Electron beam inclination angle quantification method, electronic equipment and product

Abstract

The invention provides an electron beam inclination angle quantification method, electronic equipment and a product. The method for quantifying the inclination angle of the electron beam comprises the following steps: respectively acquiring a first image and a second image of a pyramid-shaped sample with a three-dimensional rotation angle through a scanning electron microscope; enhancing edge information of the first image and the second image by using a differential filter, and extracting a key point set through a binarization method; extracting feature straight lines in the first image and the second image from the key point set; constructing a target function by combining the three-dimensional rotation matrix and the feature straight line of the first image, and calculating the three-dimensional rotation angle of the pyramid-shaped sample through a Nelder-Mead algorithm; a linear equation set is constructed by combining the tilt transformation matrix and the characteristic straight line of the second image, the two-dimensional tilt angle of the electron beam is calculated through a least square method, geometric errors caused by sample rotation can be eliminated, and interference is eliminated for quantifying the tilt angle of the electron beam; and the calculation precision and stability of the inclination angle are effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of semiconductor detection technology, and in particular to a method for quantifying an electron beam tilt angle, electronic equipment, and products. Background Art

[0002] Scanning electron microscopes (SEMs) are essential metrology tools in semiconductor manufacturing, widely used for measuring and analyzing micro- and nanostructures, such as critical dimension measurement and profile analysis. However, with increasing demands for image resolution and measurement accuracy, their application faces new technical challenges.

[0003] Among them, electron beam tilt introduces additional uncertainty and increases measurement complexity. Therefore, quantifying the electron beam tilt angle becomes the key to improving measurement accuracy. In addition, by precisely controlling the electron beam tilt angle, it is possible to generate a sidewall profile image of the sample, and combine the tilt image data from different directions to reconstruct the sample's three-dimensional profile, thereby significantly improving the accuracy and comprehensiveness of the measurement. Therefore, quantifying the electron beam tilt angle has important technical significance in meeting the needs of advanced process control, improving metrology accuracy, and monitoring complex profile changes.

[0004] In the existing technology, the following method is generally used to quantify the electron beam tilt angle: first, SEM images of the pyramidal sample from top-down and tilted perspectives are obtained, pre-processed by differential filtering, and then the qualified straight lines are screened out through Hough line detection. Finally, the sample rotation angle and electron beam tilt angle are obtained through a numerical optimization algorithm.

[0005] However, the solutions in the existing technology have the following problems: under different acquisition conditions, SEM images may be affected by factors such as blur, distortion or noise, and the existing technology uses differential filtering processing with fixed parameters, without dynamic adjustment based on image characteristics, resulting in unsatisfactory edge enhancement effects; obtaining a straight line that meets the requirements through a single Hough line detection requires repeated parameter adjustment for different images, which increases the operational complexity and process uncertainty; the existing technology uses a numerical optimization algorithm to solve the electron beam tilt angle, but because the optimization process may fall into a local minimum, the solution accuracy is not high, which affects the quantitative accuracy of the electron beam tilt angle and makes it difficult to meet the needs of high-precision measurement. Summary of the Invention

[0006] One object of the present invention is to accurately estimate the three-dimensional rotation angle of a pyramidal sample and eliminate interference for quantifying the electron beam tilt angle.

[0007] A further object of the present invention is to directly calculate the tilt angle of the electron beam to avoid falling into a local minimum and to improve the calculation accuracy and stability.

[0008] Another further object of the present invention is to improve the accuracy of extracting image key point sets and characteristic straight lines, and further ensure the accuracy of quantifying the tilt angle of the electron beam.

[0009] In particular, the present invention provides a method for quantifying the tilt angle of an electron beam, comprising:

[0010] Acquire a first image and a second image of the pyramid-shaped sample having a three-dimensional rotation angle by a scanning electron microscope, wherein the electron beam for acquiring the first image is perpendicular to a projection plane of the pyramid-shaped sample, and the electron beam for acquiring the second image is at a two-dimensional tilt angle relative to the projection plane;

[0011] Using a differential filter to enhance edge information of the first image and the second image, and extracting a key point set by a binarization method;

[0012] Extracting characteristic straight lines in the first image and the second image from the key point set;

[0013] Constructing an objective function by combining the three-dimensional rotation matrix and the characteristic line of the first image, and calculating the three-dimensional rotation angle of the pyramidal sample using the Nelder-Mead algorithm; and

[0014] A linear equation system is constructed by combining the tilt transformation matrix and the characteristic straight line of the second image, and the two-dimensional tilt angle of the electron beam is calculated by the least squares method.

