Imaging system of coaxial projection structure, auto-focusing method and topography measuring method
Patent Information
- Application Number
- CN202610978348.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-07-02
- Publication Date
- 2026-10-13
Smart Images
Figure CN122902029A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the fields of optical inspection, automatic image focusing, surface topography measurement, and three-dimensional point cloud reconstruction, specifically to an imaging system with a coaxial projection structure, an automatic focusing method, and a topography measurement method. Background Technology
[0002] In high-end manufacturing fields such as semiconductor wafers, optical communication products, chips, and MicroLEDs, it is necessary to perform automatic focusing and three-dimensional surface morphology measurement on low-contrast or complex surface samples to ensure product quality and process control precision.
[0003] Existing image autofocus systems typically determine focus by analyzing changes in sharpness in acquired images. This approach is relatively simple in structure, low in cost, and usually does not require an additional focus sensor. During focusing, the system moves up and down along the Z-axis while continuously acquiring images and calculating a sharpness evaluation function based on the acquired images, such as a gradient function, variance function, or Laplacian operator response value. Theoretically, the sharpness value has a unique maximum at the optimal focal plane and exhibits a monotonically decreasing change on both sides of the peak. Therefore, the optimal focal plane can be determined by finding the location corresponding to the maximum sharpness during the scanning process.
[0004] To improve focusing speed, existing image focusing schemes typically employ a scanning method that involves moving while acquiring data, calculating sharpness, and dynamically adjusting the step size. This method is adaptable to samples with obvious texture features, but when the sample surface texture is weak or the contrast is low, the sharpness evaluation function is prone to failure, leading to decreased focusing accuracy or even focusing failure.
[0005] Existing image sharpness focusing solutions have at least the following problems: 1) It relies on the texture features of the surface of the object being tested, has poor adaptability to low-contrast samples, and is prone to focusing failure in scenarios such as bare wafers and photoresist surfaces.
[0006] 2) The sharpness evaluation function is not stable enough due to the significant impact of changes in ambient light and noise.
[0007] 3) When the sample surface is uneven, has steps that cause local shadows or overall tilt, the overall image sharpness calculation results are easily interfered with, resulting in a decrease in focusing accuracy.
[0008] 4) Traditional image autofocus solutions can usually only output a single optimal focal plane position and cannot identify whether the sample has spatial morphological features such as tilt, warping, or steps.
[0009] 5) Existing solutions generally use the overall image sharpness as the basis for focusing, which involves a large amount of calculation and is not conducive to real-time fast focusing under high magnification and high resolution conditions. Summary of the Invention
[0010] To address the aforementioned technical problems, this invention proposes an imaging system with a coaxial projection structure, an automatic focusing method, and a morphology measurement method.
[0011] To achieve the above objectives, the technical solution of the present invention is as follows: In a first aspect, the present invention discloses an imaging system with a coaxial projection structure, comprising: An image acquisition device is used to acquire images of the surface of a test object with superimposed regularly arranged projection patterns; An imaging component is used to receive imaging light from the surface of the object being measured, form an imaging optical path, and guide the imaging light to an image acquisition device. The projection component is used to generate a projection beam carrying a regularly arranged pattern, form a projection optical path and guide the projection beam to the surface of the object being measured. The regularly arranged pattern has multiple feature points that are equally spaced, of the same size and have no brightness difference. A beam splitter is placed between the projection optical path and the imaging optical path to achieve coaxial beam combining and splitting of the projection optical path and the imaging optical path, so that the projection optical path and the imaging optical path share the main optical axis of the imaging component. Z-axis moving device, used to drive the object under test or imaging component to move along the Z-axis direction; An image processing device, connected to an image acquisition device, is used to identify multiple feature points in an image acquired by the image acquisition device and set regions of interest around each feature point, and to process the sharpness value of the regions of interest to determine the optimal focus position.
[0012] Based on the above technical solution, the following improvements can be made: As a preferred embodiment, the imaging assembly includes an objective lens and a main imaging tube. The objective lens is used to image the surface of the object under test, and the main imaging tube is used to cooperate with the image acquisition device for imaging. The projection optical path and the imaging optical path share the main optical axis of the objective lens.
[0013] As a preferred embodiment, the projection component includes: Projection light source, used to provide projection illumination; A pattern carrier on which a pattern is arranged in a regular pattern; A projection imaging component is used to project a regularly arranged pattern on a pattern carrier onto the surface of the object being measured.
[0014] Secondly, this invention discloses an autofocus method that utilizes an imaging system with any of the aforementioned coaxial projection structures to achieve autofocus, comprising: Step S1: Project a regularly arranged pattern onto the surface of the object being measured using the projection component. The regularly arranged pattern has multiple feature points that are equally spaced, of the same size, and have no difference in brightness. Step S2: Select several feature points as target points, and define a corresponding region of interest for each target point. Use the regions of interest of all target points as the focus area. Step S3: The Z-axis moving device drives the object under test or imaging component to move along the Z-axis direction according to the current scanning step size. During the movement, the image acquisition device simultaneously acquires images of the surface of the object under test with regularly arranged projection patterns superimposed on it. Step S4: The image processing device calculates the comprehensive sharpness evaluation value of the focused area based on the image acquired in real time by the image acquisition device, records the correspondence between the current Z-axis position and the comprehensive sharpness evaluation value, and constructs a curve showing the change of the comprehensive sharpness evaluation value with the Z-axis position. Step S5: In real time, determine whether the curve of the overall sharpness evaluation value changing with the Z-axis position has reached its maximum peak and entered a monotonically decreasing state; If the peak value has not been reached or the decline range has not yet been entered, the scanning step size is dynamically adjusted according to the current comprehensive sharpness evaluation value, and steps S3-S5 are repeated. If the peak value is reached and the range decreases, the optimal focus range is determined. Multiple sampling points are taken on both sides of the optimal focus range, and a fitting algorithm is used to fit the curve. The Z-axis position corresponding to the maximum value of the fitted curve is taken as the optimal focus position.
