Method for detecting the boundary and center of light source illumination

Abstract

The present application provides a method for detecting the boundary and center of illumination of a light source, including scanning the illumination of the original position data. If the illumination value at the detection position exceeds the illumination range (i.e., it represents that the illumination at the current position has exceeded a certain illumination range or is beyond the field of view), the scanning direction is changed, the scanning step length is reduced, and the number of iterations is increased to perform illumination scanning on the detection position; until the number of iterations is completed and there is no illumination value exceeding the field of view, the detection set is determined, that is, the range of the original position data is obtained to predict the boundary. Furthermore, if the predicted boundary is a polygon, an unsupervised clustering algorithm is used to roughly classify the site data, and the target classification and center point are determined by calculating the center of mass in each classification, and finally the RANSC algorithm is used to fit the boundary, and finally a point map is obtained. The embodiments of this specification improve the speed of light source illumination detection and the accuracy of illumination detection.

Description

Technical Field

[0001] The present application relates to the field of semiconductor manufacturing technology, and in particular to a method for detecting the boundary and center of light source illumination. Background Art

[0002] The semiconductor equipment manufacturing industry places high demands on the precision, uniformity, and reliability of UV light sources. Light source uniformity and illumination centering can vary slightly due to production factors, requiring corrections through algorithms and design methods after integration and commissioning.

[0003] Traditional detection methods, such as using a UV intensity sensor to scan the illumination range, obtain the distribution of the relationship between all light intensities within the range and actual positions. Then, through fitting, the boundaries and center of the illumination are determined, and this method is used to calibrate the light source for the integrated device. Another example is the use of image data analysis algorithms such as the Hough transform, which suffers from data redundancy, time-consuming detection, and is only suitable for special occasions.

[0004] Therefore, a new solution for detecting the boundary and center of light source illumination is needed. Summary of the Invention

[0005] In view of this, an embodiment of the present specification provides a method for detecting the boundary and center of illumination of a light source.

[0006] The embodiments of this specification provide the following technical solutions:

[0007] An embodiment of this specification provides a method for detecting the boundary and center of a light source, including:

[0008] Scan positions along a scanning direction using an initial step size and detect changes in illumination values ​​at the detection positions. If the illumination value at a detection position exceeds a preset illumination range, the current detection position is determined to be outside the detection set. Each scanning direction, scanning step size, and number of iterations are preset.

[0009] Reducing the scanning step size from the initial scanning step size to the second step size, increasing the initial iteration number to the second iteration number, and changing the scanning direction to scan. When the iteration number is greater than the second iteration number and the illumination value of the detection position does not exceed the field of view, a detection set is determined, and each point in the detection set is used to predict the boundary.

[0010] If the boundary information is predicted to be a polygon based on the point data in the detection set, an unsupervised clustering algorithm is used to roughly classify the point data, and the target classification and center point are determined by calculating the centroid of each classification;

[0011] The ransc algorithm is used to perform straight line fitting based on each target classification and the corresponding center point. The fitting result is to determine the intersection point of the corresponding boundaries of two adjacent classifications.

[0012] According to the fitting results, after filtering out the abnormal points from the detection set, the target graphic boundary is determined from the predicted boundary and the point drawing is obtained.

[0013] The embodiments of this specification also provide a method for light intensity correction, which adopts the method for detecting the boundary and center of the light source illumination described in the above technical solution. During the debugging process of the template printing equipment, the relationship between the fitting accuracy, initial step size and number of iterations is set, and the ransc confidence threshold is set to fit the field of view of the light intensity and obtain the field of view coordinates.

[0014] An embodiment of this specification also provides a method for wafer edge finding, which adopts the detection method of the light source illumination boundary and center described in the above technical solution, uses a spectral sensor in the wafer bonding equipment to collect data based on the set boundary and the equation of the circle to perform RANSC least squares fitting, and determines the center and radius of the wafer after removing the interference points.