[0015] Optionally, the surface of the pyramidal sample is formed with inverted N pyramids downwards,

[0016] The characteristic straight lines include baselines and ridge lines. The baselines include the N projection lines of the surface of the pyramidal sample and the N intersection lines of the N pyramids on the projection plane. The ridge lines include the N projection lines of the lines connecting the apex P0 of the N pyramid and the other N vertices on the projection plane.

[0017] The coordinates of the cone top P0 in the projection plane are defined as the origin.

[0018] Optionally, the step of constructing the objective function by combining the three-dimensional rotation matrix and the characteristic straight line of the first image includes:

[0019] Determine the coordinates of the vertices P1 to P1 of the N-pyramid sample with no rotation angle, except the apex P0. N ;

[0020] Using a 3D rotation matrix The pyramidal sample is rotated in three dimensions by Perform rotation, three-dimensional rotation matrix for:

[0021]

[0022]

[0023] The x-axis is the first coordinate direction of the projection plane, the y-axis is the second coordinate direction of the projection plane, and the z-axis is perpendicular to the third coordinate direction of the projection plane.

[0024] By formula Set the vertex coordinates P1 to P of the inverted pyramid N Perform the conversion;

[0025] The transformed vertex P 1A To P NA Project onto the projection plane and get the projection point P 1B To P NB , connect the cone top P0 and the projection point P 1B To P NB , we get the straight line L 1B To L NB , whose angle relative to the x-axis is calculated by simulating the three-dimensional rotation matrix The angles relative to the x-axis correspond to the angles α1 to α2 of the N ridge lines in the first image. N ;

[0026] Constructing the objective function To characterize the spatial rotation relationship of pyramidal samples.

[0027] Optionally, the step of constructing a system of linear equations by combining the tilt transformation matrix and the characteristic straight line of the second image includes:

[0028] Use the skew transformation matrix R θ The electron beam is tilted at a two-dimensional angle (θ x ,θ y ) for tilting, tilt transformation matrix R θ for:

[0029]

[0030] R θ =R θy *R θx ;

[0031] By formula The vertex P 1A To P NA Perform the conversion;

[0032] By connecting the cone top P0 and the transformed projection point P 1C To P NC , we get the straight line L 1C To L NC , its angle α relative to the x-axis θ1 to α θNBy the tilt transformation matrix R θ The simulation calculation shows that the angle α θ1 to α θN Corresponding to the angles of the N ridge lines in the second image, the inclination angle formula is obtained

[0033] Transforming the tilt angle formula to construct a linear system of equations

[0034] Optionally, the step of extracting characteristic straight lines in the first image and the second image from the key point set includes:

[0035] Use the Hough line detection algorithm to extract multiple candidate lines from the key point set;

[0036] Filtering 2N target straight lines in the first image and the second image from the plurality of candidate straight lines using a screening optimization algorithm; and

[0037] The pixel area near the 2N target lines is scanned by a fitting optimization algorithm, and the pixel points with the most significant gradient changes are extracted as the best representation points of the edge. The edge point set composed of the best representation points is fitted to determine the characteristic lines in the first image and the second image.

[0038] Optionally, before the step of enhancing edge information of the first image and the second image by using a differential filter, the method further includes:

[0039] The size of the difference filter is determined according to the blurriness of the first image and the second image, wherein the blurriness is positively correlated with the size.

[0040] Optionally, the step of enhancing edge information of the first image and the second image using a differential filter includes:

[0041] By calculating the difference between the pixel values of the first image and the second image, edge regions of the first image and the second image where the grayscale changes dramatically are enhanced.

