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

Abstract

The invention discloses an overlay error measurement method and device, a storage medium and computer equipment, and relates to the technical field of semiconductor laser measurement, and the method comprises the steps: firstly, scanning an overlay mark to be measured at a plurality of focal positions in the vertical direction to obtain scanning images, then arranging the scanning images to construct a three-dimensional space model, and constructing a three-dimensional space model; the method comprises the following steps: firstly, carrying out three-dimensional scanning on a to-be-measured overlay mark, then respectively carrying out vertical section processing on a three-dimensional space on two sides of a center line of the to-be-measured overlay mark, obtaining two over-focus scanning images, carrying out differential processing on the two over-focus scanning images, obtaining a differential image, finally inputting the differential image into a preset deep learning training model, extracting structural feature information of the overlay mark, and obtaining an overlay error of the overlay mark. According to the method, the scanning structure information relative to the overlay marks on the two sides of the center line is measured through the differential overfocus scanning method, quantitative analysis and calculation are conducted through the image processing and deep learning method, and high-precision lossless measurement of the overlay error with the symmetrical structure characteristic is achieved.

Description

Technical Field

[0001] The present invention relates to the field of semiconductor laser measurement technology, and in particular to an overlay error measurement method, device, storage medium and computer equipment. Background Art

[0002] Photolithography is an indispensable key technology in the integrated circuit manufacturing process. With the continuous development of photolithography, higher requirements are also put forward for the accuracy of measuring overlay error. Overlay error specifically refers to the deviation of the pattern on the wafer relative to the standard reference pattern in the X-axis and Y-axis directions in integrated circuit manufacturing. The deviation must be less than 1 / 3 to 1 / 5 of the wafer feature size to ensure that the circuit can be connected reliably as much as possible.

[0003] In the prior art, there are mainly two ways to measure overlay error, one is the imaging based overlay (IBO) measurement technology, and the other is the diffraction based overlay (DBO) measurement technology. Among them, the DBO measurement technology has become the mainstream method in overlay error measurement due to its non-contact, non-destructive and fast characteristics. The overlay mark in the DBO measurement technology is a specially designed nano grating structure. By measuring the diffraction signal of the overlay mark, such as the spectrum or angle-resolved spectrum, the overlay error is extracted by a certain method. However, the DBO measurement technology cannot be applied to the measurement of larger overlay errors, and it is mainly aimed at periodic grating structures and cannot adapt to the overlay error measurement of other symmetrical structures. Summary of the invention

[0004] In view of the shortcomings of the above-mentioned prior art, the present invention provides an overlay error measurement method and device, the main purpose of which is to solve the technical problems that the overlay error measurement technology in the prior art has a small application range 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, there is provided a method for measuring an overlay error, the method comprising:

[0006] Determine an overlay mark to be measured, and scan the overlay mark to be measured at multiple focal positions along a vertical direction to obtain multiple scanned images;

[0007] Arrange the plurality of scanned images in sequence according to the setting order of the plurality of focal positions, and construct a three-dimensional space model based on the arranged plurality of scanned images, wherein the three-dimensional space model includes the light intensity information of the overlay mark to be measured;

[0008] Performing vertical section processing on the three-dimensional space at both sides of the center line of the overlay mark to be measured to obtain a first overfocus scanning image and a second overfocus scanning image, and performing differential processing on the first overfocus scanning image and the second scanning image to obtain a differential image;

[0009] 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.

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

[0011] An image scanning module, used for determining an overlay mark to be tested, scanning the overlay mark to be tested at multiple focal positions along a vertical direction to obtain multiple scanned images;

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

[0013] A differential processing module, used to perform vertical section processing on the three-dimensional space on both sides of the center line of the overlay mark to be measured, obtain a first overfocus scanning image and a second overfocus scanning image, and perform differential processing on the first overfocus scanning image and the second scanning image to obtain a differential image;

[0014] The result output module is used to input the differential 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, there is provided a storage medium on which a computer program is stored, and when the program is executed by a processor, the above-mentioned overlay error measurement method is implemented.

