Image quality detection method and device, electronic equipment and storage medium

Abstract

The invention provides an image quality detection method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring edge contour information corresponding to a plurality of target images; determining process change bandwidth information of the plurality of target images based on the edge contour information corresponding to the plurality of target images; according to the process change bandwidth information of the plurality of target images and predetermined reference bandwidth information of a reference image, determining edge position error information between each of the plurality of target images and the reference image; and according to the edge position error information between each of the plurality of target images and the reference image, determining image quality corresponding to each of the plurality of target images. The image quality detection method and device are used for solving the problems that a traditional image quality detection method is low in detection accuracy due to low precision and low in detection efficiency due to large sample demand, and the technical effects of improving image processing precision and reducing the demand for the number of sampling samples to improve the detection efficiency are achieved.

Description

Technical Field

[0001] The present application relates to digital image processing technology, and in particular to an image quality detection method, device, electronic device and storage medium. Background Art

[0002] In the field of semiconductor material preparation, the quality requirements for semiconductor preparation processes are extremely high, especially for advanced process nodes. In order to characterize the process quality, the pattern of the prepared semiconductor material needs to be measured after the key process steps are completed.

[0003] At present, in the field of semiconductor pattern edge quality detection, the conventional SEM pattern quality detection method is: select the measurement area (sampling), set the measurement window, extract the structure edge within the measurement window, measure the geometric dimensions between the edges (interval, width, depth, etc.), and calculate the average measurement parameters.

[0004] It can be seen from the above conventional measurement methods that, on the one hand, a large number of test samples are needed to ensure that the statistical coverage of structural dimensions is comprehensive enough, but the conventional averaging operation overwhelms many abnormal situations in the process, and the statistical processing method of the data will inevitably lead to a reduction in the granularity of the data, thereby being not sensitive to abnormal data, resulting in low accuracy of the obtained statistical characteristic values; on the other hand, since the traditional sampling method needs to traverse a large amount of image data, the loading and visual presentation of high-resolution image data consumes a lot of resources, resulting in slow data processing. Summary of the invention

[0005] The present application provides an image quality detection method, device, electronic device and storage medium to solve the problems of low detection accuracy caused by low precision of traditional image quality detection methods and low detection efficiency caused by large sample demand, so as to achieve the technical effect of improving the resolution of processed images and reducing the demand for the number of sampling samples to improve detection efficiency.

[0006] On the one hand, the present application provides a method for detecting image quality, comprising:

[0007] Obtain edge contour information corresponding to each of multiple target images;

[0008] Determining process variation bandwidth information of the plurality of target images based on edge contour information corresponding to each of the plurality of target images;

[0009] Determine edge position error information between each of the plurality of target images and the reference image according to the process variation bandwidth information of the plurality of target images and the reference bandwidth information of a predetermined reference image;

[0010] The image quality corresponding to each of the plurality of target images is determined according to the edge position error information between each of the plurality of target images and the reference image.

[0011] Optionally, before obtaining edge contour information corresponding to each of the plurality of target images, the method further includes:

[0012] Acquire multiple initial images, wherein the multiple initial images are obtained by scanning the same structure in the same process manufacturing link;

[0013] Performing image denoising processing on the plurality of initial images respectively to obtain a plurality of denoised images;

[0014] Perform image alignment processing on the multiple denoised images to obtain the multiple target images.

[0015] Optionally, edge contour information corresponding to each of the multiple target images is obtained, including:

[0016] An edge extraction algorithm or an edge detection algorithm is used to extract edge contour information corresponding to each of the plurality of target images.

[0017] Optionally, determining the process variation bandwidth information of the plurality of target images based on the edge contour information corresponding to each of the plurality of target images comprises:

[0018] Performing binary conversion processing on the edge contour information corresponding to each of the plurality of target images to obtain binary edge images corresponding to each of the plurality of target images;

[0019] Performing matrix OR operation on the binary edge images corresponding to the plurality of target images to obtain edge band images to be filled;

[0020] Perform gap filling processing on the edge band image to be filled to obtain a complete edge band image;

[0021] Based on the edges extracted from the complete edge band image, process variation bandwidth information of the plurality of target images is determined.

[0022] Optionally, determining the image quality corresponding to each of the plurality of target images according to edge position error information between each of the plurality of target images and the reference image includes:

[0023] According to the edge position error information between each of the plurality of target images and the reference image, statistically analyzing the error distribution of the plurality of edge position error information;

[0024] The error distributions of the plurality of edge position error information are fitted to obtain standard deviations for characterizing the image quality corresponding to each of the plurality of target images.

