CN119472177A - Overlay error measurement method, device, storage medium and computer equipment - Google Patents
Overlay error measurement method, device, storage medium and computer equipment Download PDFInfo
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- CN119472177A CN119472177A CN202310993432.6A CN202310993432A CN119472177A CN 119472177 A CN119472177 A CN 119472177A CN 202310993432 A CN202310993432 A CN 202310993432A CN 119472177 A CN119472177 A CN 119472177A
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- G—PHYSICS
- G03—PHOTOGRAPHY; CINEMATOGRAPHY; ANALOGOUS TECHNIQUES USING WAVES OTHER THAN OPTICAL WAVES; ELECTROGRAPHY; HOLOGRAPHY
- G03F—PHOTOMECHANICAL PRODUCTION OF TEXTURED OR PATTERNED SURFACES, e.g. FOR PRINTING, FOR PROCESSING OF SEMICONDUCTOR DEVICES; MATERIALS THEREFOR; ORIGINALS THEREFOR; APPARATUS SPECIALLY ADAPTED THEREFOR
- G03F7/00—Photomechanical, e.g. photolithographic, production of textured or patterned surfaces, e.g. printing surfaces; Materials therefor, e.g. comprising photoresists; Apparatus specially adapted therefor
- G03F7/70—Microphotolithographic exposure; Apparatus therefor
- G03F7/70483—Information management; Active and passive control; Testing; Wafer monitoring, e.g. pattern monitoring
- G03F7/70605—Workpiece metrology
- G03F7/70616—Monitoring the printed patterns
- G03F7/70633—Overlay, i.e. relative alignment between patterns printed by separate exposures in different layers, or in the same layer in multiple exposures or stitching
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- G—PHYSICS
- G03—PHOTOGRAPHY; CINEMATOGRAPHY; ANALOGOUS TECHNIQUES USING WAVES OTHER THAN OPTICAL WAVES; ELECTROGRAPHY; HOLOGRAPHY
- G03F—PHOTOMECHANICAL PRODUCTION OF TEXTURED OR PATTERNED SURFACES, e.g. FOR PRINTING, FOR PROCESSING OF SEMICONDUCTOR DEVICES; MATERIALS THEREFOR; ORIGINALS THEREFOR; APPARATUS SPECIALLY ADAPTED THEREFOR
- G03F7/00—Photomechanical, e.g. photolithographic, production of textured or patterned surfaces, e.g. printing surfaces; Materials therefor, e.g. comprising photoresists; Apparatus specially adapted therefor
- G03F7/70—Microphotolithographic exposure; Apparatus therefor
- G03F7/70483—Information management; Active and passive control; Testing; Wafer monitoring, e.g. pattern monitoring
- G03F7/70491—Information management, e.g. software; Active and passive control, e.g. details of controlling exposure processes or exposure tool monitoring processes
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Abstract
本发明公开了一种套刻误差测量方法、装置、存储介质及计算机设备，涉及半导体激光测量技术领域，其中方法包括：首先沿竖直方向在多个焦点位置对待测套刻标记进行扫描得到扫描图像，然后将扫描图像进行排列以构建三维空间模型，之后在待测套刻标记的中心线两侧分别对三维空间进行垂直剖面处理，获取两个过焦扫描图像并进行差分处理，得到差分图像，最后将差分图像输入至预设深度学习训练模型中，提取套刻标记的结构特征信息，得到套刻标记的套刻误差。上述方法利用差分过焦扫描方法测量相对于中心线两侧的套刻标记的扫描结构信息，通过图像处理及深度学习的方法进行定量分析和解算，实现具有对称结构特征的套刻误差的高精度无损测量。
The present invention discloses an overlay error measurement method, device, storage medium and computer equipment, which relate to the field of semiconductor laser measurement technology, wherein the method comprises: firstly scanning the overlay mark to be measured at multiple focal positions in the vertical direction to obtain a scanned image, then arranging the scanned images to construct a three-dimensional space model, then respectively performing vertical section processing on the three-dimensional space on both sides of the center line of the overlay mark to be measured, obtaining two overfocus scanned images and performing differential processing to obtain a differential image, and finally inputting the differential image into a preset deep learning training model to extract the structural feature information of the overlay mark and obtain the overlay error of the overlay mark. The above method uses a differential overfocus scanning method to measure the scanning structural information of the overlay mark relative to both sides of the center line, and performs quantitative analysis and solution through image processing and deep learning methods to achieve high-precision non-destructive measurement of overlay errors with symmetrical structural features.
Description
Technical Field
The present invention relates to the field of semiconductor laser measurement technologies, and in particular, to an overlay error measurement method, an overlay error measurement device, a storage medium, and a computer device.
Background
Photolithography is an indispensable key technology in integrated circuit manufacturing processes, and as photolithography is continuously developed, higher requirements are also put on the accuracy of measuring overlay errors. The overlay error is specifically the deviation of the pattern on the wafer in the X-axis and Y-axis directions relative to the standard reference pattern in the integrated circuit fabrication, and the deviation is required to be less than 1/3-1/5 of the feature size of the wafer to ensure reliable connection of the circuits as much as possible.