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

[0017] The present invention provides an overlay error measurement method, device, storage medium and computer equipment, which first determine the overlay mark to be measured, scan the overlay mark to be measured at multiple focal positions in the vertical direction to obtain multiple scanned images, and then arrange the multiple scanned images in sequence according to the setting order of the multiple focal positions, and construct a three-dimensional space model based on the arranged multiple scanned images, wherein the three-dimensional space model includes light intensity information of the overlay mark to be measured, and then vertical profile processing is performed on the three-dimensional space on both sides of the center line of the overlay mark to be measured to obtain a first overfocus scanned image and a second overfocus scanned image, and the first overfocus scanned image and the second scanned image are differentially processed to obtain a differential image, and finally the differential image is input into a preset deep learning training model to extract 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 mark to be measured, without contacting the marker, reducing possible physical damage or impact; by scanning both sides of the center line of the overlay mark to be measured at different focal positions, information from different perspectives of the overlay mark with symmetrical structural features can be obtained, so that the structure of the overlay mark can be more comprehensively understood; then, by constructing a three-dimensional space model based on the arranged scanned images, the morphology and structure of the overlay mark can be more accurately restored, which is helpful to accurately analyze and evaluate the characteristics and errors of the overlay mark; using a deep learning model, complex patterns and features can be learned and recognized, and the details and differences of the overlay mark can be more accurately analyzed; finally, by calculating the overlay error of the overlay mark, the quality and accuracy of the mark are evaluated. The above method uses the differential overfocus scanning method to measure the scanning structure information of the overlay mark relative to both sides of the center line, and uses image processing and deep learning methods to perform quantitative analysis and solution, so as to achieve high-precision non-destructive measurement of the overlay error with symmetrical structural features.

[0019] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings.

[0020] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0022] Figure 1A schematic diagram of a flow chart of an overlay error measurement method provided by an embodiment of the present invention is shown;

[0023] Figure 2 A schematic diagram of a flow chart of an overlay error measurement method provided by an embodiment of the present invention is shown;

[0024] Figure 3 The overlay marks of three symmetrical structural features in an overlay error measurement method provided by an embodiment of the present invention are shown;

[0025] Figure 4 A flow chart showing a method for measuring an overlay error provided by an embodiment of the present invention is shown;

[0026] Figure 5 A schematic structural diagram of an overlay error measurement device provided by an embodiment of the present invention is shown;

[0027] Figure 6 A schematic structural diagram of an overlay error measurement device provided by an embodiment of the present invention is shown;

[0028] Figure 7 A schematic diagram of the device structure of a computer device provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0029] The exemplary embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present application are shown in the accompanying drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present application and to fully convey the scope of the present application to those skilled in the art.

[0030] The present application embodiment provides a method for measuring overlay error, such as Figure 1 As shown, the method comprises the following steps:

[0031] 101. Determine an overlay mark to be measured, and scan the overlay mark to be measured at multiple focus positions along a vertical direction to obtain multiple scanned images.

[0032] Specifically, overlay marks are an important element used in semiconductor manufacturing, flat panel display manufacturing, and other micro-nano processing fields. Overlay marks are a specific pattern or structure whose main function is to provide accurate positioning references, so that the pattern alignment between various levels in the micro-nano processing process is more accurate. They are often placed on the edge of the chip or wafer, or distributed at key positions on the chip surface. The design form and structure of overlay marks vary depending on the application and manufacturing process. Common types include cross, grid, circle, linear structure, etc., which are usually composed of lines or structures with multiple shape edges to provide more information for precise positioning and alignment. Overlay marks play an important role in micro-nano manufacturing. They can help manufacturers improve product quality, increase production efficiency, and ensure precise alignment of each manufacturing step.

[0033] In an embodiment of the present application, by scanning at multiple focal positions, image information of multiple perspectives of the overlay mark to be tested can be obtained, more comprehensive and detailed visual data can be obtained, the morphology and structure of the overlay mark can be analyzed and evaluated more accurately, and the scanned images at different focal positions can reveal the details and changes of the overlay mark at different positions, which is helpful to more comprehensively understand and analyze the structural characteristics of the overlay mark; during the scanning process, the overlay mark to be tested is scanned non-contactly without physical contact or interference, which helps to reduce the risk of interference and damage to the object to be tested and maintain the integrity of the object to be tested; and by setting multiple focal positions for scanning in the vertical direction, high-precision positioning and quantitative analysis of the overlay mark to be tested can be achieved, and the scanned image of each focal position provides information on a specific position, making the judgment of the position of the overlay mark 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 structure and characteristics of the overlay mark.

