Semiconductor machine measuring and positioning method and semiconductor machine

Abstract

The invention provides a semiconductor machine measuring and positioning method and a semiconductor machine, and the method comprises the following steps: obtaining a sampling image of the semiconductor machine when the semiconductor machine reports an error, and inputting a template image; analyzing the sampling image and the template image, acquiring a plurality of first feature points from the sampling image, and acquiring a plurality of second feature points from the template image; intercepting a sampling area from the sampling image according to the correlation between the first feature point and the second feature point, and mapping the template image to the sampling area; setting a feature threshold value, and when the similarity between the template image and the sampling region is smaller than the feature threshold value, increasing the acquisition number of the first feature points and the second feature points, and re-acquiring the first feature points and the second feature points; and when the similarity between the template image and the sampling area is greater than or equal to the characteristic threshold value, taking the sampling area as an electron microscope scanning area of the semiconductor machine. According to the invention, the camera shooting focus can be automatically adjusted when the measurement error of the machine table occurs, so that automatic goods passing is realized.

Description

Technical Field

[0001] The present invention relates to the field of semiconductor manufacturing technology, and in particular to a semiconductor machine measurement and positioning method and a semiconductor machine. Background Art

[0002] The physical size features on a chip are called feature sizes, among which the smallest feature size is called the critical dimension (CD). The size of the critical dimension represents the complexity level of the semiconductor manufacturing process. In the chip manufacturing process, the measurement of the critical dimension can also be called the nanoscale line width measurement. As the etching line width of the semiconductor gradually decreases, the accurate measurement of the critical dimension directly determines the performance of the device.

[0003] The number of chips on a wafer can reach tens of thousands. When measuring key dimensions, if the chip object to be measured cannot be accurately located, the machine will report an error or even cause the measurement to fail. Handling this problem with manual intervention is not only inefficient, but if manual intervention is not timely, the wafer will be shelved, affecting wafer production efficiency. In addition, hasty manual processing may also lead to processing errors. If there are defects in the product, this problem cannot be discovered in time because the measurement has not been carried out, which may also lead to serious waste of production capacity. Summary of the invention

[0004] The object of the present invention is to provide a semiconductor machine measurement and positioning method and a semiconductor machine, which can automatically adjust the camera focus when the machine measurement error occurs, thereby realizing automatic product transfer when the machine measurement error occurs.

[0005] To solve the above technical problems, the present invention is achieved through the following technical solutions: The present invention provides a semiconductor machine measurement and positioning method, comprising the following steps: When the semiconductor machine reports an error, acquiring a sample image of the semiconductor machine and inputting a template image; Analyze the sampled image and the template image, obtain a plurality of first feature points from the sampled image, and obtain a plurality of second feature points from the template image; According to the correlation between the first feature point and the second feature point, a sampling area is intercepted from the sampling image, and the template image is mapped onto the sampling area; Setting a feature threshold, when the similarity between the template image and the sampling area is less than the feature threshold, increasing the number of the first feature points and the second feature points to be acquired, and reacquiring the first feature points and the second feature points; and When the similarity between the template image and the sampling area is greater than or equal to the feature threshold, the sampling area is used as the electron microscope scanning area of ​​the semiconductor machine.

[0006] In one embodiment of the present invention, in the step of acquiring the first feature points and the second feature points, the first feature points and the second feature points are acquired in multiple cycles, wherein the number of feature points in a later cycle is greater than the number of feature points in a previous cycle.

[0007] In one embodiment of the present invention, in the step of acquiring the first feature point and the second feature point, an initial number, a loop increment and a loop round are set, wherein the number of the first feature point and the second feature point is the sum of the product of the loop increment and the loop round and the initial number.

[0008] In one embodiment of the present invention, the step of analyzing the sample image and the template image includes: Get any pixel point in the image as the pixel point to be analyzed; Setting a comparison area in the image with the pixel to be analyzed as the center; and A grayscale difference threshold is set. If the grayscale value difference between the pixel to be analyzed and any pixel in the comparison area is greater than or equal to the grayscale difference threshold, the pixel to be analyzed is used as a feature point.