In the prior art, there are two main ways for overlay error measurement, one is an imaging-based overlay error (Imaging Based Overlay, IBO) measurement technique, and the other is a diffraction-based overlay error (Diffraction Based Overlay, DBO) measurement technique. Among them, the DBO measurement technique is the dominant method in overlay error measurement due to its non-contact, non-destructive and fast characteristics. Overlay marks in the DBO measurement technology are specially designed nano grating structures, diffraction signals of the overlay marks, such as spectra or angle-resolved spectra, are measured, and overlay errors are extracted through a certain method. However, the DBO measurement technique cannot be applied to measurement of large overlay errors, and is mainly aimed at periodic grating structures, and cannot be adapted to measurement of overlay errors of other symmetrical structures.
Disclosure of Invention
The invention provides an overlay error measurement method and device aiming at the defects of the prior art, and mainly aims to solve the technical problems of small application range of the overlay error measurement technology, and low accuracy of measurement results aiming at a symmetrical structure and a large overlay error in the prior art.
According to a first aspect of the present invention, there is provided an overlay error measurement method comprising:
Determining an overlay mark to be detected, and scanning the overlay mark to be detected at a plurality of focus positions along the vertical direction to obtain a plurality of scanning images;
Sequentially arranging a plurality of scanning images according to the setting sequence of the focus positions, and constructing a three-dimensional space model based on the arranged plurality of scanning images, wherein the three-dimensional space model comprises the light intensity information of the overlay mark to be detected;
Respectively carrying out vertical section processing on the three-dimensional space at two sides of the center line of the overlay mark to be detected, obtaining a first over-focus scanning image and a second over-focus scanning image, and carrying out differential processing on the first over-focus scanning image and the second scanning image to obtain a differential image;
And 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 based on the structural feature information.
According to a second aspect of the present invention, there is provided an overlay error measurement apparatus comprising:
The image scanning module is used for determining the overlay mark to be detected, and scanning the overlay mark to be detected at a plurality of focus positions along the vertical direction to obtain a plurality of scanning images;
The model building module is used for sequentially arranging a plurality of scanning images according to the setting sequence of the focus positions and building a three-dimensional space model based on the arranged scanning images, wherein the three-dimensional space model comprises the light intensity information of the overlay mark to be detected;
The differential processing module is used for respectively carrying out vertical section processing on the three-dimensional space at two sides of the central line of the overlay mark to be detected, obtaining a first overfocal scanning image and a second overfocal scanning image, and carrying out differential processing on the first overfocal scanning image and the second scanning image to obtain a differential image;
And the result output module is used for inputting the differential image into a preset deep learning training model, extracting structural feature information of the overlay mark, and obtaining the overlay error of the overlay mark based on the structural feature information.
According to a third aspect of the present invention, there is provided a storage medium having stored thereon a computer program which, when executed by a processor, implements the above-described overlay error measurement method.
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 on the memory and executable on the processor, the processor implementing the above overlay error measurement method when executing the program.
The invention provides an overlay error measurement method, a device, a storage medium and computer equipment, which are characterized in that an overlay mark to be measured is firstly determined, the overlay mark to be measured is scanned at a plurality of focus positions along a vertical direction to obtain a plurality of scanning images, then the scanning images are sequentially arranged according to the arrangement sequence of the focus positions, a three-dimensional space model is constructed based on the arranged scanning images, wherein the three-dimensional space model comprises light intensity information of the overlay mark to be measured, then vertical section processing is respectively carried out on two sides of a central line of the overlay mark to be measured to obtain a first overfocus scanning image and a second overfocus scanning image, differential processing is carried out on the first overfocus scanning image and the second scanning image to obtain differential images, finally the differential images are input into a preset deep learning training model, structural characteristic information of the overlay mark is extracted, and the overlay error of the overlay mark is obtained based on the structural characteristic information.
The method comprises the steps of carrying out measurement analysis on the overlay mark to be detected by adopting a differential overfocal scanning mode, reducing physical damage or influence caused by the fact that the mark is not required to be contacted, scanning two sides of a center line of the overlay mark to be detected at different focal positions, obtaining information of different visual angles of the overlay mark with symmetrical structure characteristics, enabling the structure of the overlay mark to be more comprehensively recognized, constructing a three-dimensional space model based on arranged scanning images, accurately restoring the shape and structure of the overlay mark, accurately analyzing and evaluating the characteristics and errors of the overlay mark, learning and identifying complex patterns and characteristics by utilizing a deep learning model, accurately analyzing details and differences of the overlay mark, and finally evaluating the quality and accuracy of the overlay mark by calculating the overlay error of the overlay mark. The method utilizes a differential overfocal scanning method to measure the scanning structure information of the overlay marks on two sides relative to the central line, and carries out quantitative analysis and resolution by an image processing and deep learning method, thereby realizing high-precision nondestructive measurement of the overlay error with symmetrical structure characteristics.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
fig. 1 is a schematic flow chart of an overlay error measurement method according to an embodiment of the present invention;
Fig. 2 is a schematic flow chart of an overlay error measurement method according to an embodiment of the present invention;
FIG. 3 shows overlay marks of three symmetrical structural features in an overlay error measurement method according to an embodiment of the present invention;
fig. 4 is a flow chart of an overlay error measurement method according to an embodiment of the present invention;
Fig. 5 shows a schematic structural diagram of an overlay error measurement apparatus according to an embodiment of the present invention;
Fig. 6 shows a schematic structural diagram of an overlay error measurement apparatus according to an embodiment of the present invention;
fig. 7 shows a schematic device structure of a computer device according to an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present application will be described in more detail below 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 embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the application to those skilled in the art.
