Critical dimension measurement method and device, related equipment and computer program product
By acquiring edge region images from high-magnification lenses and combining them with grayscale information and relative offset, the problem of limited field of view of high-magnification lenses was solved, and high-precision measurement of key dimensions was achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SUZHOU MEGAROBO TECH CO LTD
- Filing Date
- 2025-12-24
- Publication Date
- 2026-04-17
AI Technical Summary
In existing technologies, the limited field of view of high-magnification lenses makes it impossible to accurately measure large critical dimensions, which affects the measurement accuracy of semiconductor manufacturing processes.
By using a high-magnification lens to acquire images of the two edge regions of the object under test, and combining the relative offset, the grayscale information of the edge regions is analyzed to determine the target measurement size.
Without changing the lens, the accuracy and efficiency of key dimension measurement were improved, the field of view limitation was broken, and high-precision CD measurement was achieved.
Smart Images
Figure CN121876807A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of semiconductor manufacturing process technology, and more specifically, to a method, apparatus, related equipment, and computer program product for measuring key dimensions. Background Technology
[0002] Critical Dimension (CD) refers to a special line pattern designed to evaluate and control the pattern processing accuracy in integrated circuit photomask manufacturing and photolithography processes. It reflects the width of characteristic lines in the integrated circuit. By measuring the CD, detailed information about the pattern dimensions after processes such as photolithography and etching can be obtained. This helps engineers to promptly identify and correct deviations in the production process, thereby reducing the generation of defective products, ensuring product performance meets design requirements, and improving production yield.
[0003] Optical imaging is typically used for CD (Discrete Cosmetic) measurement of samples. Specifically, a high-resolution visual image of the sample surface is acquired through a lens, capturing minute features and details. CD measurement is then performed through image processing. However, with advancements in semiconductor manufacturing processes, device sizes are shrinking, necessitating increasingly higher lens magnification to ensure measurement accuracy. However, this increased magnification limits the lens's field of view. When measuring larger targets, the lens's field of view cannot encompass the entire target, hindering accurate CD measurement.
[0004] Therefore, there is an urgent need for a critical dimension (CD) measurement method that can overcome the limitations of field of view and achieve accurate CD measurement while ensuring high precision. Summary of the Invention
[0005] In view of the above problems, this application is made to provide a critical dimension measurement method, related equipment, and computer program product to achieve accurate CD measurement. The specific solution is as follows:
[0006] In a first aspect, this application provides a method for measuring critical dimensions, including:
[0007] Two edge regions are identified to define the target measurement size of the object under test. Images of each edge region are acquired through a lens to obtain a first field-of-view image and a second field-of-view image, as well as the relative offset between the lens and the object under test when acquiring the two edge regions.
[0008] Based on the grayscale information of each pixel in the edge region of the first field of view image, a first edge point is determined, and based on the grayscale information of each pixel in the edge region of the second field of view image, a second edge point is determined.
[0009] The target measurement size of the object to be measured is determined based on the first edge point, the second edge point, and the relative offset.
[0010] In one possible design, in another implementation of the first aspect of the embodiments of this application, the process of determining the first edge point based on the grayscale information of each pixel in the edge region of the first field-view image includes:
[0011] Based on the box of interest in the preset first field of view image template, the first key measurement region in the first field of view image is located. The box of interest is a geometric box used to locate and define the edge region of the target measurement size.
[0012] Based on the grayscale information of each pixel within the first key measurement area, the first edge point of the first key measurement area is determined.
[0013] In one possible design, in another implementation of the first aspect of the present application, the box of interest in the first field of view image template includes: a template positioning frame for locating the edge region in the first field of view image template for defining the target measurement size of the sample to be measured, and a first template measurement frame for locating the key measurement region within the edge region;
[0014] The process of locating the first key measurement region in the first field of view image based on the preset bounding box of interest in the first field of view image template includes:
[0015] Match the first field-of-view image with the image within the template positioning box range in the first field-of-view image template, and locate the target positioning box in the first field-of-view image;
[0016] From the target positioning frame, locate the initial first measurement frame that has a target relative positional relationship with it, wherein the target relative positional relationship is the relative positional relationship between the template positioning frame and the first template measurement frame;
[0017] Based on the positional relationship between the edge region of the object under test and the target positioning box in the first field of view image, and the positional relationship between the edge region of the sample under test and the template positioning box in the first field of view image template, a first offset of the edge region of the object under test relative to the edge region of the sample under test is determined.
[0018] In the first field-of-view image, along the direction of the edge region of the object to be measured, the position of the initial first measurement box is adjusted according to the first offset to obtain the target first measurement box;
[0019] The region within the first measurement frame of the target in the first field of view image is determined as the first key measurement region.
[0020] In one possible design, in another implementation of the first aspect of the embodiments of this application, the box of interest in the second field-of-view image template includes: a second template measurement box for locating key measurement regions within the edge region;
[0021] The process of determining the second edge point based on the grayscale values of each pixel in the edge region of the second field of view image includes:
[0022] Based on the position of the second template measurement box in the second field of view image template, an initial second measurement box is located at the same position in the second field of view image;
[0023] In the second field-of-view image, along the direction of the edge region of the object to be measured, the initial second measurement frame is adjusted according to the first offset;
[0024] The position of the initial second measurement box is adjusted according to the first offset to obtain the target second measurement box. The relative positional relationship between the target second measurement box and the target first measurement box is the same as the relative positional relationship between the first template measurement box and the second template measurement box.
[0025] The second edge point is determined based on the grayscale information of each pixel within the second measurement frame of the target in the second field of view image.