[0015] As a preferred embodiment, step S4 includes: Step S4.1: The image processing device identifies the feature points corresponding to each target point in the image acquired at the current Z-axis position, and calculates the sharpness value of the region of interest for each target point; Step S4.2: Calculate the sharpness values of each region of interest within the focus area using weighted averages to obtain a comprehensive sharpness evaluation value; Step S4.3: Record the correspondence between the current Z-axis position and the overall sharpness evaluation value, and construct a curve showing the change of the overall sharpness evaluation value with the Z-axis position.
[0016] As a preferred approach, in step S5, the scanning step size is dynamically adjusted based on the current overall sharpness evaluation value, and the next scanning step size is... Specifically, it is obtained from the following formula: ; ; ; in: This is the coarse search step size; For finer search step size, and ; This is the overall sharpness evaluation value at the estimated focus area position; This is the overall sharpness evaluation value at the initial focus position; This is the overall sharpness evaluation value at the current Z-axis position.
[0017] As a preferred approach, in step S5, after obtaining the next scan step size, the... Perform out-of-bounds checks, including: like ; like ; Otherwise keep constant.
[0018] Thirdly, this invention discloses a morphology measurement method, which utilizes an imaging system with any of the aforementioned coaxial projection structures to measure the surface morphology of an object, including: Step M1: Project a regularly arranged pattern onto the surface of the object being measured using a projection component. The regularly arranged pattern has multiple feature points that are equally spaced, of the same size, and have no difference in brightness. Step M2: Define the corresponding region of interest for each feature point; Step M3: The Z-axis moving device drives the object under test or the imaging component to move along the Z-axis direction. During the movement, the image acquisition device simultaneously acquires images of the surface of the object under test with regularly arranged projection patterns superimposed on it. Step M4: Repeat step M3 to obtain multiple images that correspond one-to-one with the Z-axis position; Step M5: The image processing device identifies all feature points in each image and calculates the sharpness evaluation value of the region of interest for each feature point, and establishes a curve showing the change of the sharpness evaluation value of the feature point with the Z-axis position; Step M6: Based on the curve of the sharpness evaluation value of each feature point changing with the Z-axis position, determine the optimal focus position corresponding to each feature point; Step M7: Calculate the relative height value of each feature point based on the optimal focus position corresponding to each feature point, and combine it with the physical space coordinates obtained by converting the pixel coordinates of each feature point in the image to restore the three-dimensional shape of the surface of the object under test.
[0019] As a preferred embodiment, step M6 includes: Step M6.1: Calculate the curve of the sharpness evaluation value of each feature point as a function of the Z-axis position, and find its peak range; Step M6.2: Take multiple sampling points on the left and right sides of the peak interval, use a fitting algorithm to fit the curve, find the Z-axis position corresponding to the maximum value of the fitted curve, and take it as the best focus position corresponding to the feature point.
[0020] As a preferred embodiment, step M7 includes: Step M7.1: Set the reference plane and calculate the Z height value of each feature point relative to the reference plane based on the optimal focus position corresponding to each feature point; Step M7.2: Based on the preset parameters of the image acquisition device, convert the pixel coordinates of each feature point in the image into X and Y coordinates in physical space; Step M7.3: Register the X and Y coordinates of each feature point with the corresponding Z height value to form full-field 3D point cloud data; Step M7.4: Reconstruct the morphological features of the sample surface based on the 3D point cloud data.
[0021] This invention discloses an imaging system, an automatic focusing method, and a topography measurement method for a coaxial projection structure, which have the following beneficial effects: First, the present invention uses a coaxial projection structure to project a regularly arranged pattern onto the surface of the object being measured, so that the position of the projected pattern in the image acquired by the image acquisition device is basically fixed and does not shift significantly with the change of the defocus amount of the object being measured. This ensures the spatial consistency of the focusing area at different Z-axis positions and improves the focusing stability and reliability.
[0022] Secondly, this invention projects a regular pattern of multiple feature points with equal spacing, the same size and no brightness difference onto the surface of the object being tested. By using the artificially projected pattern to replace the texture of the sample itself as the basis for evaluating sharpness, it overcomes the technical bottleneck of traditional focusing methods that rely on the texture features of the sample surface. It is suitable for automatic focusing of low-contrast samples such as bare wafers and photoresist surfaces.
[0023] Third, by selecting several feature points as target points and defining the focus area, this invention can flexibly select all or part of the region of interest as the focus area according to the detection requirements, realize flexible switching between full-image focusing and local focusing, effectively reduce the amount of calculation, and improve the focusing speed under high magnification and high resolution conditions.