The embodiment of the application provides an overlay error measurement method, as shown in fig. 1, comprising the following steps:
101. And determining the overlay mark to be detected, and scanning the overlay mark to be detected at a plurality of focus positions along the vertical direction to obtain a plurality of scanning images.
In particular, overlay mark is an important element for semiconductor fabrication, flat panel display fabrication, and other micro-nano processing fields. The overlay mark is a specific pattern or structure, and the main function of the overlay mark is to provide accurate positioning reference, so that the pattern alignment precision between all layers in the micro-nano processing process is higher, and the overlay mark is often placed at the edge of a chip or a wafer or distributed at the key position of the chip surface. The design and configuration of overlay marks may vary from application to application and from manufacturing process to manufacturing process, and common types include cross-shaped, checkered, circular, linear structures, etc., typically consisting of lines or structures with multiple shaped edges to provide more information for accurate positioning and alignment. Overlay marks play an important role in micro-nano manufacturing, they can help manufacturers improve product quality, improve production efficiency, and ensure precise alignment of individual manufacturing steps.
In the embodiment of the application, the image information of multiple visual angles of the overlay mark to be detected can be obtained by scanning at multiple focal positions, more comprehensive and detailed visual data can be obtained, the form and structure of the overlay mark can be analyzed and evaluated more accurately, the scanning images of different focal positions can reveal the details and changes of the overlay mark at different positions, the structure characteristics of the overlay mark can be known and analyzed more comprehensively, the overlay mark to be detected is scanned in a non-contact manner without physical contact or interference in the scanning process, the interference and damage risk of the object to be detected can be reduced, the integrity of the object to be detected is maintained, the scanning is carried out at multiple focal positions in the vertical direction, the high-precision positioning and quantitative analysis of the overlay mark to be detected can be realized, the scanning image of each focal position provides information of a specific position, the judgment of the overlay mark position is more accurate and reliable, the abundant data information can be obtained through the arrangement and analysis of the images, and the accuracy of the structure characteristics of the overlay mark can be improved.
102. And sequentially arranging the plurality of scanning images according to the setting sequence of the plurality of focus positions, and constructing a three-dimensional space model based on the arranged plurality of scanning images, wherein the three-dimensional space model comprises light intensity information of the overlay mark to be detected.
In the embodiment of the application, the accuracy of data can be improved by arranging and constructing the three-dimensional space model based on the scanning images of a plurality of focus positions, the shape and structure of the overlay mark to be detected can be restored more accurately by considering the light intensity information on different focus positions, specifically, the arranged plurality of scanning images provide information from different angles and focus positions, the information can be combined into a comprehensive model by constructing the three-dimensional space model, the comprehensive characteristics of the overlay mark to be detected are included, and further, richer and comprehensive data are obtained, so that better understanding and analysis of the overlay mark are facilitated, wherein the three-dimensional space model can display the spatial relationship of the overlay mark to be detected in a visual mode, and can more accurately represent the shape, size and position relationship of the overlay mark relative to a single two-dimensional image, so that analysis of the structural characteristics and overlay error of the mark is more visual and accurate, and the analysis of the deviation or error of the mark is facilitated to be checked, and the light intensity information is fused into the three-dimensional space model to more accurately extract the structural characteristics of the overlay mark.
103. And respectively carrying out vertical section processing on the three-dimensional space at two sides of the center line of the overlay mark to be detected, obtaining a first overfocal scanning image and a second overfocal scanning image, and carrying out differential processing on the first overfocal scanning image and the second scanning image to obtain a differential image.
In particular, the application is mainly applied to measuring errors of overlay marks of symmetrical structural features, wherein the overlay marks of the symmetrical structural features generally comprise Bar-in-Bar, frame-in-Frame and Box-in-Box, as shown in figure 3, bar-in-Bar is formed by surrounding a small rectangular or linear Bar by a large rectangular or linear Frame and is generally used for positioning and alignment calibration and is widely used in micro-nano processing, frame-in-Frame is formed by surrounding a small Frame by a large Frame, the structure can be used for positioning, alignment calibration, detection precision, shape and the like, and Box-in-Box is an overlay mark and is formed by surrounding a small rectangular Frame by a large rectangular Frame and is commonly used for positioning, alignment calibration and evaluation of shape changes in corrosion or photoetching processes. The above common symmetric overlay mark structure is often applied to the fields of semiconductor chip manufacturing, MEMS device manufacturing, microelectronic packaging, etc. by providing features for positioning, alignment calibration, and evaluation and analysis of overlay errors.
