Measurement method and device and storage medium

Abstract

The embodiment of the invention relates to a measurement method and device and a storage medium. The method provided herein comprises: in an image having a measurement object, determining a reference start point and a reference end point for a first contour segment associated with the measurement object; determining a target path connected between the reference start point and the reference end point based on respective confidence levels of a plurality of pixels located between the reference start point and the reference end point in the image, where a confidence level of a pixel of the plurality of pixels indicates a likelihood of taking the pixel as part of the first contour segment; and determining a measurement result for the measurement object by measuring a second contour segment associated with the measurement object and a first contour segment generated based on the target path.

Description

TECHNICAL FIELD

[0001] Embodiments of the present application generally relate to the field of computer technology, and more particularly, to a metrology method, device and storage medium. BACKGROUND

[0002] In a semiconductor production process, the size of each component has a decisive influence on the structural function and electrical performance of the device, so it is essential to accurately obtain the size information of these components. However, as the structure of semiconductor components becomes increasingly complex, the metrology object presents a variety of morphological features. Therefore, how to design a versatile and widely adaptable metrology method has become a technical problem to be solved. SUMMARY

[0003] In a first aspect of the present application, a metrology method is provided. The method comprises: determining, in an image having a metrology object, a reference starting point and a reference ending point for a first contour segment associated with the metrology object; determining a target path connecting between the reference starting point and the reference ending point based on respective confidences of a plurality of pixels in the image located between the reference starting point and the reference ending point, wherein the confidence of a pixel in the plurality of pixels indicates a likelihood of taking the pixel as a part of the first contour segment; and determining a metrology result for the metrology object by performing metrology on a second contour segment associated with the metrology object and a first contour segment generated based on the target path.

[0004] In a second aspect of the present application, an electronic device is provided. The device comprises at least one processor; and at least one memory coupled to the at least one processor and storing instructions for execution by the at least one processor. The instructions, when executed by the at least one processor, cause the device to perform the method of the first aspect.

[0005] In a third aspect of the present application, a computer-readable storage medium is provided. The computer-readable storage medium has stored thereon computer-executable instructions that are executable by a processor to implement the method of the first aspect.

[0006] In a fourth aspect of the present application, a computer program product is provided. The computer program product comprises computer-executable instructions that, when executed by a processor, implement the method according to the first aspect of the present application.

[0007] It should be understood that the content described in this section is not intended to limit the key features or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become apparent through the following description. BRIEF DESCRIPTION OF DRAWINGS

[0008] The above-described and other features, advantages, and aspects of embodiments of the present application will become more apparent as various embodiments of the present application are described in conjunction with the following detailed description. In the detailed description, reference will be made to the following drawings, in which: Figure 1 A schematic diagram illustrating an example environment in accordance with some embodiments of the present application is shown; Figure 2 A flowchart illustrating an example process of a metrology method in accordance with some embodiments of the present application is shown; Figure 3 A schematic diagram illustrating a first profile segment of a metrology object in accordance with some embodiments of the present application is shown; Figure 4 A schematic diagram illustrating an example of a plurality of groups of pixels in accordance with embodiments of the present application is shown; Figure 5 A schematic diagram illustrating an example of a plurality of groups of candidate pixels in accordance with embodiments of the present application is shown; and Figure 6 A block diagram of an electronic device in which one or more embodiments of the present application can be implemented is shown. DETAILED DESCRIPTION

[0009] Embodiments of the present application will be described below in greater detail with reference to the accompanying drawings. While certain embodiments of the present application are shown in the drawings, it is understood that the present application can be embodied in various forms and should not be interpreted as being limited to the embodiments set forth herein; rather, these embodiments are provided so that the present application will be more thoroughly and completely understood. It is understood that the drawings of the present application and the embodiments thereof are for illustrative purposes only and are not intended to limit the scope of the present application.

[0010] It is noted that the headings provided herein are not limitations of the various embodiments described herein. The various embodiments are described throughout this document and can be included under any heading. Additionally, embodiments described in any heading can be combined with any other embodiment described in the same heading and / or a different heading in any manner.

[0011] In the description of embodiments of the present application, the term "includes" and its variants are to be read as open-ended terms that mean "includes, but is not limited to." The term "based on" is to be read as "based, at least in part, on." The term "one embodiment" or "an embodiment" are to be read as "at least one embodiment." The term "some embodiments" is to be read as "at least some embodiments." Other explicit and implicit definitions can be found throughout this document. The terms "first," "second," etc. can refer to different or the same objects. Other explicit and implicit definitions can be found throughout this document.

[0012] The embodiments of the present application can involve data of users, acquisition and / or use of data, etc. These aspects are in compliance with the corresponding laws and regulations and relevant provisions. In the embodiments of the present application, all data collection, acquisition, processing, processing, forwarding, use, etc. are performed on the premise that the user is aware of and confirms. Accordingly, in the implementation of the embodiments of the present application, the type, use range, use scenario, etc. of the data or information that can be involved should be informed to the user and the authorization of the user should be obtained through appropriate means according to the relevant laws and regulations. The specific informing and / or authorization manner can vary according to the actual situation and application scenario, and the scope of the present application is not limited in this respect.

[0013] As briefly described above, in a semiconductor production process, the size of each component has a decisive influence on the structural function and electrical performance of a device. In one solution, a scanning electron microscope (SEM) can be used to image a measurement object, and then an image processing and recognition technique can be used to extract size parameters. This measurement solution supports various measurement methods, such as a contour segment-based measurement method, a threshold-based measurement method based on an image grayscale curve, and a linear measurement method, etc. However, these measurement methods have obvious limitations in practical applications. For example, different measurement methods need to be switched for different pattern measurement objects, and the user needs to manually adjust the algorithm parameters to make a complex measurement recipe. For another example, the above measurement methods are mainly suitable for regular geometric shapes (such as straight lines, circles, etc.), and it is difficult to effectively process more complex curves or irregular structures.

[0014] Embodiments of the present application provide a measurement solution. According to the solution, in an image having a measurement object, a reference starting point and a reference ending point for a first contour segment (e.g., a contour segment associated with the measurement object) are determined. Based on respective reliabilities of a plurality of pixels in the image located between the reference starting point and the reference ending point, a target path connecting between the reference starting point and the reference ending point is determined. The reliability of a pixel indicates a possibility of taking the pixel as a part of the first contour segment. By measuring a second contour segment (e.g., another contour segment associated with the measurement object) and the first contour segment generated based on the target path, a measurement result for the measurement object is determined.