[0015] Compared with the prior art, the at least one technical solution adopted in the embodiments of this specification can achieve the following beneficial effects:

[0016] The embodiments of this specification use variable step-size scanning to obtain valid data points. By always detecting the data corresponding to the boundary position, the field of view edge information corresponding to the entire boundary range is divided. Prior information is obtained through prediction, and the prior conditions are added to subsequent calculations to improve the fitting results. The fitting process can not only fit geometric figures with general trajectory equations, but also fit polygons, ultimately obtaining accurate results. While improving fitting accuracy, it also improves detection efficiency; it can also adapt to different scenarios and fit a variety of different graphics, with universal applicability. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0018] Figure 1 is a flow chart of a detection method for obtaining boundaries and centers provided in an embodiment of this specification;

[0019] Figure 2 This is a flow chart of determining the boundary and center based on the geometric boundary of the illumination range provided in the embodiment of this specification;

[0020] Figure 3This is a flowchart of obtaining the most central position as the center provided in an embodiment of this specification;

[0021] Figure 4 Schematic diagram of the field of view coordinates during the light intensity calibration process provided in the embodiments of this specification. DETAILED DESCRIPTION

[0022] The embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0023] The following describes the embodiments of the present application through specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The present application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that, in the absence of conflict, the features in the following embodiments and embodiments can be combined with each other. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without making creative work are within the scope of protection of this application.

[0024] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein can be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on this application, it should be understood by those skilled in the art that an aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number and aspect described herein can be used to implement an apparatus and / or practice a method. In addition, other structures and / or functionalities other than one or more of the aspects described herein can be used to implement this apparatus and / or practice this method.

[0025] It should also be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present application. The illustrations only show components related to the present application and are not drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component can be changed at will, and the component layout type may also be more complicated.

[0026] It should be noted that the terms "first," "second," "third," "fourth," and so on (if any) in the specification and claims of this application and the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the terms used in this manner are interchangeable where appropriate, so that the embodiments of the present invention described herein can be implemented in sequences other than those illustrated or described herein.

[0027] Additionally, in the following description, specific details are provided to provide a thorough understanding of the examples, however, one skilled in the art will appreciate that the examples can be practiced without these specific details.

[0028] Traditional detection methods require traversing the illumination range. For example, a UV intensity sensor is used to traverse the illumination range, obtaining the distribution of the relationship between all light intensities within the range and actual positions. The boundary and center of the illumination are then determined through fitting. Another example is the use of image data analysis, such as the Hough transform. This requires traversing the entire field of view, scanning both outside and within the boundaries, resulting in redundant data processing and a lengthy processing time. Furthermore, this method is only applicable to specific detection scenarios and is not universally applicable.

[0029] In addition, traditional detection methods lack the ability to distinguish the real-time and authenticity of data, and sometimes require manual deletion of interfering information, resulting in reduced detection credibility.

[0030] Based on this, the embodiments of this specification propose a new detection scheme for the boundary and center of light source illumination: it mainly obtains parameters such as the center and boundary through fitting using the geometric figures corresponding to the illumination range. This scheme uses a variable step size to scan the illumination at the detection position to obtain valid data points, reducing data redundancy processing. It uses an unsupervised clustering algorithm to predict the data and obtain prior information, and preprocesses the data to remove interference. It is also applicable to fitting targets with multiple deformations, performs coarse classification on the original data and obtains the center point. Finally, it uses ransc to perform boundary fitting to obtain the final accurate result.

[0031] The following describes the technical solutions provided by various embodiments of the present application in conjunction with the accompanying drawings.

[0032] like Figure 1 and Figure 2 As shown, the embodiment of this specification provides a method for detecting the boundary and center of light source illumination.

[0033] First, the issue is obtaining valid data. In practice, a large amount of data falls within the geometric boundaries of the illumination range. Processing all of this data results in redundancy, time-consuming, and inflexible. Therefore, in the embodiments of this specification, variable-step scanning is used to obtain valid data points. This means that by consistently detecting data corresponding to the boundary positions, the field of view edge information corresponding to the entire boundary range is divided.

[0034] Secondly, after obtaining the field of view edge information, the data can be preprocessed to remove interference, that is, prior information can be obtained through prediction, and the prior conditions can be added to subsequent calculations to improve the fitting results.

[0035] Thirdly, the target is increased as polygon in the fitting process, that is, the fitting of a mathematical model without a general trajectory equation is added. Specifically, the final accurate result is obtained by roughly classifying the original data and then using ransc to fit it.

[0036] Specifically, the process includes steps S101 to S105. In step S101, a position scan is performed along a scanning direction using an initial step length, and changes in illumination values ​​at the detection positions are detected. When the illumination value at a detection position exceeds a preset illumination range, it is determined that the current detection position exceeds the detection set.