[0042] Optionally, the pyramidal sample is a pyramid sample, and the N-pyramid is a 4-pyramid.

[0043] According to another aspect of the present invention, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein when the processor executes the computer program, any of the above-mentioned methods for quantifying the electron beam tilt angle is implemented.

[0044] According to yet another aspect of the present invention, there is provided a computer program product, comprising a computer program, wherein the computer program implements any of the above-mentioned methods for quantifying the electron beam tilt angle when executed by a processor.

[0045] The electron beam tilt angle quantification method of the present invention accurately estimates the three-dimensional rotation angle of the pyramidal sample by constructing an objective function, eliminates the geometric error caused by the rotation of the pyramidal sample, and eliminates interference for the subsequent quantification of the electron beam tilt angle; uses the least squares method to directly solve the electron beam tilt angle, avoids falling into the local minimum, and effectively improves the solution accuracy and stability.

[0046] Furthermore, the electron beam tilt angle quantification method of the present invention selects a suitable filter size according to the degree of blur of the image, thereby improving the flexibility and effectiveness of edge enhancement; based on Hough line detection, a screening optimization algorithm is used to screen out 2N target straight lines that meet the conditions from multiple candidate straight lines, and a fitting optimization algorithm is combined to search for the best characterization point of the edge, and high-precision feature straight lines are accurately fitted, which generally improves the accuracy of extracting image key point sets and feature straight lines, further ensuring the accuracy of quantizing the electron beam tilt angle.

[0047] Based on the following detailed description of specific embodiments of the present invention in conjunction with the accompanying drawings, those skilled in the art will become more aware of the above and other objects, advantages and features of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Hereinafter, some specific embodiments of the present invention will be described in detail in an exemplary and non-limiting manner with reference to the accompanying drawings. The same reference numerals in the accompanying drawings indicate the same or similar components or parts. It should be understood by those skilled in the art that these drawings are not necessarily drawn to scale. In the accompanying drawings:

[0049] Figure 1 is a schematic diagram of a method for quantifying an electron beam tilt angle according to an embodiment of the present invention;

[0050] Figure 2 is a schematic diagram of a pyramid-shaped sample in a method for quantifying an electron beam tilt angle according to an embodiment of the present invention;

[0051] Figure 3 is a schematic diagram of a differential filter in a method for quantifying an electron beam tilt angle according to an embodiment of the present invention;

[0052] Figure 4 is a schematic diagram of a differential filter in a method for quantifying an electron beam tilt angle according to another embodiment of the present invention;

[0053] Figure 5 is a simulation diagram of a pyramidal sample without a rotation angle in a method for quantifying an electron beam tilt angle according to one embodiment of the present invention;

[0054] Figure 6 is a simulation diagram of a pyramid-shaped sample in a first image in a method for quantifying an electron beam tilt angle according to an embodiment of the present invention;

[0055] Figure 7 is a simulation diagram of a pyramid-shaped sample in a second image in a method for quantifying an electron beam tilt angle according to an embodiment of the present invention;

[0056] Figure 8 is a schematic diagram of a computer program product according to one embodiment of the present invention;

[0057] Figure 9 is a schematic diagram of a computer-readable storage medium according to one embodiment of the present invention; and

[0058] Figure 10 is a schematic diagram of a computer device according to one embodiment of the present invention. DETAILED DESCRIPTION

[0059] This embodiment provides a method for quantifying the electron beam tilt angle. By constructing an objective function, it accurately estimates the three-dimensional rotation angle of the pyramidal sample, eliminating interference for the subsequent quantification of the electron beam tilt angle; it uses the least squares method to directly solve the electron beam tilt angle to avoid falling into the local minimum, effectively improving the solution accuracy and stability. Figure 1 FIG. 1 is a schematic diagram of a method for quantifying an electron beam tilt angle according to an embodiment of the present invention. Figure 1 As shown, the method for quantifying the electron beam tilt angle of this embodiment may generally include:

[0060] Step S102, obtaining a first image and a second image of a pyramid-shaped sample having a three-dimensional rotation angle by a scanning electron microscope;

[0061] Step S104, using a differential filter to enhance edge information of the first image and the second image, and extracting a key point set by a binarization method;

[0062] Step S106, extracting characteristic straight lines in the first image and the second image from the key point set;

[0063] Step S108, constructing an objective function by combining the three-dimensional rotation matrix and the characteristic straight line of the first image, and calculating the three-dimensional rotation angle using the Nelder-Mead algorithm; and

[0064] Step S110 , combining the tilt transformation matrix and the characteristic straight line of the second image to construct a linear equation system, and solving the two-dimensional tilt angle by the least square method.