[0015] According to the measurement scheme provided in the embodiments of the present application, after the reference start point and the reference end point used for indicating the start and end positions of the contour segment are determined, a path connecting the two points is constructed and a corresponding contour segment is generated based on the reliability of each pixel between the two points (i.e. the possibility that the pixel belongs to the real contour segment of the measurement object). In this way, the user only needs to specify the reference start point and the reference end point, and the system can automatically complete the generation of the contour segment and the related measurement. This process does not need to preset specific geometric models (such as straight lines, circles, etc.) or rely on manual repeated parameter adjustment, and thus can be adapted to various measurement objects with various trends and morphologies (such as circular arcs, complex curves, and even irregular structures, etc.), and has high versatility.

[0016] The example implementation of the object rendering scheme of the embodiments of the present application will be described below with reference to the accompanying drawings. Figure 1 A schematic diagram of an example environment 100 according to some embodiments of the present application is shown. Referring to Figure 1 , the example environment 100 includes an electronic device 110, an image 120, and a measurement result 130. It should be understood that the structure and function of each element in the environment 100 are described below only for exemplary purposes, without implying any limitation on the scope of the present application.

[0017] The measurement scheme of the embodiments of the present application can be applied to any appropriate measurement scenario. As an example, in the field of semiconductors, the electronic device 110 can utilize the measurement scheme of the embodiments of the present application to implement the measurement of any physical structure on a chip 140 that can be measured. Such physical structures include but are not limited to: semiconductor devices, interconnection lines, or composite structures composed of semiconductor devices and interconnection lines, etc. The electronic device 110 can utilize the measurement scheme of the embodiments of the present application to implement the measurement of the critical dimension (CD) of these physical structures. In this case, the image 120 can be an image obtained after these physical structures are captured by a SEM or other appropriate acquisition device. The image has a measurement object, which can include the corresponding pattern of these physical structures in the image 120. The measurement result 130 can include the specific value of the critical dimension. The measurement result 130 can be used for design verification or fault analysis of the chip 140, etc.

[0018] It should be noted that the above description of the measurement object is only an example and does not constitute a limitation on the embodiments of the present application. Depending on the application scenario, the specific reference of the measurement object can also be different.

[0019] In the example environment 100, the electronic device 110 can be any type of device with computing capability, such as a terminal device or a server device. In some embodiments, the terminal device can be any type of mobile terminal, fixed terminal, or portable terminal including a mobile handset, a desktop computer, a laptop computer, a notebook computer, a netbook computer, a tablet computer, a media computer, a multimedia tablet, a personal communication system (PCS) device, a personal navigation device, a personal digital assistant (PDA), an audio / video player, a digital camera / camcorder, a positioning device, a television receiver, a radio broadcast receiver, an electronic book device, a game device, or any combination thereof, including accessories and peripherals of such devices or any combination thereof.

[0020] In some embodiments, the server device can be a standalone physical server, a server cluster or a distributed system composed of multiple physical servers, a cloud server providing cloud service, cloud database, cloud computing, cloud function, cloud storage, network service, cloud communication, middleware service, domain name service, security service, content distribution network, and basic cloud computing services such as big data and artificial intelligence platform, etc. The server device can include a computing system / server, such as a mainframe, an edge computing node, a computing device in a cloud environment, etc.

[0021] Figure 2 A flowchart of an example process 200 of a metrology method according to some embodiments of the present application is shown. Figure 3 A schematic diagram of a first profile segment 300 of a metrology object according to some embodiments of the present application is shown. The first profile segment 300 is associated with a metrology object, such as the metrology object 100. The first profile segment 300 can be a segment of a profile of the metrology object 100. Figure 1 and Figure 3 The process 200 is described. The process 200 can be implemented at the electronic device 110, for example.

[0022] Referring to Figure 2 At block 210, the electronic device 110 determines, in the image 120 having the metrology object, a reference start point 301 and a reference end point 302 for a profile segment (e.g., the first profile segment 300) associated with the metrology object. The profile segment associated with the metrology object can refer to a profile segment that can indicate a segment of a physical edge of the metrology object. For example, the profile segment can indicate a segment of a side of the metrology object, etc. The profile segment can be used for metrology of line width and / or pitch, etc.

[0023] The reference start point 301 and the reference end point 302 can be reference points manually specified by a user or automatically identified by the electronic device 110 to indicate two end points of the first contour segment 300. The reference start point 301 and the reference end point 302 can be located in a Region of Interest (ROI) selected by the user. The reference start point 301 and the reference end point 302 are at least used as constraints in space for a subsequent path search, such as a process of generating a target path 303 or a candidate path as mentioned below. In some embodiments, the electronic device 110 can display the image 120 through a visualization interface. The user can select two positions on the image 120 as the reference start point 301 and the reference end point 302 through a mouse, a stylus, or a coordinate input, etc. Alternatively, in some embodiments, the electronic device 110 can automatically identify possible end points of the contour segment by using an image 120 processing algorithm to obtain the reference start point 301 and the reference end point 302.

[0024] In some embodiments, the positions of the reference start point 301 and the reference end point 302 can be represented by two-dimensional coordinates. The reference start point 301 and the reference end point 302 have the same coordinate in one dimension. For example, the position of the reference start point 301 can be represented as (xl, yO), and the position of the reference end point 302 can be represented as (x2, yO), where xl < x2.

[0025] In block 220, the electronic device 110 determines a target path 303 connecting between the reference start point 301 and the reference end point 302 based on respective confidences of a plurality of pixels in the image 120 located between the reference start point 301 and the reference end point 302. The respective confidence of the plurality of pixels can refer to the respective confidence of each pixel. The confidence of a pixel indicates the likelihood of the pixel being part of the first contour segment 300. The higher the confidence, the more likely the pixel belongs to the first contour segment 300.

[0026] In some embodiments, the confidence of a pixel can be determined based on the brightness of the pixel by using a plurality of strategies. For example, the electronic device 110 can evaluate the brightness of the pixel based on a plurality of strategies respectively to obtain a plurality of evaluation results indicating whether the pixel is a high brightness pixel. The electronic device 110 can determine the confidence of the pixel based on at least the number of evaluation results in which the pixel is evaluated as a high brightness pixel. In some embodiments, the evaluation result can be a binary evaluation result, for example, "1" indicating that the pixel is a high brightness pixel, and "0" indicating that the pixel is a non-high brightness pixel. By using a plurality of strategies to determine the confidence of each pixel, it is helpful to accurately calculate the confidence of each pixel and thus provide better data support for the subsequent path search.

[0027] The high-brightness pixel can refer to a pixel in the image 120 whose brightness meets the brightness requirement. For example, if the brightness of a certain pixel is higher than the average brightness of the region in which it is located, the electronic device 110 can determine that the pixel is a high-brightness pixel, and the like. The above-mentioned multiple strategies can include any appropriate image analysis algorithm or rule, and the like. In some embodiments, the multiple strategies can include a first strategy and multiple second strategies. The first strategy can indicate a specific algorithm for determining whether a certain pixel is a "high-brightness pixel", and the multiple second strategies can indicate multiple sampling scales for determining whether a certain pixel is a "high-brightness pixel".