In the embodiment of the application, the overfocal scanning images at two sides of the center line of the overlay mark to be detected are subjected to differential processing, so that the fine difference and the characteristic at two sides of the center line in the symmetrical structure of the overlay mark to be detected can be highlighted, the change and the characteristic of the overlay mark are highlighted, the detail is easier to observe and analyze, the differential image obtained after the differential processing can provide more definite and accurate structural information, particularly comprises the information of the structural change such as the offset, the deformation and the like of the overlay mark, the form, the positioning, the deviation and the like of the overlay mark can be further analyzed, the normal characteristic and the abnormal characteristic can be obviously distinguished through the differential processing, and the detection and the screening accuracy of the overlay mark can be improved.
104. And 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 based on the structural feature information.
In the embodiment of the application, the deep learning model can automatically learn the representation 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 is helpful for more accurately detecting and screening defects, flaws or abnormal conditions in the overlay mark, and the deep learning model can analyze and extract the structural features of the overlay mark by utilizing the global information in the differential image. The deep learning model is trained through a large number of training samples, and can automatically learn and adapt to the structural characteristics of various overlay marks. Regardless of the shape, size or symmetry of the overlay mark, the deep learning model can perform effective feature extraction and overlay error estimation, and compared with the traditional manual analysis and measurement method, a great amount of time and labor cost can be saved. In a word, the differential image is input into a preset deep learning training model, so that the structural feature information of the overlay mark can be efficiently and accurately extracted, and the overlay error estimation can be obtained based on the information.
The invention provides an overlay error measurement method, which comprises the steps of firstly determining an overlay mark to be measured, scanning the overlay mark to be measured at a plurality of focus positions along a vertical direction to obtain a plurality of scanning images, sequentially arranging the plurality of scanning images according to the arrangement sequence of the plurality of focus positions, constructing a three-dimensional space model based on the arranged plurality of scanning images, wherein the three-dimensional space model comprises light intensity information of the overlay mark to be measured, then respectively carrying out vertical section processing on two sides of a central line of the overlay mark to be measured to obtain a first overfocus scanning image and a second overfocus scanning image, carrying out differential processing on the first overfocus scanning image and the second scanning image to obtain a differential image, finally inputting the differential image into a preset deep learning training model, extracting structural characteristic information of the overlay mark, and obtaining an overlay error of the overlay mark based on the structural characteristic information. The method comprises the steps of carrying out measurement analysis on the overlay mark to be detected by adopting a differential overfocal scanning mode, reducing physical damage or influence caused by the fact that the mark is not required to be contacted, scanning two sides of a center line of the overlay mark to be detected at different focal positions, obtaining information of different visual angles of the overlay mark with symmetrical structure characteristics, enabling the structure of the overlay mark to be more comprehensively recognized, constructing a three-dimensional space model based on arranged scanning images, accurately restoring the shape and structure of the overlay mark, accurately analyzing and evaluating the characteristics and errors of the overlay mark, learning and identifying complex patterns and characteristics by utilizing a deep learning model, accurately analyzing details and differences of the overlay mark, and finally evaluating the quality and accuracy of the overlay mark by calculating the overlay error of the overlay mark. The method utilizes a differential overfocal scanning method to measure the scanning structure information of the overlay marks on two sides relative to the central line, and carries out quantitative analysis and resolution by an image processing and deep learning method, thereby realizing high-precision nondestructive measurement of the overlay error with symmetrical structure characteristics.
The embodiment of the application provides an overlay error measurement method, as shown in fig. 2, comprising the following steps:
201. And scanning the overlay mark to be detected at a plurality of focus positions along the vertical direction to obtain a plurality of scanning images.
Specifically, firstly determining an overlay mark to be detected, obtaining the structure type of the overlay mark to be detected, wherein the structure type comprises at least one of Bar-in-Bar, frame-in-Frame and Box-in-Box, then selecting a plurality of different focus positions in the vertical direction according to the structure type of the overlay mark to be detected, determining scanning parameters on each focus position, wherein the scanning parameters comprise scanning speed and scanning range, and finally scanning the overlay mark to be detected on each focus position in the vertical direction according to a preset scanning sequence to obtain a scanning image corresponding to each focus position.
According to the embodiment of the application, a plurality of different focus positions are selected in the vertical direction, scanning images of the overlay mark can be obtained at different depths to obtain more comprehensive structural information, according to the structural type of the overlay mark to be detected, a proper focus position can be selected according to actual requirements, and proper scanning parameters are set at each focus position to fully display the structural characteristics of the overlay mark to be detected, the structural characteristics of the overlay mark can be observed and analyzed from different angles through the scanning images of the focus positions, the shape, the size, the symmetry and the like of the overlay mark can be comprehensively evaluated, and on each focus position, scanning can be performed according to a preset scanning sequence to obtain a high-resolution image, and the details and the morphological characteristics of the overlay mark can be reflected more accurately.
202. A three-dimensional space model is constructed based on the plurality of scanned images arranged in the order of setting the focal positions.
Specifically, firstly, acquiring a setting sequence of a plurality of focus positions, determining an arrangement sequence of the scanning images based on the setting sequence, wherein the arrangement sequence corresponds to a vertical direction of the plurality of focus positions, sequentially arranging the plurality of scanning images according to the arrangement sequence of the scanning images, extracting a pixel value of each scanning image and light intensity information in the pixel value, finally constructing a three-dimensional space frame based on pixel points, embedding the light intensity information into corresponding positions in the three-dimensional space frame, and performing interpolation operation on intervals in the three-dimensional space frame to obtain a three-dimensional space model.