[0037] In actual operation, various conditions exist within the illumination field of view, such as uniform illumination, illumination attenuation, and intermittent illumination. The embodiments of this specification use a variable step size to perform preliminary segmentation of effective illumination data, primarily by detecting data points at the boundaries of illumination changes to obtain edge information for the entire illuminated area. Various scanning directions, scanning step sizes, number of iterations, and search ranges can be preset.

[0038] An initial step size is set to scan along a scanning direction and detect the illumination value in real time. When the illumination value of a detection position exceeds a preset illumination range, it is determined that the current detection position exceeds the detection set.

[0039] The preset illumination range can be specifically limited based on actual conditions. For example, if the preset illumination range includes conditions outside the field of view, i.e., if the illumination value cannot be detected, the preset illumination range may be less than the preset illumination value. For another example, if the preset illumination range corresponds to a transition from bright to dark, the preset illumination range may be the preset illumination range corresponding to strong light.

[0040] When a large change in the illuminance value is detected, it means that the detection position has reached the illumination boundary; or when the illuminance value is less than the preset illuminance value, that is, it exceeds the field of view range, it is necessary to further determine the field of view edge information by changing the scanning step size, etc.

[0041] Step S102: Reduce the scanning step size from the initial scanning step size to the second step size, increase the initial iteration number to the second iteration number, and change the scanning direction. When the overlap number is greater than the second iteration number and the illumination value of the detection position does not exceed the field of view, the detection set is determined, and the points in each detection set are used to predict the boundary.

[0042] In conjunction with the above embodiment, variable-step-size scanning is performed by changing the scanning direction, reducing the step size, and increasing the number of iterations. For example, the scanning step size is reduced from the initial scanning step size to a second step size, the initial number of iterations is increased to the second number of iterations, and the scanning direction is changed. When the number of iterations exceeds the second number of iterations and no illumination value at the detection position exceeds the field of view, a detection set is determined, and each point in the detection set is used to predict the edge. The points in the detection set correspond to the edge points of the field of view.

[0043] In some embodiments, if the initial scanning direction shows a significant change in illumination, it indicates that the detection position has reached the illumination boundary. The scanning direction is changed, the step size is reduced, and the number of iterations is increased. If the illumination value at the detection position changes again, such as if it falls below a preset illumination value, it indicates that the detection position has exceeded the illumination range. The scanning direction is changed again, the step size is reduced, and the number of iterations is increased. When the number of iterations exceeds the set value, the required accuracy is achieved. In this way, the boundary information is always detected, and the edge information of the field of view within the entire boundary range is ultimately obtained.

[0044] It should be noted that the variable-step scanning employed in this manual not only minimizes the step size, thereby improving detection accuracy, but also utilizes the Ransc algorithm for post-processing fitting. Applied to semiconductor inspection, this algorithm automatically removes interfering data based on a set confidence threshold, resulting in superior fitting results. The Ransc algorithm has been widely applied in image-related fields, such as image stitching and feature matching.

[0045] It should also be noted that the predicted boundary of the light source change is obtained, the data is stored, and subsequent calculations are performed. The scanning algorithm can control the accuracy based on the set number of iterations. Here, a binary search or gradient descent algorithm is used.

[0046] In some embodiments, the method further includes scanning according to the unreduced scanning step size and the current number of iterations when the illumination value at the detection position does not exceed the preset illumination range, until the number of iterations is greater than the set value to complete the boundary scan.

[0047] Specifically, when the illumination value is within a corresponding field of view, such as a light-dark area, there is no need to perform variable step scanning, and the current scan is continued, that is, scanning is performed according to the unreduced scanning step and the current number of iterations until the number of iterations is greater than the set value to complete the boundary scan.

[0048] Step S103: If the boundary information is predicted to be a polygon based on the point data in the detection set, an unsupervised clustering algorithm is used to minimize the influence of the effective boundary data, and the point data is roughly classified. The centroid of each classification is calculated to determine the target classification and center point. The unsupervised clustering algorithm includes but is not limited to k-means, SVM, k-medoids algorithm, etc.