[0028] In some embodiments, the first strategy can indicate that a Gaussian threshold and / or mean threshold algorithm is used. The multiple second strategies can indicate that multiple scale windows are used. In some embodiments, the scale of the window can be set to 9, 11, 13, 15, or other appropriate values. As an example, assuming that the first strategy indicates that a mean threshold is used, and the second strategy indicates that a window with a scale of 9 is used. Then, the electronic device 110 can collect the brightness of a total of 9 pixels including the current pixel and its nearby pixels to calculate a brightness threshold (for example, the mean brightness of the 9 pixels). Such a brightness threshold can also be referred to as an adaptive threshold of each window. If the brightness of the current pixel is greater than the brightness threshold, the electronic device 110 can determine that the pixel is a "high-brightness pixel". If the brightness of the current pixel is less than or equal to the brightness threshold, the electronic device 110 can determine that the pixel is a "non-high-brightness pixel". It should be noted that in addition to the above-mentioned scales, the window can also use other scales. This can be determined according to the size of the contour segment, the size of the image 120, and the size of the ROI, and the like, and embodiments of the present application do not limit this.

[0029] In some embodiments, for each pixel, the electronic device 110 can record the number of evaluation results that evaluate the pixel as a high-brightness pixel. For example, the electronic device 110 can assign an index M to each pixel. Each time it is determined that there is an evaluation result indicating that the pixel is a high-brightness pixel, the index M is updated. The updated index M' = M + 1. The target path 303 can refer to a trajectory located between the reference starting point 301 and the reference ending point 302, which is composed of a series of consecutive pixels. In some embodiments, the target path 303 can be composed of a series of consecutive pixels with high confidence. The target path 303 can be regarded as a guide path, which serves as the basis for generating the first contour segment 300.

[0030] In some embodiments, the electronic device 110 can directly generate the target path 303 based on the trustworthiness of the pixels, using a path search algorithm or other appropriate algorithm. Alternatively, in some embodiments, the electronic device 110 can generate a plurality of candidate paths based on the trustworthiness of the pixels, using a path search algorithm or other appropriate algorithm. On this basis, the electronic device 110 can select a better path from the candidate paths as the target path 303 based on rules related to the properties of the pixels passed by the candidate paths.

[0031] For the convenience of discussion, the path search process in the embodiments of the present application is described below with the target path 303 as an example. It should be understood that the path search process introduced below can also be used to generate the candidate paths described above according to actual needs.

[0032] In some embodiments, the target path 303 can include a plurality of passing points 305. The plurality of passing points 305 can be determined based on multiple iterations. For example, taking a passing point 305 (e.g., a first passing point) in the plurality of passing points 305 as an example, the electronic device 110 can obtain a passing point 305 (e.g., a second passing point) determined in the last iteration. The electronic device 110 can determine a first reference range for generating the first passing point based on the second passing point. The electronic device 110 can determine a target pixel (e.g., a target pixel) in at least one first pixel (e.g., a first pixel) based on the respective trustworthiness of the at least one first pixel located in the first reference range in the image 120. The electronic device 110 can determine the first passing point based on the second target pixel.

[0033] The passing point 305 can refer to an intermediate node on the target path 303, used to guide the generation of the target path 303. The plurality of passing points 305 are sequentially connected and collectively constitute the target path 303 from the reference starting point 301 to the reference ending point 302. The first reference range can refer to an area defined with the second passing point 305 as a reference point, used to limit the search space of the first passing point. The shape and size of the first reference range can be determined according to actual needs, and the embodiments of the present application do not limit this. The second target pixel can refer to a pixel in the first reference range that is most likely to belong to the first contour segment 300 according to the trustworthiness of the pixels.

[0034] In some embodiments, the electronic device 110 may start from a reference starting point 301 and proceed sequentially forward. In each iteration, a search area (e.g., a first reference range) is defined based on a path point 305 (e.g., a second path point) determined in the previous iteration. Within this search area, the electronic device 110 may select the optimal pixel as the second target pixel based on the pixel's confidence level. The electronic device 110 may define the location of the second target pixel, or a certain range including that location, as the location of the first path point. This process is repeated until the path approaches the reference ending point 302, thereby obtaining the target path 303.

[0035] It should be noted that, in addition to the methods described above, the electronic device 110 may also use other iterative methods to generate the target path 303. For example, in some embodiments, the electronic device 110 may start from the reference starting point 301 and the reference ending point 302 respectively, and simultaneously search for path points 305 until they converge, thereby obtaining the target path 303. As another example, the electronic device 110 may determine a reference midpoint between the reference starting point 301 and the reference ending point 302. The electronic device 110 may start from the reference midpoint and search for path points 305 towards the reference starting point 301 and the reference ending point 302 respectively, thereby obtaining the target path 303, and so on.

[0036] By iteratively determining multiple waypoints 305 on the target path 303, the search space can be decomposed into multiple segments, avoiding large-scale path searches. Furthermore, this method is applicable to paths with complex orientations (such as curves), and therefore can be flexibly applied to the determination of various contour segments.

[0037] In some embodiments, the electronic device 110 may adjust the confidence level of at least one first pixel based on the confidence level of the pixel corresponding to the currently generated waypoint 305. For example, the confidence level of a first pixel with better continuity to the currently generated waypoint 305 may be increased. Conversely, the confidence level of a first pixel with poor continuity to the currently generated waypoint 305 may be decreased. And so on. The electronic device 110 may determine a second target pixel among at least one first pixel based on the adjusted confidence level of the at least one first pixel.

[0038] As an example, electronic device 110 can use formula (1) to determine the adjusted confidence level. : (1) in Indicates in step, The location of the second waypoint on the path. representing a position of a first waypoint, In the step of, the total confidence of the path is i.e., according to the path from the position walks to the position pixels on the path passed by the path The sum of the pixels. By taking into account the information of the generated waypoint 305, the continuity and smoothness of the target path 303 can be more effectively maintained.

[0039] In some embodiments, the electronic device 110 can pre-process (e.g., screen and / or merge, etc.) the at least one first pixel to obtain pre-processed at least one first pixel. The electronic device 110 can determine the second target pixel among the pre-processed at least one first pixel based on the respective confidence of the pre-processed at least one first pixel. In this way, the set of first pixels can be optimized, thereby reducing computational redundancy and improving processing efficiency.

[0040] In some embodiments, the pre-processing can include at least one of a first pre-processing, a second pre-processing, and a third pre-processing. In the first pre-processing, the electronic device 110 can remove a portion of the at least one first pixel based on the respective confidence of the at least one first pixel. In the second pre-processing, the electronic device 110 can remove a portion of the at least one first pixel based on a predetermined ratio. In the third pre-processing, the electronic device 110 can merge adjacent pixels among the at least one first pixel.