[0025] On the other hand, the present application provides an image quality detection device, the device comprising:

[0026] An acquisition module, used for acquiring edge contour information corresponding to each of a plurality of target images;

[0027] A first determination module is used to determine the process variation bandwidth information of the plurality of target images based on the edge contour information corresponding to each of the plurality of target images;

[0028] a second determining module, configured to determine edge position error information between each of the plurality of target images and the reference image according to the process variation bandwidth information of the plurality of target images and the reference bandwidth information of a predetermined reference image;

[0029] The third determination module is used to determine the image quality corresponding to each of the plurality of target images according to the edge position error information between each of the plurality of target images and the reference image.

[0030] Optionally, the above device further includes:

[0031] An image acquisition module is used to acquire multiple initial images, wherein the multiple initial images are obtained by scanning the same structure in the same process manufacturing link;

[0032] An alignment processing module, used to perform image denoising processing on the plurality of initial images to obtain a plurality of denoised images;

[0033] The denoising processing module is used to perform image alignment processing on the multiple denoised images to obtain the multiple target images.

[0034] Optionally, the acquisition module includes:

[0035] The extraction unit is used to extract edge contour information corresponding to each of the plurality of target images by using an edge extraction algorithm or an edge detection algorithm.

[0036] Optionally, the first determining module includes:

[0037] Performing binary conversion processing on the edge contour information corresponding to each of the plurality of target images to obtain binary edge images corresponding to each of the plurality of target images;

[0038] Performing matrix OR operation on the binary edge images corresponding to the plurality of target images to obtain edge band images to be filled;

[0039] Perform gap filling processing on the edge band image to be filled to obtain a complete edge band image;

[0040] Based on the edges extracted from the complete edge band image, process variation bandwidth information of the plurality of target images is determined.

[0041] Optionally, the third determining module includes:

[0042] An error statistics unit, used for counting error distribution of a plurality of edge position error information based on edge position error information between each of the plurality of target images and the reference image;

[0043] The fitting unit is used to fit the error distribution of the plurality of edge position error information to obtain a standard deviation for characterizing the image quality corresponding to each of the plurality of target images.

[0044] On the other hand, the present application provides an electronic device, comprising: a processor, and a memory connected to the processor;

[0045] The memory stores computer-executable instructions;

[0046] The processor executes the computer-executable instructions stored in the memory to implement any of the above methods.

[0047] On the other hand, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, they are used to implement any of the above methods.

[0048] On the other hand, the present application provides a computer program product, including a computer program, which implements any of the above methods when executed by a processor.

[0049] The image quality detection method provided by the present application obtains edge contour information corresponding to each of the multiple target images; determines the process variation bandwidth information of the multiple target images based on the edge contour information corresponding to each of the multiple target images; determines the edge position error information between each of the multiple target images and the reference image according to the process variation bandwidth information of the multiple target images and the reference bandwidth information of the predetermined reference image; determines the image quality corresponding to each of the multiple target images according to the edge position error information between each of the multiple target images and the reference image. It is used to solve the problem that the traditional image quality detection method has low precision resulting in low detection accuracy and large sample demand resulting in low detection efficiency, and achieves the technical effect of improving the accuracy of processed images and reducing the demand for the number of sampling samples to improve detection efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0051] Figure 1 A flowchart of a method for detecting image quality provided in an embodiment of the present application;

[0052] Figure 2 A flowchart of an optional image quality detection method provided in an embodiment of the present application;

[0053] Figure 3 A schematic flow chart of an optional method for detecting image quality provided in an embodiment of the present application;

[0054] Figure 4 A flowchart of an optional image quality detection method provided in an embodiment of the present application;

[0055] FIG5(1) is a denoised image obtained by performing image denoising on an optional initial image provided in an embodiment of the present application;

[0056] FIG5(2) is an optional superposition result after edge superposition obtained by performing image contour extraction on a denoised image according to an embodiment of the present application;

[0057] FIG5(3) is a schematic diagram of an optional process variation bandwidth based on edge information provided in an embodiment of the present application:

[0058] FIG5(4) is a schematic diagram of an optional radius change measured from the center of a circle provided in an embodiment of the present application;

[0059] FIG5 (5) is a schematic diagram of an optional layout structure of a measurement line and a reference image starting from the center of a circle provided in an embodiment of the present application;