[0026] In one possible design, in another implementation of the first aspect of the embodiments of this application, the process of acquiring images of the two edge regions separately through the lens includes:
[0027] The motion stage on which the object to be tested is placed is controlled to move, so that the edge region of the object to be tested is moved into the field of view of the lens, and the lens is controlled to acquire an image of the edge region of the object to be tested, thereby obtaining a first field of view image;
[0028] The lens remains stationary, and after the motion stage is moved according to the calibrated offset, the lens is controlled to acquire images within the field of view to obtain a second field of view image. The calibrated offset is the relative offset between the lens and the sample under test when acquiring the first field of view image template and the second field of view image template respectively.
[0029] In one possible design, in another implementation of the first aspect of the embodiments of this application, the process of determining the target measurement size of the object to be measured based on the first edge point, the second edge point, and the relative offset includes:
[0030] A coordinate system is established with the center of the field of view of the first visual field image and the center of the field of view of the second visual field image as the origin, resulting in a first coordinate system and a second coordinate system.
[0031] Based on the positional relationship between the first edge point and the center of the field of view in the first visual field image, the first coordinate point of the first edge point in the first coordinate system is determined; based on the positional relationship between the second edge point and the center of the field of view in the second visual field image, the second coordinate point of the second edge point in the second coordinate system is determined.
[0032] Based on the positional relationship between the centers of the first and second fields of view as represented by the relative offset, the first and second coordinate systems are fused to obtain the first target coordinate point corresponding to the first coordinate point and the second target coordinate point corresponding to the second coordinate point in the fused coordinate system.
[0033] Based on the first target coordinate point and the second target coordinate point, the target measurement size of the object to be measured is determined.
[0034] Secondly, this application provides a key dimension measuring device, including: a camera, a lens, a motion stage, and a processing module;
[0035] The camera is used to acquire images formed by focusing on two edge regions of the object to be measured placed on the motion stage through the lens, thereby obtaining a first field-of-view image and a second field-of-view image, as well as the relative offset between the lens and the motion stage when acquiring the two edge regions; and to send the first field-of-view image, the second field-of-view image and the relative offset to the processing module, wherein the two edge regions are used to define the target measurement size of the object to be measured;
[0036] The processing module is configured to receive the first field-of-view image, the second field-of-view image, and the relative offset sent by the camera, and process the first field-of-view image, the second field-of-view image, and the relative offset according to the key size measurement method described in any of the preceding first aspects of this application to obtain the target measurement size of the object to be measured.
[0037] Thirdly, this application provides an electronic device, including: a memory and a processor;
[0038] The memory is used to store programs;
[0039] The processor is configured to execute the program to implement the key dimension measurement method described in any of the first aspects of this application.
[0040] Fourthly, this application provides a readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the key dimension measurement method described in any of the first aspects of this application.
[0041] Fifthly, a computer program product is provided, including a computer program that, when executed by a processor, implements the key dimension measurement method described in any of the first aspects of this application.
[0042] By employing the aforementioned technical solution, this application, without replacing the high-magnification lens, uses a high-magnification lens to image the two edge regions used to define the target measurement size of the object under test, obtaining a first field-of-view image and a second field-of-view image. Therefore, this application no longer pursues capturing the entire object under test in a single field of view for CD measurement, but instead only acquires and processes small field-of-view images of two key edge regions, overcoming the strong correlation between measurement accuracy and the overall field of view. Furthermore, by analyzing the grayscale values of each pixel in the edge region of each independent field-of-view image, pixel-level edge point localization is achieved, thereby significantly improving the measurement accuracy of the target measurement size determined based on the edge points and the relative offset between the lens and the object under test when acquiring the two field-of-view images. Attached Figure Description
[0043] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:
[0044] Figure 1 This is a schematic diagram of the structure of a key dimension measuring device provided in an embodiment of this application;
[0045] Figure 2 A flowchart illustrating a method for measuring critical dimensions provided in this application embodiment;
[0046] Figure 3 Example diagram of the field of view image template provided in the embodiments of this application;
[0047] Figure 4 Example diagram of ROI positioning provided in the embodiments of this application;
[0048] Figure 5 Example diagram of grayscale projection provided for embodiments of this application;
[0049] Figure 6 An example diagram of coordinate transfer provided in an embodiment of this application;
[0050] Figure 7This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation
[0051] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0052] This application can be applied to the field of semiconductor manufacturing process technology. The following section uses CD measurement as an example to introduce several application scenarios that have been implemented in products.
[0053] First, we introduce one possible application scenario of this application. When using optical imaging to measure the CD (Crystal Disc Representation) of silicon wafers, high-magnification lenses are typically used to ensure high image accuracy. However, as the lens magnification increases, the field of view also shrinks, making it impossible to capture a complete image of the silicon wafer, thus making it impossible to measure the CD value from the image. To address this, a low-magnification lens is usually used to capture a complete image of the silicon wafer; however, this results in lower accuracy of the final measured CD value, affecting the performance of devices manufactured using the process.
[0054] To address the aforementioned issues, this application provides a method, apparatus, related equipment, and computer program product for measuring critical dimensions. The following detailed description of the method, apparatus, related equipment, and computer program product for measuring critical dimensions according to embodiments of this application is provided in conjunction with the accompanying drawings.
[0055] This application provides a method for measuring critical dimensions, which can be applied to, for example... Figure 1 The processing module in the critical dimension measuring device shown. This critical dimension measuring device may include: a camera, a lens, a motion stage, and a processing module.
[0056] The camera is used to acquire images formed by focusing the lens on two edge regions of the object to be measured placed on the motion stage, respectively, to obtain a first field-of-view image and a second field-of-view image, as well as the relative offset between the lens and the motion stage when acquiring the two edge regions; and to send the first field-of-view image, the second field-of-view image and the relative offset to the processing module. The two edge regions are used to define the target measurement size of the object to be measured.