[0009] In one embodiment of the present invention, the step of analyzing the correlation between the first feature point and the second feature point includes: Taking the feature point as the center, setting an adjustable area on the image, wherein the adjustable area includes a plurality of auxiliary pixel points, wherein the feature point is the first feature point or the second feature point; Acquiring matching parameters of the feature points according to the structural similarity and mean square error of the adjustable region; and A matching threshold is set, and when a matching parameter error between the first feature point and the second feature point is smaller than the matching threshold, the first feature point and the second feature point are correlated.

[0010] In one embodiment of the present invention, the step of obtaining the similarity between the sampling area and the template image includes: Setting the area of ​​the sampling region, mapping the second feature points to the related first feature points, and cutting out the sampling region on the sampling image according to the mapping area of ​​the second feature points; and According to the mapping relationship from the second feature point to the first feature point, a geometric consistency transformation verification is performed on the sampling area and the template image to obtain the similarity between the sampling area and the template image.

[0011] In one embodiment of the present invention, the step of analyzing the correlation between the first feature point and the second feature point includes: Establishing a coordinate system on the template image and the sampled image, and acquiring coordinates of the first feature point, the second feature point, and the auxiliary pixel point; and According to the coordinates of the first feature point, the second feature point and the auxiliary pixel point, matching parameters of the first feature point and the second feature point are obtained.

[0012] In one embodiment of the present invention, the center point of the sampling area is obtained as the focusing coordinate of the electron microscope of the semiconductor machine.

[0013] The present invention provides a semiconductor machine, comprising: a memory storing a computer program; A processor, wherein when the processor executes the computer program, the semiconductor tool measurement and positioning method as described above is implemented, wherein the processor comprises: An alarm module, used for acquiring a sample image of the semiconductor machine and inputting a template image when the semiconductor machine reports an error; A feature point acquisition module, used for analyzing the sample image and the template image, acquiring a plurality of first feature points from the sample image, and acquiring a plurality of second feature points from the template image; A region division module, used for cutting out a sampling region from the sampling image according to the correlation between the first feature point and the second feature point, and mapping the template image to the sampling region; a loop module, configured to obtain a feature threshold, and when the similarity between the template image and the sampling area is less than the feature threshold, increase the number of the first feature points and the second feature points to be obtained, and re-acquire the first feature points and the second feature points; and The result output module is used to use the sampling area as the electron microscope scanning area of ​​the semiconductor machine when the similarity between the template image and the sampling area is greater than or equal to the feature threshold.

[0014] In one embodiment of the present invention, the semiconductor machine further includes: A carrier table, used for carrying wafers; A sampling camera is arranged on the carrier platform, and faces the surface of the wafer; The manipulator is connected to the sampling camera, allowing the manipulator to drive the sampling camera to move so that the sampling camera focuses on the sampling area.

[0015] As described above, the present invention provides a semiconductor machine measurement and positioning method and a semiconductor machine, and its unexpected technical effect is that it can automatically adjust the camera focus when the machine measurement reports an error, thereby realizing automatic transfer of goods when the machine measurement makes an error, improving the measurement efficiency of the machine, and reducing the frequency of manual intervention. In addition, the present invention can not only accurately locate the key dimensions to be measured, but also reduce the resource occupation efficiency, quickly complete the measurement and repositioning of the key dimensions, avoid waste of production capacity, and solve many hidden dangers in the semiconductor production process.

[0016] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0018] Figure 1 FIG. 4 is a schematic structural diagram of a semiconductor machine according to an embodiment of the present invention.

[0019] Figure 2 FIG. 4 is a flow chart of a method for measuring and positioning a semiconductor machine according to an embodiment of the present invention.

[0020] Figure 3 FIG. 4 is a schematic diagram of measuring the first sampling area in one embodiment of the present invention.

[0021] Figure 4 FIG. 4 is a schematic diagram of measuring the second sampling area in one embodiment of the present invention.