[0060] FIG5(6) is an optional distribution diagram obtained according to EPE statistics provided in an embodiment of the present application;

[0061] Figure 6 A structural block diagram of an image quality detection device provided in an embodiment of the present application;

[0062] Figure 7 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0063] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0064] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0065] First, the terms involved in this application are explained:

[0066] Scanning electron microscope (SEM): also known as scanning electron microscope, is an observation method between transmission electron microscope and optical microscope. It uses a focused narrow high-energy electron beam to scan the sample, and stimulates various physical information through the interaction between the beam and the material. This information is collected, amplified, and re-imaged to achieve the purpose of characterizing the microscopic morphology of the material.

[0067] Process variation bandwidth (PV Band): refers to the contour simulated by lithography under a given set of {focus point, exposure dose} conditions in the lithography process of integrated circuit manufacturing, that is, the area between the outer contour and the inner contour.

[0068] At present, in the field of semiconductor material preparation, the existing semiconductor material pattern detection is mainly based on SEM scanning electron microscope (SEM) images. SEM images have high resolution characteristics and can provide clear pattern structures. The accuracy of the pattern is directly determined by the edge / contour quality of the pattern. Usually, due to the complexity of the process environment, the stability of the equipment, the differences in materials, etc., the same design structure often cannot obtain completely consistent process results. The edge of the pattern usually varies within a certain range (bandwidth), and the quality (accuracy) of the edge plays a decisive role in the process quality.

[0069] In the semiconductor process, Scanning Electron Microscope (SEM) is widely used to detect the edge quality of SEM patterns due to its high resolution.

[0070] The traditional SEM pattern edge quality detection method currently has the following main problems: 1) large sample demand, that is, it is necessary to summarize historical data for a long time to obtain a more accurate statistical distribution; 2) low sampling resolution, on the basis of large sample demand, it is also necessary to sample and measure specific parts of the SEM pattern to obtain the corresponding sampling data. Affected by the number, position and size of the sampling window, the sampling data is not representative of the pattern structure characteristics, and the window sampling cannot cover all similar structures.

[0071] In view of the above problems of the semiconductor SEM pattern edge quality detection method, the present invention proposes a SEM pattern edge quality detection method, which not only proposes a complete process method for obtaining edge quality characterization, but also provides a method for calculating the process variation bandwidth based on pattern edge information. The proposed method combines edge extraction with process bandwidth calculation to achieve high-precision processing of SEM images, reduce the demand for the number of sampling samples, and improve detection efficiency.

[0072] The image quality detection method provided by the present application is intended to solve the above technical problems of the prior art. The technical solution of the present application and how the technical solution of the present application solves the above technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0073] Figure 1 is a flow chart of an image quality detection method provided in an embodiment of the present application, such as Figure 1 As shown, the image quality detection method includes the following steps:

[0074] S101, obtaining edge contour information corresponding to each of a plurality of target images.

[0075] S102: Determine process variation bandwidth information of the plurality of target images based on edge contour information corresponding to each of the plurality of target images.

[0076] S103, determining edge position error information between each of the plurality of target images and the reference image according to the process variation bandwidth information of the plurality of target images and the predetermined reference bandwidth information of the reference image.

[0077] S104, determining the image quality corresponding to each of the plurality of target images according to the edge position error information between each of the plurality of target images and the reference image.

[0078] Optionally, the above-mentioned image quality detection method can be applied to the field of semiconductor technology, for example, the field of semiconductor material preparation technology. In this technical field, the preparation of semiconductor materials is mainly composed of two basic elements: the preparation system and the monitoring system. The monitoring system is mainly responsible for monitoring and controlling the material preparation process, and plays an important role in improving the preparation process and providing feedback.

[0079] Scanning electron microscope (SEM) is a commonly used semiconductor preparation monitoring equipment. The imaging of semiconductor growth material samples can reflect the microscopic characterization structure of the material. Optionally, the target image in the embodiment of the present application is a scanning electron microscope (SEM) image, for example, it can be obtained by scanning the same conductor material in multiple different process preparation links using a scanning electron microscope during the preparation process of the semiconductor material.

[0080] In the embodiment of the present application, the semiconductor SEM image is further qualitatively and quantitatively analyzed using image processing methods based on the characteristics of the semiconductor microstructure image, so as to determine the process quality corresponding to different process preparation links in the preparation process of the semiconductor material by detecting the image quality corresponding to each of the multiple SEM images.