In the embodiment of the application, the three-dimensional space model of the overlay mark is constructed 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, and in the process, the space is filled in the three-dimensional space frame through interpolation operation to obtain complete three-dimensional representation. Specifically, a three-dimensional space model of the overlay mark can be constructed based on pixel values and light intensity information of the multi-focus scanning image, so that a more comprehensive visual angle is provided, the shape, the size, the symmetry and other characteristics of the overlay mark are conveniently analyzed and evaluated, in the three-dimensional space frame, the detailed information of each pixel point can be reserved by embedding the light intensity information into a corresponding position, the internal structure and the surface morphology of the overlay mark can be more accurately reflected, the interval in the three-dimensional space frame can be filled through interpolation operation, a complete three-dimensional space model is obtained, and thus the obtained model can provide more consistent and continuous visual display, so that an observer can more clearly understand and analyze the characteristics of the overlay mark, and finally, the information of the overlay mark can be displayed in a visual mode by constructing the three-dimensional space model, so that an analyst can observe, compare and make a decision. In summary, by constructing the three-dimensional space model of the overlay mark based on the multi-focus scanning image, a more comprehensive and accurate visual expression can be provided, and feature analysis, evaluation and visualization of the overlay mark are facilitated, so that the shape and structure of the overlay mark can be further comprehensively and accurately understood.
203. And carrying out vertical section processing on the three-dimensional space, obtaining two over-focus scanning images, and respectively preprocessing the two over-focus scanning images.
Specifically, a first overfocal scanning image and a second scanning image are firstly obtained for differential processing, denoising, smoothing and contrast enhancement are sequentially carried out on the first overfocal scanning image and the second scanning image respectively, then image processing is carried out on the first overfocal scanning image, the outline of the first overfocal scanning image is extracted, the central line of the first overfocal scanning image is determined based on the outline, straight line fitting is carried out on the central line of the first overfocal scanning image, the central line equation of the first overfocal scanning image is determined, finally image processing is carried out on the second overfocal scanning image, the outline of the second overfocal scanning image is extracted, the central line of the second overfocal scanning image is determined based on the outline, straight line fitting is carried out on the central line of the second overfocal scanning image, and the central line equation of the second overfocal scanning image is determined.
In the embodiment of the application, the edge information of the overlay mark can be highlighted by carrying out differential processing on the two over-focus scanning images, the shape information of the overlay mark can be obtained by carrying out contour extraction, the subsequent analysis and measurement are facilitated, the central line equation of the overlay mark can be obtained by carrying out straight line fitting on the central lines of the first over-focus scanning image and the second over-focus scanning image, the shape and shape characteristics of the overlay mark are further described, the method can be used for positioning the position and the gesture of the overlay mark after the central line equation of the overlay mark is obtained, and other quantitative analysis based on the central line, such as size measurement, shape evaluation and the like, can be realized through an automatic algorithm and an image processing technology, the efficiency and the accuracy are improved, and the dependence on manual operation is reduced.
204. And carrying out differential processing on the two over-focus scanning images to obtain a differential image.
Specifically, converting a first over-focus scanning image into a first gray level image, converting a second over-focus scanning image into a second gray level image, performing differential calculation on the first gray level image and the second gray level image by using a preset differential operator to obtain a differential image, and performing image enhancement processing on the differential image, wherein the image enhancement processing comprises thresholding, gradient enhancement and normalization.
In the embodiment of the application, the scanning image is converted into the gray image, the difference image reflecting the structural characteristics of the overlay mark can be obtained by performing differential calculation, and then the differential image is subjected to thresholding, gradient enhancement, normalization and other treatments through image enhancement processing operation so as to increase the contrast and definition of the image. The application can highlight the structural characteristics of the overlay mark by the differential calculation, can analyze and detect the shape and the edge information of the overlay mark more easily, and can increase the contrast of the differential image by the image enhancement processing such as thresholding, gradient enhancement, normalization and other methods, highlight the details of the overlay mark and 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 effects. In conclusion, the two over-focus scanning images are converted into gray images, differential calculation and enhancement processing are carried out, the structural features of the overlay mark can be highlighted, the contrast and the definition of the images are improved, and further image analysis and quality evaluation are facilitated.
205. And 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 based on the structural feature information.
Specifically, first, the structural feature information of the overlay mark is obtained, the structural feature information is preprocessed, then, a plurality of 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 value of the overlay error feature is subjected to statistical analysis, so that an overlay error measurement result is obtained.