[0041] For example, the electronic device 110 can select to discard a portion of pixels having a lower confidence in T% of the maximum confidence, T being a real number. For another example, the electronic device 110 can discard a portion of pixels in a predetermined ratio For another example, the electronic device 110 can group two adjacent pixels as a pixel cluster. The electronic device 110 can take the pixel cluster as the first pixel. In this case, the formula (1) can be further expressed as formula (2): ; (2) wherein is the number of pixels in the pixel cluster, the pixel belongs to the pixel cluster , represents the number of evaluation results indicating that the pixel is a highlight pixel.

[0042] The electronic device 110 can use the first preprocessing, the second preprocessing, and the third preprocessing alone or in combination. In addition, the preprocessing can include more manners according to actual needs, as long as the first pixel can be optimized. Embodiments of the present application are not listed one by one here. In this way, a lightweight, flexible, and efficient pixel optimization mechanism is provided, which effectively improves the efficiency of target pixel selection.

[0043] In some embodiments, the electronic device 110 can determine whether the respective confidence of the at least one first pixel meets a second confidence requirement. If the respective confidence of one or more first pixels meets the second confidence requirement, the electronic device 110 can determine the second target pixel based on the above-mentioned manner. If the respective confidence of the at least one first pixel does not meet the second confidence requirement, the electronic device 110 can determine a second reference range for generating the first via point based on the second via point. The second reference range is larger than the first reference range. The electronic device 110 can determine the second target pixel from at least one second pixel in the image 120 located in the second reference range based on the respective confidence of the at least one second pixel.

[0044] The second confidence requirement is used to determine whether the confidence of the first pixel is too low. If none of the first pixels meets the second confidence requirement, the range expansion mechanism is triggered to generate the second reference range. Compared with the first reference range, the second reference range can search for the second target pixel in a larger area. Its spatial range can cover the first reference range and extend outward. The second pixel can refer to the pixel located in the second reference range. The process of the electronic device 110 determining the second target pixel from the at least one second pixel can refer to the process of determining the second target pixel from the at least one first pixel, which is not described here.

[0045] As an example, it is assumed that the position of the second via point is the position of the first pixel , and the respective confidence of each first pixel is 0. Then, the electronic device 110 can determine that the confidence of each first pixel does not meet the second confidence requirement. In this case, the electronic device 110 can search for the second target pixel in the second reference range, thereby skipping the at least one first pixel. It should be noted that the second confidence requirement can also be introduced in the process of searching for the second target pixel from the second reference range. For example, if the confidence of each second pixel does not meet the second confidence requirement, the search range can be further expanded. For example, the electronic device 110 can search for the second target pixel in a larger third reference range, thereby skipping the at least one second pixel.

[0046] After determining the second target pixel, the electronic device 110 can determine a location where the second target pixel is located or a certain range including the location as a first passing point. The electronic device 110 can connect the first passing point and the second passing point, and set a confidence level (e.g., an adjusted confidence level) of a pixel (e.g., the first pixel) on the connection path to 0 to prevent affecting the calculation of the subsequent passing point 305. By introducing a confidence level driven adaptive search range expansion mechanism, the risk of path breaking or deviation can be reduced.

[0047] The above describes the generation process of the target path 303 (or the candidate path). As mentioned above, for the generated multiple candidate paths, the electronic device 110 can select a better path as the target path 303 from the candidate paths based on a rule (hereinafter also referred to as a predetermined rule) related to the attributes of the pixels passed by the multiple candidate paths. For example, the predetermined rule can be related to at least one of the confidence level and the brightness of the pixels. For example, the predetermined rule can include a requirement related to the confidence level of the pixels and a requirement related to the brightness (hereinafter also referred to as a confidence level requirement and a brightness requirement), etc. In addition, according to actual needs, the predetermined rule can also include more requirements. For example, the predetermined rule can also include a requirement related to the direction of the candidate path, etc. This can be determined according to actual needs, and embodiments of the present application will not be listed one by one here. For the convenience of discussion, the following takes the example of the predetermined rule including the confidence level requirement and the brightness requirement to describe the example implementation of selecting the target path 303 from the multiple candidate paths.

[0048] In some embodiments, the electronic device 110 can determine the pixels passed by each candidate path in the image 120 in the multiple candidate paths to obtain multiple groups of pixels respectively corresponding to the multiple candidate paths. For example, the multiple groups of pixels correspond to the multiple candidate paths one by one. In other words, each group of pixels includes a set of pixels passed by the corresponding candidate path. The electronic device 110 can determine the target path 303 from the multiple candidate paths based on the respective confidence levels and the respective brightnesses of the pixels in the multiple groups of pixels. The respective brightness of the multiple pixels can refer to the brightness of each of the multiple pixels. In some embodiments, the electronic device 110 can count the pixels in each group of pixels that meet the confidence level requirement and / or the brightness requirement based on the respective confidence levels and the respective brightnesses of the pixels in the multiple groups of pixels. The electronic device 110 can determine the target path 303 from the multiple candidate paths based on the counting result. For example, the electronic device can select the target path 303 from the multiple candidate paths based on the number of pixels that meet the confidence level requirement and / or the brightness requirement. By introducing the confidence level and the brightness of the pixels for comprehensive selection, it is beneficial to select the target path 303 closer to the direction of the real contour segment.

[0049] Figure 4 ​A schematic diagram of an example 400 of multiple groups of pixels is shown according to embodiments of the present application. In the example 400, multiple groups of pixels 401-1, 401-2 and 401-3 are shown. It is noted that the number of groups of pixels shown in the example 400 is merely illustrative and is not a limitation of embodiments of the present application.

[0050] In some embodiments, the electronic device 110 can determine, in the multiple groups of pixels, multiple target pixels (e.g., first target pixels) that satisfy a trustworthiness requirement (e.g., a first trustworthiness requirement) and a brightness requirement based on respective trustworthiness and respective brightness of each pixel 402 in the multiple groups of pixels. The electronic device 110 can select the target path 303 from the multiple candidate paths based on a number of the first target pixels that each candidate path passes through in the multiple candidate paths.

[0051] The first trustworthiness requirement can include any suitable filtering condition for trustworthiness. As an example, in the multiple groups of pixels, the electronic device 110 can determine a pixel 402 as satisfying the first trustworthiness requirement if the trustworthiness of the pixel 402 is greater than a threshold trustworthiness. The brightness requirement can include any suitable filtering condition for brightness. As an example, in the multiple groups of pixels, the electronic device 110 can determine a pixel 402 in a row or a column of pixels 402 with the greatest brightness as satisfying the brightness requirement.