[0034] 102. Arrange the multiple scanned images in sequence according to the setting order of the multiple focus positions, and construct a three-dimensional space model based on the arranged multiple scanned images, wherein the three-dimensional space model includes light intensity information of the overlay mark to be measured.

[0035] In an embodiment of the present application, arranging and constructing a three-dimensional spatial model based on scanned images of multiple focal positions can improve the accuracy of the data. By considering the light intensity information at different focal positions, the morphology and structure of the overlay mark to be tested can be more accurately restored. Specifically, the arranged multiple scanned images provide information from different angles and focal positions, and by constructing a three-dimensional spatial model, this information can be combined into a comprehensive model, including the full range of features of the overlay mark to be tested, thereby obtaining richer and more comprehensive data, which is helpful to better understand and analyze the overlay mark. Among them, the three-dimensional spatial model can display the spatial relationship of the overlay mark to be tested in a visual manner. Compared with a single two-dimensional image, the three-dimensional model can more accurately represent the shape, size and position relationship of the overlay mark, making the analysis of the structural characteristics and overlay errors of the mark more intuitive and accurate, and helping to check the morphological consistency of the mark, qualitatively and quantitatively analyze the deviation or error of the mark, and at the same time, the light intensity information is integrated into the three-dimensional spatial model, which can more accurately extract the structural feature information of the overlay mark.

[0036] 103. Perform vertical section processing on the three-dimensional space on both sides of the center line of the overlay mark to be measured to obtain a first overfocus scanning image and a second overfocus scanning image, and perform differential processing on the first overfocus scanning image and the second scanning image to obtain a differential image.

[0037] Specifically, the present application is mainly used to measure the error of the overlay mark of the symmetrical structural feature, and the overlay mark of the symmetrical structural feature generally includes Bar-in-Bar, Frame-in-Frame and Box-in-Box; Figure 3 As mentioned above, Bar-in-Bar is a structure in which a small rectangular or straight bar is surrounded by a large rectangular or straight frame, which is usually used for positioning and alignment, and is widely used in micro-nano processing; Frame-in-Frame is a structure in which a small frame is surrounded by a large frame, and this structure can be used for positioning, alignment, and detection of accuracy and shape; Box-in-Box is an overlay mark, which is surrounded by a small rectangular frame by a large rectangular frame, and is often used for positioning, alignment, and the evaluation of shape changes during etching or lithography. The above common symmetrical overlay mark structures are often used in semiconductor chip manufacturing, MEMS device manufacturing, microelectronic packaging and other fields by providing features for positioning, alignment, and overlay error evaluation and analysis.

[0038] In an embodiment of the present application, differential processing is performed on the overfocus scanned images on both sides of the center line of the overlay mark to be tested, which 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 highlight the changes and features of the overlay mark, making the details easier to observe and analyze. The differential image obtained after the differential processing can provide clearer and more accurate structural information, including information on structural changes such as offset and deformation of the overlay mark, and 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 detection and screening of overlay marks.

[0039] 104. Input the differential 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.

[0040] In an embodiment of the present application, the deep learning model can automatically learn to represent features from the differential image, and can extract the structural feature information of the overlay mark more efficiently and accurately. The differential image highlights the subtle differences in the overlay mark, which helps to more accurately detect and screen out defects, flaws or abnormalities in the overlay mark, and the deep learning model can use the global information in the differential image to analyze and extract the structural features of the overlay mark. Compared with local features, global features can provide more comprehensive and integrated information and can better describe the shape, size and symmetry of the overlay mark. The deep learning model is trained with a large number of training samples and can automatically learn and adapt to the structural features of various overlay marks. Regardless of how the shape, size or symmetry of the overlay mark changes, the deep learning model can perform effective feature extraction and overlay error estimation, which can save a lot of time and labor costs compared to traditional manual analysis and measurement methods. In short, inputting the differential image into the preset deep learning training model can efficiently and accurately extract the structural feature information of the overlay mark, and obtain an estimate of the overlay error based on the above information.

