GSG probe tip positioning method based on image processing
Through Gaussian blur and downsampling compressed images, combined with adaptive threshold segmentation and rotation matching, the accuracy and efficiency problems of GSG probe tip positioning are solved, and high-precision probe tip positioning and accurate positioning of wafer areas are achieved.
Patent Information
- Application Number
- CN202510471305.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-15
- Publication Date
- 2025-07-22
AI Technical Summary
In the prior art, the accuracy and efficiency of the GSG probe tip positioning are low, and are susceptible to noise and background interference, which affects the subsequent measurement accuracy.
The image is compressed by Gaussian blur and downsampling algorithm, combined with adaptive threshold segmentation and rotation matching methods, and the probe tip is positioned through image preprocessing and linear detection algorithm.
It improves the accuracy and efficiency of probe tip positioning, enhances the accuracy of Z-direction displacement measurement, and reduces human interference.
Smart Images
Figure CN120355687A_ABST
Abstract
Description
Technical Field
[0001] Embodiments of the present disclosure relate to the field of image processing technologies, and in particular, to a method for positioning the tip of a GSG probe based on image processing. Background Art
[0002] Currently, in measuring the Z-direction displacement of a microwave probe station in the wafer and tip regions, visual images are required to determine the positions of the two. The accuracy of tip positioning seriously affects the subsequent focusing measurement accuracy. In image processing, the template matching algorithm is commonly used to achieve the recognition and positioning of the target. After setting the template, the search starts from the upper left corner of the image to be matched, and the similarity metric value between the template and each region is calculated according to the feature relationship, and the image matching situation is judged by the metric value. However, the area occupied by the tip in the image is small, and directly matching it is vulnerable to noise and background interference, and the positioning accuracy is low.
[0003] It can be seen that there is an urgent need for a method for positioning the tip of a GSG probe based on image processing with high positioning efficiency and accuracy. Summary of the Invention
[0004] In view of this, embodiments of the present disclosure provide a method for positioning the tip of a GSG probe based on image processing, which at least partially solves the problem of poor positioning efficiency and accuracy in the prior art.
[0005] Embodiments of the present disclosure provide a method for positioning the tip of a GSG probe based on image processing, including:
[0006] Step 1, obtaining the original images of the wafer and the probe station and compressing them based on the Gaussian blur and downsampling algorithms to obtain compressed images;
[0007] Step 2, performing threshold segmentation on the compressed images to obtain initial binary images;
[0008] Step 3, using the rotation matching method to locate the probe region and the probe angle in the initial binary images;
[0009] Step 4, performing image preprocessing on the probe region in the original images according to the probe angle to obtain binary images of the probe region;
[0010] Step 5, performing line detection in the binary images of the probe region to locate the probe tip.
[0011] According to a specific implementation manner of the embodiments of the present disclosure, the specific steps of Step 1 include:
[0012] Step 1.1, taking the original images as the bottom layer of the Gaussian pyramid and performing convolution operations on the images using Gaussian convolution kernels;
[0013] Step 1.2, delete the even rows and even columns of the convolved image, and place the image on the upper layer of the original image;
[0014] Step 1.3, according to the required number of layers, repeat Gaussian blurring and downsampling, and finally reduce the image information to obtain a compressed image.
[0015] According to a specific implementation manner of an embodiment of the present disclosure, the step 2 specifically includes:
[0016] Step 2.1, calculate the grayscale histogram of the compressed image, and find the peak points of the histogram;
[0017] Step 2.2, determine the leftmost peak point in the grayscale histogram, determine the adjacent right trough, and use the value corresponding to the trough position as the adaptive threshold;
[0018] Step 2.3, based on the adaptive threshold, perform threshold segmentation on the compressed image to obtain an initial binary image.
[0019] According to a specific implementation manner of an embodiment of the present disclosure, the step 3 specifically includes:
[0020] After obtaining the template image, perform a rotation transformation on it, and match it with the initial binary image to determine the probe area and probe angle in the initial binary image.