[0081] In one example, since the same structure may appear repeatedly in a semiconductor process, the same structure may change in manufacturing configuration as the stability of the semiconductor process changes. Optionally, the above-mentioned multiple SEM images are obtained by scanning the same structure of semiconductor material in different semiconductor processes.

[0082] For example, for each type of product that goes online, its process needs to be combined with specific machines, environments, and materials in multiple dimensions, and multiple attempts and explorations are needed to find out the range of variation of each step of the process and the control standards of each step of the manufacturing process, that is, by selecting the target image / structure of the corresponding process for measurement.

[0083] As an optional embodiment, in the embodiment of the present application, by performing edge extraction on multiple target images, the edge / contour of the corresponding pattern is obtained (the same structure of multiple target images is detected), and by superimposing the above multiple contours to form a contour band, that is, the above process variation bandwidth information of the above multiple target images, it can be understood as the image variation range obtained by the corresponding process flow. By fitting / calibrating the image variation range, the benchmark for calculating the measurement parameters is determined, that is, the benchmark bandwidth information of the predetermined benchmark image.

[0084] According to the standard bandwidth information and the process variation bandwidth information of the target images, the edge position error information between each of the target images and the reference image is calculated, that is, the edge position error EPE used to characterize the edge quality. According to the edge position error information between each of the target images and the reference image, the variation statistical parameters of the same measurement parameter are determined, which is usually also used in the card control manufacturing process. For example, the standard deviation that characterizes the edge variation can further determine the image quality corresponding to each of the target images.

[0085] The embodiment of the present invention proposes an efficient SEM pattern edge quality detection method, which adopts a non-machine learning framework, does not require a training data set, and has no model training process. It uses a high-performance edge profile extraction method to obtain the edge transformation range of the pattern, that is, the process variation (PV) bandwidth, and then combines the target fitting / calibration to quickly calculate the EPE. The main features of the present invention include three aspects: 1) The number of SEM image samples required is small, and the edge quality of a specific pattern can be detected; 2) The measurement accuracy is high, reaching the image pixel level, non-sampling measurement, and specific structure measurement to achieve full coverage; 3) The detection speed is fast, and the SEM pattern edge quality detection time consumption can be completed in seconds.

[0086] In an optional embodiment, Figure 2 is a flow chart of an optional image quality detection method provided in an embodiment of the present application, such as Figure 2 As shown, before obtaining edge contour information corresponding to each of the plurality of target images, the method further includes:

[0087] S201, acquiring a plurality of initial images, wherein the plurality of initial images are obtained by scanning the same structure in the same process manufacturing link in multiple times.

[0088] S202, performing image denoising processing on the plurality of initial images respectively to obtain a plurality of denoised images.

[0089] S203, performing image alignment processing on the plurality of denoised images respectively to obtain the plurality of target images.

[0090] In the embodiment of the present application, the target image may be a SEM image obtained after denoising and alignment processing is performed on the initial SEM image.

[0091] In one example, since the same structure will appear repeatedly in the semiconductor process, usually the same structure will change in the manufacturing structure as the stability of the semiconductor process changes. Since the process bandwidth is different for the same structure in different process manufacturing links, and each type of product goes online, its process needs to be combined with specific machines, environments, and materials for multiple dimensions, and multiple attempts and explorations are made to find out the range of changes corresponding to each step of the process manufacturing link and the card control standard corresponding to each step of the process manufacturing link. Therefore, in the embodiment of the present application, the same structure of the semiconductor material can be scanned multiple times in the same semiconductor process manufacturing link to select the initial image corresponding to the process manufacturing link, so as to process the initial image into the corresponding target image for measurement.

[0092] It should be noted that the same process manufacturing link in the embodiments of the present application refers to the same process production step, rather than multiple identical or similar process production steps. The same process manufacturing link is based on the same process menu, so the production process for different semiconductor products is consistent. It is on this basis that the embodiments of the present application discuss the multiple scanning results of the same structure of semiconductor materials in this scenario to determine the image quality of the same structure under the same process production step.

[0093] In the embodiment of the present application, due to semiconductor manufacturing, the same pattern is repeatedly produced. For a certain structure, there will be many repetitions on a wafer and also repetitions on multiple wafers. The overlay image in the embodiment of the present application is the overlap of the same detection structure on different wafers at different exposure square grid shots.