[0057] The processing module receives the first field-of-view image, the second field-of-view image, and the relative offset sent by the camera, and processes the first field-of-view image, the second field-of-view image, and the relative offset according to the key dimension measurement method to obtain the target measurement dimension of the object to be measured.
[0058] In this embodiment, the camera can be an industrial area scan camera, a 3D camera, etc., and the lens can be an optical lens with a magnification that meets the current measurement accuracy requirements, such as a high-magnification microscope lens, a zoom lens, a telecentric lens, etc. The motion stage can be a movable platform, such as an electric linear motion stage, a rotary motion stage, a multi-axis linkage motion stage, etc. By moving the motion stage, different positions / areas of the object under test are moved into the imaging field of view of the lens, and the field of view image of the corresponding local area at the current position of the object under test is acquired.
[0059] Understandably, for regularly shaped objects such as rectangles and cylinders, their linear dimensions, such as length, width, and diameter, can be determined by the positions of their two opposite edges. Similarly, consider the component dimension (CD) on a wafer, such as the width of a circuit line. In a two-dimensional plane, the width of this circuit line is determined by the distance between its left and right edges in the direction perpendicular to the line. These two edges constitute the boundary of this linear dimension (width). Regardless of the shape and position of the line, as long as the positions of these two edges are accurately determined, the CD value can be obtained by calculating the distance between the two edge positions.
[0060] Based on this, in this embodiment, by controlling a moving motion stage, the two edge regions of the object to be measured, used to define the target measurement size, are sequentially moved into the field of view of the lens, where they are captured by a camera. The field of view image corresponding to each edge region is then transmitted to a processing module. By processing the two field of view images, the distance between the two edge regions is determined, thereby determining the target measurement size of the object to be measured.
[0061] Specifically, the processing module can be an embedded processor, industrial computer, cloud computing platform, or any other device or module with image processing capabilities, without any limitations.
[0062] Next, we will illustrate this with an example of applying critical dimension measurement methods to computer equipment. (Refer to...) Figure 2 The present application provides a flowchart of a critical dimension measurement method, which may include steps S100 to S140, and these steps are described in detail below.
[0063] Step S100: Determine two edge regions used to define the target measurement dimension of the object to be measured.
[0064] In the field of semiconductor manufacturing processes, critical dimension (CD) usually refers to the width or spacing of the smallest structure on a chip. In photolithography and etching processes, it usually refers to the lateral dimension of the mask pattern transferred onto the wafer, such as the width of the transistor gate and the width of the metal interconnect. It is a core parameter for measuring process accuracy and device performance.
[0065] However, different objects are measured, and the target measurement size is different, resulting in different corresponding edge regions. Therefore, this step first identifies and defines the two edge regions corresponding to the target measurement size, laying the foundation for subsequent high-precision measurements.
[0066] It is understood that this step can be a coarse-grained determination of edge regions, at least ensuring that each edge region is within the field of view of the lens. Furthermore, it should be noted that all "edge regions" mentioned in the embodiments of this application refer to the edge regions that define the target measurement dimensions, and will not be elaborated further below.
[0067] In one possible implementation, a coordinate system can be established based on the motion table, and the position coordinates corresponding to the two edge regions that define the target measurement size can be determined according to the position of the object to be measured on the motion table.
[0068] Step S110: Acquire images of each edge region through the lens to obtain a first field-of-view image and a second field-of-view image, as well as the relative offset between the lens and the object under test when acquiring the two edge regions.
[0069] The original image data of the two edge regions determined in step S100 are collected, and the relative positional changes of the lens and the object under test are recorded, providing basic data for edge localization and size calculation in subsequent steps S120-S140.
[0070] In one possible implementation, based on the two edge regions determined in step S100, the motion stage is controlled to keep the lens stationary. First, one of the edge regions is moved into the lens's shooting range, and a lens image of that edge region is acquired to obtain a first field-of-view image. Then, the other unacquired edge region is moved into the lens's shooting range, and a lens image of that edge region is acquired to obtain a second field-of-view image.
[0071] During this process, a spatial coordinate system can be calibrated with the lens as a reference, the position of the motion stage during the first and second acquisitions can be recorded, and the offset between the two acquisition positions can be determined as the relative offset between the object under test and the lens when acquiring two edge areas.
[0072] Step S120: Determine the first edge point based on the grayscale information of each pixel in the edge region of the first field of view image.
[0073] Step S130: Determine the second edge point based on the grayscale information of each pixel in the edge region of the second field of view image.
[0074] In steps S120 and S130, the grayscale information of each pixel in the edge region of the field of view image is analyzed, and precise edge points are extracted from the edge positions where the grayscale changes drastically, serving as two reference points for calculating the target measurement size.
[0075] It is understandable that the accuracy of edge points directly affects the precision of the final measurement results. In this embodiment, the pixel with the most dramatic grayscale change is located by using the grayscale information of the pixels in the image as the edge point, thereby achieving pixel-level edge positioning and improving the precision of image processing.
[0076] In one possible implementation, steps S120 and S130 can employ the same method for locating edge points, ensuring that the first and second edge points are strictly aligned in space to guarantee consistency. Specifically, the method for locating edge points may include: smoothing the field-view image corresponding to each acquired edge region to remove noise. Further, using an operator similar to Sobel, the gradient magnitude and direction of each pixel in the edge region are calculated. Each pixel in the edge region is iterated to determine if its gradient direction is a local maximum; if so, it is listed as a candidate edge point. Further, the pixel with the maximum gradient value is selected from the candidate edge points as the final edge point.
[0077] Step S140: Determine the target measurement size of the object to be measured based on the first edge point, the second edge point, and the relative offset.
[0078] By capturing two edge points that define the target measurement size through dual-view capture, and using the relative offset between the lens and the object under test during dual-view acquisition as offset compensation, the actual physical size of the object under test, such as width and length, is calculated, thus achieving a reliable conversion from pixels to physical size.