[0021] According to a specific implementation manner of an embodiment of the present disclosure, step 4 specifically includes:
[0022] Step 4.1, rotate the original image according to the probe angle so that the probe tip points downward, and intercept the probe area;
[0023] Step 4.2, convert the three-channel color image of the probe area into a single-channel grayscale image;
[0024] Step 4.3, perform image smoothing on the grayscale image through Gaussian filtering;
[0025] Step 4.4, use threshold segmentation to obtain a binary image of the probe area for the smoothed grayscale image, and use morphological processing to fill the holes to enhance the straight-line feature of the tip.
[0026] According to a specific implementation manner of an embodiment of the present disclosure, the step 5 specifically includes:
[0027] Step 5.1, set a straight-line ratio threshold, traverse row by row from the bottom of the probe in the binary image of the probe area, and record the straight-line points to form a data point set;
[0028] Step 5.2, find the minimum value and the maximum value in the data point set, and determine the straight-line coordinates on the left and right sides of the tip;
[0029] Step 5.3: Search upward step by step along the left and right straight line ranges until the sizes of all pixel points are 0, determine the connection part between the tip and the needle body, and finally determine the position of the probe tip.
[0030] The GSG probe tip positioning solution based on image processing in the embodiments of the present disclosure includes: Step 1, obtain the original images of the wafer and the probe stage and compress them based on the Gaussian blur and downsampling algorithms to obtain compressed images; Step 2, perform threshold segmentation on the compressed images to obtain initial binary images; Step 3, use the rotation matching method to locate the probe area and the probe angle in the initial binary images; Step 4, perform image preprocessing on the probe area in the original images according to the probe angle to obtain a binary image of the probe area; Step 5, perform line detection in the binary image of the probe area to locate the probe tip.
[0031] The beneficial effects of the embodiments of the present disclosure are as follows: Through the solution of the present disclosure, first use the multi-angle template matching algorithm with adaptive threshold to perform threshold segmentation and image processing on the original images to locate the probe area; then determine the tip position through the image preprocessing algorithm and the line detection algorithm to eliminate the interference of human factors in manual positioning, realize high-precision probe tip positioning and wafer area positioning, improve the accuracy of the Z-direction displacement measurement of the probe stage, and improve the positioning efficiency and accuracy. Description of the Drawings
[0032] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings required to be used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present disclosure. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.
[0033] Figure 1 It is a flowchart of a method for positioning the tip of a GSG probe based on image processing provided by an embodiment of the present disclosure;
[0034] Figure 2 It is a schematic diagram of a sampling pyramid provided by an embodiment of the present disclosure;
[0035] Figure 3 It is a flowchart of a multi-angle template matching algorithm with adaptive threshold provided by an embodiment of the present disclosure;
[0036] Figure 4 It is a flowchart of a tip line detection algorithm provided by an embodiment of the present disclosure. Detailed Embodiments
[0037] The embodiments of the present disclosure will be described in detail below with reference to the drawings.
[0038] The following describes the embodiments of the present disclosure through specific examples, and those skilled in the art can easily understand the other advantages and effects of the present disclosure from the content disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all the embodiments. The present disclosure can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present disclosure. It should be noted that, without conflict, the following embodiments and the features in the embodiments can be combined with each other. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present disclosure without creative efforts belong to the scope of protection of the present disclosure.
[0039] It should be noted that the following describes various aspects of embodiments within the scope of the appended claims. It should be obvious that the aspects described herein can be embodied in a wide variety of forms, and any specific structure and / or function described herein is illustrative only. Based on the present disclosure, those skilled in the art should understand that one 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 of aspects described herein can be used to implement the device and / or practice the method. Additionally, this device and / or method can be implemented using other structures and / or functionality in addition to one or more of the aspects described herein.
[0040] It should also be noted that the diagrams provided in the following embodiments only schematically illustrate the basic concept of the present disclosure. The diagrams only show the components related to the present disclosure and are not drawn according to the number, shape, and size of the components in actual implementation. The type, quantity, and ratio of each component in its actual implementation can be arbitrarily changed, and the component layout type may also be more complex.