[0041] The present invention provides a method for measuring overlay error. First, an overlay mark to be measured is determined, and the overlay mark to be measured is scanned at multiple focal positions in a vertical direction to obtain multiple scanned images. Then, the multiple scanned images are arranged in sequence according to the setting order of the multiple focal positions. A three-dimensional space model is constructed based on the arranged multiple scanned images, wherein the three-dimensional space model includes light intensity information of the overlay mark to be measured. Then, vertical profile processing is performed on the three-dimensional space on both sides of the center line of the overlay mark to be measured to obtain a first overfocus scanned image and a second overfocus scanned image, and the first overfocus scanned image and the second scanned image are differentially processed to obtain a differential image. Finally, the differential image is input into a preset deep learning training model to extract 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 mark to be measured, without contacting the marker, reducing possible physical damage or impact; by scanning both sides of the center line of the overlay mark to be measured at different focal positions, information from different perspectives of the overlay mark with symmetrical structural features can be obtained, so that the structure of the overlay mark can be more comprehensively understood; then, by constructing a three-dimensional space model based on the arranged scanned images, the morphology and structure of the overlay mark can be more accurately restored, which is helpful to accurately analyze and evaluate the characteristics and errors of the overlay mark; using a deep learning model, complex patterns and features can be learned and recognized, and the details and differences of the overlay mark can be more accurately analyzed; finally, by calculating the overlay error of the overlay mark, the quality and accuracy of the mark are evaluated. The above method uses the differential overfocus scanning method to measure the scanning structure information of the overlay mark relative to both sides of the center line, and uses image processing and deep learning methods to perform quantitative analysis and solution, so as to achieve high-precision non-destructive measurement of the overlay error with symmetrical structural features.

[0042] The present application embodiment provides a method for measuring overlay error, such as Figure 2 As shown, the method comprises the following steps:

[0043] 201. Scan the overlay mark to be measured at multiple focus positions along the vertical direction to obtain multiple scanned images.

[0044] Specifically, first determine the overlay mark to be tested, and 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; then select a plurality of 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 the scanning speed and the scanning range; finally, scan the overlay mark to be tested at each focal position along the vertical direction according to a preset scanning order, and obtain a scanning image corresponding to each focal position.

[0045] In an embodiment of the present application, by selecting multiple different focal positions in the vertical direction, scanning images of the overlay mark can be obtained at different depths to obtain more comprehensive structural information; and according to the structural type of the overlay mark to be tested, a suitable focal position can be selected according to actual needs, and appropriate scanning parameters can be set at each focal position to fully display the structural characteristics of the overlay mark to be tested; through the scanning images of multiple focal positions, the structural characteristics of the overlay mark can be observed and analyzed from different angles, which is helpful 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 order to obtain a high-resolution image that can more accurately reflect the details and morphological characteristics of the overlay mark.

[0046] 202. Construct a three-dimensional space model based on a plurality of scanned images arranged in a setting order of focus positions.

[0047] Specifically, first, the setting order of multiple focus positions is obtained, and the arrangement order of the scanned images is determined based on the setting order, and the arrangement order corresponds to the vertical direction of the multiple focus positions. Then, the multiple scanned images are arranged in sequence according to the arrangement order of the scanned images, and the pixel values ​​of each scanned image and the light intensity information in the pixel values ​​are extracted. Finally, a three-dimensional space framework is constructed based on pixel points, the light intensity information is embedded in the corresponding position in the three-dimensional space framework, and the intervals in the three-dimensional space frame are interpolated to obtain a three-dimensional space model.

[0048] In an embodiment of the present application, by extracting the pixel value of each scanned image and the light intensity information in the pixel value, and embedding the light intensity information into the corresponding position in the three-dimensional space frame, a three-dimensional space model of the overlay mark is constructed. In this process, the intervals in the three-dimensional space frame are filled by interpolation operations to obtain a complete three-dimensional representation. Specifically, by constructing a three-dimensional space model of the overlay mark based on the pixel value and light intensity information of the multi-focus scanned image, a more comprehensive perspective is provided, which is convenient for analyzing and evaluating the characteristics of the overlay mark such as shape, size and symmetry. In the three-dimensional space frame, by embedding the light intensity information into the corresponding position, the detailed information of each pixel point can be retained, which can more accurately reflect the internal structure and surface morphology of the overlay mark. Then, the intervals in the three-dimensional space frame can be filled by interpolation operations to obtain a complete three-dimensional space model. The model obtained in this way can provide a more coherent and continuous visual display, so that the observer can more clearly understand and analyze the characteristics of the overlay mark. Finally, by constructing a three-dimensional space model, the information of the overlay mark can be displayed in a visual way, which is convenient for analysts to observe, compare and make decisions. In summary, by constructing a three-dimensional spatial model of the overlay mark based on the multi-focus scanning image, a more comprehensive and accurate visual expression can be provided, which is convenient for the feature analysis, evaluation and visualization of the overlay mark, and helps to further fully and accurately understand the shape structure of the overlay mark.