[0065] In the above steps, the scanning electron microscope (SEM) in step S102 scans the sample surface with an electron beam, collects signals such as secondary electrons and backscattered electrons emitted by the sample, and generates an image reflecting the surface morphology of the sample after processing. The image obtained by the scanning electron microscope can be called an SEM image, that is, the first image and the second image are both SEM images. In addition, the electron beam used to obtain the first image is perpendicular to the projection plane of the pyramid-shaped sample, and the electron beam used to obtain the second image is at a two-dimensional tilt angle relative to the projection plane.

[0066] The change in the pyramidal sample's geometric shape in the image is determined by the electron beam tilt angle and the sample rotation angle. These parameters are subsequently quantified in the first and second images respectively. Separating and independently optimizing the angle calculation process can effectively improve the calculation accuracy of the electron beam tilt angle.

[0067] Figure 2 FIG. 1 is a schematic diagram of a pyramidal sample in a method for quantifying an electron beam tilt angle according to an embodiment of the present invention. Figure 2 As shown, the surface 110 of the pyramidal sample is formed with an inverted N-pyramid 120. The pyramidal sample can be a wafer sample. In a specific embodiment, the wafer is placed in an etching solution and heated to a certain temperature. The etching solution etches the wafer surface. Due to the different etching rates in different crystal directions, a microscopic pyramid structure is formed on the wafer surface.

[0068] Step S104 of enhancing edge information of the first image and the second image using a differential filter may specifically include: enhancing edge regions of the first image and the second image where grayscale changes dramatically by calculating the difference between pixel values of the first image and the second image. In a preferred embodiment, before step S104 of enhancing edge information of the first image and the second image using a differential filter, the step may further include: determining the size of the differential filter based on the degree of blur between the first image and the second image, where the degree of blur is positively correlated with the size.

[0069] That is, the higher the blur level of the image, the larger the size of the differential filter; and the lower the blur level of the image, the smaller the size of the differential filter. Figure 3 FIG. 1 is a schematic diagram of a differential filter in a method for quantifying an electron beam tilt angle according to an embodiment of the present invention. Specifically, Figure 3 A 3*3 differential filter matrix is shown. Figure 4 FIG. 1 is a schematic diagram of a differential filter in a method for quantifying an electron beam tilt angle according to another embodiment of the present invention. Specifically, Figure 4A 5*5 differential filter matrix is shown. Selecting an appropriate filter size based on the blur level of the image and preprocessing the image can effectively improve the flexibility and effectiveness of image edge enhancement, thereby improving the accuracy of key point extraction.

[0070] After enhancing the edge information of the first and second images, step S104 extracts key points using a binarization method. Specifically, binarization is the process of converting a grayscale image into an image containing only two pixel values: "black" (0) and "white." This method can highlight the contrast between the target area and the background, thereby quickly locating and extracting key points.

[0071] The characteristic lines in step S106 include baselines and ridge lines. The baselines include the N projections of the N intersection lines of the pyramidal sample surface 110 and the N pyramids 120 on the projection plane. The ridge lines include the N projections of the lines connecting the apex P0 of the N pyramids and the other N vertices on the projection plane. In a preferred embodiment, the coordinates of the apex P0 in the projection plane are defined as the origin.