[0024] Fourth, this invention dynamically adjusts the scanning step size by considering the changing trend of the comprehensive sharpness evaluation value during the focusing scanning process. It uses a large step size for fast searching at a distance from the focus and a small step size for fine searching near the focal plane. It also fits the sharpness evaluation value curve near the focal plane to obtain the peak position, thus balancing focusing speed and accuracy and improving the overall performance of the system.
[0025] Fifth, the present invention utilizes an imaging system with a coaxial projection structure to achieve three-dimensional morphological measurement of the sample surface, and can identify morphological features such as surface warping, step height difference, overall tilt and local unevenness of the sample, thus expanding the functional boundaries of the imaging system. Attached Figure Description
[0026] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.
[0027] Figure 1 This is a schematic diagram of the imaging system with a coaxial projection structure provided in an embodiment of the present invention.
[0028] Figure 2 A flowchart of an autofocus method provided in an embodiment of the present invention.
[0029] Figure 3 A flowchart of a morphology measurement method provided in an embodiment of the present invention.
[0030] Figure 4 This is a schematic diagram of the ROI region provided in an embodiment of the present invention.
[0031] Among them: 1-image acquisition device, 2-objective lens, 3-main imaging tube lens, 4-projection light source, 5-pattern carrier, 6-projection imaging component, 7-beam splitter, 8-Z-axis moving device, 9-image processing device. Detailed Implementation
[0032] The preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings.
[0033] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0034] The expression “includes” is an “open-ended” expression, which means that there is a corresponding component or step, and should not be interpreted as excluding additional components or steps.
[0035] like Figure 1 As shown, in this embodiment, the imaging system of the coaxial projection structure of the present invention includes: an image acquisition device 1 (e.g., an industrial camera), an imaging component, a projection component, a beam splitter 7, a Z-axis moving device 8, and an image processing device 9.
[0036] This invention discloses an imaging system with a coaxial projection structure, comprising: The imaging assembly includes an objective lens 2 and a main imaging tube 3. The objective lens 2 is used to image the surface of the object under test, collecting and guiding the imaging light from the surface of the object to the main imaging tube 3. The main imaging tube 3 is used in conjunction with the image acquisition device 1 to image, further guiding the imaging light from the objective lens 2 to the photosensitive surface of the image acquisition device 1. The imaging assembly forms an imaging optical path.
[0037] The projection assembly includes a projection light source 4, a pattern carrier 5, and a projection imaging component 6. The pattern carrier 5 has a regularly arranged pattern, which has multiple feature points that are equally spaced, of the same size, and have no brightness difference. A projection beam emitted from the projection light source 4 illuminates the pattern carrier 5, and the projection beam carrying pattern information passes through the projection imaging component 6 to form a pattern projection beam. The projection assembly forms the projection light path.
[0038] Beam splitter 7 is positioned between the projection optical path and the imaging optical path to achieve coaxial beam combining and splitting of the two paths. Specifically, the pattern projection beam generated by the projection component is reflected by beam splitter 7 and propagates along the principal optical axis of objective lens 2, projecting onto the surface of the object under test through objective lens 2. The imaging light from the surface of the object under test (containing image information of the object surface itself and the projected image information of the pattern projected onto its surface) is collected by objective lens 2, passes through beam splitter 7, and enters the imaging component, ultimately being acquired by image acquisition device 1. In this way, the projection optical path and the imaging optical path share the principal optical axis of objective lens 2, achieving coaxial projection.
[0039] Z-axis moving device 8 is used to drive the object under test or imaging component to move relative to each other along the Z-axis direction. In one embodiment, Z-axis moving device 8 is a stage that drives the object under test to move up and down along the Z-axis direction; in another embodiment, Z-axis moving device 8 is a driving mechanism that drives the imaging component as a whole to move along the Z-axis direction.
[0040] Image processing device 9 is connected to image acquisition device 1. Image processing device 9 receives images acquired by image acquisition device 1, identifies multiple feature points in a regularly arranged pattern within the image, sets regions of interest (ROIs) around each feature point, and processes the sharpness values of the ROIs to determine the optimal focus position. Image processing device 9 also sends control commands to Z-axis moving device 8 to control Z-axis moving device 8 to perform focusing scanning motion.
[0041] In this invention, since the projection optical path and the imaging optical path share the principal optical axis of the objective lens, i.e., a coaxial projection method is adopted, the position of the regularly arranged pattern in the image acquired by the image acquisition device 1 is basically fixed. It does not undergo significant translation with changes in the vertical defocus of the object being measured, but rather manifests as changes in pattern sharpness or contrast under different defocus states. This characteristic ensures the spatial consistency of the focus area at different Z-axis positions during subsequent autofocusing.
[0042] In a preferred embodiment, the regularly arranged pattern is a cross-grid pattern, meaning the pattern carrier 5 has a grid pattern formed by equally spaced and equally sized cross marks arranged in rows and columns, with no brightness difference between the cross marks. In another preferred embodiment, the regularly arranged pattern is a dot matrix pattern, meaning the pattern carrier 5 has a dot matrix pattern formed by equally spaced and equally sized dots arranged in rows and columns. In yet another preferred embodiment, the regularly arranged pattern is a checkerboard pattern or a hexagonal array pattern. All of the above-mentioned regularly arranged patterns have multiple feature points that are equally spaced, of the same size, and without brightness differences, and all can achieve the technical effects of the present invention.