[0094] In order to ensure accurate edge bandwidth based on edge overlay, for the same structure (i.e. the same test structure, used to detect manufacturing process quality), during the measurement process, various reasons such as the stability of the measuring machine for different types of measuring structures will cause pattern offset. The offset will produce position error through edge overlay, and image alignment can be achieved by correlation calculation and other methods; denoising is to optimize image quality and improve edge extraction effects, which can be accomplished using denoising algorithms based on prior models and machine learning.

[0095] Therefore, in the embodiment of the present application, the accuracy of contour superposition position is ensured by performing alignment processing on the initial image, and the accuracy of contour extraction of the target image is ensured by performing denoising processing on the image after the alignment processing.

[0096] As an optional embodiment, obtaining edge contour information corresponding to each of the plurality of target images includes:

[0097] An edge extraction algorithm or an edge detection algorithm is used to extract edge contour information corresponding to each of the plurality of target images.

[0098] For example, edge extraction of SEM images can be performed using any high-performance edge extraction method or traditional operators such as Canny.

[0099] An optional embodiment, Figure 3 is a flow chart of an optional image quality detection method provided in an embodiment of the present application, such as Figure 3 As shown, based on the edge contour information corresponding to each of the plurality of target images, the process variation bandwidth information of the plurality of target images is determined, including:

[0100] S301 , performing binary conversion processing on the edge contour information corresponding to each of the plurality of target images to obtain binary edge images corresponding to each of the plurality of target images.

[0101] S302, performing matrix OR operation on the binary edge images corresponding to the plurality of target images to obtain edge band images to be filled.

[0102] S303, performing gap filling processing on the edge band image to be filled to obtain a complete edge band image.

[0103] S304: Determine process variation bandwidth information of the plurality of target images based on the complete edge band image.

[0104] In the above embodiment of the present application, the edge contour information corresponding to each of the multiple target images is binary-converted to obtain the binary edge images corresponding to each of the multiple target images. Then, the multiple binary edge images are superimposed, for example, by performing a matrix or operation to obtain a SEM edge superposition image.

[0105] Since the SEM edge overlay image is an edge band image to be filled, a graphic morphological closing operation can be used to fill the gaps in the edge band image to be filled to obtain a complete edge band image. Finally, the edge is extracted based on the complete edge band image, that is, the process variation bandwidth information of the above-mentioned target images is determined.

[0106] Since the core of the semiconductor process (usually described by process nodes, for example, some 7nm, 5nm, 4nm, and 3nm process nodes in the semiconductor industry) is lithography, which is exposure, and the resulting pattern (structure) is usually compared with the reference image (design index). To measure and obtain key parameter indicators from the actual SEM image, it is usually necessary to determine the reference bandwidth information (reference points / lines) of the reference image, and then complete the calculation of the key parameters for characterizing the accuracy of the structure. For images that can be calibrated through the design layout, they can be calibrated with the help of design parameters, and for those that cannot be calibrated, they can be obtained through pattern shape fitting (because the semiconductor process flow is very long and the intermediate state structures vary greatly, for some target structures that cannot be obtained through the layout, and because some measurements require an origin, such as the radius of the wafer, the width and position of the semiconductor input gate, etc., can only be obtained through local pattern fitting, or the target is manually determined to complete the measurement.

[0107] In an optional embodiment, based on the edge information (image form) obtained from each SEM image, the binary conversion of the edge image is first completed to obtain a binary image, which is the basis for subsequent graphical operations; secondly, by superimposing the image edges, it is realized through matrix OR operation, that is, all SEM binary edge images are ORed to obtain an "edge band" with partial gaps; then, the gaps in the "edge band" are filled by the closing operation of image morphology to obtain a complete edge band; finally, based on the complete edge band image, the edge of the edge band is extracted, which characterizes the process variation range of the corresponding process step.

[0108] In an optional embodiment, Figure 4 A flowchart of an optional image quality detection method provided in an embodiment of the present application, such as Figure 4 As shown, according to the edge position error information between each of the plurality of target images and the reference image, determining the image quality corresponding to each of the plurality of target images comprises:

[0109] S401 , according to edge position error information between each of the plurality of target images and the reference image, statistically analyzing error distribution of the plurality of edge position error information.

[0110] S402, fitting the error distributions of the plurality of edge position error information to obtain standard deviations for characterizing the image quality corresponding to the plurality of target images.

