Mask pattern processing method, electronic device, and storage medium

Abstract

Example embodiments of the present disclosure provide a mask pattern processing method, an electronic device and a storage medium. The method comprises obtaining an exposure energy distribution map corresponding to a mask pattern; determining at least two groups of distribution regions in the mask pattern representing distribution states of exposure energy based on the exposure energy distribution map; and transforming exposure energy distribution maps corresponding to the at least two groups of distribution regions respectively to generate an exposure energy distribution transformation map corresponding to the mask pattern. Embodiments of the present disclosure can efficiently generate a large number of electron microscope images by processing the mask pattern, overcoming the defects of high cost and low efficiency of known solutions.

Description

Technical Field

[0001] The embodiments of this disclosure primarily relate to the semiconductor field, and more specifically, to methods for processing mask layouts, electronic devices, and storage media. Background Technology

[0002] In the process of Electronic Design Automation (EDA), or for EDA design or equipment manufacturing companies, it is necessary to verify the effectiveness of EDA yield tools or equipment (usually photolithography defect detection equipment). This requires a large number of Scanning Electron Microscope (SEM) images (hereinafter referred to as SEM images or electron microscope images) for verification. In other words, a large number of electron microscope images are needed to verify whether the photolithography defect detection equipment can detect defects in the electron microscope images. However, electron microscope images are usually obtained through actual photolithography, which is very costly in both time and money.

[0003] Therefore, an improved scheme for generating electron micrographs is needed. Summary of the Invention

[0004] According to an example embodiment of this disclosure, a mask layout processing scheme is provided to at least partially overcome the above or other potential defects.

[0005] In a first aspect of this disclosure, a method for processing a mask layout is provided. The method includes: obtaining an exposure energy distribution map corresponding to the mask layout; determining at least two sets of distribution regions in the mask layout representing the exposure energy distribution state based on the exposure energy distribution map; and transforming the exposure energy distribution maps corresponding to the at least two sets of distribution regions respectively to generate an exposure energy distribution transformation map corresponding to the mask layout.

[0006] In a second aspect of this disclosure, an electronic device is provided. The electronic device includes a processor and a memory coupled to the processor, the memory having instructions stored therein, the instructions causing the device to perform actions when executed by the processor. The actions include: obtaining an exposure energy distribution map corresponding to a mask pattern; determining at least two sets of distribution regions in the mask pattern representing the exposure energy distribution state based on the exposure energy distribution map; and transforming the exposure energy distribution maps corresponding to the at least two sets of distribution regions respectively to generate an exposure energy distribution transformation map corresponding to the mask pattern.

[0007] In some embodiments, obtaining an exposure energy distribution map corresponding to a mask pattern includes: performing photolithography simulation on the mask pattern to generate an exposure energy distribution map corresponding to the mask pattern.

[0008] In some embodiments, performing photolithography simulation on a mask pattern to generate an exposure energy distribution map corresponding to the mask pattern includes: setting parameters of a photolithography simulation model, wherein the parameters include at least a light source and a photoresist; and performing photolithography simulation on the mask pattern using the photolithography simulation model to generate an exposure energy distribution map corresponding to the mask pattern; wherein the exposure energy distribution map is an energy distribution matrix composed of the energy values ​​of each exposure point in the mask pattern.

[0009] In some embodiments, determining at least two sets of distribution regions in a mask pattern representing the distribution state of exposure energy based on an exposure energy distribution map includes: comparing the energy value of the exposure energy distribution map with at least one predetermined threshold to determine at least two sets of exposure energy distribution ranges; and determining the regions in the mask pattern corresponding to each set of exposure energy distribution ranges as the distribution regions of the exposure energy distribution state.

[0010] In some embodiments, determining the region in the mask pattern corresponding to each group of exposure energy distribution ranges as the distribution region of the exposure energy distribution state includes: when the first energy value is greater than a first threshold, determining the distribution region corresponding to the first energy distribution range as a graphic region, wherein the first energy value is the energy value corresponding to the first energy distribution range in the exposure energy distribution map; when the second energy value is greater than a second threshold and less than the first threshold, determining the distribution region corresponding to the second energy distribution range as a graphic boundary blurred region, wherein the second energy value is the energy value corresponding to the second energy distribution range in the exposure energy distribution map; and when the third energy value is less than the second threshold, determining the distribution region corresponding to the third energy distribution range as a non-graphic region, wherein the third energy value is the energy value corresponding to the third energy distribution range in the exposure energy distribution map.