[0043] like Figure 2 As shown, in some other embodiments, the present invention discloses an autofocus method that utilizes the aforementioned coaxial projection structure imaging system to achieve autofocus, specifically including the following steps: Step S101: Project a regularly arranged pattern onto the surface of the object being measured using a projection component. The regularly arranged pattern has multiple feature points that are equally spaced, of the same size, and have no brightness difference. Step S102: Select several feature points as target points, and define a corresponding region of interest for each target point, and use the regions of interest of all target points as the focus area; Step S103: The Z-axis moving device 8 drives the object under test or the imaging component to move along the Z-axis direction according to the current scanning step length. During the movement, the image acquisition device 1 synchronously acquires images of the surface of the object under test with regularly arranged projection patterns superimposed on it. Step S104: The image processing device 9 calculates the comprehensive sharpness evaluation value of the focus area based on the image acquired in real time by the image acquisition device 1, records the correspondence between the current Z-axis position and the comprehensive sharpness evaluation value, and constructs a curve showing the change of the comprehensive sharpness evaluation value with the Z-axis position. Step S105: In real time, determine whether the curve of the overall sharpness evaluation value changing with the Z-axis position has reached its maximum peak and entered a monotonically decreasing state; If the peak value has not been reached or the decline range has not yet been entered, the scanning step size is dynamically adjusted according to the current comprehensive sharpness evaluation value, and steps S103-S105 are repeated. If the peak value is reached and the range decreases, the optimal focus range is determined. Multiple sampling points are taken on both sides of the optimal focus range, and a fitting algorithm is used to fit the curve. The Z-axis position corresponding to the maximum value of the fitted curve is taken as the optimal focus position.
[0044] The following is a detailed explanation of each of the above steps.
[0045] Step S101 projects a regularly arranged pattern onto the surface of the object being measured using a projection component. Specifically, the projection light source 4 emits a projection beam, which illuminates the pattern carrier 5. The pattern carrier 5 has a regularly arranged pattern (e.g., a cross grid pattern) on it, which has multiple feature points that are equally spaced, of the same size, and without brightness differences. After carrying the pattern information, the projection beam is passed through the projection imaging component 6 to form a pattern projection beam. The pattern projection beam is reflected by the beam splitter 7 and propagates along the principal optical axis of the objective lens, then projected onto the surface of the object being measured through the objective lens 2, forming a projected image of the regularly arranged pattern on the surface of the object. In this step, because a coaxial projection method is used, the position of the pattern projected onto the surface of the object only changes in sharpness as the object is moved up or down, without any significant horizontal translation.
[0046] Step S102 selects several feature points as target points and delineates a corresponding region of interest (ROI) for each target point, using the ROIs of all target points as the focus area. Here, "several feature points" can refer to one feature point, multiple feature points, or all feature points.
[0047] In one embodiment, if it is necessary to focus on the entire field of view, all feature points are selected as target points. At this time, the focus area is the entire region of interest within the entire image range, i.e., the full-image focus mode.
[0048] In another embodiment, if precise focusing is only required on a specific area of the surface of the object under test (e.g., a specific functional area of a chip, a specific die area of a wafer, etc.), then only a few feature points within the target area are selected as target points. In this case, the focusing area is a few regions of interest corresponding to the target area, i.e., a local focusing mode. By flexibly selecting target points, it is possible to freely switch between full-image focusing and local focusing.
[0049] In a preferred embodiment, such as Figure 4 As shown, the region of interest (ROI) is a square region centered on each feature point, such as a square region with a side length of M pixels (e.g., M=20, 30, or 50) centered on each cross point of a cross grid pattern. The size of the ROI can be set according to the actual detection accuracy requirements and computing resources.
[0050] In step S103, the Z-axis moving device 8 drives the object under test or imaging component to move along the Z-axis direction according to the current scanning step size. During the movement, the image acquisition device 1 synchronously acquires images of the surface of the object under test containing regularly arranged patterns. Each acquired image corresponds to a Z-axis position.
[0051] During the initial scan phase, the current scan step size can be set as the coarse search step size. This allows for quick coverage of a wider Z-axis search range.
[0052] In step S104, the image processing device 9 calculates the comprehensive sharpness evaluation value of the focus area based on the image acquired in real time by the image acquisition device 1, records the correspondence between the current Z-axis position and the comprehensive sharpness evaluation value, and constructs a curve showing the change of the comprehensive sharpness evaluation value with the Z-axis position.
[0053] Specifically, step S104 includes the following sub-steps: Step S104.1: The image processing device 9 identifies feature points corresponding to each target point in the image acquired at the current Z-axis position, and calculates the sharpness value of the region of interest for each target point. The sharpness value can be calculated using well-known sharpness evaluation functions in the art, such as gradient functions (e.g., Tenengrad gradient function), variance functions, or Laplacian operator response values. In a preferred embodiment, the Laplacian operator response value is used as the sharpness evaluation function; that is, after performing Laplacian filtering on the image within the region of interest, the sum of squared response values is calculated as the sharpness value of that region.