[0049] By combining the above variable-step scanning process with valid data points, we can obtain a detection set, and then acquire geometric figures based on the target requirements. If the detection set corresponds to a mathematical model with a general trajectory equation when fitting the field of view edge information, subsequent processing can directly apply the RANSC algorithm to perform noise-reduction and interference-removal fitting, thereby obtaining accurate results.

[0050] If the fitting target is a polygon, a rough classification is required, and then ransc is used for fitting to obtain the final accurate result.

[0051] In some embodiments, to improve the accuracy of field-of-view edge fitting, the raw data must be preprocessed to obtain a priori information to determine the boundaries and extent of the polygon. This is especially true when the center point is extremely inaccurate, which can distort the data distribution and result in inaccurate geometric shapes.

[0052] For example, if k-means is used to select the average value of the cluster in the detection set as the new center, it will iterate until the distribution of objects in the cluster no longer changes. However, this may result in extremely inaccurate center points. Using the k-means algorithm does not achieve high accuracy because of the significant interference detected during the illumination acquisition process. Therefore, based on the calculation of clusters in k-means, a centroid algorithm is used to eliminate this interference. That is, the center of mass of the most central object in the center of the cluster is used as the centroid. This method is similar to the k-medoids method of finding the centroid, which improves accuracy, detects faster, and converges in a fixed number of times.

[0053] In other embodiments, the process of performing rough classification on the point data and directly calculating the centroid is similar to using a priori conditions to remove interference to obtain the target classification and center point.

[0054] Step S104: perform straight line fitting using the ransc algorithm according to each target category and the corresponding center point. The fitting result is to determine the intersection point of the corresponding boundaries of two adjacent categories, thus completing the fitting process.

[0055] In some embodiments, if the boundary information predicted according to the point data in the detection set is a geometric figure with a curve equation, the ransc algorithm is directly applied to perform anti-noise and interference fitting to obtain a point map.

[0056] Specifically, in the process of fitting the edge information of the field of view, if the fitting target is a mathematical model with a general trajectory equation, the subsequent processing can directly apply the ransc algorithm to use the geometric figures represented by the curve equation to achieve anti-noise and interference fitting to obtain accurate results.

[0057] In other embodiments, if the fitting target is a polygon, the target category and center point are determined by combining the above calculation of the centroid in each category, and the ransc algorithm is used for straight line fitting. The fitting result is to determine the intersection of the corresponding boundaries of two adjacent categories.

[0058] To eliminate interference, the raw data is fitted with outliers based on the prior condition of the cluster average. This is done by combining the aforementioned clustering algorithm to obtain a priori information. Locations within the range of a polygonal edge are grouped together, while locations that are a certain distance away from the priori information are considered to be within the range of a polygonal edge and are grouped together in another category. These categories are then fitted with straight lines using the ransc algorithm. After multiple iterations, a highly reliable result is obtained, which determines the intersection of the boundaries of two adjacent categories. The intersection of these boundaries is the intersection of the polygons and is near the priori information.

[0059] Another example is to directly use the centroid to determine the target classification and center point, which is similar to using prior conditions to remove interference. The ransc algorithm is used for straight line fitting to achieve anti-noise and interference fitting, thereby determining the intersection of the corresponding boundaries of two adjacent target classifications.

[0060] In the embodiments of this specification, unsupervised clustering and other machine learning algorithms preprocess the data to obtain prior conditions, then set a range based on the prior conditions and adaptively remove interference information to obtain the result, greatly improving efficiency. Alternatively, in the process of calculating the center point, a centroid calculation method is used to directly perform a process similar to the one that includes preprocessing to obtain the prior conditions, and finally adaptively remove interference information to obtain the result, greatly improving efficiency.

[0061] Step S105: According to the fitting result, after filtering out abnormal points from the detection set, the target graphic boundary is determined from the predicted boundary and a point map is obtained.

[0062] Specifically, during the data mapping process, the fitting results are mapped using the remaining points after filtering out the original points, thereby determining the target graphic boundary within the predicted boundary and obtaining a point mapping. Observation has verified that the point mapping obtained in the embodiments of this specification is not only applicable to specific scenarios, but can also obtain precise point mapping for polygonal targets, such as those suitable for high-precision calibration of semiconductor equipment. The module design can adapt to different scenarios and target graphics, and has excellent results in calculating illumination uniformity and high-precision fitting of center and edge points, demonstrating its universal applicability.