[0049] 203. Perform vertical section processing on the three-dimensional space to obtain two over-focus scan images, and perform pre-processing on the two over-focus scan images respectively.

[0050] Specifically, firstly, the first overfocus scanning image and the second scanning image are acquired for differential processing, and denoising, smoothing and contrast enhancement are performed on the first overfocus scanning image and the second scanning image respectively. Then, image processing is performed on the first overfocus scanning image to extract the contour of the first overfocus scanning image, and the center line of the first overfocus scanning image is determined based on the contour. A straight line fitting is performed on the center line of the first overfocus scanning image to determine the center line equation of the first overfocus scanning image. Finally, image processing is performed on the second overfocus scanning image to extract the contour of the second overfocus scanning image, and the center line of the second overfocus scanning image is determined based on the contour. A straight line fitting is performed on the center line of the second overfocus scanning image to determine the center line equation of the second overfocus scanning image.

[0051] In an embodiment of the present application, the edge information of the overprint mark can be highlighted by performing differential processing on the two overfocus scan images, and the shape information of the overprint mark can be obtained by performing contour extraction, which is helpful for subsequent analysis and measurement; and the centerline equation of the overprint mark can be obtained by performing straight line fitting on the center lines of the first overfocus scan image and the second overfocus scan image, which further describes the morphology and shape characteristics of the overprint mark. After obtaining the centerline equation of the overprint mark, it can be used to locate the position and posture of the overprint mark, as well as to perform other centerline-based quantitative analysis, such as size measurement, shape evaluation, etc. This process can be achieved through automated algorithms and image processing technology, which improves efficiency and accuracy and reduces dependence on manual operations.

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

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

[0054] In an embodiment of the present application, by converting the scanned image into a grayscale image and performing a differential calculation, a differential image reflecting the structural features of the overlay mark can be obtained, and then the differential image is thresholded, gradient enhanced, and normalized through an image enhancement processing operation to increase the contrast and clarity of the image. The differential image obtained by the differential calculation in the present application can highlight the structural features of the overlay mark, and can more easily analyze and detect the shape and edge information of the overlay mark, and through image enhancement processing, such as thresholding, gradient enhancement, and normalization, the contrast of the differential image can be increased, the details of the overlay mark can be highlighted, and it is easier to observe and analyze. In the actual process, the process of enhancing the differential image is flexible, and different enhancement algorithms and parameters can be selected according to actual needs to obtain better results. In summary, converting two overfocus scanned images into grayscale images, performing differential calculations and enhancing processing can highlight the structural features of the overlay mark, improve the contrast and clarity of the image, and contribute to further image analysis and quality assessment.

[0055] 205. Input the differential 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.

[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 result.

[0057] Among them, the deep learning training model is a convolutional neural network or a recurrent neural network. The convolutional neural network is a type of deep learning model widely used in image processing and computer vision tasks. It has the characteristics of convolutional layers, pooling layers, activation functions, fully connected layers and deep structures, and can gradually extract and combine abstract features of images from low-level to high-level features. Convolutional neural networks have achieved remarkable results in the field of computer vision and are widely used in tasks such as image classification, target detection, image generation and semantic segmentation. Through the training process of deep learning, convolutional neural networks can learn the feature representation and pattern of images from a large amount of data and have strong automatic learning capabilities. The recurrent neural network is a type of neural network model that can process sequence data. It introduces recurrent connections so that information can be transmitted and persisted in the network. When processing sequence data, the recurrent neural network can associate the current input with the previous context information by introducing hidden states in the network. At each time step, the recurrent neural network will calculate the hidden state and output at the current moment based on the current input and the hidden state at the previous moment. This transmission and update of hidden states enables the recurrent neural network to gradually model and learn sequence data.