[0054] Step S104.2: Calculate the sharpness values of each region of interest within the focus area using weighted summation to obtain the overall sharpness evaluation value. When the focus area contains multiple regions of interest, the values can be weighted according to the positional weights of each region of interest (e.g., higher weight for the central region and lower weight for the edge region); when the focus area contains only one region of interest, the sharpness value of that region is the overall sharpness evaluation value.
[0055] Step S104.3: Record the correspondence between the current Z-axis position and the overall sharpness evaluation value, and transfer the data points... Add it to the curve dataset to construct a curve of the overall sharpness rating as a function of the Z-axis position.
[0056] Step S105 determines in real time whether the curve of the overall sharpness evaluation value changing with the Z-axis position has reached its maximum peak and entered a monotonically decreasing state.
[0057] If the overall sharpness evaluation value has not yet reached its peak or has not yet entered the declining range (i.e., the curve is still in the rising stage or the peak has not yet been crossed), the scanning step size is dynamically adjusted according to the current trend of the overall sharpness evaluation value, and then the process returns to step S103 to continue moving in the same direction and acquiring the image at the next position.
[0058] If the overall sharpness evaluation value has reached its peak and entered a declining range (i.e., the curve has crossed the peak and started to decline), then the optimal focus range is determined. Multiple sampling points are taken on both sides of the optimal focus range (e.g., 3 sampling points on each side, for a total of 7 sampling points). A fitting algorithm is used to fit the overall sharpness evaluation value curve, and the Z-axis position corresponding to the maximum value of the fitted curve is taken as the optimal focus position.
[0059] In a preferred embodiment, the relationship between the sharpness score and the Z-axis position approximates a unimodal Gaussian distribution, the mathematical form of which is: ; in, This is the Z-axis position corresponding to the peak value (i.e., the optimal focus position). Peak amplitude, The standard deviation is denoted as .
[0060] Taking the logarithm of the Gaussian formula above, we can expand it as follows: ; The above equation can be written as a quadratic function of z: ; There are seven points in total, including the peak point and three points on each side. Since there are three unknowns, the statically indeterminate equations can be solved using the least squares method to obtain the values of a, b, and c. Then, the Z-axis position corresponding to the maximum sharpness value can be calculated, i.e.: .
[0061] In another embodiment, the fitting algorithm may also employ Gaussian fitting, parabolic fitting, spline interpolation fitting, or other peak-finding methods.
[0062] In a preferred embodiment, the scanning step size is dynamically adjusted based on the current overall sharpness evaluation value, and the next scanning step size is... Specifically, it is obtained from the following formula: ; ; ; in: This is the coarse search step size; For finer search step size, and ; This is the overall sharpness evaluation value at the estimated focus area position; This is the overall sharpness evaluation value at the initial focus position; This is the overall sharpness evaluation value at the current Z-axis position.
[0063] Before focusing, the Z-axis moving device 8 drives the object under test or imaging component to move along the Z-axis to the estimated focal plane position (e.g., the focal plane position estimated based on historical experience or pre-scan results), and obtains the comprehensive sharpness evaluation value at that position. .
[0064] Move the object under test or imaging component along the Z-axis to the focusing start position (i.e., the Z-axis position of the system before the start of this focusing scan), and obtain the overall sharpness evaluation value at that position. .
[0065] Furthermore, after obtaining the next scan step size, for Perform out-of-bounds checks, including: like ; like ; Otherwise keep constant.
[0066] By performing out-of-bounds checks, the scanning step size is always kept within limits. Within a reasonable range, it will neither miss the focal peak due to an excessively large step size, nor cause the search efficiency to be too low due to an excessively small step size.
[0067] like Figure 3 As shown, in some other embodiments, the present invention discloses a topography measurement method, which utilizes the above-described coaxial projection structure imaging system to measure the surface topography of an object, specifically including the following steps: Step M101: Project a regularly arranged pattern onto the surface of the object being measured using a projection component. The regularly arranged pattern has multiple feature points that are equally spaced, of the same size, and have no difference in brightness. Step M102: Define the corresponding region of interest for each feature point; Step M103: The Z-axis moving device 8 drives the object under test or the imaging component to move along the Z-axis direction. During the movement, the image acquisition device 1 synchronously acquires images of the surface of the object under test with regularly arranged projection patterns superimposed on it. Step M104: Repeat step M103 to obtain multiple images that correspond one-to-one with the Z-axis position; Step M105: The image processing device 9 identifies all feature points in each image, calculates the sharpness evaluation value of the region of interest for each feature point, and establishes a curve showing the change of the sharpness evaluation value of the feature point with the Z-axis position. Step M106: Based on the curve of the sharpness evaluation value of each feature point changing with the Z-axis position, determine the optimal focus position corresponding to each feature point; Step M107: Calculate the relative height value of each feature point based on the optimal focus position corresponding to each feature point, and combine it with the physical space coordinates obtained by converting the pixel coordinates of each feature point in the image to restore the three-dimensional shape of the surface of the object under test.
[0068] The following is a detailed explanation of each of the above steps.