[0072] Figure 5 is a simulation diagram of a pyramidal sample without a rotation angle in a method for quantifying an electron beam tilt angle according to an embodiment of the present invention. Figure 6 is a simulation diagram of a pyramid-shaped sample of the first image in a method for quantifying an electron beam tilt angle according to an embodiment of the present invention. Figure 7 This is a simulated diagram of a pyramidal sample in the second image of a method for quantifying electron beam tilt angle according to one embodiment of the present invention. The x-axis is the first coordinate direction of the projection plane, the y-axis is the second coordinate direction of the projection plane, and the z-axis is perpendicular to the third coordinate direction of the projection plane. In other words, the projection plane can be the xy plane.

[0073] In a preferred embodiment, Figure 2 as well as Figures 5 to 7 As shown, the pyramidal sample can be a pyramid sample, and the N pyramid 120 is a 4-pyramid. In this case, all descriptions or formulas related to N in the text can be understood by substituting 4. Figure 5 Taking the pyramid sample with no rotation angle as an example, the baseline includes the four projection lines of the surface of the pyramid sample and the four intersection lines of the four pyramids on the projection plane: L0, L1, L2, and L3. The ridge line includes the four projection lines of the connection line between the apex P0 of the four pyramid and the other four vertices P1, P2, P3, and P4 on the projection plane: L4, L5, L6, and L7.

[0074] Step S106 extracting characteristic straight lines in the first image and the second image from the key point set may specifically include: extracting multiple candidate straight lines from the key point set using the Hough line detection algorithm; screening 2N target straight lines in the first image and the second image from the multiple candidate straight lines using the screening optimization algorithm; and scanning the pixel area near the 2N target straight lines using the fitting optimization algorithm, extracting the pixel points with the most significant gradient changes as the best representation points of the edge, and fitting the edge point set composed of the best representation points to determine the characteristic straight lines in the first image and the second image.

[0075] The Hough line detection algorithm is a feature extraction algorithm based on an image voting mechanism, capable of detecting lines in images. Specifically, the line detection problem in image space can be converted into a cumulative voting problem in parameter space, and the most likely line parameters can be identified by counting the voting results. The screening optimization algorithm, on the other hand, uses specific rules during the iterative process to select high-quality solutions and eliminate low-quality solutions, thereby guiding the search direction to converge toward more optimal areas. The screening optimization algorithm in this embodiment can select 2N target lines in the first and second images from multiple candidate lines based on quadrant division and slope characteristics.

[0076] To improve line detection accuracy and avoid deviations caused by noise and other interference, a fitting optimization algorithm scans the pixel area near the 2N target lines, extracting the pixels with the most significant gradient changes as the optimal edge characterization points. This optimal edge point set is then fitted to determine the characteristic lines in the first and second images. The resulting characteristic lines are geometrically precise and accurately represent the geometric characteristics of the pyramidal sample.

[0077] After extracting characteristic lines from the first and second images in step S106, step S108 may be performed to construct an objective function by combining the three-dimensional rotation matrix and the characteristic lines of the first image, and to calculate the three-dimensional rotation angle using the Nelder-Mead algorithm. In a specific embodiment, step S108 may include:

[0078] First, determine the coordinates of the other vertices P1 to P0 of the N pyramids of the pyramid-shaped sample without rotation angle. N In a preferred embodiment, the pyramidal sample is a pyramidal sample, and the base of the four pyramids is a square, and the sides are all equilateral triangles. Figure 5 As shown, in this case, the coordinates of the other four vertices P1, P2, P3, and P4 of the four-sided pyramid other than the apex P0 can be expressed in matrices as follows:

[0079]

[0080] Then use the 3D rotation matrix The pyramidal sample is rotated in three dimensions by Perform rotation, three-dimensional rotation matrix for:

[0081]

[0082] Then, through the formula Translate the vertex coordinates P1 to P N Perform the transformation; transform the transformed vertex P 1A To P NA Project onto the projection plane and get the projection point P 1B To P NB , connect the cone top P0 and the projection point P 1B To P NB , we get the straight line L 1B To L NB , whose angle relative to the x-axis is calculated by simulating the three-dimensional rotation matrix The angles relative to the x-axis correspond to the angles α1 to α2 of the N ridge lines in the first image. N It should be noted that the three-dimensional rotation matrix is calculated by simulation The obtained straight line L 1B To L NB The angle relative to the x-axis is the angle of the theoretical model, and the angles of the N ridge lines in the first image are α1 to α N is the actual image angle.

