Method for aligning sem images and related products
By determining the grayscale change trend on both sides of the bright stripes in the SEM image, the contour type of the dark stripes can be identified, solving the problem of inaccurate alignment when the grayscale of the grid lines and the background image are similar in the existing technology, and achieving a more efficient alignment effect.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- DONGFANG JINGYUAN ELECTRON LTD
- Filing Date
- 2026-04-27
- Publication Date
- 2026-07-31
AI Technical Summary
In existing technologies, CD-SEM image alignment algorithms cannot accurately extract contour information when the grayscale of the raster image and the background image are similar, resulting in poor alignment or even misalignment.
By identifying alternating bright and dark stripes in SEM images, the grayscale change trend on both sides of the bright stripes is obtained, the contour type of the dark stripes is determined, and the layout is aligned based on this. An image matching algorithm is then used to achieve accurate alignment.
When the grayscale of the grid line image and the background image are similar, the contour information of the grid line and the background in the SEM image can be accurately extracted, which improves the alignment effect between the design layout and the SEM image.
Smart Images
Figure CN122492768A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of integrated circuit technology, and in particular to a method for aligning SEM images, a computer-readable storage medium, a computer program product, and a computer device. Background Technology
[0002] In the integrated circuit manufacturing process, CD-SEM (Critical Dimension Scanning Electron Microscope) is used to measure the critical dimension (CD) of the wafer. A key step in the measurement process is to align the SEM image actually acquired by the CD-SEM with the design layout before measurements can be performed at the aligned position.
[0003] In related technologies, one-dimensional grid lines are typically used as feature patterns for alignment and measurement. These feature patterns usually consist of multiple grid lines arranged periodically along the width direction. Generally, in SEM images, the grid line image (corresponding to the top of the wafer trench) and the background image (corresponding to the bottom of the wafer trench) each form stripes. The grid line image forms bright stripes, and the background image forms dark stripes. The grayscale difference between the bright and dark stripes is significant, making the distinction quite obvious. Therefore, existing image alignment algorithms can easily extract the contour information of the grid line image and the background image from the SEM image, and then use the extracted information to align the SEM image with the design layout.
[0004] However, due to various measurement factors, CD-SEM cannot guarantee a sufficiently large difference in grayscale values between the raster image and the background image in each imaging process. When the grayscale values of the raster image and the background image in the SEM image are relatively similar, existing image alignment algorithms cannot accurately extract the contour information of the SEM image, resulting in poor alignment or even misalignment. Summary of the Invention
[0005] One object of the present invention is to provide a method for aligning SEM images, a computer-readable storage medium, a computer program product, and a computer device to accurately extract the contour information of the raster image and the background image in the SEM image, thereby improving the alignment effect between the design layout and the SEM image.
[0006] Specifically, according to one aspect of the present invention, the present invention provides a method for aligning SEM images, comprising: The design layout of the wafer and the SEM image obtained by acquiring the wafer are obtained. The design layout has multiple gate lines, which are arranged at intervals along its width direction. Multiple bright and dark stripes with alternating bright and dark patterns were identified in the SEM image. The target bright stripe is identified from the multiple bright stripes, and the grayscale change trend of each stripe within a preset width range on both sides of the target bright stripe is obtained. Based on the grayscale change trend, the contour type of each of the two dark stripes adjacent to the target bright stripe is determined, and the contour type includes the top surface of the groove or the bottom surface of the groove. Based on the outline type, the design layout is aligned with the SEM image.
[0007] Optionally, determining multiple alternating bright and dark stripes in the SEM image includes: Obtain pixel data from multiple consecutive rows of the SEM image; The pixel data of the consecutive rows are averaged column by column, and a one-dimensional array is formed with the average pixel value of each column as an element. Multiple peak values of element values are determined in the one-dimensional array, and the columns corresponding to each peak value are used as bright stripes.
[0008] Optionally, before the step of determining the peak values of the multiple elements arranged at intervals in the one-dimensional array, the method further includes: Get a sliding window with a set width; The sliding window is used to perform a moving average on the values of each element of the one-dimensional array to update the one-dimensional array.
[0009] Optionally, determining the target bright stripe among the plurality of bright stripes includes: One or more target peaks are identified from among the multiple peaks; The columns corresponding to each of the target peak values are respectively used as target bright stripes.