[0069] Step M101 projects a regularly arranged pattern onto the surface of the object being measured using a projection component. The specific implementation of this step is the same as step S101 of the autofocus method, and will not be described again here.
[0070] Step M102 defines the corresponding region of interest for each feature point. For example, a square region with a side length of M pixels (e.g., M=20, 30, or 50) centered on each cross point of the cross grid pattern. The size of the region of interest can be set according to the actual detection accuracy requirements and computing resources.
[0071] In steps M103 and M104, the Z-axis moving device 8 drives the object under test or imaging component to move along the Z-axis direction. During the movement, the image acquisition device 1 synchronously acquires images of the surface of the object under test containing regularly arranged patterns. This step is repeated to obtain multiple images that correspond one-to-one with the Z-axis positions, forming an image stack. Each image in the image stack corresponds to a different Z-axis position.
[0072] In one embodiment, the Z-axis moving device 8 starts from the focusing start position and moves in a fixed scanning step size (e.g., 0.5 μm or 1 μm) along the Z-axis direction, acquiring one frame of image with each step, until a predetermined Z-axis scanning range (e.g., ±20 μm) is covered.
[0073] In another embodiment, a variable step size scanning method can be used, employing a larger step size at greater distances from the focus plane and a smaller step size near the estimated focal plane. In yet another embodiment, it can be performed directly in sync with the autofocus method, recording images synchronously during the autofocus scanning process.
[0074] Step M105: Image processing device 9 identifies all feature points in each image and calculates the sharpness evaluation value of the region of interest for each feature point, and establishes a curve showing the change of the sharpness evaluation value of the feature point with the Z-axis position.
[0075] Specifically, for the i-th feature point, the sharpness value of the region of interest corresponding to that feature point is extracted from each frame of the image stack to obtain a set of data points. ,in This represents the sharpness evaluation value of the i-th feature point at position Z on the Z-axis. Connecting these data points in order of their Z-axis positions yields the sharpness-Z curve for that feature point.
[0076] Step M106 determines the optimal focus position for each feature point based on the curve of the sharpness evaluation value of each feature point changing with the Z-axis position.
[0077] Specifically, step M106 includes the following sub-steps: Step M106.1: Calculate the sharpness-Z curve for each feature point and find its peak range. The peak range refers to the Z-axis range near the maximum sharpness value.
[0078] Step M106.2: Take multiple sampling points on the left and right sides of the peak interval (for example, take 3 sampling points on the left and right sides, for a total of 7 sampling points), use the fitting algorithm to fit the sharpness curve, find the Z-axis position corresponding to the maximum value of the fitted curve, and take it as the optimal focus position corresponding to the feature point.
[0079] In a preferred embodiment, the fitting algorithm employs Gaussian fitting. For each feature point, its sharpness-Z curve approximates a unimodal Gaussian distribution. The peak position of the Gaussian curve is obtained by fitting using the least squares method, which is the optimal focus position for that feature point. .
[0080] Step M107 calculates the relative height value of each feature point based on the optimal focus position corresponding to each feature point, and combines the physical space coordinates obtained by converting the pixel coordinates of each feature point in the image to restore the three-dimensional shape of the surface of the object under test.
[0081] Specifically, step M107 includes the following sub-steps: Step M107.1: Set a reference plane and calculate the Z-height value of each feature point relative to the reference plane based on the optimal focus position corresponding to each feature point. In one embodiment, the average value of the optimal focus positions of each feature point in the center region of the field of view is used as the reference plane. For the i-th feature point, its relative height value .when When the value is positive, it indicates that the point is convex relative to the reference plane; when... When the value is negative, it indicates that the point is concave relative to the reference surface.
[0082] In another embodiment, the reference plane can also be the focal plane corresponding to the optimal focus position determined by the autofocus method. That is, after autofocus is completed, the optimal focal plane found by autofocus is used as the reference plane to calculate the relative height values of each feature point. In this way, surface topography measurement can be achieved simultaneously with autofocus.
[0083] Step M107.2: Based on the preset parameters of the image acquisition device 1 (such as magnification, pixel size, etc.) and the system geometric calibration parameters, determine the pixel coordinates of each feature point in the image. Converted to X and Y coordinates in physical space .
[0084] Step M107.3: Register the X and Y coordinates of each feature point with the corresponding Z height value to form full-field 3D point cloud data. .
[0085] Step M107.4: Reconstruct the morphological features of the sample surface based on the 3D point cloud data. Specifically, a 3D mesh model of the sample surface can be constructed using the 3D point cloud data, or a 3D morphological map of the sample surface can be generated using an interpolation algorithm. The reconstructed morphological features include, but are not limited to: surface warping (overall bending deformation), step height differences (height differences between different regions), overall tilt (the tilt angle of the sample surface relative to the ideal horizontal plane), and local unevenness (minor undulations in small areas).
[0086] In one specific application embodiment, the morphology measurement method provided by the present invention can be used to measure the warpage of semiconductor wafers. The wafer is placed on a stage, and a cross-grid pattern is projected onto the wafer surface using a coaxial projection system. Image stacks are acquired along the Z-axis, and the optimal focus position corresponding to each cross point is calculated to obtain the height information of each point on the wafer surface, generating a three-dimensional morphology map of the wafer. Based on this morphology map, parameters such as the wafer's warpage, curvature, and local flatness are calculated.