[0083] Finally, we can construct the objective function To characterize the spatial rotation relationship of the pyramidal sample. The core of this objective function is to minimize the geometric error between the theoretical position of the baseline and ridge of the pyramidal sample and the actual detection position. Therefore, the Nelder-Mead algorithm is subsequently used to optimize this objective function to obtain the estimated value of the three-dimensional rotation angle of the pyramidal sample.

[0084] Step S110 combines the tilt transformation matrix and the characteristic straight line of the second image to construct a linear equation system, which may specifically include: using the tilt transformation matrix R θ The electron beam is tilted at a two-dimensional angle (θ x ,θ y ) for tilting, tilt transformation matrix R θ for:

[0085]

[0086] R θ =R θy *R θx .

[0087] Then by the formula The vertex P 1A To P NA Perform the transformation by connecting the vertex P0 with the transformed projection point P 1C To P NC , we get the straight line L 1C To L NC , its angle α relative to the x-axis θ1 to α θN By the tilt transformation matrix R θ The simulation calculation shows that the angle α θ1 to α θN Corresponding to the angles of the N ridge lines in the second image, the inclination angle formula is obtained It should be noted that the tilt transformation matrix R is calculated by simulation. θ The obtained straight line L 1C To L NC Angle α relative to the x-axis θ1 to α θN is the angle of the theoretical model, and the angles of the N ridge lines in the second image are the angles of the actual image.

[0088] Finally, the linear equations can be constructed by transforming the tilt angle formula Then the two-dimensional tilt angle (θ x ,θ y ).

[0089] In summary, the method for quantifying the electron beam tilt angle in this embodiment accurately estimates the three-dimensional rotation angle of the pyramidal sample by constructing an objective function, eliminates the geometric error caused by the rotation of the pyramidal sample, and eliminates interference for the subsequent quantification of the electron beam tilt angle; the least squares method is used to directly solve the tilt angle of the electron beam to avoid falling into the local minimum, thereby effectively improving the solution accuracy and stability.

[0090] Furthermore, the electron beam tilt angle quantification method of this embodiment selects a suitable filter size according to the degree of image blur, thereby improving the flexibility and effectiveness of edge enhancement; based on Hough line detection, a screening optimization algorithm is used to screen out 2N target straight lines that meet the conditions from multiple candidate straight lines, and a fitting optimization algorithm is used to search for the best representation point of the edge, and high-precision feature straight lines are obtained by accurate fitting, which generally improves the accuracy of extracting image key point sets and feature straight lines, further ensuring the accuracy of quantizing the electron beam tilt angle.

[0091] The flowcharts provided in the above embodiments are not intended to indicate that the operations of the method will be performed in any particular order, or that all operations of the method are included in all every case. In addition, the method may include additional operations. Within the scope of the technical ideas provided by the above embodiment methods, additional changes can be made to the above method.

[0092] This embodiment also provides a computer program product, a computer-readable storage medium, and a computer device. Figure 8 is a schematic diagram of a computer program product 500 according to one embodiment of the present invention. Figure 9 is a schematic diagram of a computer-readable storage medium 300 according to one embodiment of the present invention. Figure 10 is a schematic diagram of a computer device 400 according to one embodiment of the present invention.

[0093] The computer program product 500 includes a computer program 310, which, when executed by a processor 410, implements any of the aforementioned methods for quantifying an electron beam tilt angle. A computer-readable storage medium 300 stores the computer program 310, which, when executed by the processor 410, implements any of the aforementioned methods for quantifying an electron beam tilt angle. A computer device 400 may include a memory 420, a processor 410, and the computer program 310 stored in the memory 420 and executed by the processor 410. When the processor 410 executes the computer program 310, it implements any of the aforementioned methods for quantifying an electron beam tilt angle.

[0094] The computer program 310 for performing the operations of the present invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, configuration data for an integrated circuit, or source code or object code written in any combination of one or more programming languages and procedural programming languages.