[0111] Optionally, the edge position error information between each of the above-mentioned multiple target images and the above-mentioned reference image is obtained by taking the absolute value of the difference between the process variation bandwidth and the reference bandwidth of a predetermined reference image.

[0112] In the embodiments of the present application, through calibration processing, the pattern or combined structural parameters that can be obtained from the reference image GDS (SEM pattern design plan, usually provided by the customer before manufacturing) are obtained; Fitting: Since the semiconductor manufacturing process is very long, the geometric structure of the intermediate SEM pattern varies greatly, and some intermediate structures cannot obtain a direct reference from the layout. Relevant measurement structures need to be fitted, such as the center of the wafer, etc., to determine the geometric size or position of the structure.

[0113] Optionally, as an optional embodiment, Figure 5(1) is a denoised image obtained after image denoising of the initial image; Figure 5(2) is a superposition result of the edge (contour) obtained after image contour extraction of the denoised image; Figure 5(3) is a schematic diagram of the process variation bandwidth based on edge information: based on the result of Figure 5(2), the gap filling in the edge band is completed through the close operation, and then a change band of the edge contour (process variation band) is formed; the inner line in Figure 5(3) is the inner edge of the process band formed after the contours of multiple SEM images of the same structure are obtained, and the outer line area is the outer edge; Figure 5(4) is the radius change measured from the center of the circle.

[0114] The left side of Figure 5 (5) is the measurement line starting from the center of the circle. The image radius line segment starting from the center of the circle to each edge is used to measure the range of the process variation band, that is, the process variation range; the circular outline on the right side of Figure 5 (5) is the layout structure of the reference image (target reference structure). Each measurement radius is compared with the prior radius, that is, the radius of the layout structure design, and the edge bandwidth information EPE is calculated, that is, the inner and outer edges. With the inner and outer edges plus the center of the circle, the variation range of the wafer hole structure during the process can be determined. To obtain EPE specifically, it is necessary to start from the center of the circle, draw a radius that intersects the inner and outer edges, and then obtain the radius of the process variation. Then, the radius of the process variation is subtracted from the reference radius to obtain the absolute value to obtain EPE.

[0115] As can be seen from the figure, the radius accuracy of the measurement can be easily achieved at the pixel level. Any abnormal changes in the target pattern will be detected and the corresponding EPE value will be obtained. Figure 5 (6) is a schematic diagram of the error distribution of EPE obtained based on EPE statistics. Usually, the horizontal axis is the error amount (usually pixel is the basic unit of error), and the vertical axis is the number of errors corresponding to the horizontal axis. According to the central limit theorem, the error distribution of EPE shown in Figure 5 (6) conforms to the normal distribution. The error distribution of EPE is fitted to obtain the standard deviation σ that characterizes the edge quality, which can be used to characterize the error range of this process change.

[0116] Compared with the existing edge quality detection technology, the embodiment of the present invention has the following beneficial effects:

[0117] The contour extraction technology in the present invention has the remarkable characteristics of high accuracy and high speed, and is very suitable for pattern edge quality detection in integrated circuit production and manufacturing, and can quickly obtain the process variation range and timely adjust the process control standard.

[0118] On the one hand, no special training data set is required, and no large number of SEM image samples need to be sampled. The method is fast, efficient, widely applicable, and highly practical. The present invention uses the edge information extracted from the SEM image to add the morphological operation of the image to obtain a complete and accurate pattern edge variation range, and obtains EPE by comparing it with the target / objective to complete the characterization of the process quality.

[0119] On the other hand, the method of the present invention can process all target patterns of SEM images by processing the pixel-level information of the pattern, compared with the traditional statistical method, and the pixel-level detection will not miss any abnormal changes in the pattern structure, so the edge quality is more comprehensive and accurate.

[0120] According to one or more embodiments of the present application, an embodiment of a device for detecting image quality is provided. Figure 6 A structural block diagram of an image quality detection device provided in an embodiment of the present application, such as Figure 6 As shown, the above device comprises:

[0121] An acquisition module 601 is used to acquire edge contour information corresponding to each of a plurality of target images;

[0122] A first determination module 602 is used to determine process variation bandwidth information of the plurality of target images based on edge contour information corresponding to each of the plurality of target images;

[0123] A second determination module 603 is used to determine edge position error information between each of the plurality of target images and the reference image according to the process variation bandwidth information of the plurality of target images and the reference bandwidth information of the predetermined reference image;

[0124] The third determination module 604 is used to determine the image quality corresponding to each of the plurality of target images according to the edge position error information between each of the plurality of target images and the reference image.