[0041] In addition, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, those skilled in the art will understand that the described aspects can be practiced without these specific details.
[0042] The embodiments of the present disclosure provide a method for positioning the tip of a GSG probe based on image processing, and the method can be applied to the process of positioning the tip of the probe.
[0043] See Figure 1 , which is a schematic flowchart of a method for positioning the tip of a GSG probe based on image processing provided by the embodiments of the present disclosure. As Figure 1 shown, the method mainly includes the following steps:
[0044] Step 1: Obtain the original images of the wafer and the probe station and compress them based on the Gaussian blur and downsampling algorithms to obtain compressed images;
[0045] Specifically, the process of compressing the images based on the Gaussian blur and downsampling algorithms can be as follows:
[0046] A. Use an image acquisition device to collect a large number of wafer probe images, including various images with different brightnesses, different angles, and different probe positions. Take the original image as the bottom layer (Level 0) of the Gaussian pyramid. As Figure 2 shown, select an appropriate Gaussian kernel to perform a convolution operation on the image, and obtain a smoothed image through Gaussian blur to reduce high-frequency information.
[0047] B. Perform a downsampling operation on the smoothed image. In this example, select to delete the even rows and even columns of the smoothed image. At this time, the image becomes 1 / 4 of the original image size. Place the downsampled image on the upper layer (Level 1) of the original image;
[0048] C. According to the required number of layers, repeat the Gaussian blur and downsampling operations to finally reduce the image information. In this example, the number of compression layers is selected to be 2 times.
[0049] Step 2: Perform threshold segmentation on the compressed images to obtain initial binary images;
[0050] Specifically, the specific process of performing threshold segmentation on the compressed images obtained in Step 1 to obtain initial binary images is as follows:
[0051] A. As Figure 3 is the flowchart of the multi-angle template matching algorithm for adaptive thresholding. Convert the compressed image into a grayscale image, traverse each pixel of the grayscale image, and count the number of pixels h(k) at each gray level k (k ∈ [0, 255]). Use the horizontal axis to represent the gray level (0 - 255), the vertical axis to represent the number of pixels h(k) at each gray level, plot the gray histogram of the compressed image, and find the peak point of the gray histogram;
[0052] B. Determine the leftmost peak point in the gray histogram and the adjacent right trough, and use the position of this trough as the adaptive threshold;
[0053] C. Based on the adaptive threshold, perform threshold segmentation on the image, set the pixels with gray values greater than or equal to the threshold to 255 (foreground), and the pixels less than the threshold to 0 (background) to obtain the initial binary image.
[0054] Step 3: Use the rotation matching method to locate the probe area and probe angle in the initial binary image;
[0055] In specific implementation, after obtaining the template image, it can be rotationally transformed to match the initial binary image obtained in step 2, and the probe region and probe angle are determined.
[0056] Step 4: Perform image preprocessing on the probe region in the original image according to the probe angle to obtain a binary image of the probe region;
[0057] In specific implementation, the specific process of performing image preprocessing on the probe region in the original image according to the probe angle to obtain a binary image of the probe region can be as follows:
[0058] A. According to the probe angle determined in step 3, rotate the original image so that the probe tip points downward, and intercept the probe region.
[0059] B. Convert the three-channel color image of the probe region into a single-channel grayscale image. In this example, the values of the three RGB channels of the color image are combined into a single grayscale value by the weighted average method;
[0060] C. Perform image smoothing through Gaussian filtering to reduce image noise;
[0061] D. Use threshold segmentation to obtain a binary image, and use morphological processing to fill holes. In this example, first invert the binary image to mark the background region, then use the connected component analysis morphological operation to mark the background and perform hole filling, and finally invert the filled image to enhance the straight line feature of the tip.
[0062] Step 5: Perform line detection in the binary image of the probe region to locate the probe tip.