[0011] In some embodiments, transforming the exposure energy distribution maps corresponding to the at least two sets of distribution regions includes: transforming the exposure energy distribution maps corresponding to each set of distribution regions into grayscale images; and using the transformed grayscale images as the corresponding electron microscope images.

[0012] In some embodiments, transforming the exposure energy distribution map corresponding to each group of distribution areas into a grayscale image includes: transforming the energy value of the exposure energy distribution map from a floating-point value between 0 and 1 to an integer value between 0 and 255.

[0013] In some embodiments, transforming the exposure energy distribution map corresponding to each of the two sets of distribution areas into a grayscale image includes: multiplying a first energy value by a first coefficient to transform the first energy value to an integer value near 255 and less than 255; multiplying a second energy value by a second coefficient, wherein the second coefficient is less than or equal to the first coefficient; and multiplying a third energy value by a third coefficient to transform the third energy value to an integer value near 0 and greater than 0, wherein the third coefficient is less than the second coefficient.

[0014] In some embodiments, converting the exposure energy distribution map corresponding to each distribution area into a grayscale image includes: using the formula G1(k,j)= X1 I1(k,j) transforms the first energy value, where I1(k,j) represents the first exposure value of the exposure energy point in the exposure energy distribution map, and the first exposure value belongs to the first energy value. X1 is the first coefficient, with a value of 255 / Imax, where Imax is the maximum exposure energy value in the energy distribution map, and G1(k,j) is the value of the corresponding pixel point after the first transformation. The formula G2(k,j) = X2 is then used. I2(k,j) performs a second transformation on the second energy value, where I2(k,j) represents the second exposure value of the exposure energy point in the exposure energy distribution map. The second exposure value belongs to the second energy value. X2 is the second coefficient, which takes the value of 255 / A. A is the first threshold. The second coefficient X2 is less than or equal to the first coefficient X1. G2(k,j) is the value of the corresponding pixel point after the second transformation. The third energy value remains unchanged.

[0015] In some embodiments, electron microscope images are trained using machine learning methods to generate new electron microscope images for verifying photolithography defect detection equipment.

[0016] In a third aspect of this disclosure, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the method according to a first aspect of this disclosure.

[0017] The solution of this disclosure can efficiently generate a large number of electron micrographs, overcoming the shortcomings of known solutions that are costly and inefficient.

[0018] It should be understood that the description in the Summary of the Invention is not intended to limit the key or essential features of the embodiments of this disclosure, nor is it intended to restrict the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0019] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. In the drawings, the same or similar reference numerals denote the same or similar elements, wherein:

[0020] Figure 1 A schematic diagram of an example environment in which embodiments of the present disclosure can be implemented is shown;

[0021] Figure 2 A flowchart illustrating a method for processing a mask layout according to some embodiments of the present disclosure is shown;

[0022] Figure 3 A schematic diagram of the original mask template is shown according to some embodiments of the present disclosure;

[0023] Figure 4 A schematic diagram of an electron microscope image generated according to an embodiment of the present disclosure is shown; and

[0024] Figure 5 A block diagram of a computing device capable of implementing several embodiments of the present disclosure is shown. Detailed Implementation

[0025] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0026] In the description of embodiments of this disclosure, the term "comprising" and similar terms should be understood as open-ended inclusion, i.e., "including but not limited to". The term "based on" should be understood as "at least partially based on". The term "one embodiment" or "the embodiment" should be understood as "at least one embodiment". The terms "first", "second", etc., may refer to different or the same objects. Other explicit and implicit definitions may also be included below.