[0079] In one possible implementation, the image coordinates can be transformed to a unified world coordinate system based on lens calibration parameters (such as pixel equivalents), and the effects of the object under test or lens movement can be corrected by offset adjustments to obtain an accurate world coordinate system. Furthermore, the target measurement dimensions of the object under test can be calculated using the coordinates of the two edge points in the world coordinate system.
[0080] In summary, this application, without replacing the high-magnification lens, uses a high-magnification lens to image the two edge regions used to define the target measurement size of the object under test, obtaining a first field-of-view image and a second field-of-view image. Therefore, this application no longer pursues capturing the entire object under test in a single field of view for CD measurement, but instead only acquires and processes small field-of-view images of two key edge regions, breaking through the strong correlation between measurement accuracy and the overall field of view. Furthermore, by analyzing the grayscale values of each pixel in the edge region of each independent field-of-view image, pixel-level edge point localization is achieved, thereby significantly improving the measurement accuracy of the target measurement size determined based on the edge points and the relative offset between the lens and the object under test when acquiring the two field-of-view images.
[0081] Next, other possible implementations of a critical dimension measurement method provided in this application will be described with reference to the embodiments below.
[0082] In one possible implementation, step S120, the process of determining the first edge point based on the grayscale information of each pixel in the edge region of the first field of view image, includes: locating the first key measurement region in the first field of view image based on the box of interest in the preset first field of view image template, wherein the box of interest is a geometric box used to locate and define the edge region of the target measurement size; and determining the first edge point of the first key measurement region based on the grayscale information of each pixel in the first key measurement region.
[0083] The process first uses feature point matching, grayscale matching, template position mapping, etc., to locate the first key measurement region in the first field of view image by using the bounding box of interest in the preset first field of view image template. Then, based on the grayscale information of each pixel in the first key measurement region, the first edge point of the region is determined.
[0084] In one possible implementation, the second field-of-view image also follows the above process. First, the second key measurement region is located in the second field-of-view image using the bounding box of interest in the preset second field-of-view image template. Then, the second edge point of the region is determined based on the grayscale information of each pixel in the second key measurement region.
[0085] This application embodiment uses a pre-defined bounding box of interest in a field-of-view image template to quickly and accurately locate key measurement areas in a complex first field-of-view image, improving the efficiency of locating edge regions in the field-of-view image. Simultaneously, it determines edge points based on the grayscale information of pixels within the edge region, achieving pixel-level edge point localization, improving the accuracy and reliability of edge point location, and laying the foundation for obtaining accurate target measurement dimensions subsequently.
[0086] The first and second field-of-view image templates are pre-acquired images of the edge regions of the defect-free sample to be measured, used to define the target measurement dimensions. It is important to note that the sample to be measured is an object or pattern of the same type as the object being measured, to ensure that the templates match the actual object being measured in the images.
[0087] Reference Figure 3 The example diagram of the field-of-view image template provided in this application is used to illustrate the measurement of the width of a sample to be tested. Referring to the process of acquiring dual-field-of-view images of the object to be tested, firstly, two vertical edge regions are determined to define the width of the sample to be tested, and images of each edge region are acquired by moving the motion stage, resulting in a first field-of-view image template and a second field-of-view image template. Further, based on the shape, position, etc. of the edge regions of the sample to be tested in the first and second field-of-view image templates, a region of interest (ROI) is drawn on the image template.
[0088] Given that a first field-of-view image template and a second field-of-view image template have been pre-collected and designed, the process of acquiring images of two edge regions through a lens can include: controlling the movement of the stage where the object to be tested is placed, so that the edge region of the object to be tested moves into the field of view of the lens, and controlling the lens to acquire images of the edge region of the object to be tested to obtain the first field-of-view image; the lens does not move, and after controlling the stage to move according to the calibrated offset, the lens is controlled to acquire images within the field of view to obtain the second field-of-view image. The calibrated offset is the relative offset between the lens and the sample to be tested when acquiring the first field-of-view image template and the second field-of-view image template respectively.
[0089] First, the processing module sends a movement command to the motion stage, controlling the stage to move. Based on preset movement parameters and direction, the motion stage precisely moves the first edge region of the object under test (one of two edge regions used to define the target measurement size of the object) into the lens's field of view. Once the edge region accurately reaches the designated position, the camera captures the image from the lens, obtaining the first field-of-view image.
[0090] After the first field-of-view image acquisition is completed, the lens remains stationary. At the same time, the motion stage is controlled to move according to the pre-calibrated offset. When the second edge region moves into the field of view of the lens, the camera is controlled to acquire the image of the lens to obtain the second field-of-view image. The calibration offset is the relative offset between the lens and the sample to be tested, which is accurately measured during the acquisition of the first and second field-of-view image templates. It can be calculated with reference to the following formula (4).
[0091] VisionOffset=Vision1_StagePos(x,y)–Vision2_StagePos(x,y)(1)
[0092] Wherein, Vision1_StagePos and Vision2_StagePos are the positions of the motion stage when acquiring the first and second field-of-view image templates, respectively.
[0093] Understandably, moving the motion stage according to the calibrated offset can reproduce the relative positional relationship between the lens and the sample under test to a great extent during template acquisition, ensuring that the position of the sample under test is highly consistent between the two image acquisitions, effectively reducing positioning errors caused by positional deviations, and making subsequent image-based positioning and analysis more accurate and reliable.