[0052] In some embodiments, the electronic device 110 can determine the target path 303 as the candidate path that passes through the greatest number of the first target pixels. Alternatively, in some embodiments, the electronic device 110 can calculate a ratio of the number of the first target pixels that a candidate path passes through to all pixels 402 that the candidate path passes through. The electronic device 110 can determine the target path 303 as the candidate path with the highest ratio. In this way, it can avoid misjudgment of a single indicator (e.g., a noise point with high trustworthiness but overexposure, or a pixel 402 with suitable brightness but located in a background region), and make the path selection closer to the actual situation.

[0053] In some embodiments, for each candidate path in the multiple candidate paths, the electronic device 110 can determine, in a group of pixels corresponding to the candidate path, a candidate pixel that satisfies the first trustworthiness requirement based on respective trustworthiness of each pixel 402 in the group of pixels. In this way, multiple groups of candidate pixels respectively corresponding to the multiple candidate paths can be obtained. Figure 5A schematic diagram of an example 500 of multiple groups of candidate pixels according to embodiments of the present application is shown. In the example 500, multiple groups of candidate pixels 501-1, 501-2, and 501-3 are shown. The multiple groups of candidate pixels correspond to the multiple candidate paths one-to-one. The electronic device 110 can determine multiple first target pixels 502 in the multiple groups of candidate pixels by comparing the brightness of the multiple groups of candidate pixels. This process is equivalent to "filtering the reliable pixels by path first, and then comparing the brightness across paths to determine the first target pixels 502". In this way, the reliability filtering can be limited within the group of pixels corresponding to each candidate path, while the brightness comparison is performed across paths. In this way, both the local and global characteristics of the pixels 402 can be taken into account, so that the determined first target pixels 502 are more representative.

[0054] In some embodiments, the electronic device 110 can determine an average reliability of a group of pixels based on the respective reliabilities of the pixels 402 in the group of pixels. The electronic device 110 can determine a pixel 402 in the group of pixels as a candidate pixel that satisfies the first reliability requirement if the reliability of the pixel 402 is greater than the average reliability. The electronic device 110 can determine a pixel 402 in the group of pixels as a pixel 402 that does not satisfy the first reliability requirement if the reliability of the pixel 402 is less than or equal to the average reliability. In this way, the reliability distribution characteristics of different groups of pixels can be automatically adapted to. Using the average within the group as a threshold, the rationality of the candidate pixel filtering can be improved. Even if the absolute reliability of the entire group of pixels is low (e.g., due to poor imaging conditions), as long as there are relatively more reliable pixels 402, they can still be identified as candidate pixels.

[0055] In some embodiments, the electronic device 110 can calculate the average reliability of a group of pixels by formula (3) : ; (3) If the indicator M of a pixel 402 in the group of pixels is greater than the average reliability , the pixel 402 can be determined as a candidate pixel (which can also be referred to as a strong boundary pixel). If the indicator M of the pixel 402 is less than or equal to the average reliability , the pixel 402 can be determined as a non-candidate pixel (which can also be referred to as a weak boundary pixel).

[0056] In some embodiments, the candidate pixels in each of the groups of candidate pixels are arranged along a first direction X. The electronic device 110 can extract the candidate pixels from the groups of candidate pixels respectively along a second direction Y to obtain the candidate pixel sets 503. The second direction Y intersects the first direction X. As an example, the first direction X can be a horizontal direction, and the second direction Y can be a vertical direction, etc. In this case, the above process can be regarded as the electronic device 110 extracting the candidate pixel sets 503 from the groups of candidate pixels in “column” units. For example, the electronic device 110 can traverse all the candidate pixels 402 in each column of pixels between the position and the position and determine the candidate pixel with the highest brightness in this column as one of the first target pixels 502. In this way, the electronic device 110 can obtain a plurality of candidate pixel sets 503 respectively corresponding to a plurality of columns of pixels.

[0057] For each candidate pixel set 503, the electronic device 110 can determine the candidate pixel with the highest brightness in the candidate pixel set 503 as one of the first target pixels 502. In this way, the electronic device 110 can determine one first target pixel 502 from each candidate pixel set 503, and thus obtain the plurality of first target pixels 502. In some embodiments, in the process of extracting the candidate pixel sets 503, the electronic device 110 can align the groups of pixels, so as to ensure that the pixels 402 in each obtained candidate pixel set 503 come from the same column.

[0058] It should be noted that the above content about the first direction X and the second direction Y is only an example. According to actual needs, the first direction X and the second direction Y can also be other directions. For example, the first direction X can be a vertical direction, and the second direction Y can be a horizontal direction, etc. In this case, the above process can be regarded as the electronic device 110 extracting the candidate pixel sets 503 from the groups of candidate pixels in “row” units. Similarly, the above process can also be regarded as the electronic device 110 extracting the candidate pixel sets 503 from the groups of candidate pixels in “diagonal” units, etc.

[0059] In the image 120 (in particular, the SEM image), due to factors such as secondary electron emission, the real profile segment of the measurement object often appears as a high-brightness line. By further screening the high-brightness pixels in the high-confidence pixels in the above manner, the first target pixels 502 are consistent with the physical imaging mechanism, which helps to accurately capture the first target pixels 502.

[0060] Referring back to Figure 2 , in block 230, the electronic device 110 determines a measurement result for the measurement object by measuring the second profile segment associated with the measurement object and the first profile segment 300 generated based on the target path 303.

[0061] For the scenario where the width of the contour segment is negligible, the electronic device 110 can directly take the target path 303 as the first contour segment 300. For the scenario where the width of the contour segment is not negligible, the electronic device 110 can employ a static or dynamic scheme to expand the boundary (e.g., the predicted boundary 304) of the first contour segment 300 in the vicinity of the target path 303, so as to generate the first contour segment 300 with a certain width. For example, in the static scheme, the electronic device 110 can expand the predicted boundary 304 symmetrically or asymmetrically in the vicinity of the target path 303 (e.g., left and / or right and / or top and / or bottom of the target path 303) based on a pre-set geometric parameter (e.g., a predetermined width) with the target path 303 as the reference (e.g., the center line), so as to generate the first contour segment 300 with a fixed width. For example, in the dynamic scheme, the electronic device 110 can determine the predicted boundary 304 of the first contour segment 300 in the image 120 (e.g., left and / or right and / or top and / or bottom of the target path 303) based on the brightness of the pixels 402 in the image 120 in the vicinity of the target path 303, so as to obtain the first contour segment 300. In this way, the electronic device 110 can flexibly select the generation strategy of the first contour segment 300 according to the application scenario, which can quickly output the result under the simplified condition, or fully utilize the details of the image 120 to dynamically adjust the boundary under the complex or high-precision requirement, so as to improve the fitting degree of the first contour segment 300 to the actual physical structure and the measurement reliability.