[0010] Optionally, obtaining the grayscale change trend within a preset width range on both sides of the target bright stripe includes: Obtain multiple consecutive element values of a preset width on both sides of the target peak value; Calculate the grayscale gradient of multiple consecutive element values on both sides of the target peak, as its grayscale change trend.
[0011] Optionally, determining the contour type of each of the two dark stripes adjacent to the target bright stripe based on the grayscale change trend includes: Compare the absolute values of the grayscale gradients on both sides of the target peak. The contour type of the dark stripe on the side with the larger absolute value of the gray-level gradient is determined as the bottom surface of the trench, and the contour type of the dark stripe on the side with the smaller absolute value of the gray-level gradient is determined as the top surface of the trench.
[0012] Optionally, aligning the design layout with the SEM image based on the outline type includes: Based on the contour types of the two dark stripes adjacent to the target bright stripe, and the positional relationship between the multiple bright stripes and dark stripes, the contour types of the other dark stripes are determined. Using an image matching algorithm, each of the dark stripes with the contour type of the top surface of the groove is aligned to the corresponding grid line, thereby aligning the SEM image to the design layout.
[0013] According to another aspect of the present invention, a computer-readable storage medium is also provided, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the steps of any of the above-described SEM image alignment methods.
[0014] According to another aspect of the present invention, a computer program product is also provided, comprising a computer program that, when executed by a processor, implements the steps of any of the above-described SEM image alignment methods.
[0015] According to another aspect of the present invention, a computer device is also provided, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the SEM image alignment method described above.
[0016] The SEM image alignment method of this invention identifies bright stripes representing the sidewalls of trenches in the SEM image and obtains the grayscale variation trend within a preset width range on both sides of the bright stripes. This allows for convenient and accurate determination of the contour type of dark stripes on both sides of the bright stripes based on the grayscale variation trend, thereby accurately obtaining the contour type of all dark stripes. This method can accurately extract the contour information of the raster image and background image in the SEM image even when the grayscale values of the raster image and background image are relatively close or even the same, thus improving the alignment effect between the SEM image and the design layout.
[0017] The above and other objects, advantages and features of the present invention will become more apparent to those skilled in the art from the following detailed description of specific embodiments of the invention in conjunction with the accompanying drawings. Attached Figure Description
[0018] The following sections will describe some specific embodiments of the invention in detail by way of example and not limitation, with reference to the accompanying drawings. The same reference numerals in the drawings denote the same or similar parts or portions. Those skilled in the art should understand that these drawings are not necessarily drawn to scale. In the drawings: Figure 1 This is a flowchart illustrating a method for aligning SEM images according to an embodiment of the present invention. Figure 2 This is a schematic diagram of a feature graphic in the design layout of an alignment method according to an embodiment of the present invention; Figure 3 It is a SEM image of the feature graphics of the alignment method according to an embodiment of the present invention; Figure 4 This is another SEM image of the feature graph of the alignment method according to an embodiment of the present invention; Figure 5 This is a SEM image of a groove according to an alignment method of an embodiment of the present invention; Figure 6 This is a schematic flowchart illustrating the process of obtaining multiple bright stripes using an alignment method according to an embodiment of the present invention; Figure 7 This is a schematic diagram of the process of obtaining a one-dimensional array from an SEM image using an alignment method according to an embodiment of the present invention; Figure 8 This is a flowchart illustrating the process of obtaining the grayscale change trend on both sides of a target peak using an alignment method according to an embodiment of the present invention. Figure 9 This is a flowchart illustrating the process of determining the dark stripe contour type using an alignment method according to an embodiment of the present invention. Figure 10 This is a schematic diagram of the process of aligning a SEM image to a design layout according to an embodiment of the present invention; Figure 11 This is a schematic flowchart of an alignment method for obtaining a contour map of a SEM image according to an embodiment of the present invention. Figure 12 This is a schematic diagram of a computer program product according to an embodiment of the present invention; Figure 13 This is a schematic diagram of a computer-readable storage medium according to an embodiment of the present invention; and Figure 14 This is a schematic diagram of a computer device according to an embodiment of the present invention. Detailed Implementation
[0019] The purpose of the SEM image alignment method in this embodiment is to accurately extract the contour information of the grid line image and the background image in the SEM image, thereby improving the alignment effect between the design layout and the SEM image.