[0022] Figure 5 FIG. 1 is a measurement diagram of an adjustment structure in an embodiment of the present invention.

[0023] Figure 6 FIG. 4 is a schematic diagram of measuring critical dimensions in an embodiment of the present invention.

[0024] Figure 7 FIG. 1 is a flow chart of step S200 in an embodiment of the present invention.

[0025] Figure 8 FIG. 1 is a flow chart of step S300 in an embodiment of the present invention.

[0026] Fig. 9 FIG. 4 is a flow chart of step S400 in an embodiment of the present invention.

[0027] Fig.10 FIG. 4 is a schematic diagram of performing geometric consistency transformation in one embodiment of the present invention.

[0028] Fig.11 Schematic diagram of an algorithm executing geometric consistency transformation in one embodiment of the present invention.

[0029] Fig.12 This is the sampling area obtained when the number of feature points is 100 in one embodiment of the present invention.

[0030] Fig.13 This is the sampling area obtained when the number of feature points is 500 in one embodiment of the present invention.

[0031] Fig.14 This is the sampling area obtained when the number of feature points is 1000 in one embodiment of the present invention.

[0032] Fig.15 FIG. 1 is a schematic diagram of the structure of a measurement and positioning system in one embodiment of the present invention.

[0033] In the figure: 10, carrier; 101, wafer; 20, sampling camera; 30, robot; 40, measurement and positioning system; 401, alarm module; 402, feature point acquisition module; 403, area division module; 404, circulation module; 405, output module. DETAILED DESCRIPTION

[0034] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0035] See also Figure 1As shown, the present invention provides a semiconductor machine. The semiconductor machine includes a carrier 10, a sampling camera 20 and a manipulator 30. The carrier 10 is used to carry a wafer 101. A plurality of chip particles are planned on the wafer 101 in the present invention. Among them, the plurality of chip particles share a substrate. It should be noted that on the wafer 101, the plurality of chip particles can be the same product or different products. In this embodiment, a semiconductor structure is arranged on the substrate. The semiconductor structure refers to a structure having an exposed surface for forming an integrated circuit. For example, it may include a dielectric layer, a conductive layer and a semiconductor layer produced by such processing. The substrate may include doped and undoped semiconductors or epitaxial semiconductor layers, which may be supported by a substrate of a semiconductor or insulator material and other semiconductor structures known to those skilled in the art. The carrier 10 may be an electrostatic adsorption device for fixing the position of the wafer 101. The sampling camera 20 is arranged on the carrier 10, and the sampling camera 20 faces the surface of the wafer 101. In this embodiment, if there is a critical dimension to be measured within the shooting range of the sampling camera 20, an optical microscope (OM) and a scanning electron microscope (EM) can be used to obtain an electron microscope image of the critical dimension CD. In this embodiment, the manipulator 30 is connected to the sampling camera 20, and can drive the sampling camera 20 to move along a horizontal plane or a vertical plane, so that the sampling camera 20 focuses on the sampling area. The semiconductor machine provided by the present invention also includes a memory and a processor, and a computer program is stored in the memory. When the processor executes the computer program, the steps of the semiconductor machine measurement and positioning method provided by the present invention are implemented, wherein the measurement and positioning system provided by the present invention can be loaded in the processor, and the measurement and positioning method provided by the present invention can be stored in the memory in the form of firmware.