[0095] The computer program 310 may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter case, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0096] In some embodiments, to implement various aspects of the present invention, electronic circuits including, for example, programmable logic circuits, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs) may execute computer-readable program instructions by utilizing state information of the computer-readable program instructions to personalize the electronic circuits.

[0097] For the purposes of the description of this embodiment, computer program product 500 is a related product that includes computer program 310. For the purposes of the description of this embodiment, computer-readable storage medium 300 is a tangible device capable of retaining and storing computer program 310, and can be any device that can contain, store, communicate, propagate, or transmit computer program 310 for use by an instruction execution system, apparatus, or device, or in conjunction with such instruction execution system, apparatus, or device.

[0098] More specific examples (a non-exhaustive list) of computer-readable storage media 300 include the following: a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, and any suitable combination of the foregoing.

[0099] It should be noted that the logic and / or steps represented in the flowchart or described in other ways herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be specifically implemented in any machine-readable storage medium for use by an instruction execution system, device or equipment (such as a computer-based system, a system including a processor or other system that can fetch instructions from an instruction execution system, device or equipment and execute instructions), or used in combination with these instruction execution systems, devices or equipment.

[0100] It should be understood that each part of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system.

[0101] Computer device 400 can be, for example, a server, a desktop computer, a laptop computer, a tablet computer, or a smartphone. In some examples, computer device 400 can be a cloud computing node. Computer device 400 can be described in the general context of computer system executable instructions (such as program modules) executed by a computer system. Generally, program modules can include routines, programs, object programs, components, logic, data structures, etc. that perform specific tasks or implement specific abstract data types. Computer device 400 can be implemented in a distributed cloud computing environment where remote processing devices linked via a communication network perform tasks. In a distributed cloud computing environment, program modules can be located on local or remote computing system storage media, including storage devices.

[0102] Computer device 400 may include a processor 410 adapted to execute stored instructions, and a memory 420 that provides temporary storage space for instructions during operation. Processor 410 may be a single-core processor, a multi-core processor, a computing cluster, or any number of other configurations. Memory 420 may include random access memory (RAM), read-only memory, flash memory, or any other suitable storage system.

[0103] The processor 410 can be connected to an I / O interface (input / output interface) suitable for connecting the computer device 400 to one or more I / O devices (input / output devices) via a system interconnect (e.g., PCI, PCI-Express, etc.). The I / O devices may include, for example, a keyboard and a pointing device, wherein the pointing device may include a touchpad or a touch screen, etc. The I / O devices may be built-in components of the computer device 400, or may be devices externally connected to the computing device.

[0104] Processor 410 can also be linked to the display interface that is suitable for connecting computer device 400 to display device through system interconnection.Display device can include the display screen that is built-in component of computer device 400.Display device can also include the computer monitor, television or projector etc. that are externally connected to computer device 400.In addition, network interface controller (network interface controller, NIC) can be suitable for connecting computer device 400 to network through system interconnection.In certain embodiments, NIC can use any suitable interface or protocol (such as Internet Small Computer System Interface etc.) to transmit data.Network can be cellular network, radio network, wide area network (WAN)), local area network (LAN) or Internet etc.Remote device can be connected to computing device through network.

[0105] At this point, those skilled in the art will recognize that, although a number of exemplary embodiments of the present invention have been shown and described in detail herein, many other variations or modifications consistent with the principles of the present invention may be directly determined or derived from the disclosure of the present invention without departing from the spirit and scope of the present invention. Therefore, the scope of the present invention should be understood and deemed to cover all such other variations or modifications.

Claims

1. A method for quantifying an electron beam tilt angle, comprising: Acquire a first image and a second image of the pyramid-shaped sample having a three-dimensional rotation angle by a scanning electron microscope, wherein an electron beam for acquiring the first image is perpendicular to a projection plane of the pyramid-shaped sample, and an electron beam for acquiring the second image is at a two-dimensional tilt angle relative to the projection plane; enhancing edge information of the first image and the second image using a differential filter, and extracting a key point set using a binarization method; extracting characteristic straight lines in the first image and the second image from the key point set; Constructing an objective function by combining the three-dimensional rotation matrix and the characteristic straight line of the first image, and calculating the three-dimensional rotation angle of the pyramid-shaped sample by using the Nelder-Mead algorithm; as well as A linear equation group is constructed by combining the tilt transformation matrix and the characteristic straight line of the second image, and the two-dimensional tilt angle of the electron beam is calculated by using the least square method.

