A semiconductor machine measurement and positioning method and a semiconductor machine

Abstract

The present invention provides a method for measuring and positioning a semiconductor machine tool and a semiconductor machine tool. The method for measuring and positioning includes the following steps: when the semiconductor machine tool reports an error, acquiring a sampling image of the semiconductor machine tool 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; according to the correlation between the first feature points and the second feature points, intercepting a sampling area from the sampling image and mapping the template image 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 acquisition quantity of the first feature points and the second feature points, 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, using the sampling area as the electron microscope scanning area of the semiconductor machine tool. The present invention can automatically adjust the camera focus when the machine tool measurement goes wrong, so as to achieve automatic goods transfer.

Description

Technical Field

[0001] The present invention relates to the technical field of semiconductor manufacturing, and particularly relates to a method for measuring and positioning a semiconductor machine tool and a semiconductor machine tool. Background Art

[0002] Physical size features on a chip are called feature sizes, and 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 nanoscale linewidth measurement. As the etching linewidth 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 the critical dimension, if the chip object to be measured cannot be accurately positioned, it will cause the machine tool to report an error and even lead to measurement failure. Handling this problem in a manual intervention manner not only has low processing efficiency, but also when manual intervention is not timely, the wafer will be left idle all the time, affecting the wafer production efficiency. And if handled manually in a hurry, it may also lead to errors in handling. If there are defects in the product, since the measurement has not been carried out, this problem cannot be discovered in time, which may also lead to serious waste of production capacity. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for measuring and positioning a semiconductor machine tool and a semiconductor machine tool, which can automatically adjust the camera focus when the machine tool measurement goes wrong, so as to achieve automatic goods transfer when the machine tool measurement goes wrong.

[0005] To solve the above technical problems, the present invention is realized through the following technical solutions:

[0006] The present invention provides a method for measuring and positioning a semiconductor machine tool, including the following steps:

[0007] When the semiconductor machine tool reports an error, obtain a sampled image of the semiconductor machine tool and input a template image;

[0008] 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;

[0009] According to the correlation between the first feature points and the second feature points, intercept a sampled area from the sampled image, and map the template image onto the sampled area;

[0010] Set a feature threshold. When the similarity between the template image and the sampling area is less than the feature threshold, increase the acquisition quantity of the first feature point and the second feature point, and re-acquire the first feature point and the second feature point; and

[0011] When the similarity between the template image and the sampling area is greater than or equal to the feature threshold, use the sampling area as the electron microscope scanning area of the semiconductor machine tool.

[0012] In an embodiment of the present invention, in the step of acquiring the first feature point and the second feature point, the first feature point and the second feature point are acquired in multiple cycles, and the number of feature points in the latter cycle is more than that in the previous cycle.

[0013] In an embodiment of the present invention, in the step of acquiring the first feature point and the second feature point, set an initial quantity, a cycle increment, and a cycle number, where the number of the first feature point and the second feature point is the sum of the product of the cycle increment and the cycle number and the initial quantity.

[0014] In an embodiment of the present invention, the step of analyzing the sampling image and the template image includes:

[0015] Acquire any pixel point in the image as the pixel point to be analyzed;

[0016] Set a comparison area in the image with the pixel point to be analyzed as the center; and

[0017] Set a gray difference threshold. If the gray value difference between the pixel point to be analyzed and any pixel point in the comparison area is greater than or equal to the gray difference threshold, use the pixel point to be analyzed as a feature point.

[0018] In an embodiment of the present invention, the step of analyzing the correlation between the first feature point and the second feature point includes:

[0019] Set an adjustable area on the image with the feature point as the center. The adjustable area includes multiple auxiliary pixel points, where the feature point is the first feature point or the second feature point;

[0020] Obtain the matching parameter of the feature point according to the structural similarity and mean square error of the adjustable area; and

[0021] Set a matching threshold. When the matching parameter error between the first feature point and the second feature point is less than the matching threshold, the first feature point and the second feature point are correlated.