[0020] Figure 1 This is a flowchart illustrating a method for aligning SEM images according to an embodiment of the present invention. The method generally includes: S100: Acquire the design layout of the wafer and the SEM image obtained by acquiring the wafer. The design layout has multiple gate lines, which are arranged at intervals along its width direction. S200, identifies multiple bright and dark stripes arranged in alternating bright and dark patterns in the SEM image; S300, identify the target bright stripe among multiple bright stripes, and obtain the grayscale change trend of each of the target bright stripe within a preset width range on both sides of the target bright stripe; S400, based on the grayscale change trend, determines the contour type of each of the two dark stripes adjacent to the target bright stripe. The contour type includes the top surface of the groove or the bottom surface of the groove. S500 aligns the design layout with the SEM image based on the outline type.
[0021] In this embodiment, one-dimensional grid lines can be set in the design layout as feature graphics for alignment and measurement. Please refer to [link to relevant documentation]. Figure 2 , Figure 2 This is a schematic diagram of a feature pattern in a design layout, where the shaded vertical lines represent gate lines, which are evenly spaced apart. Feature patterns are used to etch spaced trenches onto a wafer using photolithography, or to develop spaced trenches onto photoresist on the wafer. Generally, gate lines form the tops of the trenches within the etch profile of the wafer, the areas between the gate lines form the bottoms of the trenches within the etch profile, and the edges of the gate lines form the vertical sidewalls of the trenches.
[0022] SEM images can be etching images of a wafer acquired using CD-SEM, or development images of photoresist on a wafer. Please see [link to relevant documentation]. Figure 3-4 , Figure 3-4 For data collected using CD-SEM Figure 2 These are two etched images of a wafer with a characteristic pattern. Figure 3 It exhibits distinct light and dark stripes with significant differences in grayscale; the brighter stripes represent the grid lines (corresponding to the top of the wafer trench), while the darker stripes represent the background (corresponding to the bottom of the wafer trench). For Figure 3 As shown, existing image alignment algorithms can easily extract the contour information of the raster image and background image from the SEM image, and then use the extracted information to align the SEM image with the design layout (i.e., Figure 2 Alignment of ).
[0023] Affected by multiple weight measurement factors, Figure 4 The grayscale differences among the stripes are relatively small. Existing image alignment algorithms struggle to accurately determine which stripes belong to the raster image and which belong to the background image, resulting in poor alignment or even misalignment.
[0024] In related technologies, two approaches are typically used to solve the above problems. One approach is to improve the measurement environment of CD-SEM, hoping to acquire data such as... Figure 3The SEM image shown is an example. Another approach is to perform image restoration on the SEM image, hoping to distinguish between the raster image stripes and the background image stripes within small grayscale differences. Practice has shown that both approaches either result in excessively low image acquisition efficiency or still have a high probability of false positives.
[0025] The inventor studied a large number of things such as Figure 3-4 The SEM images shown creatively demonstrate that the raster image and background image can be distinguished by the extremely bright stripes formed by the sidewalls of the trenches. Please see [link to SEM image]. Figure 3-4 Between any two relatively coarse stripes, there are usually extremely bright and fine bright stripes. Studies have found that these extremely bright stripes are images of the sidewalls of the trenches. This is because the electron signal of a scanning electron microscope (SEM) is highly sensitive to the uneven geometry of the sample surface. When the incident electron beam is parallel to the normal to the sample surface, the secondary electron yield is minimal, resulting in a lower image brightness. A tilted sample surface increases the effective depth of electron beam penetration, leading to a higher secondary electron yield and a brighter image; the brightness is positively correlated with the tilt angle. The trench sidewalls have a large tilt angle and a narrow projection width, thus forming extremely bright and fine bright stripes in the SEM image.
[0026] The inventors astutely observed that, due to the inherent characteristics of photolithography processes such as development and etching, the extremely bright and fine stripes formed on the trench sidewalls exhibit different grayscale variation trends on the left and right sides within a certain width range. (Please refer to...) Figure 5 , Figure 5 This is a SEM image of a trench on a wafer. The thicker dark stripe in the middle represents the bottom of the trench. The two extremely bright and very thin stripes on either side of the bottom image represent the trench sidewalls. The outer part of the sidewall image represents the top of the trench. Figure 5 It is evident that the grayscale changes more rapidly on the side of the extremely bright and fine stripes near the bottom of the trench, while the grayscale changes more slowly on the side near the top of the trench. In other words, by comparing the grayscale change trends on both sides of the trench sidewall image, the contour type of the coarse stripes on both sides of the trench sidewall image can be easily and accurately determined.