[0036] See also Figures 1 to 6 As shown, the present invention provides a semiconductor machine measurement and positioning method, which can obtain the correct sampling area when the sampling camera 20 has a focus error, thereby obtaining the correct electron microscope scanning area. It should be noted that the semiconductor machine measurement and positioning method provided by the present invention automatically adjusts the position of the sampling camera 20 when the semiconductor machine reports an error. When the positioning is correct, the wafer 101 first passes through an optical microscope to obtain a first sampling area from the surface of the wafer 101, such as Figure 3 The focus of the sampling camera 20 is adjusted to focus on the first sampling area, thereby further magnifying the first sampling area. Then, a second sampling area is obtained on the first sampling area by scanning electron microscopy, such as Figure 4 Then, by comparing the template image, the feature structure to be measured is obtained from the second sampling area, such as Figure 5The characteristic structure shown in Figure 1 is then used to obtain the key dimensions of the characteristic structure, such as Figure 6 The width dimension in the ellipse. In the present invention, the error types of the semiconductor machine include the first type of alarm information, the second type of alarm information, the third type of alarm information and the fourth type of alarm information. The first type of alarm information is the first sampling area positioning error, the second type of alarm information is the second sampling area positioning error, the third type of alarm information is the characteristic structure positioning error, and the fourth type of alarm information is the key size positioning error. In the present invention, when any type of alarm information appears, the semiconductor machine will report an error.

[0037] See also Figures 1 to 6 As shown, in the present invention, before the critical dimension measurement is performed, the measurement program is input into the semiconductor machine. Specifically, for a type of wafer product, the present invention sets a master. On the master, there are standard structures for each level of this type of wafer product. The material, thickness and surrounding environment of the characteristic structure can be optically detected to determine whether the final measurement structure or measurement area meets the standard structure set by the master. Since the size of the characteristic structure is much smaller than the area of ​​the wafer 101. And the categories of semiconductor products are extremely complex, measurement positioning is prone to errors in the batch process of semiconductors. If any position does not match the master and the verification program set at the beginning fails, it will cause the semiconductor machine to report an error. It should be noted that the verification program varies too much according to the designer, the product type, the product level, the parameter preferences adopted, etc., and the present invention does not limit the verification program for finding errors. The measurement positioning method provided by the present invention only re-measures and positions when the semiconductor machine reports an error.

[0038] See also Figures 1 to 6 As shown, when a semiconductor machine reports an error, the semiconductor machine starts the semiconductor machine measurement and positioning method provided by the present invention and feeds back the error type. The semiconductor machine measurement and positioning method includes steps S100 to S500.

[0039] Step S100 , when a semiconductor machine reports an error, a sample image of the semiconductor machine is acquired, and a template image is input.

[0040] Step S200: Analyze the sampled image and the template image, obtain a plurality of first feature points from the sampled image, and obtain a plurality of second feature points from the template image.

[0041] Step S300: According to the correlation between the first feature point and the second feature point, a sampling area is cut out from the sampling image, and the template image is mapped onto the sampling area.

[0042] Step S400, setting a feature threshold, when the similarity between the template image and the sampling area is less than the feature threshold, increasing the number of first feature points and second feature points to be acquired, and reacquiring the first feature points and second feature points.

[0043] Step S500: When the similarity between the template image and the sampling area is greater than or equal to the feature threshold, the sampling area is used as the electron microscope scanning area of ​​the semiconductor machine.

[0044] See also Figures 1 to 7 As shown, in one embodiment of the present invention, in step S100, when the semiconductor machine reports an error, the error type of the semiconductor machine is recorded. The error type may be any one of the first type of alarm information, the second type of alarm information, the third type of alarm information, and the fourth type of alarm information. When an error occurs, the wafer 101 will stay in the current measurement process and will not continue to flow to the next measurement process. In step S100, the current camera image of the sampling camera 20 is used as the sampling image. The template image is the master image. In the case of the first type of alarm information, the master image corresponds to the standard image of the first sampling area. In the case of the second type of alarm information, the master image corresponds to the standard image of the second sampling area. In the case of the third type of alarm information, the master image corresponds to the standard image of the characteristic structure. In the case of the fourth type of alarm information, the master image corresponds to the standard image of the key size. Then the sampling image and the template image are analyzed, wherein step S200 includes steps S210 to S250.

[0045] Step S210: input image.

[0046] Step S220: Obtain any pixel point in the image as a pixel point to be analyzed.

[0047] Step S230: setting a comparison area in the image with the pixel to be analyzed as the center.

[0048] Step S240: setting a grayscale difference threshold. If the grayscale value difference between the pixel to be analyzed and any pixel in the comparison area is greater than or equal to the grayscale difference threshold, the pixel to be analyzed is used as a feature point.