The deep learning training model is a convolutional neural network or a cyclic neural network, wherein the convolutional neural network is a deep learning model widely applied to image processing and computer vision tasks, has the characteristics of a convolutional layer, a pooling layer, an activation function, a full connection layer, a deep structure and the like, and further can gradually extract and combine abstract features of images from low-level to high-level features. The convolutional neural network has remarkable results in the field of computer vision, is widely applied to tasks such as image classification, target detection, image generation, semantic segmentation and the like, can learn characteristic representation and mode of images from a large amount of data through a training process of deep learning, and has strong automatic learning capability. The cyclic neural network is a neural network model capable of processing sequence data, cyclic connection is introduced, information can be transferred and persisted in the network, when the cyclic neural network processes the sequence data, the current input and the previous context information can be associated by introducing a hidden state in the network, the cyclic neural network calculates the hidden state and output at the current moment according to the current input and the hidden state at the previous moment in each time step, and the transmission and updating of the hidden state can enable the cyclic neural network to gradually model and learn the sequence data.
In the embodiment of the application, the structural feature information of the overlay mark is obtained, preprocessed and extracted to obtain a plurality of overlay error features, and then the error value of each overlay error feature is calculated and statistically analyzed to obtain measurement results of the overlay error, such as average error, maximum error, error distribution and the like. The method can intuitively evaluate the quality and the manufacturing precision of the overlay mark by calculating and analyzing the overlay error characteristics, is favorable for finding and correcting the problems in the manufacturing process in time, improves the quality of the overlay mark, enables the overlay error measurement result to have objectivity by measuring and analyzing the structural characteristic information, can perform repeated measurement, reduces the influence of subjective factors, and improves the reliability and the repeatability of the measurement result.
The principle of the method is as shown in fig. 4, firstly, scanning to-be-detected overlay marks at a plurality of focus positions along the vertical direction to obtain a plurality of scanning images, then constructing a three-dimensional space model based on the plurality of scanning images arranged according to the setting sequence of the focus positions, then carrying out vertical section processing on the three-dimensional space to obtain two overfocus scanning images, respectively carrying out pretreatment on the two overfocus scanning images, carrying out differential processing on the two overfocus scanning images to obtain differential images, finally inputting the differential images into a preset deep learning training model, extracting structural characteristic information of the overlay marks, and obtaining the overlay errors of the overlay marks based on the structural characteristic information.
Further, as a specific implementation of the method of fig. 1, an overlay error measurement apparatus is provided in an embodiment of the present application, as shown in fig. 5, where the apparatus includes an image scanning module 301, a model building module 302, a differential processing module 303, and a result output module 304.
The image scanning module 301 is configured to determine an overlay mark to be detected, and scan the overlay mark to be detected at a plurality of focus positions along a vertical direction to obtain a plurality of scanned images;
the model building module 302 is configured to sequentially arrange a plurality of scan images according to a setting sequence of a plurality of focus positions, and build a three-dimensional space model based on the arranged plurality of scan images, where the three-dimensional space model includes light intensity information of an overlay mark to be measured;
the difference processing module 303 is configured to perform vertical profile processing on the three-dimensional space at two sides of a center line of the overlay mark to be detected, obtain a first over-focus scan image and a second over-focus scan image, and perform difference processing on the first over-focus scan image and the second scan image to obtain a difference image;
The result output module 304 is configured to input the differential image into a preset deep learning training model, extract structural feature information of the overlay mark, and obtain an overlay error of the overlay mark based on the structural feature information.
In a specific application scenario, the image scanning module 301 may be specifically configured to determine an overlay mark to be detected, obtain a structure type of the overlay mark to be detected, where the structure type includes at least one of Bar-in-Bar, frame-in-Frame, and Box-in-Box, select a plurality of different focus positions in a vertical direction according to the structure type of the overlay mark to be detected, and determine a scanning parameter at each focus position, where the scanning parameter includes a scanning speed and a scanning range, and scan the overlay mark to be detected at each focus position in a vertical direction according to a preset scanning sequence to obtain a scanning image corresponding to each focus position.
In a specific application scenario, the model building module 302 is specifically configured to obtain a setting sequence of a plurality of focus positions, determine an arrangement sequence of scan images based on the setting sequence, where the arrangement sequence corresponds to a vertical direction of the plurality of focus positions, sequentially arrange the plurality of scan images according to the arrangement sequence of the scan images, extract a pixel value of each scan image and light intensity information in the pixel value, build a three-dimensional space frame based on the pixel points, embed the light intensity information in a corresponding position in the three-dimensional space frame, and perform interpolation operation on intervals in the three-dimensional space frame to obtain the three-dimensional space model.
In a specific application scenario, as shown in fig. 6, the apparatus further includes an image preprocessing module 305, specifically configured to acquire a first over-focus scan image and a second scan image, perform differential processing on the first over-focus scan image and the second scan image, respectively, perform denoising, smoothing, and contrast enhancement on the first over-focus scan image in sequence, perform image processing on the first over-focus scan image, extract a contour of the first over-focus scan image, determine a center line of the first over-focus scan image based on the contour, perform straight line fitting on the center line of the first over-focus scan image, determine a center line equation of the first over-focus scan image, perform image processing on the second over-focus scan image, extract a contour of the second over-focus scan image, determine a center line of the second over-focus scan image based on the contour, and perform straight line fitting on the center line of the second over-focus scan image, and determine the center line equation of the second over-focus scan image.