[0022] In an embodiment of the present invention, the step of obtaining the similarity between the sampling area and the template image includes:

[0023] Set the area of the sampling region, map the second feature point to the relevant first feature point, and intercept the sampling region on the sampling image according to the mapped region of the second feature point; and

[0024] Verify the geometric consistency transformation between the sampling region and the template image according to the mapping relationship from the second feature point to the first feature point, and obtain the similarity between the sampling region and the template image.

[0025] In an embodiment of the present invention, the step of analyzing the correlation between the first feature point and the second feature point includes:

[0026] Establish a coordinate system on the template image and the sampling image, and obtain the coordinates of the first feature point, the second feature point, and the auxiliary pixel point; and

[0027] Obtain the matching parameters between the first feature point and the second feature point according to the coordinates of the first feature point, the second feature point, and the auxiliary pixel point.

[0028] In an embodiment of the present invention, obtain the center point of the sampling region as the electron microscope focusing coordinate of the semiconductor machine.

[0029] The present invention provides a semiconductor machine, including:

[0030] A memory storing a computer program;

[0031] A processor, when the processor executes the computer program, implements the semiconductor machine measurement and positioning method described in any one of the above, wherein the processor includes:

[0032] An alarm module for obtaining the sampling image of the semiconductor machine and inputting a template image when the semiconductor machine reports an error;

[0033] A feature point acquisition module for analyzing the sampling image and the template image, obtaining a plurality of first feature points from the sampling image, and obtaining a plurality of second feature points from the template image;

[0034] A region division module for intercepting 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;

[0035] A loop module for obtaining a feature threshold, when the similarity between the template image and the sampling region is less than the feature threshold, increasing the acquisition quantity of the first feature point and the second feature point, and re-obtaining the first feature point and the second feature point; and

[0036] A result output module, configured to use the sampling area as the electron microscope scanning area of the semiconductor machine tool when the similarity between the template image and the sampling area is greater than or equal to the feature threshold.

[0037] In an embodiment of the present invention, the semiconductor machine tool further includes:

[0038] A carrier stage for carrying a wafer;

[0039] A sampling camera, disposed on the carrier stage, and the sampling camera faces the surface of the wafer;

[0040] A manipulator, 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.

[0041] As described above, the present invention provides a method for measuring and positioning a semiconductor machine tool and a semiconductor machine tool, and its unexpected technical effects are as follows: it can automatically adjust the camera focus when the machine tool measurement reports an error, so as to realize automatic goods passing when the machine tool measurement goes wrong, improve the measurement efficiency of the machine tool, and reduce the frequency of manual intervention. Moreover, the present invention can not only accurately locate the critical dimensions to be measured, but also reduce the resource occupancy efficiency, quickly complete the repositioning of the measurement of the critical dimensions, avoid production capacity waste, and solve many hidden dangers in the semiconductor production process at the same time.

[0042] Of course, it is not necessary for any product implementing the present invention to simultaneously achieve all the above-mentioned advantages. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can also obtain other drawings based on these drawings without creative efforts.

[0044] Figure 1 It is a schematic structural diagram of a semiconductor machine tool in an embodiment of the present invention.

[0045] Figure 2 It is a flowchart of a method for measuring and positioning a semiconductor machine tool in an embodiment of the present invention.

[0046] Figure 3 It is a measurement schematic diagram of a first sampling area in an embodiment of the present invention.

[0047] Figure 4 It is a measurement schematic diagram of a second sampling area in an embodiment of the present invention.

[0048] Figure 5 Schematic diagram of the measurement of the adjustment structure in an embodiment of the present invention.

[0049] Figure 6 Schematic diagram of the measurement of key dimensions in an embodiment of the present invention.

[0050] Figure 7 Flowchart of step S200 in an embodiment of the present invention.

[0051] Figure 8 Flowchart of step S300 in an embodiment of the present invention.

[0052] Figure 9 Flowchart of step S400 in an embodiment of the present invention.

[0053] Figure 10 Schematic diagram of performing geometric consistency transformation in an embodiment of the present invention.

[0054] Figure 11 Schematic diagram of the algorithm performing geometric consistency transformation in an embodiment of the present invention.

[0055] Figure 12 Sampling area obtained when the number of feature points is 100 in an embodiment of the present invention.

[0056] Figure 13 Sampling area obtained when the number of feature points is 500 in an embodiment of the present invention.