[0094] Furthermore, since the pre-defined bounding box information in the template can be directly utilized, there is no need to perform complex region of interest searches and determinations again after each new image acquisition, greatly simplifying the localization process, reducing computational load, and significantly improving the overall efficiency of image acquisition and localization. In addition, this process can adapt to objects of different sizes, shapes, and positions. By simply moving the motion stage according to the corresponding calibration offset, accurate localization and detection of different objects can be achieved, enhancing the system's applicability in various scenarios. Moreover, the fixed calibration offset makes the system operation more stable and repeatable, reducing the impact of human factors and random errors, ensuring the consistency of detection results and the reliability of the system.
[0095] In one possible implementation, refer to Figure 3 The bounding boxes of interest (ROIs) in the first field-of-view image template include: a template positioning box (ROI) for locating the edge region defining the target measurement size of the sample in the first field-of-view image template, and a first template measurement box (ROI) for locating the key measurement region within the edge region. The bounding boxes of interest (ROIs) in the second field-of-view image template include: a second template measurement box (ROI) for locating the key measurement region within the edge region.
[0096] Based on this, the process of locating the first key measurement region in the first field-of-view image based on the bounding box of interest in the preset first field-of-view image template may include:
[0097] Match the first field-of-view image with the image within the template positioning box range of the first field-of-view image template to locate the target positioning box in the first field-of-view image; locate the initial first measurement box that has a target relative positional relationship with the target positioning box from the target positioning box, the target relative positional relationship being the relative positional relationship between the template positioning box and the first template measurement box;
[0098] Based on the positional relationship between the edge region of the object under test and the target positioning box in the first field of view image, and the positional relationship between the edge region of the sample under test and the template positioning box in the first field of view image template, the first offset of the edge region of the object under test relative to the edge region of the sample under test is determined; the position of the initial first measurement box is adjusted according to the first offset along the direction of the edge region of the object under test in the first field of view image to obtain the target first measurement box; the area within the target first measurement box in the first field of view image is determined as the first key measurement area.
[0099] Reference Figure 4 The ROI positioning example diagram provided in this application illustrates the process of locating the first key measurement area.
[0100] First, image features within the ROI range, such as grayscale, color, and pattern shape similarity, are located using templates in the first field-of-view image template. These features are then matched with images in the first field-of-view image to locate image regions with high similarity, thereby pinpointing the target ROI within the first field-of-view image. It is understandable that the actual first field-of-view image may differ from the example image, potentially containing other patterns or noise. Therefore, ROI localization is necessary to locate the edge region of the target object within the field-of-view image.
[0101] Furthermore, utilizing the positional relationship between the template-localized ROI and the first template-measured ROI in the first field-of-view image template, an initial first-measured ROI with the same positional relationship is located from the target-localized ROI in the first field-of-view image. For example, if the first template-measured ROI is located in the exact center of the template-localized ROI, then an initial first-measured ROI with the same shape as the first template-measured ROI is located in the exact center of the target-localized ROI.
[0102] Understandably, the actual movement during each measurement differs, resulting in different positions of the object's edge region within the lens's field of view. Consequently, the position of the object's edge region in the first field of view image deviates from its position in the template image. This leads to the initial first measurement ROI obtained through template position mapping failing to cover the first key measurement region covered by the template image. To achieve accurate measurement of the first key measurement region, the initial first measurement ROI needs to be adjusted so that the positional relationship between the adjusted target first measurement ROI and the object's edge region is identical to the positional relationship between the first template measurement ROI and the sample's edge region.
[0103] In the specific adjustment process, firstly, in the first field-of-view image, the position of the edge region of the object under test relative to the target ROI is determined; in the first field-of-view image template, the position of the edge region of the sample under test relative to the template ROI is determined. Treating the target ROI and the template ROI as the same ROI, the position difference between the two positions can be calculated to determine the offset of the edge region of the object under test relative to the edge region of the sample under test (denoted as the first offset).
[0104] Since the initial first measurement ROI is obtained through template position mapping, the positional relationship between the initial first measurement ROI and the target location ROI is the same as the positional relationship between the first template measurement ROI and the template location ROI. However, if there is a first offset between the edge region of the object under test and the edge region of the sample under test, and if the positional relationship between the first measurement ROI and the edge region of the object under test is to be the same as the positional relationship between the first template measurement ROI and the edge region of the sample under test, then the initial first measurement ROI must also have a first offset relative to the first template measurement ROI.
[0105] Therefore, based on the first offset, the initial first measurement ROI is adjusted along the direction approaching the edge region of the object to be measured to obtain the target first measurement ROI. For example, if the horizontal offset is Δd, and the edge region of the object to be measured is to the right of the initial first measurement ROI, then the center coordinate of the initial first measurement ROI is increased by Δd in the horizontal direction.
[0106] Finally, based on the coordinate information of the target's first measurement ROI, the image region within the bounding box is precisely cropped from the first field-of-view image; this region is the first key measurement region. This region contains crucial information that requires further measurement and analysis.
[0107] Based on this, the process of determining the second edge point according to the gray values of each pixel in the edge region of the second field of view image includes: locating an initial second measurement box at the same position in the second field of view image based on the position of the second template measurement box in the second field of view image template; adjusting the initial second measurement box in the second field of view image along the direction of the edge region of the object to be measured according to a first offset; adjusting the position of the initial second measurement box according to the first offset to obtain the target second measurement box, the relative positional relationship between the target second measurement box and the target first measurement box is the same as the relative positional relationship between the first template measurement box and the second template measurement box; and determining the second edge point according to the gray value information of each pixel in the target second measurement box in the second field of view image.
[0108] First, by using template position mapping, the second template measurement ROI in the second field of view image template is mapped to the same position in the second field of view image, thus locating the initial second measurement ROI of the second field of view image.