[0027] As mentioned earlier, EDA processes, or the needs of EDA design and equipment manufacturing companies, require verification of the effectiveness of lithography defect detection equipment. This necessitates a large number of SEM images, or electron microscope images, to validate the equipment. Providing electron microscope images to the lithography defect detection equipment verifies its performance, such as its ability to detect defects in the SEM images. For example, if the critical dimension of the feature pattern in the electron microscope image is smaller than the minimum size required by the process, a lithography defect exists. Additionally, it determines whether the equipment's detection sensitivity meets requirements. However, obtaining electron microscope images through actual lithography and metrology equipment is extremely costly in terms of both time and money.

[0028] In view of this, this disclosure aims to provide an improved solution.

[0029] According to embodiments of this disclosure, a method for processing a photomask pattern is provided. The method includes: obtaining an exposure energy distribution map corresponding to the photomask pattern. The exposure energy distribution map can be determined by parameters of a photolithography simulation model, and the exposure energy distribution map can reflect the exposure energy distribution during the photolithography process. Based on the exposure energy distribution map, at least two sets of distribution regions representing the exposure energy distribution state in the photomask pattern can be determined. For example, the first set of distribution regions can correspond to a patterned region, while the second set of distribution regions can correspond to a non-patterned region. Transforming the exposure energy distribution maps corresponding to the at least two sets of distribution regions respectively can generate an exposure energy distribution transformation map corresponding to the photomask pattern. The exposure energy distribution transformation map can be a grayscale image.

[0030] The embodiments of this disclosure can obtain an exposure energy distribution map corresponding to a mask pattern. For example, a photolithography simulation can be performed using the mask pattern to generate an exposure energy distribution map corresponding to the mask pattern. By transforming the exposure energy distribution maps corresponding to the at least two sets of distribution regions, an exposure energy distribution transformation map corresponding to the mask pattern can be generated. For example, the generated energy distribution transformation map can be used as an electron microscope (EM) image. The embodiments of this disclosure can efficiently generate a large number of EEM images, overcoming the drawbacks of high cost and low efficiency of known solutions. The EEM images can be used to verify photolithography defect detection equipment. In addition, the generated EEM images can also be used for photolithography and semiconductor-related machine learning datasets, etc.

[0031] The embodiments of this disclosure will now be described in detail with reference to the accompanying drawings. (See attached figures) Figure 1 This illustrates a schematic diagram of an example environment 100 in which various embodiments of the present disclosure can be implemented. For example... Figure 1 As shown, the example environment 100 includes a computing device 110 and a client 120.

[0032] In some embodiments, the computing device 110 can interact with the client 120. For example, the computing device 110 can receive input messages from the client 120 and output feedback messages to the client 120. In some embodiments, the input messages from the client 120 can set parameters of the lithography simulation model. The computing device 110 can set the lithography simulation model for the parameters specified in the input message. The computing device 110 can further perform corresponding numerical calculations based on other parameters specified in the input message from the client 120, such as transformation coefficients.

[0033] In some embodiments, the computing device 110 may include, but is not limited to, a personal computer, a server computer, a handheld or laptop device, a mobile device (such as a mobile phone, a personal digital assistant PDA, a media player, etc.), a consumer electronics product, a minicomputer, a mainframe computer, cloud computing resources, etc.

[0034] It should be understood that the description of the structure and functionality of example environment 100 for illustrative purposes only is not intended to limit the scope of the topics described herein. The topics described herein may be implemented in different structures and / or functionalities.

[0035] The technical solutions described above are for illustrative purposes only and are not intended to limit the invention. It should be understood that the example environment 100 can also have many other implementations. To more clearly explain the principles of this disclosure, reference will be made below. Figure 2 Let's describe it in more detail.

[0036] Figure 2 A flowchart of a mask layout processing method 200 according to some embodiments of the present disclosure is shown. For example, method 200 may be performed by, for example, Figure 1 The method is implemented using the computing device 110 shown. It should be understood that method 200 may also include additional boxes not shown and / or some boxes shown may be omitted. The scope of this disclosure is not limited in this respect.

[0037] At box 202, an exposure energy distribution map corresponding to the mask pattern is obtained. In some embodiments, photolithography simulation can be performed on the mask pattern. Photolithography simulation can generate an exposure energy distribution map corresponding to the mask pattern. In some embodiments, the mask pattern can be generated using dedicated EDA tools, etc. The mask pattern can be used for subsequent photolithography simulation processing.