[0063] The detection method of the embodiment of this specification has the following beneficial effects:

[0064] 1. Improve detection speed: The existing method traverses the entire field of view, such as the range outside the boundary of the target graphic and the range inside the boundary, there is redundant data. The present application uses a smaller step size to always detect the boundary information, and finally obtains the edge information of the field of view within the entire boundary range. The use of a reduced step size not only improves the processing accuracy, but also compared with the existing method with a time complexity of O(n^2), that is, processing n data requires n^2, that is, n*n operations, while the present application uses a variable step size method, processing log(n) data requires log2(n) operations, and the time complexity is O(log2(n)), and does not scan the range outside and inside the boundary, which shortens the time spent on scanning under high precision and shortens the detection time.

[0065] 2. It has high decoupling and is suitable for high-precision calibration of semiconductor equipment. The module design can adapt to different scenes and different target graphics, and has good effects on the calculation of illumination uniformity and high-precision fitting of center points and edge points.

[0066] 3. Improve accuracy. Variable step size can make the step size very small, which makes the detection accuracy very high. The subsequent data processing uses the ransc algorithm for fitting. When applied to the field of semiconductor detection, it can automatically delete interference data according to the set confidence threshold, thereby obtaining a good fitting result.

[0067] 4. No human intervention. Compared with the existing methods, which require designing prior conditions and removing interference points after collecting data, which consumes manpower, this application uses unsupervised clustering and other machine learning algorithms to pre-process the data to obtain prior conditions, and then sets the range according to the prior conditions, adaptively removes interference information to obtain results, greatly improving efficiency.

[0068] In some embodiments, an unsupervised clustering algorithm is used to perform a preliminary classification of site data, and the target category and center point are determined by calculating the centroid point in each category, including: presetting the preliminary category and the initial cluster center corresponding to each category from the detection set; assigning sample points to each category by calculating the distance from each site to each of the initial cluster centers in the detection set, wherein the sample points in each category are determined based on whether the distance between the sample point and the initial cluster center meets a first threshold; within each classification sample range, the initial cluster center is moved and updated to a new cluster center according to the average value of all samples, and each classification sample range is updated each time the cluster center is updated; when the moving distance when the initial cluster center is updated to the new cluster center is less than the second threshold, the first center point corresponding to each category is determined; within each classification sample range, the point closest to the first center point is updated as the center of mass; when the distance between the previous center of mass and the current center of mass is less than a third threshold, the target center of mass of the target category is obtained, that is, the target category is obtained.

[0069] Specifically, taking the K-means algorithm as an example, the cluster centers are initialized first. This step requires randomly selecting k samples as the initial cluster centers, where k is a given number. In some embodiments, the points in the detection set are used as samples, and the initial cluster centers are initialized from each detection set.

[0070] Next, samples are assigned to the k cluster centers. The distance between each sample and each cluster center is calculated, and each sample is assigned to a cluster center that meets a preset condition, such as the distance between the sample and the cluster center meeting a first threshold. The cluster center is then moved to the average value of all samples in that cluster. Simultaneously, the sample range for each category is updated. The sample assignment process is the same as above and will not be further described here.

[0071] Finally, until the distance the cluster center moves is less than a set threshold or remains unchanged, the movement stops, indicating that the data has reached convergence. According to the current scenario, the k we set is known.

[0072] That is, the K-means algorithm is used to obtain prior information by taking the cluster average as the new center, and then the centroid algorithm is used to obtain the most central position in the cluster as the center point.

[0073] Combined with the process of obtaining prior information, within each classification sample range, the point closest to the first center point is updated as the centroid, and each classification sample range is updated to obtain the target classification; when the distance between the previous centroid and the current centroid is less than the third threshold, the target centroid and target classification of the target classification are obtained.

[0074] In some embodiments, an unsupervised clustering algorithm is used to perform a preliminary classification of site data, and the target classification and center point are determined by calculating the centroid in each classification, which also includes: presetting the preliminary classification and the initial cluster center corresponding to each classification from the detection set; assigning sample points to each classification by calculating the distance from each site to each of the initial cluster centers in the detection set, wherein the sample points in each classification are determined based on whether the distance between the sample point and the initial cluster center meets a fourth threshold; within each classification sample range, the point closest to the initial cluster center is updated as the centroid, and each classification sample range is updated to obtain the target classification; when the distance between the previous centroid and the current centroid is less than the fifth threshold, the target centroid of each class is obtained and the target classification is obtained.