[0027] The following description uses an SEM image of a wafer etching process as an example to illustrate the scheme of this embodiment. After acquiring, for example... Figure 4 After viewing the SEM image, multiple alternating bright and dark stripes can be identified using grayscale recognition methods. The bright stripes are the brightest and thinnest, with a width much smaller than the width of the grid lines. These bright stripes represent the sidewalls of the trenches and correspond to the edges of the grid lines. Between the bright stripes are dark stripes, which are less bright than the bright stripes and much wider. These dark stripes may be grid line image stripes or background image stripes.
[0028] Next, one or more bright stripes are identified as target bright stripes. When the SEM image quality is good and there is little noise, only one target bright stripe can be identified. When the SEM image quality is poor and there is a lot of noise, multiple stripes can be identified as target bright stripes. This allows for cross-validation of the contour types of the dark stripes on both sides of each target bright stripe in subsequent steps, improving alignment accuracy.
[0029] Next, the grayscale change trends within a preset width range on both sides of the target bright stripe are analyzed. For example, 10 consecutive pixels can be selected along the width direction on both the left and right sides of the target bright stripe, and then the grayscale change trends of the 10 pixels on each side can be determined. For instance, the cumulative difference of the 10 pixels can be calculated, and the magnitude of the cumulative difference characterizes the rate of grayscale change. Alternatively, a linear function can be fitted to each of the 10 pixels to obtain the corresponding slope, and the magnitude of the slope can be used to characterize the rate of grayscale change.
[0030] Next, compare the grayscale change trends on both sides of the target bright stripe, determine the outline type of the dark stripe on the side with faster grayscale change as the bottom surface of the groove, and determine the outline type of the dark stripe on the side with slower grayscale change as the top surface of the groove.
[0031] Next, based on the contour types of the dark stripes on both sides of the target bright stripe, and the alternating arrangement of the bright and dark stripes, the contour types of all dark stripes are obtained. Then, image alignment algorithms using relevant techniques can be used to successfully align the SEM image with the design layout.
[0032] The SEM image alignment method in this embodiment identifies bright stripes representing the trench sidewalls in the SEM image, then obtains the grayscale change trend within a preset width range on both sides of the bright stripes. This allows for convenient and accurate determination of the contour type of the dark stripes on both sides of the bright stripes based on the grayscale change trend, thereby accurately obtaining the contour types of all dark stripes. This method can accurately extract the contour information of the grid line image and background image in the SEM image even when the grayscale values of the grid line image and the background image are relatively close or even the same, improving the alignment effect between the SEM image and the design layout.
[0033] In some embodiments of the SEM image alignment method of the present invention, such as Figure 6 As shown, multiple alternating bright and dark stripes were identified in the SEM image, including: S211, Obtain pixel data from multiple consecutive rows of the SEM image; S213: Calculate the average of pixel data from multiple consecutive rows by column, and form a one-dimensional array with the average pixel value of each column as an element. S219: Determine multiple peak values of element values in a one-dimensional array, and treat the columns corresponding to each peak value as bright stripes.
[0034] The characteristic patterns of one-dimensional grid lines typically form long bright and dark stripes in SEM images. In this embodiment, it is not necessary to extract the full length of bright and dark stripes from the SEM image; only a few rows of representative pixel data need to be extracted. This reduces computational load and improves alignment efficiency without affecting the accuracy of bright and dark stripe extraction. In practical applications, the number of rows of pixel data to be extracted can be determined based on the imaging quality of the SEM image.
[0035] Please refer to Figure 7 , Figure 7 This diagram illustrates how to obtain representative pixel data from multiple consecutive rows in an SEM image and generate a one-dimensional array. Figure 7 The image on the left is an SEM image, with a size of 512 pixels * 512 pixels. The actual pixel data extracted is from rows 245 to 265, a total of 10 rows.
[0036] Due to various measurement factors, SEM images acquired by CD-SEM may contain defects such as image distortion and warping. To improve the accuracy of extracting bright and dark stripes, the pixel data is averaged column-wise after obtaining representative pixel data from multiple consecutive rows. Please refer to [reference needed]. Figure 7 For the extracted 10 rows of pixel data, the average pixel values of each column across the 10 rows are calculated to obtain the average pixel value of that column (hereinafter referred to as the column mean). The column mean of the first column is denoted as a1, the column mean of the second column as a2, and so on, with the column mean of the 5th and 12th columns denoted as a... 512 .