[0062] For the convenience of discussion, the following describes an example implementation of generating the first contour segment 300 by taking the dynamic scheme as an example. The pixels 402 in the vicinity of the target path 303 can refer to the set of pixels 402 in the image 120 located on one side or both sides of the target path 303 and within a predetermined region. In some embodiments, the predetermined region can extend in a specific direction (e.g., the normal direction of the target path 303 as a whole or the normal direction of the target path 303 locally), for defining the space for searching the predicted boundary 304. The predicted boundary 304 of the first contour segment 300 can refer to the outermost side of the first contour segment 300. For example, assuming that the first contour segment 300 extends along the horizontal direction, the predicted boundary 304 of the first contour segment 300 can include the upper side and the lower side of the first contour segment 300, and the like.

[0063] In some embodiments, the electronic device 110 can find a position in the vicinity of the target path 303 that is most likely to belong to the predicted boundary 304 of the first contour segment 300 based on the brightness of the pixels 402 in the vicinity of the target path 303. In turn, the electronic device 110 can take the portion of the image 120 at the position as the predicted boundary 304 of the first contour segment 300. In some embodiments, the electronic device 110 can determine the position by using a brightness curve or other suitable manners. For convenience of discussion, the process is described below by taking the brightness curve as an example.

[0064] In some embodiments, the target path 303 can include a plurality of waypoints 305. For at least one waypoint 305 (e.g., the third waypoint) of the plurality of waypoints 305, the electronic device 110 can determine a brightness curve of the third waypoint. The brightness curve of the third waypoint indicates the brightness of the pixels 402 in the vicinity of the third waypoint. The electronic device 110 can determine the predicted boundary 304 of the first contour segment 300 based on the brightness curve of each waypoint 305 of the plurality of waypoints 305.

[0065] The brightness curve can refer to a sequence or function of a plurality of pixel brightness values sampled in a third direction in a vicinity of the waypoint 305 (e.g., the third waypoint). The brightness curve can reflect the brightness variation trend of the pixels 402 in the vicinity of the third waypoint. As an example, the third direction can be the normal direction of the target path 303 in a local portion (e.g., a local portion including the third waypoint). For example, if the target path 303 extends in a horizontal direction, the third direction can be a vertical direction passing through the third waypoint. By analogy.

[0066] In some embodiments, the electronic device 110 can collect the brightness of the nearby pixels in the third direction with the third waypoint as a reference point, thereby forming a brightness curve. The electronic device 110 can determine a position (e.g., a point of maximum brightness gradient, a point of brightness-dark transition, or a local extreme point, etc.) corresponding to a feature of the predicted boundary 304 on the brightness curve, and determine the portion of the image 120 corresponding to the position as a part of the predicted boundary 304.

[0067] It should be noted that the above description of the third direction is only an example. According to actual needs, the third direction can also be other directions. Alternatively, the electronic device 110 can further extract a brightness curve in a fourth direction. The fourth direction intersects the third direction. The electronic device 110 can determine the brightness curve of the third waypoint based on the brightness curves extracted in the third direction and the fourth direction.

[0068] By analyzing the brightness curve at each passing point 305 to locate the predicted boundary 304 of the first contour segment 300, high-precision boundary detection can be achieved. Moreover, each passing point 305 in the target path 303 can be processed independently, thus being able to flexibly cope with possible local topography changes (such as corners, etc.) of the first contour segment 300.

[0069] In some embodiments, the electronic device 110 can adjust the brightness curve of the third passing point based on the brightness curve of the third passing point and the brightness curves of the neighboring passing points of the third passing point in the target path 303, to obtain an adjusted brightness curve of the third passing point. The electronic device 110 can determine the predicted boundary 304 of the first contour segment 300 based on the adjusted brightness curve of each passing point 305 in the plurality of passing points 305.

[0070] The neighboring passing points of the third passing point can refer to the passing points 305 directly connected and / or indirectly connected to the third passing point in the target path 303. These passing points 305 are spatially adjacent to the third passing point and jointly reflect the local trend of the target path 303. The adjusted brightness curve refers to a new curve obtained by optimizing or correcting the brightness curve of the third passing point in combination with the brightness curve information of its neighboring passing points. In some embodiments, the electronic device 110 can fuse or weightedly fuse the brightness curve of the third passing point with the curves of the neighboring passing points to obtain the adjusted brightness curve.

[0071] By introducing the neighboring passing points of the third passing point, a more comprehensive brightness curve that better fits the third passing point can be generated. Based on such a brightness curve, it is helpful to more accurately determine the predicted boundary 304 of the first contour segment 300.

[0072] In some embodiments, the electronic device 110 can determine a position corresponding to a reference value of the brightness curve of the third passing point in the vicinity of the third passing point to obtain a target position. The electronic device 110 can take the part of the image 120 located at the target position as part of the predicted boundary 304 of the first contour segment 300.

[0073] The reference value can refer to a brightness reference for locating the target position on the brightness curve of the third passing point. The electronic device 110 can determine the reference value based on the extreme value or mean value, etc. of the brightness curve. The electronic device 110 can map the reference value to the spatial coordinates corresponding thereto in the image 120, thereby obtaining the target position. The electronic device 110 can take the single pixel 402 or multiple pixels 402 in the image 120 located at the target position as part of the predicted boundary 304 of the first contour segment 300. In this way, the predicted boundary 304 close to the actual situation can be accurately determined without complex optimization or iteration.

[0074] In some embodiments, the reference value can be determined based on an average of a maximum value and a plurality of minimum values in the luminance curve of the pass point 305 (e.g., the third pass point). The maximum value can refer to a luminance value corresponding to a sampling point with the highest luminance in the luminance curve of the third pass point. The plurality of minimum values can refer to a number of local luminance minima (or valleys) on the luminance curve. After determining the maximum luminance value from the luminance curve, the electronic device 110 can determine a plurality of minimum values on both sides of the maximum luminance value. The electronic device 110 can calculate the reference value based on an average of the maximum value and the plurality of minimum values by any appropriate manner (e.g., difference, weighted average, or median). In this way, the reference value can be constructed in combination with the peaks and valleys of the luminance curve. Thus, the rationality of the reference value can be improved. It should be noted that the number and size of the minimum values can be determined according to the actual situation of the luminance curve, and the embodiments of the present application do not limit this.

[0075] In some embodiments, the electronic device 110 can determine the measurement result 130 for the measurement object by measuring the distance or angle between the second profile segment and the first profile segment 300. The second profile segment can refer to another profile segment associated with the measurement object and different from the first profile segment 300. For example, the first profile segment 300 and the second profile segment can be the left (or right) side edges of the same interconnection line. For another example, the first profile segment 300 can be the left side edge (or the upper side edge) of one interconnection line, and the second profile segment can be the right side edge (or the lower side edge) of another interconnection line, and so on. The distance between the first profile segment 300 and the second profile segment can refer to the geometric distance between them in the image 120, used to represent the physical dimension (e.g., line width, gap, etc.) of the measurement object.