[0075] Specific as Figure 3 As shown in the figure, the k-medoids algorithm directly calculates the centroid of each classification to determine the target classification and center point. The main process is as follows:

[0076] First, input data data and the number of classes k, where data is a set of x and y points, such as the detection set. k is the number of detection sets.

[0077] k classes need to be initialized with k initial points, usually random points. The positions of the points will be continuously updated in subsequent iterations until the updated position offset is less than a certain threshold, which means the algorithm has converged.

[0078] Then, we enter a loop, traverse all points, calculate the distance of each point to the k centroids, select the centroid whose distance to the point satisfies the fourth threshold, and determine that the point belongs to this class. This is the process of assigning sample points to each category.

[0079] Calculate the centroids of all points in all classes separately, select the points closest to the centroids as the new centroids of these classes, and update the samples in each class in the same way as the process of allocating sample points mentioned above.

[0080] Finally, the cycle is repeated until the distance between the new centroid and the previous centroid is less than a certain threshold, indicating that the algorithm has reached convergence. That is, the target classification and the corresponding target centroid are obtained.

[0081] In some embodiments, each sample corresponds to a silhouette coefficient, and the range of each classification sample is determined by the silhouette coefficient; the silhouette coefficient of each sample in the target classification is greater than or equal to -1 and less than or equal to 1; wherein the silhouette coefficient includes cohesion and separation.

[0082] Specifically, in the k-means example, a contour system is used to determine the convergence of the scan during the continuous iteration process. Each sample has a corresponding silhouette coefficient, which consists of two parts:

[0083] a: The average distance between the sample and other sample points in the same cluster (quantified cohesion)

[0084] b: The average distance between the sample and all sample points in the nearest cluster (quantified separation)

[0085] max(a,b) means taking the maximum value of a and b.

[0086] The silhouette coefficient for each sample is defined as:

[0087]

[0088] In the process of obtaining the target classification and the center point, the silhouette coefficient of each sample in the target classification is greater than or equal to -1 and less than or equal to 1.

[0089] In some embodiments, the average distance between all points in the target classification and the target centroid meets a preset threshold.

[0090] Taking the k-means example again, during the continuous iteration process, SSE is used to determine the convergence of the scan.

[0091] Among them, SSE:

[0092]

[0093] Given a data set D containing n data objects, D = {x1, x2, x3, ..., x n}, define the category set generated by clustering through K-means algorithm as set C = {C1, C2, C3, ..., C n}, where m k It is cluster C k The center point is calculated as follows:

[0094]

[0095] SSE is the sum of squared errors, which mainly measures the average distance between all data in a class and the center point of the data cluster. The smaller the SSE, the denser the data cluster, that is, the better the clustering effect. That is, the average distance between all points in the target classification and the target centroid meets the preset threshold.

[0096] In some embodiments, it also includes: in each classification, with the target centroid as the center, drawing a circle with a gradually increasing radius until a target circle is formed with the initial step length as the radius, and determining that the target circle includes the initial cluster center, then determining the target centroid.

[0097] After obtaining the target centroid, the accuracy of the target centroid is verified by the following method. Taking k-means as an example, a circle with a gradually increasing radius is made with the center obtained by k-means as the center. The initial step size S is set to increase gradually until the circle contains the actual sampling point, such as Figure 3 As shown, the k-medoids algorithm is used to obtain cluster centers by obtaining sampling points such as the initial cluster center. For example, the target centroid is used to obtain the object at the center of the cluster. The initial cluster center can be similar to the first point in the k-medoids algorithm. The first point is assumed to be a theoretical point in space, calculated by calculating the distance and weight of surrounding points. This point is set as a virtual point and does not actually exist in the feature space. With this virtual point as the center, the radius is expanded until it contains the first real point, and this real point is used as the true centroid to obtain the true centroid. The initial cluster center can be a real point.

[0098] In combination with the above embodiments, the embodiments of this specification provide a method for light intensity correction. During the debugging process of the template printing equipment, the relationship between the fitting accuracy, initial step size and number of iterations is set, and the ransc confidence threshold is set to fit the light intensity field and obtain the field of view coordinates.