In a specific application scenario, the differential processing module 303 is specifically configured to convert a first over-focus scan image into a first gray-scale image, convert a second over-focus scan image into a second gray-scale image, perform differential computation on the first gray-scale image and the second gray-scale image by using a preset differential operator to obtain a differential image, and perform image enhancement processing on the differential image, where the image enhancement processing includes thresholding, gradient enhancement and normalization.
In a specific application scenario, the result output module 304 may be further configured to obtain structural feature information of the overlay mark, perform preprocessing on the structural feature information, extract a plurality of overlay error features from the preprocessed structural feature information, calculate an error value of each overlay error feature, and perform statistical analysis on the error value of the overlay error feature to obtain an overlay error measurement result.
It should be noted that, other corresponding descriptions of the functional units related to the overlay error measurement apparatus provided in this embodiment may refer to corresponding descriptions in fig. 1 and fig. 2, and are not described herein again.
Based on the above method as shown in fig. 1, correspondingly, the present embodiment further provides a storage medium, on which a computer program is stored, which when executed by a processor, implements the above overlay error measurement method.
Based on such understanding, the technical solution of the present application may be embodied in the form of a software product, where the software product to be identified may be stored in a non-volatile storage medium (may be a CD-ROM, a usb disk, a mobile hard disk, etc.), and includes several instructions for causing a computer device (may be a personal computer, a server, or a network device, etc.) to execute the method for measuring overlay error in each implementation scenario of the present application.
Based on the method shown in fig. 1 and fig. 2 and the embodiment of the overlay error measurement apparatus shown in fig. 5 and fig. 6, in order to achieve the above object, as shown in fig. 7, the embodiment further provides an overlay error measurement entity device, where the device includes a communication bus, a processor, a memory, a communication interface, and may further include an input/output interface and a display device, where each functional unit may complete communication with each other through the bus. The memory stores a computer program and a processor, which is used for executing the program stored in the memory and executing the overlay error measurement method in the above embodiment.
Optionally, the physical device may further include a user interface, a network interface, a camera, radio Frequency (RF) circuitry, sensors, audio circuitry, WI-FI modules, and the like. The user interface may include a Display screen (Display), an input unit such as a Keyboard (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 (e.g., WI-FI interface), etc.
It will be appreciated by those skilled in the art that the structure of the overlay error measurement entity device provided in this embodiment is not limited to the entity device, and may include more or fewer components, or may be combined with certain components, or may include different arrangements of components.
The storage medium may also include an operating system, a network communication module. The operating system is a program for managing the entity equipment hardware 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 for realizing communication among all components in the storage medium and communication with other hardware and software in the information processing entity equipment.
From the above description of the embodiments, it will be apparent to those skilled in the art that the present application may be implemented by means of software plus necessary general hardware platforms, or may be implemented by hardware. By applying the technical scheme of the application, the overlay mark to be detected is firstly determined, the overlay mark to be detected is scanned at a plurality of focus positions along the vertical direction to obtain a plurality of scanning images, the scanning images are sequentially arranged according to the setting sequence of the focus positions, a three-dimensional space model is constructed based on the arranged scanning images, the three-dimensional space model comprises light intensity information of the overlay mark to be detected, then vertical section processing is respectively carried out on two sides of a central line of the overlay mark to be detected to obtain a first overfocus scanning image and a second overfocus scanning image, differential processing is carried out on the first overfocus scanning image and the second scanning image to obtain differential images, finally the differential images are input into a preset deep learning training model, structural characteristic information of the overlay mark is extracted, and overlay errors of the overlay mark are obtained based on the structural characteristic information. The method comprises the steps of carrying out measurement analysis on the overlay mark to be detected by adopting a differential overfocal scanning mode, reducing physical damage or influence caused by the fact that the mark is not required to be contacted, scanning two sides of a center line of the overlay mark to be detected at different focal positions, obtaining information of different visual angles of the overlay mark with symmetrical structure characteristics, enabling the structure of the overlay mark to be more comprehensively recognized, constructing a three-dimensional space model based on arranged scanning images, accurately restoring the shape and structure of the overlay mark, accurately analyzing and evaluating the characteristics and errors of the overlay mark, learning and identifying complex patterns and characteristics by utilizing a deep learning model, accurately analyzing details and differences of the overlay mark, and finally evaluating the quality and accuracy of the overlay mark by calculating the overlay error of the overlay mark. The method utilizes a differential overfocal scanning method to measure the scanning structure information of the overlay marks on two sides relative to the central line, and carries out quantitative analysis and resolution by an image processing and deep learning method, thereby realizing high-precision nondestructive measurement of the overlay error with symmetrical structure characteristics.
Those skilled in the art will appreciate that the drawing is merely a schematic illustration of a preferred implementation scenario and that the modules or flows in the drawing are not necessarily required to practice the application. Those skilled in the art will appreciate that modules in an apparatus in an implementation scenario may be distributed in an apparatus in an implementation scenario according to an implementation scenario description, or that corresponding changes may be located in one or more apparatuses different from the implementation scenario. The modules of the implementation scenario may be combined into one module, or may be further split into a plurality of sub-modules.
The above-mentioned inventive sequence numbers are merely for description and do not represent advantages or disadvantages of the implementation scenario. The foregoing disclosure is merely illustrative of some embodiments of the application, and the application is not limited thereto, as modifications may be made by those skilled in the art without departing from the scope of the application.