[0037] Next, using the column mean of each column as the array element, a one-dimensional array [a1, a2, ..., a] with a length equal to the image width of 512 and a height of 1 is created. 512 This one-dimensional array can roughly depict the distribution of gray values in the SEM image along the width of the raster lines.
[0038] Next, we search for multiple peaks in the one-dimensional array. For example, peak-finding methods such as local maxima, derivative-based methods, or difference methods can be used to find the peaks in the one-dimensional array, resulting in a peak array [peak1, peak2, ..., peak...]. n ], peak n This represents the nth peak from left to right. The column corresponding to the peak can be considered a bright stripe, which corresponds to the boundary between the grid image stripes and the background image stripes. The columns corresponding to pixels outside the peak can be considered dark stripes. This method can quickly and accurately determine the bright stripes representing the boundary between the grid image stripes and the background image stripes in SEM images.
[0039] In some embodiments of the SEM image alignment method of the present invention, such as Figure 6 As shown, before the step of determining the peak values of multiple elements arranged at intervals in a one-dimensional array, the following steps are also included: S215, Obtain a sliding window with a set width; S217, use a sliding window to perform a sliding average on the values of each element of the one-dimensional array to update the one-dimensional array.
[0040] Due to various measurement factors, SEM images acquired by CD-SEM may contain isolated noise or individual pixel brightness deviations, which may affect the accuracy of the peaks found. Therefore, this embodiment further smooths the one-dimensional array before searching for peaks to reduce the impact of noise and individual pixel brightness deviations.
[0041] In this embodiment, the set width (window_size) can be determined based on data such as SEM image size, grid line width, and grid line spacing. For example, it can be set to 10-15 pixels. After determining the window_size, the window can be slid from left to right in the one-dimensional array, and the mean value of each element in the sliding window can be calculated sequentially as the new element value to obtain a smoothed one-dimensional array.
[0042] In some embodiments of the SEM image alignment method of the present invention, such as Figure 8 As shown, the target bright stripe was identified among multiple bright stripes, including: S311, identify one or more target peaks among multiple peaks; S313, the columns corresponding to the peak values of each target are respectively used as target bright stripes.
[0043] For example, the peak array [peak1, peak2, ..., peak] can be used. n Select the first three peaks [peak1, peak2, peak3] from the array [a1, a2, ..., a...] as the target peaks. 512 Find the value of the element corresponding to the target peak in the column; the column corresponding to the element at the corresponding position is the target bright stripe.
[0044] In some embodiments of the SEM image alignment method of the present invention, such as Figure 8 As shown, the grayscale change trend within a preset width range on both sides of the target bright stripe is obtained, including: S315, respectively obtain multiple consecutive element values of the preset width on both sides of the target peak; S317 calculates the gray-level gradient of multiple consecutive element values on both sides of the target peak, as its gray-level change trend.
[0045] In this embodiment, a one-dimensional array [a1, a2, ..., a...] can be used. 512 Ten consecutive pixels are selected to the left and right of the target peak, and then the average gray-level gradient of the ten consecutive pixels is calculated. The average gray-level gradient can accurately characterize the rate of gray-level change.
[0046] In some embodiments of the SEM image alignment method of the present invention, such as Figure 9 As shown, based on the trends of grayscale changes, the contour types of the two dark stripes adjacent to the target bright stripe are determined, including: S411 compares the absolute values of the grayscale gradients on both sides of the target peak. S413, the contour type of the dark stripe on the side with the larger absolute value of the gray-level gradient is determined as the bottom surface of the trench, and the contour type of the dark stripe on the side with the smaller absolute value of the gray-level gradient is determined as the top surface of the trench.
[0047] A one-dimensional array [a1, a2, ..., a 512 Since the grayscale gradients to the left and right of the target peak are in opposite directions, the absolute values are calculated first, and then the magnitudes are compared.
[0048] By comparing the absolute values of the grayscale gradients on the left and right sides of the target peak, the contour type of the dark stripes on the side with the larger absolute value can be conveniently and accurately determined as the bottom surface of the trench, which corresponds to the background image stripes, and the contour type of the dark stripes on the side with the smaller absolute value of the grayscale gradient can be determined as the top surface of the trench, which corresponds to the grid image stripes.