[0109] Since the second field-of-view image was acquired after shifting the calibration offset following the acquisition of the first field-of-view image, the first offset of the edge region relative to the edge region of the sample under test in the first field-of-view image still exists in the second field-of-view image. Therefore, according to the first offset, the initial second measurement ROI is adjusted along the direction approaching the edge region of the sample under test to obtain the target second measurement ROI. At this time, the positional relationship between the target second measurement ROI and the edge region of the sample under test should be the same as the positional relationship between the second template measurement ROI and the edge region of the sample under test in the second field-of-view image template, thereby locking the second key measurement region for edge point detection in the second field-of-view image.
[0110] Furthermore, the grayscale information of each pixel in the second key measurement region is processed to obtain the second edge point of the second field of view image.
[0111] In one possible implementation, the process of processing the grayscale information of each pixel in the second key measurement region to obtain the second edge point of the second field of view image is the same as the process of processing the grayscale information of each pixel in the first key measurement region to obtain the first edge point of the first field of view image. The process of determining the first edge point of the first field of view image will be used as an example for illustration.
[0112] First, along the extension direction of the edge region within the second key measurement region, grayscale projection is performed on each pixel within the first key measurement region to obtain a one-dimensional grayscale projection curve. For example... Figure 4 The image within the first measurement ROI of the target is projected in grayscale along the vertical direction of the ROI to obtain a one-dimensional grayscale projection curve (e.g., ...). Figure 5 (Grayscale projection in the image). Then, the first derivative of the one-dimensional grayscale projection curve is taken to obtain the gradient curve, as shown in the image. Figure 5 The gradient curve in the image.
[0113] Finally, the pixels corresponding to the maxima on the gradient curve are used as the first edge points. Specifically, the maxima (peaks) on the gradient curve are calculated as edge points. If there are multiple maxima, the maxima with the largest gradient is selected as the edge point.
[0114] Finally, step S140 is executed to determine the target measurement size of the object under test based on the first edge point, the second edge point, and the relative offset. In one possible implementation, step S140 may include: establishing coordinate systems with the centers of the fields of view of the first and second field of view images as the origins, respectively, to obtain a first coordinate system and a second coordinate system; determining the first coordinate point of the first edge point in the first coordinate system based on the positional relationship between the first edge point and the center of the field of view in the first field of view image; determining the second coordinate point of the second edge point in the second coordinate system based on the positional relationship between the second edge point and the center of the field of view in the second field of view image; fusing the first and second coordinate systems based on the positional relationship between the centers of the fields of view of the first and second field of view images represented by the relative offset, to obtain the first target coordinate point corresponding to the first coordinate point and the second target coordinate point corresponding to the second coordinate point in the fused coordinate system; and determining the target measurement size of the object under test based on the first target coordinate point and the second target coordinate point.
[0115] Reference Figure 6 The present application provides an example diagram of a coordinate transfer in its embodiments, which will be used to describe the process in detail.
[0116] The process first establishes coordinate systems with the centers of the two visual fields (first and second visual fields) as the origin, resulting in coordinate systems Stage1 and Stage2 corresponding to the first visual field image. The coordinates of two edge points (first and second edge points) in their respective coordinate systems are then determined. Based on the positional relationship between the two visual field centers as represented by their relative offsets, the two coordinate systems are fused to obtain coordinate system Stage3. The target coordinates corresponding to the two edge points in coordinate system Stage3 are then determined. Finally, the target measurement dimensions of the object under test are determined based on the two target coordinates.
[0117] It is understood that the embodiments of this application can accurately locate the position of edge points in two field-of-view images by establishing a coordinate system and fusing them, unifying the edge point information under different fields of view into a coordinate system, effectively avoiding measurement errors caused by differences in fields of view, and improving the accuracy and reliability of measuring the target size of the object to be measured.
[0118] Specifically, the above process will be explained in detail. In this example, the coordinates of the center of the field of view in the image are defined as (Vision_Center_X_Image, Vision_Center_Y_Image), with the unit being pixels; the coordinates of the center of the field of view in the Stage coordinate system are defined as (Vision_Center_X_Stage, Vision_Center_Y_Stage), with the unit being micrometers (µm); the coordinates of the edge points in the image are defined as (Edge_X_Image, Edge_Y_Image), with the unit being pixels; the coordinates of the edge points in the Stage coordinate system are defined as (Edge_X_Stage, Edge_Y_Stage), with the unit being micrometers (µm).
[0119] After obtaining the first visual field image Image1 and the second visual field image Image2, the coordinates of the visual field center of each image in the image coordinate system are determined, namely the coordinates of the visual field center of the first visual field image (Vision_Center_X_Image1, Vision_Center_Y_Image1) and the coordinates of the visual field center of the second visual field image (Vision_Center_X_Image2, Vision_Center_Y_Image2).
[0120] The following explanation uses the coordinate transformation of the first field of view image as an example. The coordinate transformation process of the second field of view image is the same as that of the first field of view image, and can be understood by referring to the coordinate transformation process of the first field of view image. It will not be repeated below.
[0121] Using the center of the field of view of the first visual field image as the origin, and following the definition of a coordinate system in mathematics, such as the common rectangular coordinate system, the same horizontal and vertical directions as the image coordinate system are specified, to establish the first coordinate system Stage1 corresponding to the first visual field image Image1. The coordinates O1 of the center of the field of view in the Stage1 coordinate system can be denoted as (Vision_Center_X_Stage1, Vision_Center_Y_Stage1) or (0,0).
[0122] Furthermore, in the image coordinate system of the first field of view image, the first edge point is determined ( Figure 6The position of the edge point 1) relative to the center of the field of view is determined, for example, by how much to the left or right in the horizontal direction, and by how much to the top or bottom in the vertical direction. Specifically, the relative positional relationship between the edge point coordinates (Edge_X_Image1, Edge_Y_Image1) and the center of the field of view coordinates (Vision_Center_X_Image1, Vision_Center_Y_Image1) can be determined by referring to the following formula (2). According to the coordinate system transformation rules of the established first coordinate system, such as pixel equivalent, this relative positional relationship is converted into coordinate values in the first coordinate system, thereby determining the first coordinate point (Edge_X_Stage1, Edge_Y_Stage1) of the first edge point in the first coordinate system.