[0087] In another specific application embodiment, the morphology measurement method provided by the present invention can be used to measure the step height of MicroLED chips. By measuring the difference in the optimal focus position of each feature point in different regions of the chip surface (such as the light-emitting area and the non-light-emitting area), the step height difference is calculated, and the manufacturing quality of the chip is evaluated.
[0088] It is worth noting that in this invention, autofocus and morphology measurement can be performed collaboratively in the same scanning process or independently.
[0089] Collaborative Working Mode: During steps S103-S104 of the autofocus method, the image processing device 9 calculates the overall sharpness evaluation value of the focused area while simultaneously recording the change in sharpness evaluation values of all feature points with respect to the Z-axis position. Once autofocus is complete (i.e., the optimal focus position is found), the image processing device 9 uses the recorded sharpness-Z curve data for each feature point to execute steps M106-M107 of the topography measurement method to reconstruct the three-dimensional topography of the sample surface. In this mode, autofocus and topography measurement are completed simultaneously during a single Z-axis scan, without affecting detection efficiency.
[0090] Independent working mode: In scenarios where only topographic measurement is required and autofocus is not needed, steps M101-M107 of the topographic measurement method can be executed directly. In scenarios where only autofocus is required and topographic measurement is not needed, steps S101-S105 of the autofocus method can be executed only.
[0091] This invention discloses an imaging system, an automatic focusing method, and a topography measurement method for a coaxial projection structure, which have the following beneficial effects: First, the present invention uses a coaxial projection structure to project a regularly arranged pattern onto the surface of the object being measured, so that the position of the projected pattern in the image acquired by the image acquisition device 1 is basically fixed and does not shift significantly with the change of the defocus amount of the object being measured, thus ensuring the spatial consistency of the focusing area at different Z-axis positions and improving the focusing stability and reliability.
[0092] Secondly, this invention projects a regular pattern of multiple feature points with equal spacing, the same size and no brightness difference onto the surface of the object being tested. By using the artificially projected pattern to replace the texture of the sample itself as the basis for evaluating sharpness, it overcomes the technical bottleneck of traditional focusing methods that rely on the texture features of the sample surface. It is suitable for automatic focusing of low-contrast samples such as bare wafers and photoresist surfaces.
[0093] Third, by selecting several feature points as target points and defining the focus area, this invention can flexibly select all or part of the region of interest as the focus area according to the detection requirements, realize flexible switching between full-image focusing and local focusing, effectively reduce the amount of calculation, and improve the focusing speed under high magnification and high resolution conditions.
[0094] Fourth, this invention dynamically adjusts the scanning step size by considering the changing trend of the comprehensive sharpness evaluation value during the focusing scanning process. It uses a large step size for fast searching at a distance from the focus and a small step size for fine searching near the focal plane. It also fits the sharpness evaluation value curve near the focal plane to obtain the peak position, thus balancing focusing speed and accuracy and improving the overall performance of the system.
[0095] Fifth, the present invention utilizes an imaging system with a coaxial projection structure to achieve three-dimensional morphological measurement of the sample surface, and can identify morphological features such as surface warping, step height difference, overall tilt and local unevenness of the sample, thus expanding the functional boundaries of the imaging system.
[0096] In summary, the imaging system, autofocus method, and morphology measurement method for coaxial projection structures provided by this invention establish artificial sharpness features by regularly arranging patterns through coaxial projection, effectively solving the technical problem of low-contrast samples being difficult to autofocus. Furthermore, it achieves surface three-dimensional morphology measurement while completing the focusing process, demonstrating significant technological advancements and industrial application value.
[0097] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the present invention. Various changes and modifications can be made to the present invention without departing from its spirit and scope. All such changes and modifications fall within the scope of the present invention as claimed, which is defined by the appended claims and their equivalents.
Claims
1. An imaging system with a coaxial projection structure, characterized in that, include: An image acquisition device is used to acquire images of the surface of a test object with superimposed regularly arranged projection patterns; An imaging component is used to receive imaging light from the surface of the object being measured, form an imaging optical path, and guide the imaging light to the image acquisition device; A projection component is used to generate a projection beam carrying a regularly arranged pattern, form a projection optical path, and guide the projection beam to the surface of the object being measured. The regularly arranged pattern has multiple feature points that are equally spaced, of the same size, and have no brightness difference. A beam splitter is positioned between the projection optical path and the imaging optical path to achieve coaxial beam combining and splitting of the projection optical path and the imaging optical path, so that the projection optical path and the imaging optical path share the main optical axis of the imaging component. Z-axis moving device, used to drive the object under test or imaging component to move along the Z-axis direction; An image processing device, connected to the image acquisition device, is used to identify the plurality of feature points in the image acquired by the image acquisition device and set a region of interest around each feature point, and process the sharpness value of the region of interest to determine the optimal focus position.
2. The system according to claim 1, characterized in that, The imaging assembly includes an objective lens and a main imaging tube. The objective lens is used to image the surface of the object being measured, and the main imaging tube is used to cooperate with the image acquisition device for imaging. The projection optical path and the imaging optical path share the principal optical axis of the objective lens.