[0049] In some embodiments of the SEM image alignment method of the present invention, such as Figure 10 As shown, based on the outline type, the design layout is aligned with the SEM image, including: S511, based on the contour types of the two dark stripes adjacent to the target bright stripe and the positional relationship between the multiple bright stripes and dark stripes, determine the contour types of the other dark stripes. S513 uses an image matching algorithm to align each dark stripe of the top surface of the groove contour type to the corresponding grid line, thereby aligning the SEM image to the design layout.
[0050] Since the grid lines in the feature pattern are arranged at periodic intervals, and multiple bright and dark stripes are extracted from the SEM image, after obtaining the contour type of a dark stripe, it can be extended to all bright and dark stripes according to the arrangement order of each dark stripe, so as to obtain the contour type of the dark stripes on both sides of each bright stripe.
[0051] For example, after obtaining the outline types of the bright and dark stripes corresponding to all elements in the one-dimensional array, the values of all elements corresponding to the stripes in the raster image can be set to 255, the values of all elements corresponding to the stripes in the background image can be set to 0, and the values of the peak elements can also be set to 0. Then, the result of the one-dimensional array is vertically expanded to the original image size, thus forming a binary image with the same size as the original image. See [link to relevant documentation]. Figure 11 , Figure 11 The left-hand image shows the binary image obtained by vertically expanding the result of the one-dimensional array to the original image size.
[0052] Next, based on this binary image, the outline of the original SEM image can be obtained. See also... Figure 11 , Figure 11 The image on the right shows the contour map converted from the binary image. Although this contour map only roughly reflects the shape features of the original image, it is sufficient to meet the requirements for subsequent alignment.
[0053] Next, an image matching algorithm can be used to align the contour image to the design layout. Specifically, a correspondence between the contour image and the design layout can be established based on positional information. Then, based on this correspondence, an image matching algorithm can be used to perform transformations such as translation, rotation, and scaling on the contour image to roughly align it with the design layout. Various contour-based image matching algorithms exist in the prior art; the appropriate algorithm can be selected as needed, and no specific requirements are specified here.
[0054] The flowchart provided in this embodiment is 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 every case. Furthermore, the method may include additional operations. Within the scope of the technical concept provided by the method in this embodiment, additional variations can be made to the above method.
[0055] It should be understood that in some embodiments, the components may be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods may be implemented using software or firmware stored in memory and executed by a suitable instruction execution system.
[0056] This invention also provides a computer program product 10, a computer-readable storage medium 20, and a computer device 30. Figure 12 This is a schematic diagram of a computer program product 10 according to an embodiment of the present invention. Figure 13 This is a schematic diagram of a computer-readable storage medium 20 according to an embodiment of the present invention. Figure 14This is a schematic diagram of a computer device 30 according to an embodiment of the present invention. The computer program product 10 includes a computer program 11, which, when executed by the processor 32, implements the steps of the SEM image alignment method described above. A computer-readable storage medium 20 stores the computer program 11 thereon, which, when executed by the processor 32, implements the steps of the SEM image alignment method described above. The computer device 30 may include a memory 31, a processor 32, and the computer program 11 stored in the memory 31 and running on the processor 32.
[0057] The computer program 11 used to perform the operations of this invention may be assembly instructions, Instruction Set Architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, integrated circuit configuration data, or source code or object code written in any combination of one or more programming languages and procedural programming languages. The computer program 11 may execute entirely on the user's computer, partially on the user's computer, as a standalone 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 Wide Area Network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, to perform aspects of this 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 to personalize the electronic circuits by utilizing state information from computer-readable program instructions.
[0058] For the purposes of this embodiment, computer program product 10 is a related product that includes computer program 11.
[0059] For the purposes of this embodiment, the computer-readable storage medium 20 is a tangible device capable of holding and storing a computer program 11. It can be any device capable of containing, storing, communicating, propagating, or transmitting the computer program 11 for use by or in conjunction with an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable storage medium 20 include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable optical disc read-only memory (CD-ROM), digital versatile disc (DVD), memory stick, floppy disk, mechanical encoding device, and any suitable combination thereof.