[0049] Step S250, determine whether the number of feature points reaches a preset number. When the number of feature points reaches the preset number, execute step S300. If the number of feature points does not reach the preset number, return to step S220.

[0050] See also Figures 1 to 7As shown, in one embodiment of the present invention, in step S210, the input image includes a sample image and a template image. In this embodiment, feature points of the sample image and the template image are obtained respectively, and the feature points of the sample image and the template image can be obtained in sequence, or the feature points of the sample image and the template image can be obtained simultaneously. The following takes the sample image as an example to illustrate step S200, and the template image is also executed in the same way. In step S230, the number of pixels in the comparison area is a preset number, for example, 4, 6, 8, etc. are set. Specifically, the number of pixels in the comparison area can be obtained through experiments or experience. With the pixel to be analyzed as the center, a preset number of pixels are obtained from near to far around the pixel to be analyzed, so that the area where the pixel to be analyzed is located and the area where the pixel to be analyzed is located are used as the comparison area. In step S240, the grayscale value of the pixel to be analyzed and the grayscale value of each pixel in the comparison area are compared in sequence. If the grayscale value difference between the pixel to be analyzed and any pixel in the comparison area is greater than or equal to the grayscale difference threshold, the pixel to be analyzed is used as the first feature point. Similarly, the second feature point is obtained in the manner of step S220 to step S240, wherein the grayscale difference threshold is a preset value, which can be obtained through experiments or experience.

[0051] See also Figure 2 , Figure 7 and Figure 8As shown, in one embodiment of the present invention, in step S250, the number of acquired first feature points and second feature points is equal. In this embodiment, the first feature points and the second feature points are acquired in multiple cycles, wherein the number of feature points in the latter cycle is greater than the number of feature points in the previous cycle. It should be noted that the cycle described in the present invention is not the number of times step S250 returns to step S220, but the number of times the feature points reach the preset number of the current cycle. In this embodiment, the initial number, the cycle increment and the cycle are set. Among them, the initial number is the number of first feature points and second feature points in the first cycle. The cycle increment is the increment of the number of feature points in the current cycle compared to the previous cycle. Whenever the preset number of feature points increases once, the cycle also increases once. In this embodiment, the number of first feature points and second feature points is the sum of the product of the cycle increment and the cycle and the initial number. For example, if the initial number is 500, after acquiring 500 feature points, it is determined whether the electron microscope scanning area can be obtained at present. If it cannot be obtained, the feature points are acquired incrementally. For example, if the loop increment is 50, 550 feature points are obtained in the second loop round. In the third loop round, 600 feature points are obtained. And so on. It should be noted that the loop increment in this embodiment is a fixed value. In other embodiments of the present invention, the loop increment may also increase or decrease with the loop rounds. After the number of feature points reaches a preset number, step S300 is executed, wherein step S300 includes steps S310 to S350.

[0052] Step S310: setting an adjustable area on the image with the feature point as the center, wherein the adjustable area includes a plurality of auxiliary pixel points, wherein the feature point is the first feature point or the second feature point.

[0053] Step S320: Obtain matching parameters of feature points based on the structural similarity and mean square error of the adjustable region. Step S330: Set a matching threshold, and determine whether a matching parameter error between the first feature point and the second feature point is less than the matching threshold.

[0054] Step S340: If the matching parameter error between the first feature point and the second feature point is less than the matching threshold, mark the first feature point and the second feature point as being related.

[0055] Step S350: If the matching parameter error between the first feature point and the second feature point is greater than or equal to the matching threshold, replace the first feature point and return to step S310.