3. The system according to claim 1, characterized in that, The projection component includes: Projection light source, used to provide projection illumination; A pattern carrier on which the pattern is arranged according to the rules is set; A projection imaging component is used to project a regularly arranged pattern on the pattern carrier onto the surface of the object being measured.
4. An autofocus method, characterized in that, The imaging system using the coaxial projection structure as described in any one of claims 1-3 achieves automatic focusing, comprising: Step S1: Project a regularly arranged pattern onto the surface of the object being measured using a projection component. The regularly arranged pattern has multiple feature points that are equally spaced, of the same size, and have no difference in brightness. Step S2: Select several feature points as target points, and define a corresponding region of interest for each target point. Use the regions of interest of all target points as the focus area. Step S3: The Z-axis moving device drives the object under test or imaging component to move along the Z-axis direction according to the current scanning step size. During the movement, the image acquisition device simultaneously acquires images of the surface of the object under test with regularly arranged projection patterns superimposed on it. Step S4: The image processing device calculates the comprehensive sharpness evaluation value of the focused area based on the image acquired in real time by the image acquisition device, records the correspondence between the current Z-axis position and the comprehensive sharpness evaluation value, and constructs a curve showing the change of the comprehensive sharpness evaluation value with the Z-axis position. Step S5: In real time, determine whether the curve of the overall sharpness evaluation value changing with the Z-axis position has reached its maximum peak and entered a monotonically decreasing state; If the peak value has not been reached or the decline range has not yet been entered, the scanning step size is dynamically adjusted according to the current comprehensive sharpness evaluation value, and steps S3-S5 are repeated. If the peak value is reached and the range of decline begins, the optimal focus range is determined. Multiple sampling points are taken to the left and right of the optimal focus range, and a fitting algorithm is used to fit the curve. The Z-axis position corresponding to the maximum value of the fitted curve is then taken as the optimal focus position.
5. The method according to claim 4, characterized in that, Step S4 includes: Step S4.1: The image processing device identifies the feature points corresponding to each target point in the image acquired at the current Z-axis position, and calculates the sharpness value of the region of interest for each target point; Step S4.2: Perform a weighted calculation on the sharpness values of each region of interest within the focused area to obtain a comprehensive sharpness evaluation value; Step S4.3: Record the correspondence between the current Z-axis position and the overall sharpness evaluation value, and construct a curve showing the change of the overall sharpness evaluation value with the Z-axis position.
6. The method according to claim 5, characterized in that, In step S5, the scanning step size is dynamically adjusted based on the current overall sharpness evaluation value, and the next scanning step size is... Specifically, it is obtained from the following formula: ； ； ； in: This is the coarse search step size; For finer search step size, and ; This is the overall sharpness evaluation value at the estimated focus area position; This is the overall sharpness evaluation value at the initial focus position; This is the overall sharpness evaluation value at the current Z-axis position.
7. The method according to claim 6, characterized in that, In step S5, after obtaining the next scan step size, the... Perform out-of-bounds checks, including: like ; like ; Otherwise keep constant.
8. A morphology measurement method, characterized in that, The method of measuring the surface topography of an object using an imaging system with a coaxial projection structure as described in any one of claims 1-3 includes: Step M1: Project a regularly arranged pattern onto the surface of the object being measured using a projection component. The regularly arranged pattern has multiple feature points that are equally spaced, of the same size, and have no difference in brightness. Step M2: Define the corresponding region of interest for each feature point; Step M3: The Z-axis moving device drives the object under test or the imaging component to move along the Z-axis direction. During the movement, the image acquisition device simultaneously acquires images of the surface of the object under test with regularly arranged projection patterns superimposed on it. Step M4: Repeat step M3 to obtain multiple images that correspond one-to-one with the Z-axis position; Step M5: The image processing device identifies all feature points in each image and calculates the sharpness evaluation value of the region of interest for each feature point, and establishes a curve showing the change of the sharpness evaluation value of the feature point with the Z-axis position; Step M6: Based on the curve of the sharpness evaluation value of each feature point changing with the Z-axis position, determine the optimal focus position corresponding to each feature point; Step M7: Calculate the relative height value of each feature point based on the optimal focus position corresponding to each feature point, and combine it with the physical space coordinates obtained by converting the pixel coordinates of each feature point in the image to restore the three-dimensional shape of the surface of the object under test.
9. The method according to claim 8, characterized in that, Step M6 includes: Step M6.1: Calculate the curve of the sharpness evaluation value of each feature point as a function of the Z-axis position, and find its peak range; Step M6.2: Take multiple sampling points on the left and right sides of the peak interval, use a fitting algorithm to fit the curve, find the Z-axis position corresponding to the maximum value of the fitted curve, and take it as the best focus position corresponding to the feature point.
10. The method according to claim 8, characterized in that, Step M7 includes: Step M7.1: Set a reference plane and calculate the Z height value of each feature point relative to the reference plane based on the optimal focus position corresponding to each feature point; Step M7.2: Based on the preset parameters of the image acquisition device, convert the pixel coordinates of each feature point in the image into X and Y coordinates in physical space; Step M7.3: Register the X and Y coordinates of each feature point with the corresponding Z height value to form full-field 3D point cloud data; Step M7.4: Reconstruct the morphological features of the sample surface based on the three-dimensional point cloud data.