[0123] Edge_X_Stage=Vision_Center_X_Stage+(Edge_X_Image-Vision_Center_X_Image)*Pixel_Size_X;
[0124] Edge_Y_Stage=Vision_Center_Y_Stage+(Edge_Y_Image-Vision_Center_Y_Image)*Pixel_Size_Y (2)
[0125] Where Pixel_Size_X and Pixel_Size_Y are the pixel equivalents of the image coordinate system in the X and Y directions, respectively, with the unit um / Pixel, representing the conversion relationship between pixels and um.
[0126] Based on the above process, Stage 1 corresponding to the first visual field image, the coordinates of the visual field center O1 and edge point 1 under Stage 1, and Stage 2 corresponding to the second visual field image, the coordinates of the visual field center O2 and edge point 2 under Stage 2 are obtained.
[0127] Furthermore, Stage 1 and Stage 2 are merged into a single coordinate system, Stage 3, based on the relative offset. It can be understood that the relative offset reflects the spatial difference in the position of the visual field centers of the first and second visual field images. Using the relative offset, Stage 1 and Stage 2 are transformed and merged, unifying the two coordinate systems under a common reference frame. In the merged coordinate system, Stage 3, the coordinates of the first and second edge points are redefined, thus obtaining the coordinates of the first target point and the second target point.
[0128] Optionally, the process of merging the two coordinate systems may include: arbitrarily selecting either Stage1 or Stage2 as the reference coordinate system Stage3. Taking Stage1 as the reference coordinate system as an example, the following explanation is provided: Figure 6 Based on the relative offset, the coordinates A of the origin of Stage 2 (i.e., the center of the second visual field image) in Stage 1 are determined. Based on the positional relationship between the visual field center O2 in Stage 2 and edge point 2, the position of edge point 2 relative to coordinate A is determined, thus determining the second target coordinate point of edge point 2 in Stage 1. It can be understood that, since Stage 1 is the reference coordinate system, the first target coordinate point of edge point 1 in Stage 3 is the same as its coordinates in Stage 1.
[0129] Furthermore, referring to the following formula (3), the target measurement size of the object to be measured is calculated based on the coordinates of edge point 1 and edge point 2 in the fused coordinate system Stage3.
[0130] (3)
[0131] Where CD represents the critical dimension of the object to be measured; CD X Indicates the critical dimension in the X direction; CD Y This represents the critical dimension in the Y direction. (Edge_X_Stage1, Edge_Y_Stage1) are the coordinates of the first target, and (Edge_X_Stage2, Edge_Y_Stage2) are the coordinates of the second target.
[0132] In summary, to address the issue of insufficient measurement capacity for a single field of view due to the large size of the target, this application directly utilizes a high-magnification lens for dual-field-of-view acquisition. This eliminates the need to replace the lens with a lower-magnification one, achieving high-precision measurement and thus significantly enhancing measurement accuracy. Furthermore, this application pre-plans the location of the area to be measured using the measurement ROI, cleverly avoiding locations prone to interference or defects by leveraging prior knowledge, effectively ensuring the accuracy and stability of the measurement. Moreover, this application employs an innovative method of one-time positioning and two-time measurement, greatly improving computational efficiency and effectively saving computational resources, making it perfectly suitable for scenarios requiring high throughput.
[0133] This application also provides an electronic device in its embodiments. (See reference...) Figure 7 The diagram illustrates a structural schematic suitable for implementing the electronic device in the embodiments of this application. The electronic device in the embodiments of this application may include, but is not limited to, fixed terminals such as mobile phones, tablets, large-screen teaching displays, wearable devices, etc. Figure 7 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.
[0134] like Figure 7 As shown, the electronic device may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 1, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 2 or a program loaded from a storage device 8 into a random access memory (RAM) 3, to implement the correlation dimension measurement method of the foregoing embodiments of this application. When the electronic device is powered on, the RAM 3 also stores various programs and data required for the operation of the electronic device. The processing unit 1, ROM 2, and RAM 3 are interconnected via a bus 4. An input / output (I / O) interface 5 is also connected to the bus 4.
[0135] Typically, the following devices can be connected to I / O interface 5: input devices 6 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 7 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 8 including, for example, memory cards, hard drives, etc.; and communication devices 9. Communication device 9 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 7 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown. More or fewer devices may be implemented or have alternatively.
[0136] This application also provides a computer program product including computer-readable instructions, which, when executed on an electronic device, cause the electronic device to implement any of the associated dimension measurement methods provided in this application.
[0137] This application also provides a computer-readable storage medium carrying one or more computer programs. When the one or more computer programs are executed by an electronic device, the electronic device can implement any of the key dimension measurement methods provided in this application.
[0138] It should also be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. In addition, in the device embodiment drawings provided in this application, the connection relationship between modules indicates that they have a communication connection, which can be implemented as one or more communication buses or signal lines.
[0139] Through the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware, or it can be implemented by special-purpose hardware including application-specific integrated circuits, special-purpose CPUs, special-purpose memory, special-purpose components, etc. Generally, any function performed by a computer program can be easily implemented by corresponding hardware, and the specific hardware structure used to implement the same function can also be diverse, such as analog circuits, digital circuits, or special-purpose circuits. However, for this application, software program implementation is more often the preferred implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a readable storage medium, such as a computer floppy disk, USB flash drive, mobile hard disk, ROM, RAM, magnetic disk, or optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, training equipment, or network device, etc.) to execute the methods described in the various embodiments of this application.
[0140] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product.