[0056] See also Figure 2 , Figure 7 and Figure 8As shown, in one embodiment of the present invention, in step S310, the feature point refers to the first feature or the second feature point. In this embodiment, the matching parameters of the sampled image and the template image are obtained respectively, and the matching parameters of the sampled image and the template image can be obtained in sequence, or the matching parameters of the sampled image and the template image can be analyzed and obtained simultaneously. The adjustable area includes a plurality of auxiliary pixel points, wherein the number of auxiliary pixel points is, for example, 4, 6, 8, and the like. The number of auxiliary pixel points can be obtained through experiments or experience. In step S320, the mean square error (MSE) of the pixel points in the adjustable area is obtained. The mean square error is calculated according to formula (1).

[0057] (1).

[0058] In formula (1), MSE is the mean square error of pixels in the adjustable area, patchsize is the area of ​​the adjustable area, and I view(i，j，k) is the pixel value of the sampled image, I template(i，j，k) is the pixel value of the template image.

[0059] See also Figure 2 , Figure 7 and Figure 8 As shown, in one embodiment of the present invention, in step S320, the structural similarity (Structural Similarity, SS1M) of the pixels in the adjustable region is obtained, wherein the structural similarity of the pixels in the adjustable region is calculated according to formula (2).

[0060] (2).

[0061] In formula (2), SSIM is the structural similarity of pixels in the adjustable region, μ x is the average value of the horizontal coordinates of the pixels in the adjustable area, μ y is the average ordinate of the pixels in the adjustable area, σ x 2 is the horizontal coordinate variance of the pixel points in the adjustable area, σ y 2 is the vertical coordinate variance of the pixel points in the adjustable area, σ xy is the covariance of the horizontal and vertical coordinates of the pixel points in the adjustable area, and c1 and c2 are constants obtained experimentally. c1 and c2 can be used to help improve the measurement accuracy.

[0062] See also Figure 2 , Figure 7 and Figure 8As shown, in one embodiment of the present invention, in step S320, the matching parameter of the feature point is the sum of the structural similarity SSIM of the pixel points in the adjustable region and the mean square error MSE of the pixel points in the adjustable region. In step S330, the matching limit parameter GM is greater than the sum of the structural similarity SSIM of the pixel points in the adjustable region and the mean square error MSE of the pixel points in the adjustable region. In this embodiment, the matching threshold is less than 0.8 times the matching limit parameter GM. If the difference in matching parameters between the first feature point and the second feature point is less than the matching threshold, step S340 is executed to mark the first feature point as being related to the second feature point. Then the next second feature point and the next first feature point are obtained, and correlation matching is performed until all second feature points are matched. In this embodiment, if the difference in matching parameters between the first feature point and the second feature point is greater than or equal to the matching threshold, step S350 is executed to replace a first feature point, and re-match the newly selected first feature point and the current second feature point, and so on, until all second feature points are matched. It should be noted that a coordinate system is established on the template image and the sampled image, and the coordinates of the first feature point, the second feature point and the auxiliary pixel point are obtained. According to the coordinates of the first feature point, the second feature point and the auxiliary pixel point, the matching parameters of the first feature point and the second feature point are obtained.

[0063] See also Figure 2 , Figures 7 to 10 As shown, in one embodiment of the present invention, in step S400, the step of obtaining the similarity between the sampling area and the template image includes step S410 and step S420.

[0064] Step S410: Set the area of ​​the sampling region, map the second feature point to the related first feature point, and cut out the sampling region on the sampling image according to the mapping area of ​​the second feature point.

[0065] Step S420: According to the mapping relationship from the second feature point to the first feature point, a geometric consistency transformation verification is performed on the sampling area and the template image to obtain the similarity between the sampling area and the template image.