[0076] In some embodiments, the electronic device 110 can generate the second profile segment in a similar manner as generating the first profile segment 300. The embodiments of the present application do not repeat here. In this way, the user only needs to give four reference points (i.e., the reference start point 301 and the reference end point 302 indicating the start and end positions of the first profile segment 300, and the reference start point and the reference end point indicating the start and end positions of the second profile segment), and two profile segments can be automatically generated, so that the measurement for the two profile segments can be performed.

[0077] In some embodiments, the first profile segment 300 and the second profile segment can be regarded as two point sets (e.g., point sets composed of pass points 305). In this case, for each point in the first profile segment 300, there is a corresponding point in the second profile segment. Such two points can also be referred to as a “point pair”. The electronic device 110 can determine the distance between the first profile segment 300 and the second profile segment by calculating the distance between a plurality of point pairs.

[0078] In some embodiments, if the structure of the measurement object does not have connectivity (e.g., is broken) or has multiple connectivity paths (e.g., a "T" shaped structure), the electronic device 110 can split the structure of the measurement object into multiple sub-structures. In some embodiments, the electronic device 110 can perform the above-described process to determine the profile segments and obtain the measurement results 130 for each sub-structure. The electronic device 110 can then aggregate the obtained measurement results 130 to obtain the final measurement results 130.

[0079] In some embodiments, the electronic device 110 can determine the distance between two profile segments in any suitable manner. For example, the electronic device 110 can determine a measurement direction, such as the X direction, the Y direction, or a 45 degree direction, etc. The electronic device 110 can measure the maximum distance, the minimum distance, and / or the average distance between pairs of midpoints of the two profile segments, etc. For example, assuming the unit vector of the measurement direction is r, the electronic device 110 can iterate through a point a of the first profile segment 300 and solve for t such that a point b of the second profile segment satisfies b = [a + t x r], where [] denotes the rounding of the pixel number. The electronic device 110 can iterate through t from the point a until the end of the second profile segment to find the pairs of points between the two profile segments. For each pair of points, the electronic device 110 can calculate the Euclidean distance d. The electronic device 110 can determine the distance between the two profile segments based on the maximum, the minimum, or the average of the Euclidean distances d of the pairs of points.

[0080] As can be clearly understood from various embodiments of the present application, the embodiments of the present application provide a solution that is convenient for user customization and applicable to SEM measurement of CD. The solution of the embodiments of the present application solves the problem of identification of complex profile segments. The distance measurement method between profile segments of the embodiments of the present application is flexible and easy to use, and can meet various measurement requirements.

[0081] Figure 6 A block diagram of an electronic device 600 in which one or more embodiments of the application can be implemented is shown. The electronic device 600 may, for example, be used to implement the electronic device 110 as shown in Figure 1 It should be understood that the electronic device 600 shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments described herein. Figure 6 The electronic device 600 shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments described herein.

[0082] Reference is made to Figure 6The electronic device 600 is in the form of a general electronic device. Components of the electronic device 600 can include, but are not limited to, one or more processors 610, a memory 620, a storage device 630, one or more communication units 640, one or more input devices 650, and one or more output devices 660. The processor 610 can be a real or virtual processor and is capable of performing various processing according to programs stored in the memory 620. In a multi-processor system, multiple processors perform computer-executable instructions in parallel to improve parallel processing capability of the electronic device 600.

[0083] The electronic device 600 typically includes a plurality of computer storage media. Such media can be any available media that is accessible by the electronic device 600 and includes both volatile and non-volatile media, removable and non-removable media. The memory 620 can be a volatile memory (e.g., registers, cache, random access memory (RAM)), a non-volatile memory (e.g., read-only memory (ROM), electrically erasable programmable read only memory (EEPROM), flash memory), or some combination thereof. The storage device 630 can be a removable or non-removable media and can include machine-readable media such as a flash drive, a magnetic disk drive, or any other media that can be used to store information and / or data and that can be accessed by the electronic device 600.

[0084] The electronic device 600 can further include additional removable / non-removable, volatile / non-volatile storage media. Although not shown in Figure 6 disk drives for reading from or writing to a removable, non- volatile magnetic disk (e.g., a "floppy disk"), and an optical disk drive for reading from or writing to a removable, non-volatile optical disk (e.g., a CD-ROM). In these instances, each drive can be connected to the bus (not shown) by one or more data media interfaces. The memory 620 can include a computer program product 625 having one or more program modules configured to carry out the various methods or actions of the various embodiments of the present application.

[0085] The communication unit 640 enables communication with other electronic devices over communication media. Additionally, functionality of the components of the electronic device 600 can be implemented in a single computing cluster or a plurality of computer machines that are capable of communicating over a communication connection. As such, the electronic device 600 can operate in a networked environment using logical connections to one or more other servers, network personal computers (PCs), or another network nodes in a distributed computing environment.

[0086] The input device 650 can be one or more input devices such as a mouse, a keyboard, a trackball, etc. The output device 660 can be one or more output devices such as a display, a speaker, a printer, etc. The electronic device 600 can also communicate with one or more external devices (not shown) such as a storage device, a display device, etc., one or more devices that enable a user to interact with the electronic device 600, or any devices (e.g., a network card, a modem, etc.) that enable the electronic device 600 to communicate with one or more other electronic devices, as desired via the communication unit 640. Such communication can be carried out via an input / output (I / O) interface (not shown).

[0087] According to an example implementation of the present application, a computer readable storage medium is provided having computer executable instructions stored thereon, where the computer executable instructions are executed by a processor to implement the method described above. According to an example implementation of the present application, a computer program product is also provided that is tangibly stored on a non-transitory computer readable medium and includes computer executable instructions, where the computer executable instructions are executed by a processor to implement the method described above.

[0088] The computer readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0089] These computer readable program instructions can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks. These computer readable program instructions can also be stored in a computer readable storage medium that can include a non-transitory computer readable storage medium. The instructions stored on the computer readable storage medium can be used to program a computer, a programmable data processing apparatus, and / or other devices to produce a manufactured product, such that the instructions which run on the computer, the programmable data processing apparatus, and / or other devices implement the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0090] The computer readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0091] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction, which contains one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0092] The various implementations of this application have been described above. The foregoing description is exemplary and not exhaustive, nor is it limited to the disclosed implementations. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described implementations. The terminology used herein is intended to best explain the principles, practical applications, or improvements to technology in the market, or to enable others skilled in the art to understand the various implementations of this application.