[0125] According to one or more embodiments of the present application, the above device further includes:

[0126] An image acquisition module is used to acquire multiple initial images, wherein the multiple initial images are obtained by scanning the same structure in the same process manufacturing link;

[0127] An alignment processing module, used to perform image denoising processing on the plurality of initial images to obtain a plurality of denoised images;

[0128] The denoising processing module is used to perform image alignment processing on the multiple denoised images to obtain the multiple target images.

[0129] According to one or more embodiments of the present application, the acquisition module includes:

[0130] The extraction unit is used to extract edge contour information corresponding to each of the plurality of target images by using an edge extraction algorithm or an edge detection algorithm.

[0131] According to one or more embodiments of the present application, the first determining module includes:

[0132] Performing binary conversion processing on the edge contour information corresponding to each of the plurality of target images to obtain binary edge images corresponding to each of the plurality of target images;

[0133] Performing matrix OR operation on the binary edge images corresponding to the plurality of target images to obtain edge band images to be filled;

[0134] Perform gap filling processing on the edge band image to be filled to obtain a complete edge band image;

[0135] Based on the edges extracted from the complete edge band image, process variation bandwidth information of the plurality of target images is determined.

[0136] According to one or more embodiments of the present application, the third determination module includes:

[0137] An error statistics unit, used for counting error distribution of a plurality of edge position error information based on edge position error information between each of the plurality of target images and the reference image;

[0138] The fitting unit is used to fit the error distribution of the plurality of edge position error information to obtain a standard deviation for characterizing the image quality corresponding to each of the plurality of target images.

[0139] In an exemplary embodiment, the present application also provides an electronic device, including: a processor, and a memory connected to the processor;

[0140] The memory stores computer-executable instructions;

[0141] The processor executes the computer-executable instructions stored in the memory to implement any of the above methods.

[0142] In an exemplary embodiment, the embodiment of the present application further provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement any of the above methods.

[0143] In an exemplary embodiment, the present application also provides a computer program product, including a computer program, which implements any of the above methods when executed by a processor.

[0144] In order to implement the above embodiment, the present application embodiment also provides an electronic device. Figure 7 , which shows a schematic diagram of the structure of an electronic device 700 suitable for implementing the embodiment of the present application, and the electronic device 700 may be a terminal device or a server. The terminal device may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, message transceiver devices, game consoles, medical equipment, fitness equipment, personal digital assistants (PDAs), tablet computers (Portable Android Devices, PADs), portable multimedia players (PMPs), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 7 The electronic device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0145] like Figure 7 As shown, the electronic device 700 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 701, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 702 or a program loaded from a storage device 708 to a random access memory (RAM) 703. Various programs and data required for the operation of the electronic device 700 are also stored in the RAM 703. The processing device 701, the ROM 702, and the RAM 703 are connected to each other via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0146] Typically, the following devices may be connected to the I / O interface 705: input devices 706 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; output devices 707 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 708 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 709. The communication devices 709 may allow the electronic device 700 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 7 The electronic device 700 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead.

[0147] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication device 709, or installed from the storage device 708, or installed from the ROM 702. When the computer program is executed by the processing device 701, the above-mentioned functions defined in the method of the embodiment of the present application are executed.

[0148] It should be noted that the computer-readable medium mentioned above in the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution device or device. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution device or device. The program code contained on the computer readable medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0149] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0150] The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device executes the method shown in the above embodiment.

[0151] The computer program code for performing the operation of the present application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on the remote computer, or entirely on the remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., using an Internet service provider to connect through the Internet).

[0152] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the method and computer program product according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the box can also occur in a sequence different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0153] The units involved in the embodiments described in the present application may be implemented by software or hardware. The name of the unit does not limit the unit itself in some cases. For example, the first acquisition unit may also be described as a "unit for acquiring at least two Internet Protocol addresses".

[0154] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0155] In the context of the present application, a machine-readable medium may be a tangible medium that may contain or store a program for use by an instruction execution device or device or used in combination with an instruction execution device or device. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor devices or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0156] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention disclosed herein. The present application is intended to cover any modification, use or adaptation of the present application, which follows the general principles of the present application and includes common knowledge or customary techniques in the art that are not disclosed in the present application. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present application are indicated by the following claims.