[0066] See also Figure 2 , Figures 7 to 14 As shown, in one embodiment of the present invention, in step S410, all second feature points are projected onto the relevant first feature points to form a plurality of mapping line bundles. The principle is as follows Fig.10 In the present invention, the second feature point is mapped to the first feature point through a two-dimensional affine transformation algorithm. Based on the mapping line bundles from the second feature point to the first feature point, the geometric consistency of the template image and the sampled image can be verified. Based on the two-dimensional affine transformation algorithm, the similarity between the sampled area and the template image is obtained, as shown in FIG. Fig.10 and Fig.11As shown. The similarity depends on the average skew line angle between multiple mapping line bundles. The smaller the average skew line angle, the higher the similarity. In step S400, the feature threshold can be set through experiments or experience. When the similarity between the sampling area and the template image is less than the feature threshold, it returns to step S200 and enters a new loop round. The preset number of feature points is increased according to the loop increment, and the first feature point and the second feature point are re-acquired. In this embodiment, the above-mentioned similarity judgment process can also be directly implemented through an affine transformation algorithm. Specifically, the correlation between the sampling area and the template image is judged according to the value output by the affine transformation algorithm matrix. If the matrix output value is 1, it means that the sampling area and the template image are strongly correlated, and the sampling area and the template image are similar. If the matrix output value is a value other than 1, it returns to step S200. Fig.12 Shown to Fig.14 As shown, each time the cycle is repeated, the fitting of the sampling area will be more accurate due to the increase in the number of feature points. The area indicated by the box is the sampling area. In this embodiment, the center point of the sampling area is obtained as the focusing coordinate of the electron microscope of the semiconductor machine, so that the electron microscope scanning can evenly scan the entire sampling area, thereby improving the focusing accuracy of the electron microscope scanning in the current scanning area. Until the similarity between the sampling area and the template image is greater than or equal to the feature threshold, the current sampling area is used as the electron microscope scanning area. Specifically, the system outputs the center point of the current sampling area as the focal coordinate of the electron microscope scanning.

[0067] See also Figure 2 and Fig.15 As shown, the present invention provides a measurement and positioning system 40 for a semiconductor machine, and the measurement and positioning system 40 includes an alarm module 401, a feature point acquisition module 402, a region division module 403, a loop module 404 and a result output module 405. The alarm module 401 is used to obtain a sampling image of the semiconductor machine and input a template image when the semiconductor machine reports an error. The feature point acquisition module 402 is used to analyze the sampling image and the template image, obtain multiple first feature points from the sampling image, and obtain multiple second feature points from the template image. The region division module 403 is used to intercept a sampling area from the sampling image according to the correlation between the first feature point and the second feature point, and map the template image to the sampling area. The loop module 404 is used to obtain a feature threshold. When the similarity between the template image and the sampling area is less than the feature threshold, the number of the first feature point and the second feature point to be obtained is increased, and the first feature point and the second feature point are obtained again. The result output module 405 is used to take the sampling area as the electron microscope scanning area of ​​the semiconductor machine when the similarity between the template image and the sampling area is greater than or equal to the feature threshold.

[0068] The present invention provides a semiconductor machine measurement and positioning method and a semiconductor machine, and its unexpected technical effect is that it can automatically adjust the camera focus when the machine measurement reports an error, so as to realize automatic transfer when the machine measurement makes an error, improve the measurement efficiency of the machine, and reduce the frequency of manual intervention. In addition, the present invention can not only accurately locate the key dimensions to be measured, but also reduce the resource occupation efficiency, quickly complete the measurement and repositioning of the key dimensions, avoid waste of production capacity, and solve many hidden dangers in the semiconductor production process.

[0069] The embodiments of the present invention disclosed above are only used to help illustrate the present invention. The embodiments do not describe all the details in detail, nor do they limit the invention to the specific embodiments described. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A semiconductor machine measurement and positioning method, characterized in that: The following steps are involved: When the semiconductor machine reports an error, acquiring a sample image of the semiconductor machine and inputting a template image; Analyze the sampled image and the template image, obtain a plurality of first feature points from the sampled image, and obtain a plurality of second feature points from the template image; According to the correlation between the first feature point and the second feature point, a sampling area is intercepted from the sampling image, and the template image is mapped onto the sampling area; Setting a feature threshold, when the similarity between the template image and the sampling area is less than the feature threshold, increasing the number of the first feature points and the second feature points to be acquired, and reacquiring the first feature points and the second feature points; as well as When the similarity between the template image and the sampling area is greater than or equal to the feature threshold, the sampling area is used as the electron microscope scanning area of ​​the semiconductor machine.

