks. This method utilizes differential overfocus scanning to measure the scanned structural information of the overlay marks relative to both sides of the center line, and employs image processing and deep learning methods for quantitative analysis and calculation, achieving high-precision, non-destructive measurement of overlay errors with symmetrical structural features.

[0078] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of a preferred embodiment, and the modules or processes shown in the drawings are not necessarily essential for implementing this application. Those skilled in the art will understand that the modules in the apparatus of the embodiment can be distributed within the apparatus of the embodiment as described, or can be modified to be located in one or more apparatuses different from this embodiment. The modules of the above-described embodiment can be combined into one module, or further divided into multiple sub-modules.

[0079] The serial numbers in this application are for descriptive purposes only and do not represent the superiority or inferiority of any particular implementation scenario. The above disclosures are merely a few specific implementation scenarios of this application; however, this application is not limited thereto, and any variations conceived by those skilled in the art should fall within the protection scope of this application.

Claims

1. A method for measuring overlay error, characterized in that, include: The mark to be tested is identified, and the mark is scanned at multiple focal positions along the vertical direction to obtain multiple scan images; Multiple scanned images are arranged sequentially according to the setting order of the multiple focal positions, and a three-dimensional spatial model is constructed based on the arranged multiple scanned images, wherein the three-dimensional spatial model includes the light intensity information of the overlay mark to be tested; Vertical cross-sectional processing is performed on both sides of the center line of the overlay mark to be tested to obtain a first overfocus scan image and a second overfocus scan image. The first overfocus scan image and the second overfocus scan image are then differentially processed to obtain a differential image. The difference image is input into a preset deep learning training model to extract the structural feature information of the overlay mark, and the overlay error of the overlay mark is obtained based on the structural feature information.

2. The overlay error measurement method according to claim 1, characterized in that, The process involves determining the overlay mark to be tested, scanning the mark at multiple focal positions along the vertical direction to obtain multiple scan images, including: Determine the overlay mark to be tested, and obtain the structure type of the overlay mark to be tested, wherein the structure type includes at least one of Bar-in-Bar, Frame-in-Frame, and Box-in-Box; Based on the structural type of the overlay mark to be tested, multiple different focal positions are selected in the vertical direction, and scanning parameters are determined at each focal position, wherein the scanning parameters include scanning speed and scanning range; According to a preset scanning sequence, the marking to be tested is scanned at each of the focal positions along the vertical direction to obtain a scanned image corresponding to each of the focal positions.

3. The overlay error measurement method according to claim 1, characterized in that, The step of arranging multiple scanned images sequentially according to the setting order of multiple focal positions, and constructing a three-dimensional spatial model based on the arranged multiple scanned images, includes: The setting order of the plurality of focal positions is obtained, and the arrangement order of the scanned images is determined based on the setting order, wherein the arrangement order corresponds to the vertical direction of the plurality of focal positions; The multiple scanned images are arranged sequentially according to their arrangement order, and the pixel value and light intensity information within the pixel value of each scanned image are extracted. A three-dimensional spatial framework is constructed based on the pixel positions in each scanned image. The light intensity information is embedded into the corresponding positions in the three-dimensional spatial framework, and an interpolation operation is performed on the intervals in the three-dimensional spatial framework to obtain a three-dimensional spatial model.

4. The overlay error measurement method according to claim 1, characterized in that, Before performing differential processing on the first overfocus scan image and the second overfocus scan image to obtain a differential image, the method includes: The first overfocus scan image and the second overfocus scan image are acquired and differentially processed. The first overfocus scan image and the second overfocus scan image are then denoised, smoothed and contrast enhanced in sequence. Image processing is performed on the first overfocus scan image to extract the contour of the first overfocus scan image, the center line of the first overfocus scan image is determined based on the contour, and a straight line fitting is performed on the center line of the first overfocus scan image to determine the equation of the center line of the first overfocus scan image. Image processing is performed on the second overfocus scan image to extract the contour of the second overfocus scan image. Based on the contour, the center line of the second overfocus scan image is determined, and a straight line fitting is performed on the center line of the second overfocus scan image to determine the equation of the center line of the second overfocus scan image.

5. The overlay error measurement method according to claim 1, characterized in that, The step of performing differential processing on the first overfocus scan image and the second overfocus scan image to obtain a differential image includes: The first overfocus scan image is converted into a first grayscale image, and the second overfocus scan image is converted into a second grayscale image; A difference image is obtained by performing difference calculation on the first grayscale image and the second grayscale image using a preset difference operator, and then performing image enhancement processing on the difference image, wherein the image enhancement processing includes thresholding, gradient enhancement and normalization.

6. The overlay error measurement method according to claim 1, characterized in that, The deep learning training model is a convolutional neural network or a recurrent neural network.

7. The overlay error measurement method according to claim 1, characterized in that, The process of obtaining the overlay error of the overlay mark based on the structural feature information includes: Obtain the structural feature information of the overlay mark, and preprocess the structural feature information; Multiple overlay error features are extracted from the preprocessed structural feature information, and the error value of each overlay error feature is calculated. Statistical analysis is performed on the error values ​​of the overlay error characteristics to obtain the overlay error measurement results.

8. A device for measuring overlay error, characterized in that, The device includes: The image scanning module is used to determine the overlay mark to be tested, and to scan the overlay mark to be tested at multiple focal positions along the vertical direction to obtain multiple scan images; The model building module is used to arrange multiple scanned images sequentially according to the setting order of the multiple focal positions, and to construct a three-dimensional spatial model based on the arranged multiple scanned images, wherein the three-dimensional spatial model includes the light intensity information of the overlay mark to be tested; The differential processing module is used to perform vertical cross-sectional processing on both sides of the center line of the overlay mark to be tested, to obtain a first overfocus scan image and a second overfocus scan image, and to perform differential processing on the first overfocus scan image and the second overfocus scan image to obtain a differential image. The result output module is used to input the difference image into a preset deep learning training model, extract the structural feature information of the overlay mark, and obtain the overlay error of the overlay mark based on the structural feature information.

9. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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