2. The method according to claim 1, wherein The surface of the pyramid-shaped sample is formed with inverted N pyramids downward. The characteristic straight line includes a baseline and a ridge line, wherein the baseline includes N projection lines of the surface of the pyramid sample and the N intersection lines of the N pyramids on the projection plane, and the ridge line includes N projection lines of the line connecting the cone top P0 of the N pyramid and the other N vertices on the projection plane, and The coordinates of the cone top P0 in the projection plane are defined as the origin.

3. The method according to claim 2, wherein the step of constructing the objective function by combining the three-dimensional rotation matrix and the characteristic straight line of the first image comprises: Determine the coordinates of the other vertices P1 to P0 of the N-pyramid of the pyramid-shaped sample without a rotation angle except the apex P0. N ; Using the three-dimensional rotation matrix The pyramidal sample is rotated at the three-dimensional angle To perform rotation, the three-dimensional rotation matrix for: The x-axis is the first coordinate direction of the projection plane, the y-axis is the second coordinate direction of the projection plane, and the z-axis is perpendicular to the third coordinate direction of the projection plane; By formula The vertex coordinates P1 to P N Perform the conversion; The transformed vertex P 1A To P NA Projected onto the projection plane, the projection point P is obtained 1B To P NB , connect the cone top P0 and the projection point P 1B To P NB , we get the straight line L 1B To L NB , whose angle relative to the x-axis is calculated by simulating the three-dimensional rotation matrix The angles relative to the x-axis correspond to the angles α1 to α2 of the N ridge lines in the first image. N ; Construct the objective function To characterize the spatial rotation relationship of the pyramidal sample.

4. The method according to claim 3, wherein the step of constructing a system of linear equations by combining the tilt transformation matrix and the characteristic straight lines of the second image comprises: Using the tilt transformation matrix R θ The electron beam is tilted at the two-dimensional angle (θ x ,θ y ) is tilted, and the tilt transformation matrix R θ for: R θ =R θy *R θx ; By formula The vertex P 1A To P NA Perform the conversion; By connecting the cone top P0 with the transformed projection point P 1C To P NC , we get the straight line L 1C To L NC , whose angle α relative to the x-axis θ1 to α θN By the tilt transformation matrix R θ The simulation calculation shows that the angle α θ1 to α θN Corresponding to the angles of the N ridge lines in the second image, the inclination angle formula is obtained: Transform the tilt angle formula to construct the linear equation system 5. The method according to claim 2, wherein the step of extracting characteristic straight lines in the first image and the second image from the key point set comprises: Extracting multiple candidate lines from the key point set using the Hough line detection algorithm; Filtering 2N target straight lines in the first image and the second image from the plurality of candidate straight lines using a screening optimization algorithm; and The pixel areas near the 2N target straight lines are scanned by a fitting optimization algorithm, the pixel points with the most significant gradient changes are extracted as the optimal representation points of the edge, and the edge point set composed of the optimal representation points is fitted to determine the characteristic straight lines in the first image and the second image.

6. The method according to claim 1, further comprising: The size of the difference filter is determined according to blurriness of the first image and the second image, wherein the blurriness is positively correlated with the size.

7. The method according to claim 1, wherein the step of enhancing edge information of the first image and the second image using a difference filter comprises: By calculating the difference between the pixel values of the first image and the second image, edge regions with drastic grayscale changes in the first image and the second image are enhanced.

8. The method according to any one of claims 1 to 7, wherein: The pyramid-shaped sample is a pyramid sample, and the N-pyramid is a 4-pyramid.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein when the processor executes the computer program, the method for quantifying the electron beam tilt angle according to any one of claims 1 to 8 is implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method for quantifying the electron beam tilt angle according to any one of claims 1 to 8 is implemented.

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