[0057] Figure 14 Sampling area obtained when the number of feature points is 1000 in an embodiment of the present invention.

[0058] Figure 15 Schematic diagram of the structure of the measurement and positioning system in an embodiment of the present invention.

[0059] In the figure: 10, carrier stage; 101, wafer; 20, sampling camera; 30, manipulator; 40, measurement and positioning system; 401, alarm module; 402, feature point acquisition module; 403, area division module; 404, loop module; 405, output module. Detailed implementation manners

[0060] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0061] Please refer to Figure 1As shown in the figure, the present invention provides a semiconductor machine platform. The semiconductor machine platform includes a carrier 10, a sampling camera 20, and a robot arm 30. Among them, the carrier 10 is used to carry the wafer 101. Multiple chip particles are planned on the wafer 101 in the present invention. Among them, the multiple chip particles share a substrate. It should be noted that on the wafer 101, the multiple chip particles can be the same product or different products. In this embodiment, a semiconductor structure is provided on the substrate. The semiconductor structure refers to a structure having an exposed surface for forming an integrated circuit. For example, it may include dielectric layers, conductive layers, and semiconductor layers generated by such processing. The substrate may include doped and undoped semiconductors or epitaxial semiconductor layers, which may be supported by a substrate of semiconductor or insulator material and other semiconductor structures known to those skilled in the art. Among them, the carrier 10 can be an electrostatic adsorption device for fixing the position of the wafer 101. Among them, 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, within the shooting range of the sampling camera 20, if there is a key dimension to be measured, an electron microscope image of the key dimension CD can be obtained through an optical microscope (OM) and a scanning electron microscope. In this embodiment, the robot arm 30 is connected to the sampling camera 20 and can drive the sampling camera 20 to move along the horizontal plane or the vertical plane so that the sampling camera 20 focuses on the sampling area. The semiconductor machine platform provided by the present invention further 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 measurement and positioning method of the semiconductor machine platform provided by the present invention are realized. Among them, the measurement and positioning system provided by the present invention can be loaded into the processor, and the measurement and positioning method provided by the present invention can be stored in the memory in the form of firmware.

[0062] Please refer to Figures 1 to 6 As shown in the figure, the present invention provides a measurement and positioning method for a semiconductor machine platform, which can obtain the correct sampling area when the sampling camera 20 has a focusing error, so as to obtain the correct electron microscope scanning area. It should be noted that the measurement and positioning method of the semiconductor machine platform provided by the present invention automatically adjusts the position of the sampling camera 20 when the semiconductor machine platform reports an error. In the case of correct positioning, the wafer 101 first passes through an optical microscope, and a first sampling area is obtained from the surface of the wafer 101, such as Figure 3 the white frame area shown in the figure. Adjust the focus of the sampling camera 20 to focus on the first sampling area, so as to further magnify the first sampling area. Then, through a scanning electron microscope, a second sampling area is obtained on the first sampling area, such as Figure 4 the white frame area shown in the figure. Then, by comparing the template pictures, the characteristic structure to be measured is addressed and obtained from the second sampling area, such as Figure 5The characteristic structure shown. Then, obtain the key dimensions of the characteristic structure, such as Figure 6 The width dimension in the elliptical circle. In the present invention, the types of error reports 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. Among them, the first type of alarm information is the positioning error of the first sampling area, the second type of alarm information is the positioning error of the second sampling area, the third type of alarm information is the positioning error of the characteristic structure, and the fourth type of alarm information is the positioning error of the key dimension. In the present invention, when any type of alarm information appears, the semiconductor machine reports an error.

[0063] Please refer to Figures 1 to 6 As shown, in the present invention, before measuring the key dimension, a measurement program is input into the semiconductor machine. Specifically, for a type of wafer product, the present invention sets a master wafer. On the master wafer, 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 detected optically, so as to determine whether the finally obtained measurement structure or measurement area conforms to the standard structure set on the master wafer. Since the size of the characteristic structure is much smaller than the area of the wafer 101. And the types of semiconductor products are extremely complex. Therefore, in the batch manufacturing process of semiconductors, measurement positioning is prone to errors. If any position does not match the master wafer and the initially set verification program fails, it will cause the semiconductor machine to report an error. It should be noted that the verification program varies too much according to designers, the types of products targeted, the levels of products targeted, and parameter preferences adopted, etc. The present invention does not limit the verification program for finding errors. The measurement positioning method provided by the present invention only re-performs measurement positioning when the semiconductor machine reports an error.