2. A semiconductor machine measurement and positioning method according to claim 1, characterized in that: In the step of acquiring the first feature points and the second feature points, the first feature points and the second feature points are acquired in multiple cycles, wherein the number of feature points in a later cycle is greater than the number of feature points in a previous cycle.

3. A semiconductor machine measurement and positioning method according to claim 2, characterized in that: In the step of acquiring the first feature point and the second feature point, an initial number, a loop increment and a loop round are set, wherein the number of the first feature point and the second feature point is the sum of the product of the loop increment and the loop round and the initial number.

4. The semiconductor machine measurement and positioning method according to claim 1, characterized in that: The step of analyzing the sample image and the template image comprises: Get any pixel point in the image as the pixel point to be analyzed; Setting a comparison area in the image with the pixel to be analyzed as the center; and A grayscale difference threshold is set. If the grayscale value difference between the pixel to be analyzed and any pixel in the comparison area is greater than or equal to the grayscale difference threshold, the pixel to be analyzed is used as a feature point.

5. The semiconductor machine measurement and positioning method according to claim 1, characterized in that: The step of analyzing the correlation between the first feature point and the second feature point includes: Taking the feature point as the center, setting an adjustable area on the image, wherein the adjustable area includes a plurality of auxiliary pixel points, wherein the feature point is the first feature point or the second feature point; Acquiring matching parameters of the feature points according to the structural similarity and mean square error of the adjustable region; and A matching threshold is set, and when a matching parameter error between the first feature point and the second feature point is smaller than the matching threshold, the first feature point and the second feature point are correlated.

6. A semiconductor machine measurement and positioning method according to claim 5, characterized in that: The step of obtaining the similarity between the sampling area and the template image comprises: Setting the area of ​​the sampling region, mapping the second feature points to the related first feature points, and cutting out the sampling region on the sampling image according to the mapping area of ​​the second feature points; and According to the mapping relationship from the second feature point to the first feature point, a geometric consistency transformation verification is performed on the sampling area and the template image to obtain the similarity between the sampling area and the template image.

7. A semiconductor machine measurement and positioning method according to claim 5, characterized in that: The step of analyzing the correlation between the first feature point and the second feature point includes: Establishing a coordinate system on the template image and the sampled image, and acquiring coordinates of the first feature point, the second feature point, and the auxiliary pixel point; and According to the coordinates of the first feature point, the second feature point and the auxiliary pixel point, matching parameters of the first feature point and the second feature point are obtained.

8. The semiconductor machine measurement and positioning method according to claim 1, characterized in that: The center point of the sampling area is obtained as the focusing coordinate of the electron microscope of the semiconductor machine.

9. A semiconductor machine, characterized in that: include: a memory storing a computer program; A processor, wherein when executing the computer program, the processor implements the semiconductor machine measurement and positioning method according to any one of claims 1 to 8, wherein the processor comprises: An alarm module, used for acquiring a sample image of the semiconductor machine and inputting a template image when the semiconductor machine reports an error; A feature point acquisition module, used for analyzing the sample image and the template image, acquiring a plurality of first feature points from the sample image, and acquiring a plurality of second feature points from the template image; A region division module, used for cutting out a sampling region from the sampling image according to the correlation between the first feature point and the second feature point, and mapping the template image to the sampling region; a loop module, configured to obtain a feature threshold, and when the similarity between the template image and the sampling area is less than the feature threshold, increase the number of the first feature points and the second feature points to be obtained, and re-acquire the first feature points and the second feature points; and The result output module is used to use the sampling area as the electron microscope scanning area of ​​the semiconductor machine when the similarity between the template image and the sampling area is greater than or equal to the feature threshold.

10. The semiconductor machine according to claim 9, characterized in that: Semiconductor machines also include: A carrier table, used for carrying wafers; A sampling camera is arranged on the carrier platform, and faces the surface of the wafer; The manipulator is connected to the sampling camera, allowing the manipulator to drive the sampling camera to move so that the sampling camera focuses on the sampling area.

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