[0141] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, training device, or data center to another website, computer, training device, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a training device or data center that integrates one or more available media. The available media may be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state drives (SSDs)).
[0142] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can be referred to each other.
Claims
1. A critical dimension measurement method, characterized by, include: Two edge regions are identified to define the target measurement size of the object under test. Images of each edge region are acquired through a lens to obtain a first field-of-view image and a second field-of-view image, as well as the relative offset between the lens and the object under test when acquiring the two edge regions. Based on the grayscale information of each pixel in the edge region of the first field of view image, a first edge point is determined, and based on the grayscale information of each pixel in the edge region of the second field of view image, a second edge point is determined. The target measurement size of the object to be measured is determined based on the first edge point, the second edge point, and the relative offset.
2. The critical dimension measurement method of claim 1, wherein, The process of determining the first edge point based on the grayscale information of each pixel in the edge region of the first field-of-view image includes: Based on the box of interest in the preset first field of view image template, the first key measurement region in the first field of view image is located. The box of interest is a geometric box used to locate and define the edge region of the target measurement size. Based on the grayscale information of each pixel within the first key measurement area, the first edge point of the first key measurement area is determined.
3. The critical dimension measurement method of claim 2, wherein, The bounding box in the first field-of-view image template includes: a template positioning box for locating the edge region in the first field-of-view image template that defines the target measurement size of the sample to be measured, and a first template measurement box for locating the key measurement region within the edge region; The process of locating the first key measurement region in the first field of view image based on the preset bounding box of interest in the first field of view image template includes: Match the first field-of-view image with the image within the template positioning box range in the first field-of-view image template, and locate the target positioning box in the first field-of-view image; From the target positioning frame, locate the initial first measurement frame that has a target relative positional relationship with it, wherein the target relative positional relationship is the relative positional relationship between the template positioning frame and the first template measurement frame; Based on the positional relationship between the edge region of the object under test and the target positioning box in the first field of view image, and the positional relationship between the edge region of the sample under test and the template positioning box in the first field of view image template, a first offset of the edge region of the object under test relative to the edge region of the sample under test is determined. In the first field-of-view image, along the direction of the edge region of the object to be measured, the position of the initial first measurement box is adjusted according to the first offset to obtain the target first measurement box; The region within the first measurement frame of the target in the first field of view image is determined as the first key measurement region.
4. The critical dimension measurement method of claim 3, wherein, The bounding box in the second field-of-view image template includes: a second template measurement box used to locate key measurement regions within the edge region; The process of determining the second edge point based on the grayscale values of each pixel in the edge region of the second field of view image includes: Based on the position of the second template measurement box in the second field of view image template, an initial second measurement box is located at the same position in the second field of view image; In the second field-of-view image, along the direction of the edge region of the object to be measured, the initial second measurement frame is adjusted according to the first offset; The position of the initial second measurement box is adjusted according to the first offset to obtain the target second measurement box. The relative positional relationship between the target second measurement box and the target first measurement box is the same as the relative positional relationship between the first template measurement box and the second template measurement box. The second edge point is determined based on the grayscale information of each pixel within the second measurement frame of the target in the second field of view image.
5. The critical dimension measurement method of claim 4, wherein, The process of acquiring images of the two edge regions separately using a lens includes: The motion stage on which the object to be tested is placed is controlled to move, so that the edge region of the object to be tested is moved into the field of view of the lens, and the lens is controlled to acquire an image of the edge region of the object to be tested, thereby obtaining a first field of view image; The lens remains stationary, and after the motion stage is moved according to the calibrated offset, the lens is controlled to acquire images within the field of view to obtain a second field of view image. The calibrated offset is the relative offset between the lens and the sample under test when acquiring the first field of view image template and the second field of view image template respectively.
6. The critical dimension measurement method according to any one of claims 1 to 5, wherein, The process of determining the target measurement size of the object under test based on the first edge point, the second edge point, and the relative offset includes: A coordinate system is established with the center of the field of view of the first visual field image and the center of the field of view of the second visual field image as the origin, resulting in a first coordinate system and a second coordinate system. Based on the positional relationship between the first edge point and the center of the field of view in the first visual field image, the first coordinate point of the first edge point in the first coordinate system is determined; based on the positional relationship between the second edge point and the center of the field of view in the second visual field image, the second coordinate point of the second edge point in the second coordinate system is determined. Based on the positional relationship between the centers of the first and second fields of view as represented by the relative offset, the first and second coordinate systems are fused to obtain the first target coordinate point corresponding to the first coordinate point and the second target coordinate point corresponding to the second coordinate point in the fused coordinate system. Based on the first target coordinate point and the second target coordinate point, the target measurement size of the object to be measured is determined.
7. A critical dimension measuring apparatus characterized by comprising: include: Camera, lens, motion table, and processing module; The camera is used to acquire images formed by focusing on two edge regions of the object to be measured placed on the motion stage through the lens, thereby obtaining a first field-of-view image and a second field-of-view image, as well as the relative offset between the lens and the motion stage when acquiring the two edge regions; and to send the first field-of-view image, the second field-of-view image and the relative offset to the processing module, wherein the two edge regions are used to define the target measurement size of the object to be measured; The processing module is configured to receive the first field-of-view image, the second field-of-view image, and the relative offset sent by the camera, and process the first field-of-view image, the second field-of-view image, and the relative offset according to any one of claims 1 to 6 to obtain the target measurement size of the object to be measured.
8. An electronic device, characterized in that, include: Memory and processor; The memory is used to store programs; The processor is configured to execute the program to implement the various steps of the critical dimension measurement method as described in any one of claims 1 to 6.
9. A readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements each step of the critical dimension measurement method as described in any one of claims 1 to 6.
10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the various steps of the critical dimension measurement method as described in any one of claims 1 to 6.