[0064] Please refer to Figures 1 to 6 As shown, when the semiconductor machine reports an error, the semiconductor machine enables the semiconductor machine measurement positioning method provided by the present invention and feedbacks the type of error report. Among them, the semiconductor machine measurement positioning method includes step S100 to step S500.

[0065] Step S100: When the semiconductor machine reports an error, obtain the sampling image of the semiconductor machine and input the template image.

[0066] Step S200: Analyze the sampling image and the template image, obtain a plurality of first feature points from the sampling image, and obtain a plurality of second feature points from the template image.

[0067] Step S300: According to the correlation between the first feature points and the second feature points, intercept the sampling area from the sampling image and map the template image onto the sampling area.

[0068] Step S400: Set a feature threshold. When the similarity between the template image and the sampling area is less than the feature threshold, increase the number of acquired first feature points and second feature points, and re-acquire the first feature points and the second feature points.

[0069] 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 tool.

[0070] Please refer to Figures 1 to 7 As shown, in an embodiment of the present invention, in step S100, when the semiconductor machine tool reports an error, the error type of the semiconductor machine tool is recorded. Among them, the error type can 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 the error occurs, the wafer 101 will stay in the current measurement process and will not continue to the next measurement process. In step S100, the current captured image of the sampling camera 20 is used as the sampling image. Among them, the template image is the master wafer image. In the case of the first type of alarm information, the master wafer image corresponds to the standard image of the first sampling area. In the case of the second type of alarm information, the master wafer image corresponds to the standard image of the second sampling area. In the case of the third type of alarm information, the master wafer image corresponds to the standard image of the feature structure. In the case of the fourth type of alarm information, the master wafer image corresponds to the standard image of the critical dimension. Then, the sampling image and the template image are analyzed, and step S200 includes steps S210 to S250.

[0071] Step S210: Input the image.

[0072] Step S220: Obtain any pixel point in the image as the pixel point to be analyzed.

[0073] Step S230: Set a comparison area in the image with the pixel point to be analyzed as the center.

[0074] Step S240: Set the gray difference threshold. If the gray value difference between the pixel point to be analyzed and any pixel point in the comparison area is greater than or equal to the gray difference threshold, the pixel point to be analyzed is used as a feature point.

[0075] Step S250: Determine whether the number of feature points reaches the 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.

[0076] Please refer to Figures 1 to 7As shown, in an embodiment of the present invention, in step S210, the input image includes a sampled image and a template image. In this embodiment, the feature points of the sampled image and the template image are respectively obtained, and the feature points of the sampled image and the template image can be obtained sequentially, or the feature points of the sampled image and the template image can be obtained simultaneously. The following takes the sampled image as an example to illustrate step S200, and the same execution process applies to the template image. In step S230, the number of pixel points in the comparison region is a preset number, for example, 4, 6, 8, etc. Specifically, the number of pixel points in the comparison region can be obtained through experiments or experience. Taking the pixel point to be analyzed as the center, a preset number of pixel points are obtained from near to far around the pixel point to be analyzed, and the region where the obtained pixel points are located and the region where the pixel point to be analyzed is located are used as the comparison region. In step S240, the gray value of the pixel point to be analyzed is sequentially compared with the gray value of each pixel point in the comparison region. If the difference between the gray value of the pixel point to be analyzed and the gray value of any pixel point in the comparison region is greater than or equal to the gray difference threshold, the pixel point to be analyzed is taken as the first feature point. Similarly, the second feature point is obtained in the manner of steps S220 to S240. The gray difference threshold is a preset value and can be obtained through experiments or experience.