[0157] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A method for detecting image quality, characterized in that: The method comprises: Obtain edge contour information corresponding to each of multiple target images; Determining process variation bandwidth information of the plurality of target images based on edge contour information corresponding to each of the plurality of target images; Determine edge position error information between each of the plurality of target images and the reference image according to the process variation bandwidth information of the plurality of target images and the reference bandwidth information of a predetermined reference image; The image quality corresponding to each of the plurality of target images is determined according to the edge position error information between each of the plurality of target images and the reference image.

2. The method according to claim 1, characterized in that Before acquiring edge contour information corresponding to each of the plurality of target images, the method further includes: Acquire multiple initial images, wherein the multiple initial images are obtained by scanning the same structure in the same process manufacturing link; Performing image denoising processing on the plurality of initial images respectively to obtain a plurality of denoised images; Perform image alignment processing on the multiple denoised images respectively to obtain multiple target images.

3. The method according to claim 1, characterized in that Get the edge contour information corresponding to multiple target images, including: An edge extraction algorithm or an edge detection algorithm is used to extract edge contour information corresponding to each of the plurality of target images.

4. The method according to claim 1, characterized in that: Determining process variation bandwidth information of the plurality of target images based on edge contour information corresponding to each of the plurality of target images includes: Performing binary conversion processing on the edge contour information corresponding to each of the plurality of target images to obtain a binary edge image corresponding to each of the plurality of target images; Performing matrix OR operation on the binary edge images corresponding to each of the plurality of target images to obtain an edge band image to be filled; Perform gap filling processing on the edge band image to be filled to obtain a complete edge band image; Based on the edges extracted from the complete edge band image, process variation bandwidth information of a plurality of the target images is determined.

5. The method according to any one of claims 1 to 4, characterized in that: Determining the image quality corresponding to each of the plurality of target images according to edge position error information between each of the plurality of target images and the reference image, comprising: According to the edge position error information between each of the plurality of target images and the reference image, statistically analyzing the error distribution of the plurality of edge position error information; The error distributions of the plurality of edge position error information are fitted to obtain standard deviations for characterizing the image quality corresponding to each of the plurality of target images.

6. An image quality detection device, characterized in that: The device comprises: An acquisition module, used for acquiring edge contour information corresponding to each of a plurality of target images; A first determination module, configured to determine process variation bandwidth information of a plurality of target images based on edge contour information corresponding to each of the plurality of target images; a second determining module, configured to determine edge position error information between each of the plurality of target images and the reference image according to the process variation bandwidth information of the plurality of target images and the reference bandwidth information of a predetermined reference image; The third determination module is used to determine the image quality corresponding to each of the plurality of target images according to the edge position error information between each of the plurality of target images and the reference image.

7. The device according to claim 6, characterized in that The device also includes: An image acquisition module, used to acquire a plurality of initial images, wherein the plurality of initial images are obtained by scanning the same structure in multiple steps during the same process manufacturing step; An alignment processing module, used for performing image denoising processing on the plurality of initial images respectively to obtain a plurality of denoised images; The denoising processing module is used to perform image alignment processing on the multiple denoised images respectively to obtain multiple target images.

8. The device according to claim 6, characterized in that The acquisition module comprises: The extraction unit is used to extract edge contour information corresponding to each of the plurality of target images by using an edge extraction algorithm or an edge detection algorithm.

9. The device according to claim 6, characterized in that The first determining module includes: Performing binary conversion processing on the edge contour information corresponding to each of the plurality of target images to obtain a binary edge image corresponding to each of the plurality of target images; Performing matrix OR operation on the binary edge images corresponding to each of the plurality of target images to obtain an edge band image to be filled; Perform gap filling processing on the edge band image to be filled to obtain a complete edge band image; Based on the edges extracted from the complete edge band image, process variation bandwidth information of a plurality of the target images is determined.

10. The device according to any one of claims 6 to 9, characterized in that The third determining module comprises: an error statistics unit, configured to count error distribution of a plurality of edge position error information based on edge position error information between each of the plurality of target images and the reference image; The fitting unit is used to fit the error distribution of the plurality of edge position error information to obtain a standard deviation for characterizing the image quality corresponding to each of the plurality of target images.

11. An electronic device, characterized in that: include: A processor, and a memory connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 5.

12. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 5 when executed by a processor.

13. A computer program product, characterized in that The invention comprises a computer program, which implements the method according to any one of claims 1 to 5 when being executed by a processor.

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