Claims

1. A measurement method, characterized in that, The method includes: In an image containing a measurement object, a reference start point and a reference end point are determined for a first contour segment associated with the measurement object; Based on the corresponding confidence levels of multiple pixels in the image located between the reference start point and the reference end point, a target path connecting the reference start point and the reference end point is determined, wherein the confidence level of a pixel among the multiple pixels indicates the probability that the pixel is part of the first contour segment; and The measurement results for the measurement object are determined by measuring a second contour segment associated with the measurement object and a first contour segment generated based on the target path.

2. The method according to claim 1, characterized in that, Determining the target path connecting the reference starting point and the reference ending point includes: Based on the corresponding confidence levels of the plurality of pixels, multiple candidate paths connecting the reference starting point and the reference ending point are determined; and The target path is determined from the plurality of candidate paths based on rules relating to the attributes of the pixels traversed by the plurality of candidate paths.

3. The method according to claim 2, characterized in that, Determining the target path from the plurality of candidate paths includes: Based on the pixels traversed by each of the multiple candidate paths in the image, multiple sets of pixels corresponding to the multiple candidate paths are determined; and The target path is determined from the multiple candidate paths based on the corresponding confidence level and brightness of each pixel in the multiple groups of pixels.

4. The method according to claim 3, characterized in that, Determining the target path from the plurality of candidate paths includes: Based on the corresponding confidence level and brightness of each pixel in the plurality of pixel groups, a plurality of first target pixels that satisfy the first confidence level requirement and the first brightness requirement are determined from the plurality of pixel groups; and The target path is selected from the multiple candidate paths based on the number of first target pixels traversed by each candidate path.

5. The method according to claim 4, characterized in that, Among the plurality of pixels, the plurality of first target pixels that satisfy the first confidence requirement and the brightness requirement are determined to include: For each of the plurality of candidate paths, based on the corresponding confidence level of each pixel in a set of pixels corresponding to the candidate path, candidate pixels that satisfy the first confidence level requirement are determined from the set of pixels, so as to obtain multiple sets of candidate pixels corresponding to the plurality of candidate paths respectively; and The plurality of first target pixels are determined from the plurality of candidate pixels by comparing the brightness of the plurality of candidate pixels.

6. The method according to claim 5, characterized in that, Among the set of pixels, candidate pixels that satisfy the first confidence requirement are identified as follows: Based on the corresponding confidence level of each pixel in the set of pixels, determine the average confidence level of the set of pixels; and Pixels in the set of pixels whose confidence level is greater than the average confidence level are identified as candidate pixels that meet the first confidence level requirement.

7. The method according to claim 5, characterized in that, The candidate pixels in each of the multiple groups of candidate pixels are arranged along a first direction, and determining the plurality of first target pixels from the multiple groups of candidate pixels includes: In a second direction, candidate pixels are extracted from the plurality of candidate pixel groups to obtain a candidate pixel set, wherein the second direction intersects the first direction; and The candidate pixel with the highest brightness in the candidate pixel set is determined as one of the plurality of first target pixels.

8. The method according to claim 1, characterized in that, The confidence level of a pixel among the plurality of pixels is generated in the following manner: The brightness of the pixel is evaluated based on multiple strategies to obtain multiple evaluation results indicating whether the pixel is a high-brightness pixel; as well as The confidence level of a pixel is determined based on at least the number of evaluation results in which the pixel is evaluated as a high-brightness pixel.

9. The method according to claim 1, characterized in that, The target path includes multiple waypoints, which are determined based on multiple iterations. The first waypoint among these multiple waypoints is determined in the following way: Obtain the path point determined in the previous iteration to obtain the second path point; Based on the second waypoint, a first reference range for generating the first waypoint is determined; Based on the corresponding confidence level of at least one first pixel in the image that is located within the first reference range, a second target pixel is determined among the at least one first pixel; as well as The first waypoint is determined based on the second target pixel.

10. The method according to claim 9, characterized in that, Determining the second target pixel in the at least one first pixel includes: In response to the fact that the corresponding confidence levels of at least one first pixel do not meet the second confidence level requirement, a second reference range for generating the first path point is determined based on the second path point, the second reference range being larger than the first reference range; and The second target pixel is determined from at least one second pixel in the image that is located within the second reference range based on the corresponding confidence level of that second pixel.

11. The method according to claim 8, characterized in that, Determining the second target pixel in the at least one first pixel includes: Based on the confidence level of the pixels corresponding to the currently generated waypoints, the confidence level of the at least one first pixel is adjusted; and The second target pixel is determined from the at least one first pixel based on the adjusted corresponding confidence level of the at least one first pixel.

12. The method according to claim 8, characterized in that, Determining the second target pixel in the at least one first pixel includes: The at least one first pixel is preprocessed to obtain the at least one preprocessed first pixel; and The second target pixel is determined from the preprocessed at least one first pixel based on the corresponding confidence level of the preprocessed at least one first pixel.

13. The method according to claim 12, characterized in that, The preprocessing includes at least one of the following: Based on the corresponding confidence level of the at least one first pixel, a portion of the at least one first pixel is removed. Based on a predetermined ratio, a portion of the at least one first pixel is removed, and Merge adjacent pixels in at least one first pixel.

14. The method according to claim 1, characterized in that, The target path includes multiple waypoints, and the first contour segment is generated in the following manner: For the third path point among the plurality of path points, a brightness curve for the third path point is determined, wherein the brightness curve for the third path point indicates the brightness of the pixels located near the third path point; as well as Based on the brightness curves of each of the multiple waypoints, the prediction boundary of the first contour segment is determined to obtain the first contour segment.

15. The method according to claim 14, characterized in that, Determining the prediction boundary of the first contour segment includes: Based on the brightness curve of the third waypoint and the brightness curves of adjacent waypoints in the target path, the brightness curve of the third waypoint is adjusted to obtain the adjusted brightness curve of the third waypoint; and The predicted boundary of the first contour segment is determined based on the adjusted brightness curve of each of the plurality of waypoints.

16. The method according to claim 14, characterized in that, Determining the prediction boundary of the first contour segment includes: Near the third path point, determine the position corresponding to the reference value of the brightness curve of the third path point to obtain the target position; and The portion of the image located at the target position is used as part of the prediction boundary.

17. The method according to claim 16, characterized in that, The reference value is determined based on the average of the maximum value and multiple minimum values ​​in the brightness curve of the third path point.

18. An electronic device, characterized in that, include: At least one processor; as well as At least one memory coupled to the at least one processor and storing instructions for execution by the at least one processor, the instructions causing the electronic device to perform the method according to any one of claims 1 to 17 when executed by the at least one processor.

19. A computer-readable storage medium having computer-executable instructions stored thereon, characterized in that, The computer-executable instructions can be executed by a processor to implement the method according to any one of claims 1 to 17.

20. A computer program product comprising computer-executable instructions, characterized in that, The computer-executable instructions, when executed by a processor, implement the method according to any one of claims 1 to 17.

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