[0077] Please refer to Figure 2 , Figure 7 and Figure 8As shown, in an embodiment of the present invention, in step S250, the number of first feature points and second feature points obtained is equal. In this embodiment, the first feature points and second feature points are obtained in multiple cycles, and the number of feature points in the latter cycle is more than that 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, cycle increment, and number of cycles 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 in the number of feature points in the current cycle compared to the previous cycle. Whenever the preset number of feature points is incremented once, the number of cycles is also incremented by one. 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 number of cycles and the initial number. For example, if the initial number is 500, after obtaining 500 feature points, it is judged whether the electron microscope scanning area can be obtained currently. If it cannot be obtained, the feature points are incrementally obtained. For example, if the cycle increment is 50, then in the second cycle, 550 feature points are obtained. In the third cycle, 600 feature points are obtained, and so on. It should be noted that the cycle increment is a fixed value in this embodiment. In other embodiments of the present invention, the cycle increment can also increase or decrease with the number of cycles. After the number of feature points reaches the preset number, step S300 is executed, where step S300 includes steps S310 to S350.

[0078] Step S310: Set an adjustable area on the image centered on the feature point. The adjustable area includes a plurality of auxiliary pixel points, where the feature point is the first feature point or the second feature point.

[0079] Step S320: Obtain the matching parameters of the feature point according to the structural similarity and mean square error of the adjustable area.

[0080] Step S330: Set a matching threshold, and judge whether the matching parameter error between the first feature point and the second feature point is less than the matching threshold.

[0081] 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 relevant.

[0082] 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.

[0083] Please refer to Figure 2 、 Figure 7 and Figure 8As shown, in an embodiment of the present invention, in step S310, the feature points refer to the first feature or the second feature points. In this embodiment, the matching parameters of the sampled image and the template image are respectively obtained, and the matching parameters of the sampled image and the template image can be obtained sequentially, or the matching parameters of the sampled image and the template image can be analyzed and obtained simultaneously. The adjustable region includes a plurality of auxiliary pixel points, and the number of auxiliary pixel points is, for example, 4, 6, 8, etc. 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 region is obtained. The calculation of the mean square error is based on Equation (1).

[0084] (1).

[0085] In Equation (1), MSE is the mean square error of the pixel points in the adjustable region, patchsize is the area of the adjustable region, I view(i，j，k) is the pixel value of the sampled image, and I template(i，j，k) is the pixel value of the template image.

[0086] Please refer to Figure 2 、 Figure 7 and Figure 8 As shown, in an embodiment of the present invention, in step S320, the structural similarity (SSIM) of the pixel points in the adjustable region is obtained, and the calculation of the structural similarity of the pixel points in the adjustable region is based on Equation (2).

[0087] (2).

[0088] In Equation (2), SSIM is the structural similarity of the pixel points in the adjustable region, μ x is the average value of the abscissas of the pixel points in the adjustable region, μ y is the average value of the ordinates of the pixel points in the adjustable region, σ x 2 is the variance of the abscissas of the pixel points in the adjustable region, σ y 2 is the variance of the ordinates of the pixel points in the adjustable region, σ xy is the covariance of the abscissas and ordinates of the pixel points in the adjustable region, c 1 and c 2 are constants obtained through experiments. Among them, c 1 and c 2 can be used to help improve the measurement accuracy.

[0089] Please refer to Figure 2 、 Figure 7 and Figure 8As shown, in an embodiment of the present invention, in step S320, the matching parameter of the feature points 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 between the matching parameters of the first feature point and the second feature point is less than the matching threshold, then step S340 is executed to mark the first feature point as related to the second feature point. Then, the next second feature point and the next first feature point are obtained for correlation matching until the matching of all second feature points is completed. In this embodiment, if the difference between the matching parameters of the first feature point and the second feature point is greater than or equal to the matching threshold, then 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 the matching of all second feature points is completed. It should be noted that a coordinate system is established on the template image and the sampling image, and the coordinates of the first feature point, the second feature point, and the auxiliary pixel points are obtained. According to the coordinates of the first feature point, the second feature point, and the auxiliary pixel points, the matching parameters of the first feature point and the second feature point are obtained.

[0090] Please refer to Figure 2 、 Figures 7 to 10 As shown, in an embodiment of the present invention, in step S400, the steps of obtaining the similarity between the sampling region and the template image include step S410 and step S420.