[0058] In the embodiment of the present application, by obtaining the structural feature information of the overlay mark, and preprocessing and extracting it, a plurality of overlay error features can be obtained, and then by calculating the error value of each overlay error feature and performing statistical analysis, the measurement results of the overlay error, such as the average error, the maximum error, the error distribution, etc., can be obtained. By calculating and analyzing the overlay error features, the present application can intuitively evaluate the quality and manufacturing accuracy of the overlay mark, which helps to timely discover and correct problems in the manufacturing process and improve the quality of the overlay mark. By measuring and analyzing the structural feature information, the overlay error measurement results are objective, and repeatable measurements can be performed to reduce the influence of subjective factors and improve 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, first, the overlay mark to be tested is scanned at multiple focal positions in the vertical direction to obtain multiple scanned images, and then a three-dimensional space model is constructed based on the multiple scanned images arranged in the setting order of the focal positions. After that, the three-dimensional space is vertically profiled to obtain two overfocus scanned images, and the two overfocus scanned images are preprocessed respectively, and then the two overfocus scanned images are differentially processed to obtain a differential image, and 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.

[0060] Further, as Figure 1 The specific implementation of the method, the embodiment of the present 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] An image scanning module 301 is used to determine the overlay mark to be measured, and scan the overlay mark to be measured at multiple focus positions along the vertical direction to obtain multiple scanned images;

[0062] A model building module 302 is used to sequentially arrange the multiple scanned images according to the setting order of the multiple focus positions, and construct a three-dimensional space model based on the arranged multiple scanned images, wherein the three-dimensional space model includes the light intensity information of the overlay mark to be measured;

[0063] The differential processing module 303 is used to perform vertical section processing on the three-dimensional space on both sides of the center line of the overlay mark to be measured, obtain a first overfocus scanning image and a second overfocus scanning image, and perform differential processing on the first overfocus scanning image and the second scanning image to obtain a differential image;

[0064] The result output module 304 is used to input the differential 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 a specific application scenario, the image scanning module 301 can be specifically used to determine the overlay mark to be tested and 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 the scanning speed and the scanning range; scan the overlay mark to be tested at each focal position along the vertical direction according to a preset scanning order to obtain a scanning image corresponding to each focal position.

[0066] In a specific application scenario, the model building module 302 can be specifically used to obtain the setting order of multiple focus positions, determine the arrangement order of the scanned images based on the setting order, and the arrangement order corresponds to the vertical direction of the multiple focus positions; arrange the multiple scanned images in sequence according to the arrangement order of the scanned images, and extract the pixel values ​​of each scanned image and the light intensity information in the pixel values; construct a three-dimensional space frame based on pixel points, embed the light intensity information into the corresponding position in the three-dimensional space frame, and perform interpolation operations on the intervals in the three-dimensional space frame to obtain a three-dimensional space model.

[0067] In specific application scenarios, such as Figure 6 As shown, the device also includes an image preprocessing module 305, which can be specifically used to obtain the first overfocus scanning image and the second scanning image for differential processing, and perform denoising, smoothing and contrast enhancement on the first overfocus scanning image and the second scanning image respectively; perform image processing on the first overfocus scanning image, extract the contour of the first overfocus scanning image, determine the center line of the first overfocus scanning image based on the contour, and perform straight line fitting on the center line of the first overfocus scanning image to determine the center line equation of the first overfocus scanning image; perform image processing on the second overfocus scanning image, extract the contour of the second overfocus scanning image, determine the center line of the second overfocus scanning image based on the contour, and perform straight line fitting on the center line of the second overfocus scanning image to determine the center line equation of the second overfocus scanning image.

[0068] In a specific application scenario, the differential processing module 303 can be used to convert the first over-focus scan image into a first grayscale image, and convert the second over-focus scan image into a second grayscale image; use a preset differential operator to perform differential calculation on the first grayscale image and the second grayscale image 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 a specific application scenario, the result output module 304 can also be used to obtain the structural feature information of the overlay mark and preprocess the structural feature information; extract multiple overlay error features from the preprocessed structural feature information, and calculate the error value of each overlay error feature; perform statistical analysis on the error values ​​of the overlay error features to obtain the overlay error measurement results.

[0070] It should be noted that for other corresponding descriptions of the functional units involved in the overlay error measurement device provided in this embodiment, reference can be made to Figure 1 and Figure 2 The corresponding description in will not be repeated here.

[0071] Based on the above Figure 1The method shown, accordingly, this embodiment also provides a storage medium, on which a computer program is stored, and when the program is executed by a processor, the above-mentioned overlay error measurement method is implemented.