Claims (10)
1. An overlay error measurement method, comprising:
Determining an overlay mark to be detected, and scanning the overlay mark to be detected at a plurality of focus positions along the vertical direction to obtain a plurality of scanning images;
Sequentially arranging a plurality of scanning images according to the setting sequence of the focus positions, and constructing a three-dimensional space model based on the arranged plurality of scanning images, wherein the three-dimensional space model comprises the light intensity information of the overlay mark to be detected;
Respectively carrying out vertical section processing on the three-dimensional space at two sides of the center line of the overlay mark to be detected, obtaining a first over-focus scanning image and a second over-focus scanning image, and carrying out differential processing on the first over-focus scanning image and the second scanning image to obtain a differential image;
And 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 based on the structural feature information.
2. The overlay error measurement method according to claim 1, wherein determining the overlay mark to be measured, scanning the overlay mark to be measured at a plurality of focus positions along a vertical direction, and obtaining a plurality of scanned images, comprises:
Determining an overlay mark to be detected, and obtaining a structure type of the overlay mark to be detected, wherein the structure type comprises at least one of Bar-in-Bar, frame-in-Frame and Box-in-Box;
Selecting a plurality of different focus positions in the vertical direction according to the structure type of the overlay mark to be detected, and determining scanning parameters on each focus position, wherein the scanning parameters comprise scanning speed and scanning range;
And scanning the overlay mark to be detected on each focus position along the vertical direction according to a preset scanning sequence to obtain a scanning image corresponding to each focus position.
3. The overlay error measurement method according to claim 1, wherein the sequentially arranging the plurality of scan images in accordance with the order of arrangement of the plurality of focus positions, and constructing a three-dimensional space model based on the arranged plurality of scan images, comprises:
acquiring a setting sequence of the plurality of focus positions, and determining an arrangement sequence of the scanned images based on the setting sequence, wherein the arrangement sequence corresponds to a vertical direction of the plurality of focus positions;
Sequentially arranging a plurality of scanning images according to the arrangement sequence of the scanning images, and extracting the pixel value of each scanning image and the light intensity information in the pixel value;
and constructing a three-dimensional space frame based on the pixel points, embedding the light intensity information into corresponding positions in the three-dimensional space frame, and performing interpolation operation on intervals in the three-dimensional space frame to obtain a three-dimensional space model.
4. The overlay error measurement method according to claim 1, wherein before the differential processing is performed on the first over-focus scanned image and the second scanned image to obtain a differential image, the method comprises:
Acquiring the first over-focus scanning image and the second scanning image, performing differential processing, and respectively sequentially denoising, smoothing and enhancing contrast on the first over-focus scanning image and the second scanning image;
performing image processing on the first overfocal scanning image, extracting the outline of the first overfocal scanning image, determining the central line of the first overfocal scanning image based on the outline, performing straight line fitting on the central line of the first overfocal scanning image, and determining a central line equation of the first overfocal scanning image;
and performing image processing on the second overfocal scanning image, extracting the outline of the second overfocal scanning image, determining the central line of the second overfocal scanning image based on the outline, performing straight line fitting on the central line of the second overfocal scanning image, and determining a central line equation of the second overfocal scanning image.
5. The overlay error measurement method according to claim 1, wherein the performing differential processing on the first over-focus scan image and the second scan image to obtain a differential image includes:
Converting the first over-focus scanning image into a first gray level image, and converting the second over-focus scanning image into a second gray level image;
And carrying out differential calculation on the first gray level image and the second gray level image by using a preset differential operator to obtain a differential image, and carrying out image enhancement processing on the differential image, wherein the image enhancement processing comprises thresholding, gradient enhancement and normalization.
6. The overlay error measurement method of claim 1, wherein 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, wherein the obtaining the overlay error of the overlay mark based on the structural feature information comprises:
Obtaining the 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 overlay error feature;
And carrying out statistical analysis on the error value of the overlay error characteristic to obtain an overlay error measurement result.
8. An overlay error measurement apparatus, the apparatus comprising:
The image scanning module is used for determining the overlay mark to be detected, and scanning the overlay mark to be detected at a plurality of focus positions along the vertical direction to obtain a plurality of scanning images;
The model building module is used for sequentially arranging a plurality of scanning images according to the setting sequence of the focus positions and building a three-dimensional space model based on the arranged scanning images, wherein the three-dimensional space model comprises the light intensity information of the overlay mark to be detected;
The differential processing module is used for respectively carrying out vertical section processing on the three-dimensional space at two sides of the central line of the overlay mark to be detected, obtaining a first overfocal scanning image and a second overfocal scanning image, and carrying out differential processing on the first overfocal scanning image and the second scanning image to obtain a differential image;
And the result output module is used for inputting the differential image into a preset deep learning training model, extracting structural feature information of the overlay mark, and obtaining the overlay error of the overlay mark based on the structural feature information.
9. A storage medium having stored thereon a computer program, which when executed by a processor, implements the steps of the method of any of claims 1 to 7.
10. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the computer program when executed by the processor implements the steps of the method according to any one of claims 1 to 7.
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