[0060] Computer device 30 can be, for example, a server, desktop computer, laptop computer, tablet computer, or smartphone. In some examples, computer device 30 can be a cloud computing node. Computer device 30 can be described in the general context of computer system executable instructions (such as program modules) executed by a computer system. Typically, 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 30 can be implemented in a distributed cloud computing environment where tasks are performed by remote processing devices linked through a communication network. In a distributed cloud computing environment, program modules can reside on local or remote computing system storage media, including storage devices.
[0061] Computer device 30 may include a processor 32 adapted to execute stored instructions and a memory 31 that provides temporary storage space for the operation of said instructions during operation. The processor 32 may be a single-core processor, a multi-core processor, a computing cluster, or any other configuration. The memory 31 may include random access memory (RAM), read-only memory, flash memory, or any other suitable storage system.
[0062] Computer device 30 may also include a network adapter / interface and an input / output (I / O) interface. The I / O interface allows external devices that can be connected to the computer device to input and output data. The network adapter / interface provides communication between the computer device and a network, typically represented as a communication network.
[0063] Therefore, those skilled in the art should recognize that although numerous exemplary embodiments of the present invention have been shown and described in detail herein, many other variations or modifications conforming to the principles of the present invention can be directly determined or derived from the disclosure of the present invention without departing from the spirit and scope of the invention. Thus, the scope of the present invention should be understood and construed as covering all such other variations or modifications.
Claims
1. A method for aligning SEM images, characterized in that, include: The design layout of the wafer and the SEM image obtained by acquiring the wafer are obtained. The design layout has multiple gate lines, which are arranged at intervals along its width direction. Multiple bright and dark stripes with alternating light and dark patterns were identified in the SEM image. The target bright stripe is identified from the multiple bright stripes, and the grayscale change trend of each stripe within a preset width range on both sides of the target bright stripe is obtained. Based on the grayscale change trend, the contour type of each of the two dark stripes adjacent to the target bright stripe is determined, and the contour type includes the top surface of the groove or the bottom surface of the groove. Based on the outline type, the design layout is aligned with the SEM image.
2. The alignment method according to claim 1, characterized in that, The process of identifying multiple alternating bright and dark stripes in the SEM image includes: Obtain pixel data from multiple consecutive rows of the SEM image; The pixel data of the consecutive rows are averaged column by column, and a one-dimensional array is formed with the average pixel value of each column as an element. Multiple peak values of element values are determined in the one-dimensional array, and the columns corresponding to each peak value are used as bright stripes.
3. The alignment method according to claim 2, characterized in that, Before the step of determining the peak values of multiple elements arranged at intervals in the one-dimensional array, the method further includes: Get a sliding window with a set width; The sliding window is used to perform a moving average on the values of each element of the one-dimensional array to update the one-dimensional array.
4. The alignment method according to claim 2 or 3, characterized in that, The step of identifying the target bright stripe among the plurality of bright stripes includes: One or more target peaks are identified from among the multiple peaks; The columns corresponding to each of the target peak values are respectively used as target bright stripes.
5. The alignment method according to claim 4, characterized in that, The step of obtaining the grayscale change trend within a preset width range on both sides of the target bright stripe includes: Obtain multiple consecutive element values of a preset width on both sides of the target peak value; Calculate the grayscale gradient of multiple consecutive element values on both sides of the target peak, as its grayscale change trend.
6. The alignment method according to claim 5, characterized in that, The step of determining the contour type of each of the two dark stripes adjacent to the target bright stripe based on the grayscale change trend includes: Compare the absolute values of the grayscale gradients on both sides of the target peak. The contour type of the dark stripe on the side with the larger absolute value of the gray-level gradient is determined as the bottom surface of the trench, and the contour type of the dark stripe on the side with the smaller absolute value of the gray-level gradient is determined as the top surface of the trench.
7. The alignment method according to claim 1, characterized in that, Aligning the design layout with the SEM image based on the outline type includes: Based on the contour types of the two dark stripes adjacent to the target bright stripe, and the positional relationship between the multiple bright stripes and dark stripes, the contour types of the other dark stripes are determined. Using an image matching algorithm, each of the dark stripes with the contour type of the top surface of the groove is aligned to the corresponding grid line, thereby aligning the SEM image to the design layout.
8. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the steps of the SEM image alignment method as described in any one of claims 1 to 7.
9. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the SEM image alignment method as described in any one of claims 1 to 7.
10. A computer device, characterized in that, The device includes a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the SEM image alignment method according to any one of claims 1 to 7.