[0099] Specifically, during the debugging process of the template printing device, the center of the field of view for calibrating the light intensity is applied to the detection algorithm for the light source illumination boundary and center of the above embodiment. If the fitting accuracy is set to be less than a, the initial step size is s, and the number of iterations is n, the following relationship is obtained:

[0100]

[0101] Right now

[0102]

[0103] Set parameters according to accuracy requirements, communicate with the industrial computer to control the coarse motion stage and each sensor to collect data in real time. When the collection is completed, determine whether a priori is needed based on the mathematical model of the scan target, and then set the ransc confidence threshold and the number of iterations to obtain the fitting result, as shown below Figure 4 As shown in the figure, the straight line is the final accurate fit result, the large X marks the intersection of the accurate fit, and the small X marks the prior conditions. The trusted points considered by the ransc algorithm are dark dots, and the removed points are light dots. Obtaining accurate field of view coordinates can improve the accuracy of the device.

[0104] In combination with the above embodiments, the embodiments of this specification provide a method for wafer edge finding, which uses a spectral sensor in a wafer bonding device to collect data based on the set boundary and the equation of the circle to perform ransc least squares fitting, and determines the center and radius of the wafer after removing interference points.

[0105] Specifically, in wafer bonding equipment, a spectral confocal sensor is used to find the edge of the wafer. Due to problems such as unstable cutting angle and position deviation at the edge of the wafer, the sensor collects unstable data. In this scenario, the above-mentioned light source illumination boundary and center detection algorithm is applicable to fit the edge and calculate the center.

[0106] Set the boundary acquisition data as required. When the spectral confocal sensor data has a sudden change, it means that the current position is near the wafer boundary. Collect multiple boundary information and perform RANSC least squares fitting based on the general equation of the three-dimensional circle as the mathematical model according to the following formula.

[0107] x 2 +y 2 +Dx+Ey+F=0 (Formula 3)

[0108] Collect data at multiple data points to obtain a linear equation system:

[0109]

[0110] Set the number of iterations to the confidence level and perform ransc iteration to obtain D, E, and F after removing the interference. The center and radius of the circle are obtained according to the formula:

[0111]

[0112] Among them, D, E, and F are constants.

[0113] The embodiments of this specification are applicable to semiconductor equipment precision calibration, equipment stability testing, system error correction, non-contact anti-interference high-precision fitting, and are particularly suitable for high-precision calibration of semiconductor testing equipment, imprinting equipment, lithography equipment, etc.

[0114] In combination with the above embodiments, the traditional detection method has the following shortcomings:

[0115] 1. Obtain data redundancy. According to the test performance, we only need the boundary information of the geometric figure within the illumination range to obtain its center, boundary and other parameters through fitting. However, in actual operation, a large amount of data is within the geometric boundary range of this illumination. This data is redundant, not only time-consuming but also inflexible. If you want to improve the detection accuracy, you need a smaller detection step size.

[0116] 2. Low flexibility. After obtaining the data, the traditional method requires post-processing to reduce interference, and then perform least squares fitting to obtain parameters. This method has very high requirements for the real-time and authenticity of the data. If necessary, it is necessary to manually delete the interference information to obtain more reliable results.

[0117] 3. The scope of application is narrow, and traditional methods can only be applied to specific scenarios.

[0118] 4. Low anti-interference ability. If the traditional method is directly fitted, noise, anomalies and other information will be added to the result. Therefore, manual post-processing of the data is required, which is time-consuming and labor-intensive.

[0119] The same or similar parts between the various embodiments in this specification can be referred to in conjunction with each other. Each embodiment focuses on the differences from other embodiments. In particular, for the product embodiments described later, since they correspond to the methods, the description is relatively simple. For relevant parts, please refer to the partial description of the system embodiment.

[0120] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A method for detecting the boundary and center of a light source, characterized in that: include: Scan positions along a scanning direction using an initial step size and detect changes in illumination values ​​at the detection positions. If the illumination value at a detection position exceeds a preset illumination range, the current detection position is determined to be outside the detection set. Each scanning direction, scanning step size, and number of iterations are preset. Reducing the scanning step size from the initial scanning step size to the second step size, increasing the initial iteration number to the second iteration number, and changing the scanning direction to scan. When the iteration number is greater than the second iteration number and the illumination value of the detection position does not exceed the field of view, a detection set is determined, and each point in the detection set is used to predict the boundary. If the boundary information is predicted to be a polygon based on the point data in the detection set, an unsupervised clustering algorithm is used to roughly classify the point data, and the target classification and center point are determined by calculating the centroid of each classification; The ransc algorithm is used to perform straight line fitting based on each target classification and the corresponding center point. The fitting result is to determine the intersection point of the corresponding boundaries of two adjacent classifications. According to the fitting results, after filtering out the abnormal points from the detection set, the target graphic boundary is determined from the predicted boundary and the point drawing is obtained.