[0072] Based on this understanding, the technical solution of the present 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 (which can be a CD-ROM, U disk, mobile hard disk, etc.), including a number of instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute the overlay error measurement method of each implementation scenario of the present application.

[0073] Based on the above Figure 1 and Figure 2 The method shown, and Figure 5 and Figure 6 In order to achieve the above-mentioned purpose, the embodiment of the overlay error measuring device shown in the figure is as follows: Figure 7 As shown, this embodiment also provides a physical device for measuring overlay error, which includes a communication bus, a processor, a memory and a communication interface, and may also include an input and output interface and a display device, wherein each functional unit can communicate with each other through the bus. The memory stores a computer program, and the processor is used to execute the program stored in the memory and execute the overlay error measurement method in the above embodiment.

[0074] Optionally, the physical device may also include a user interface, a network interface, a camera, a radio frequency (RF) circuit, a sensor, an audio circuit, a WI-FI module, etc. The user interface may include a display, an input unit such as a keyboard, etc., and the optional user interface may also include a USB interface, a card reader interface, etc. The network interface may optionally include a standard wired interface, a wireless interface (such as a WI-FI interface), etc.

[0075] Those skilled in the art will appreciate that the structure of an overlay error measurement entity device provided in this embodiment does not constitute a limitation on the entity device, and may include more or fewer components, or a combination of certain components, or 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 of the physical device and the software resources to be identified, and supports the operation of the information processing program and other software and / or programs to be identified. The network communication module is used to realize the communication between the components inside the storage medium, and the communication with other hardware and software in the information processing physical device.

[0077] Through the description of the above implementation methods, the technical personnel in this field can clearly understand that the present application can be implemented by means of software plus the necessary general hardware platform, or by hardware. By applying the technical solution of the present application, the overlay mark to be tested is first determined, and the overlay mark to be tested is scanned at multiple focal positions in the vertical direction to obtain multiple scanned images, and then the multiple scanned images are arranged in sequence according to the setting order of the multiple focal positions, and a three-dimensional space model is constructed based on the arranged multiple scanned images, wherein the three-dimensional space model includes the light intensity information of the overlay mark to be tested, and then the three-dimensional space is vertically profiled on both sides of the center line of the overlay mark to be tested, and the first overfocus scanned image and the second overfocus scanned image are obtained, and the first overfocus scanned image and the second scanned image are differentially processed to obtain a differential image, and 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 mark to be measured, without contacting the marker, reducing possible physical damage or impact; by scanning both sides of the center line of the overlay mark to be measured at different focal positions, information from different perspectives of the overlay mark with symmetrical structural features can be obtained, so that the structure of the overlay mark can be more comprehensively understood; then, by constructing a three-dimensional space model based on the arranged scanned images, the morphology and structure of the overlay mark can be more accurately restored, which is helpful to accurately analyze and evaluate the characteristics and errors of the overlay mark; using a deep learning model, complex patterns and features can be learned and recognized, and the details and differences of the overlay mark can be more accurately analyzed; finally, by calculating the overlay error of the overlay mark, the quality and accuracy of the mark are evaluated. The above method uses the differential overfocus scanning method to measure the scanning structure information of the overlay mark relative to both sides of the center line, and uses image processing and deep learning methods to perform quantitative analysis and solution, so as to achieve high-precision non-destructive measurement of the overlay error with symmetrical structural features.

[0078] Those skilled in the art will appreciate that the accompanying drawings are only schematic diagrams of a preferred implementation scenario, and the modules or processes in the accompanying drawings are not necessarily necessary for implementing the present application. Those skilled in the art will appreciate that the modules in the devices in the implementation scenario can be distributed in the devices of the implementation scenario according to the description of the implementation scenario, or can be changed accordingly and located in one or more devices different from the present implementation scenario. The modules of the above-mentioned implementation scenario can be combined into one module, or can be further split into multiple submodules.

[0079] The above serial numbers of this application are only for description and do not represent the advantages and disadvantages of the implementation scenarios. The above disclosure is only a few specific implementation scenarios of this application, but this application is not limited to them, and any changes that can be thought of by technicians in this field should fall within the scope of protection of this application.