[0091] In step S410, the area of the sampling region is set, the second feature points are mapped onto the relevant first feature points, and according to the mapping regions of the second feature points, the sampling region is intercepted on the sampling image.

[0092] In step S420, according to the mapping relationship from the second feature points to the first feature points, the geometric consistency transformation between the sampling region and the template image is verified to obtain the similarity between the sampling region and the template image.

[0093] Please refer to Figure 2 、 Figures 7 to 14 As shown, in an embodiment of the present invention, in step S410, all the second feature points are projected onto the relevant first feature points to form multiple projection ray bundles, the principle of which is as Figure 10 shown. In the present invention, the mapping from the second feature points to the first feature points is completed through a two-dimensional affine transformation algorithm. Based on the multiple projection ray bundles from the second feature points to the first feature points, the geometric consistency between the template image and the sampling image can be verified. Based on the two-dimensional affine transformation algorithm, the similarity between the sampling region and the template image is obtained, as Figure 10 and Figure 11As shown in the figure. The similarity depends on the average skew line angle between multiple projection beams. 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, return to step S200 to enter a new loop iteration, increase the preset number of feature points according to the loop increment, and re-obtain the first feature point and the second feature point. In this embodiment, the above 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, return to step S200. As Figure 12 shown to Figure 14 shown, in each loop, due to the increase in the number of feature points, the fitting of the sampling area will be more accurate. The boxed area is the sampling area. In this embodiment, the center point of the sampling area is obtained as the electron microscope focusing coordinate of the semiconductor machine tool, so that the electron microscope scan can evenly scan the entire sampling area and improve the focusing accuracy of the electron microscope scan in the current scan 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 scan area. Specifically, the system outputs the center point of the current sampling area as the focus coordinate of the electron microscope scan.

[0094] Please refer to Figure 2 and Figure 15 shown, the present invention provides a measurement and positioning system 40 for a semiconductor machine tool. 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. Among them, the alarm module 401 is used to obtain the sampling image of the semiconductor machine tool and input the template image when the semiconductor machine tool 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 the 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 the feature threshold. When the similarity between the template image and the sampling area is less than the feature threshold, increase the acquisition quantity of the first feature point and the second feature point, and re-obtain the first feature point and the second feature point. The result output module 405 is used to use the sampling area as the electron microscope scan area of the semiconductor machine tool when the similarity between the template image and the sampling area is greater than or equal to the feature threshold.

[0095] The present invention provides a method for measuring and positioning a semiconductor machine tool and a semiconductor machine tool. Its unexpected technical effects are as follows: it can automatically adjust the camera focus when the machine tool measurement reports an error, so as to realize automatic goods transfer when the machine tool measurement goes wrong, improve the measurement efficiency of the machine tool, and reduce the frequency of manual intervention. Moreover, the present invention can not only accurately locate the key dimensions to be measured, but also reduce the resource occupancy efficiency, quickly complete the repositioning of the measurement of the key dimensions, avoid waste of production capacity, and at the same time solve many potential hazards in the semiconductor production process.

[0096] The embodiments of the present invention disclosed above are only used to help explain 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 variations can be made according to the content of this specification. These embodiments are selected and specifically described in this specification to better explain the principle and practical application of the present invention, so that those skilled in the art can well understand and utilize the present invention. The present invention is only limited by the claims and the 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, the surface of the wafer on the semiconductor machine is captured by camera to obtain a sample image, and a master wafer image is input as 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; 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, obtaining matching parameters of the feature point according to the structural similarity and mean square error of the adjustable area, setting a matching threshold, and when the matching parameter error between the first feature point and the second feature point is less than the matching threshold, correlating the first feature point with the second feature point, setting the area of ​​a sampling area, mapping the second feature point to the related first feature point, and cutting out the sampling area on the sampling image according to the mapping area of ​​the second feature point; 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 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.

6. 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: 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.

7. 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.

8. 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 7, 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 setting the area of ​​a sampling region, mapping the second feature point to the related first feature point, and cutting out the sampling region on the sampling image according to the mapping area of ​​the second feature point; 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.

9. The semiconductor machine according to claim 8, 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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