2. The method for detecting the light source illumination boundary and center according to claim 1, characterized in that: An unsupervised clustering algorithm is used to roughly classify the site data, and the centroid points in each classification are calculated to determine the target classification and center point, including: Preset preliminary classification and initial cluster centers corresponding to each classification from the detection set; Assigning a sample point to each category by calculating the distance from each site to each of the initial cluster centers in the detection set, wherein the sample point in each category is determined based on whether the distance between the sample point and the initial cluster center satisfies a first threshold; Moving and updating the initial cluster center to a new cluster center according to the average value of all samples within each classification sample range, and updating each classification sample range each time the cluster center is updated; When the initial cluster center is updated to the new cluster center and the moving distance between the two is less than a second threshold, determining the first center point corresponding to each classification; In each classification sample range, the point closest to the first center point is updated as the centroid; When the distance between the previous centroid and the current centroid is less than a third threshold, the target centroid of the target classification is obtained and the target classification is obtained.

3. The method for detecting the light source illumination boundary and center according to claim 1, characterized in that: An unsupervised clustering algorithm is used to roughly classify the site data, and the centroid of each classification is calculated to determine the target classification and center point. It also includes: Preset preliminary classification and initial cluster centers corresponding to each classification from the detection set; Assigning a sample point to each category by calculating the distance from each point to each of the initial cluster centers in the detection set, wherein the sample point in each category is determined based on whether the distance between the sample point and the initial cluster center satisfies a fourth threshold; In each classification sample range, the point closest to the initial cluster center is updated as the centroid, and each classification sample range is updated to obtain the target classification; When the distance between the previous centroid and the current centroid is less than the fifth threshold, the target centroid of each category is obtained and the target classification is obtained.

4. The method for detecting the light source illumination boundary and center according to claim 2 or 3, characterized in that: Each sample has a corresponding silhouette coefficient, and the range of each classification sample is determined by the silhouette coefficient; The silhouette coefficient of each sample in the target classification is greater than or equal to -1 and less than or equal to 1; The silhouette coefficient includes cohesion and separation.

5. The method for detecting the light source illumination boundary and center according to claim 2 or 3, characterized in that: The average distance between all points in the target classification and the target centroid meets a preset threshold.

6. The method for detecting the light source illumination boundary and center according to claim 2 or 3, characterized in that: Also includes: In each classification, a circle with a gradually increasing radius is drawn with the target centroid as the center until a target circle is formed with the initial step length as the radius, and it is determined that the target circle includes the initial cluster center, and then the target centroid is determined.

7. The method for detecting the light source illumination boundary and center according to claim 1, characterized in that: Also includes: When the illumination value at the detection position does not exceed the preset illumination range, scanning is performed according to the unreduced scanning step size and the current number of iterations until the number of iterations is greater than the set value to complete the boundary scan.

8. The method for detecting the light source illumination boundary and center according to claim 1, characterized in that: Also includes: If the boundary information predicted according to the point data in the detection set is a geometric figure with a curve equation, the ransc algorithm is directly applied to perform anti-noise and interference fitting to obtain a point drawing.

9. A method for light intensity calibration, characterized in that: The method for detecting the boundary and center of light source illumination as described in any one of claims 1 to 8 is adopted. During the debugging process of the template printing equipment, the relationship between the fitting accuracy, the initial step size and the number of iterations is set, and the ransc confidence threshold is set to fit the field of view of the light intensity and obtain the field of view coordinates.

10. A wafer edge finding method, characterized in that: The method for detecting the boundary and center of light source illumination as described in any one of claims 1 to 8 is adopted, and a spectral sensor is used in the wafer bonding equipment to collect data according to the set boundary and the equation of the circle to perform RANSC least squares fitting, and the center and radius of the wafer are determined after removing the interference points.

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