Claims

1. A method for measuring overlay error, characterized in that: include: Determine an overlay mark to be measured, and scan the overlay mark to be measured at multiple focal positions along a vertical direction to obtain multiple scanned images; Arrange the plurality of scanned images in sequence according to the setting order of the plurality of focal positions, and construct a three-dimensional space model based on the arranged plurality of scanned images, wherein the three-dimensional space model includes the light intensity information of the overlay mark to be measured; Performing vertical section processing on the three-dimensional space at both sides of the center line of the overlay mark to be measured to obtain a first overfocus scanning image and a second overfocus scanning image, and performing differential processing on the first overfocus scanning image and the second scanning image to obtain a differential image; 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.

2. The overlay error measurement method according to claim 1, characterized in that: The determining of the overlay mark to be measured, scanning the overlay mark to be measured at multiple focal positions along a vertical direction to obtain multiple scanned images, includes: Determine an overlay mark to be tested, and obtain a 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; Selecting a plurality of different focal positions in the vertical direction according to the structural type of the overlay mark to be measured, and determining scanning parameters at each of the focal positions, wherein the scanning parameters include a scanning speed and a scanning range; The overlay mark to be measured is scanned at each focal position along the vertical direction according to a preset scanning sequence to obtain a scanning image corresponding to each focal position.

3. The overlay error measurement method according to claim 1, characterized in that: The step of sequentially arranging the plurality of scanned images according to the setting order of the plurality of focal positions, and constructing a three-dimensional space model based on the plurality of scanned images after the arrangement includes: Acquire a setting order of the multiple focus positions, and determine an arrangement order of the scanned images based on the setting order, wherein the arrangement order corresponds to a vertical direction of the multiple focus positions; Arrange the plurality of scanned images in sequence according to the arrangement order of the scanned images, and extract the pixel value of each scanned image and the light intensity information in the pixel value; A three-dimensional space frame is constructed based on the pixel points, the light intensity information is embedded in a corresponding position in the three-dimensional space frame, and an interpolation operation is performed on the intervals in the three-dimensional space frame to obtain a three-dimensional space model.

4. The overlay error measurement method according to claim 1, characterized in that: Before performing differential processing on the first over-focus scanned image and the second scanned image to obtain a differential image, the method includes: Acquire the first over-focus scan image and the second scan image for differential processing, and perform denoising, smoothing, and contrast enhancement on the first over-focus scan image and the second scan image respectively; Performing image processing on the first overfocus scan image, extracting a contour of the first overfocus scan image, determining a 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 a center line equation of the first overfocus scan image; Perform image processing on the second overfocus scanning image, extract the contour of the second overfocus scanning image, determine the center line of the second overfocus scanning image based on the contour, and perform straight line fitting on the center line of the second overfocus scanning image to determine the center line equation of the second overfocus scanning image.

5. The overlay error measurement method according to claim 1, characterized in that: The step of performing differential processing on the first over-focus scanned image and the second scanned image to obtain a differential image includes: Converting the first over-focus scan image into a first grayscale image, and converting the second over-focus scan image into a second grayscale image; A preset differential operator is used to perform differential calculation on the first grayscale image and the second grayscale image to obtain a differential image, and image enhancement processing is performed on the differential 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 obtaining the overlay error of the overlay mark based on the structural feature information includes: Acquiring structural feature information of the overlay mark, and preprocessing the structural feature information; Extracting a plurality of overlay error features from the preprocessed structural feature information, and calculating an error value of each of the overlay error features; Statistical analysis is performed on the error values ​​of the overlay error characteristics to obtain an overlay error measurement result.

8. An overlay error measuring device, characterized in that: The device comprises: An image scanning module, used for determining an overlay mark to be tested, scanning the overlay mark to be tested at multiple focal positions along a vertical direction to obtain multiple scanned images; A model building module, used to sequentially arrange the plurality of scanned images according to the setting order of the plurality of focal positions, and construct a three-dimensional space model based on the arranged plurality of scanned images, wherein the three-dimensional space model includes the light intensity information of the overlay mark to be measured; A differential processing module, used to perform vertical section processing on the three-dimensional space on both sides of the center line of the overlay mark to be measured, obtain a first overfocus scanning image and a second overfocus scanning image, and perform differential processing on the first overfocus scanning image and the second scanning image to obtain a differential image; The result output module is used to input the differential 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, the steps of the method according to any one of claims 1 to 7 are implemented.

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, the steps of the method according to any one of claims 1 to 7 are implemented.

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