[0063] In specific implementation, the specific process of performing line detection in the binary image of the probe region to locate the probe tip includes:
[0064] A. As Figure 4 is the flow chart of the tip straight line detection algorithm by directly analyzing pixel points. Calculate the pixel point size of each row of the probe region image, count the number of pixel points with a value of 0 in each row, denoted as n. If the ratio of n in two adjacent rows is greater than the set ratio threshold S, in this example, S = 0.9, then these two rows are regarded as the tip straight line, and the data points are stored in the straight line coordinate container. If four consecutive rows are all identified as the tip straight line, then the current region is considered as the tip straight line position.
[0065] B. Sort the coordinates of the obtained straight line points, and calculate the minimum and maximum values in the coordinates, that is, the straight line coordinates on the left and right sides of the tip;
[0066] C. Gradually search upward along the left and right straight line ranges until all pixel point sizes are 0. At this time, it is considered that the connection part between the tip and the needle body is found. At this time, the tip region position of the probe is obtained according to the four straight line coordinates.
[0067] The GSG probe tip positioning method based on image processing provided in this embodiment performs threshold segmentation and image processing on the original image through a multi-angle template matching algorithm with an adaptive threshold to locate the probe area; and then determines the tip position through an image preprocessing algorithm and a line detection algorithm. Since the image consists of a probe area and a wafer area, the position of the wafer area can be calculated while locating the probe area. Finally, the positioning of the tip area and the wafer area is achieved.
[0068] It should be understood that each part of the present disclosure can be implemented by hardware, software, firmware, or a combination thereof.
[0069] As mentioned above, the above are only specific embodiments of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present disclosure should be covered by the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be subject to the protection scope of the claims.
Claims
1. A method for positioning the tip of a GSG probe based on image processing, characterized in that, Including: Step 1: Obtain the original images of the wafer and the probe station and compress them based on the Gaussian blur and downsampling algorithms to obtain compressed images; Step 2: Perform threshold segmentation on the compressed images to obtain initial binary images; Step 3: Use the rotation matching method to locate the probe area and the probe angle in the initial binary images; Step 4: Perform image preprocessing on the probe area in the original images according to the probe angle to obtain binary images of the probe area; Step 5: Perform line detection in the binary images of the probe area to locate the probe tips.
2. The method according to claim 1, wherein The specific steps of Step 1 include: Step 1.1: Take the original image as the bottom layer of the Gaussian pyramid and perform convolution operations on the image using a Gaussian convolution kernel; Step 1.2: Delete the even rows and even columns of the convolved image and place the image on the upper layer of the original image; Step 1.3: Repeat Gaussian blur and downsampling according to the required number of layers to finally reduce the image information and obtain compressed images.
3. The method according to claim 2, wherein The specific steps of Step 2 include: Step 2.1: Calculate the grayscale histogram of the compressed images and find the peak points of the histogram; Step 2.2: Determine the leftmost peak point in the grayscale histogram, determine the adjacent right trough, and use the value corresponding to the trough position as the adaptive threshold; Step 2.3: Based on the adaptive threshold, perform threshold segmentation on the compressed images to obtain initial binary images.
4. The method according to claim 3, characterized in that The specific steps of Step 3 include: After obtaining the template image, perform rotation transformation on it and match it with the initial binary images to determine the probe area and the probe angle in the initial binary images.
5. The method according to claim 4, wherein The specific steps of Step 4 include: Step 4.1: Rotate the original images according to the probe angle so that the probe tips face downwards and intercept the probe area; Step 4.2: Convert the three-channel color images of the probe area into single-channel grayscale images; Step 4.3: Smooth the grayscale images through Gaussian filtering; Step 4.4: Perform threshold segmentation on the smoothed grayscale images to obtain binary images of the probe area, and use morphological processing to fill the holes to enhance the straight line features of the tips.
6. The method according to claim 5, characterized in that, The specific steps of Step 5 include: Step 5.1: Set the line ratio threshold, traverse row by row from the bottom of the probe in the binary images of the probe area, and record the line points to form a data point set; Step 5.2: Find the minimum and maximum values in the data point set to determine the left and right line coordinates of the tips; Step 5.3: Gradually search upwards along the left and right line ranges until the sizes of all pixel points are 0, determine the connection part between the tips and the needle bodies, and finally determine the positions of the probe tips.