[0038] In some embodiments, parameters of the photolithography simulation model can be set. These parameters may include a light source, photoresist, etc. The photolithography simulation model with pre-set parameters can be used to perform photolithography simulation on a mask pattern. Depending on the set simulation parameters, simulation effects under different parameters can be obtained, thereby generating simulation images under different simulation conditions. In some embodiments, the exposure energy distribution map is an energy distribution matrix composed of the energy values ​​of each exposure point in the mask pattern. The data in this energy distribution matrix can be determined by the parameters of the photolithography simulation model. For example, the stronger the light source intensity, the larger the values ​​of the data in the energy distribution matrix.

[0039] It should be noted that the exposure energy distribution map corresponding to the mask pattern can be generated through photolithography model simulation. Therefore, by adjusting the photolithography model parameters, exposure energies displaying different defects can be generated based on the mask pattern. By increasing the types of exposure energy distribution maps, the mask processing capability of the embodiments of the invention can be further improved to cope with various different process environments. In some embodiments, the energy values ​​of the exposure energy distribution map are floating-point values ​​between 0 and 1. The magnitude of the floating-point value reflects the magnitude of the light intensity. In some embodiments, the larger the floating-point value, the greater the corresponding light intensity value. Conversely, the smaller the floating-point value, the smaller the corresponding light intensity value.

[0040] As described above, by using a photolithography simulation model to simulate the mask pattern, an exposure energy distribution map corresponding to the mask pattern can be generated. This exposure energy distribution map can then be used to obtain subsequent electron micrographs.

[0041] At box 204, at least two sets of distribution regions in the mask pattern can be determined based on the exposure energy distribution map. These at least two sets of distribution regions represent two distribution states of exposure energy. As will be described in detail later, the two sets of distribution regions may include one set corresponding to higher exposure energy, which may represent a graphic region. The two sets of distribution regions may also include one set corresponding to lower exposure energy, which may represent a non-graphic region. Embodiments of this disclosure are not limited thereto. For example, in some embodiments, three sets of distribution regions may be determined as needed, such as graphic regions, non-graphic regions, and blurred regions. In other embodiments, more sets of distribution regions may be set as needed to provide a finer division of the mask pattern.

[0042] The following describes how to determine the distribution area in the mask pattern based on the exposure energy distribution map.

[0043] In some embodiments, the energy values ​​of the exposure energy distribution map can be compared with at least one predetermined threshold, and based on the comparison result, at least two sets of exposure energy distribution ranges can be determined. The regions in the mask pattern corresponding to each set of exposure energy distribution ranges can be defined as the distribution areas of the exposure energy distribution state.

[0044] For example, by setting a threshold, the area corresponding to the energy values ​​of the exposure energy distribution map in the mask layout that are greater than the threshold is called the first energy distribution range, and the area corresponding to the energy values ​​of the exposure energy distribution map in the mask layout that are less than the threshold is called the second energy distribution range. The distribution area in the mask layout can be further determined based on the energy distribution range. Specifically, it can be further determined what kind of area the energy distribution range corresponds to, such as a graphic area, a non-graphic area, or a blurred area, etc.

[0045] In some embodiments, when the first energy value is greater than a first threshold, the distribution area corresponding to the first energy distribution range is determined as a graphic region, wherein the first energy value is the energy value in the exposure energy distribution map corresponding to the first energy distribution range. For example, in some embodiments, the first threshold is represented by A. Assuming A is 0.5, when the first energy value in the exposure energy distribution map corresponding to the first energy distribution range is greater than 0.5, the distribution area corresponding to the first energy distribution range is determined as a graphic region.

[0046] In some embodiments, when the second energy value is greater than a second threshold and less than a first threshold, the distribution area corresponding to the second energy distribution range is defined as a blurred region of the graphic boundary, wherein the second energy value is the energy value in the exposure energy distribution map corresponding to the second energy distribution range. For example, in some embodiments, the second threshold is represented by B. Assuming B is 0.4, when the second energy value in the exposure energy distribution map corresponding to the second energy distribution range is greater than 0.4 and less than 0.5, the distribution area corresponding to the second energy distribution range is defined as a blurred region of the graphic boundary.

[0047] In some embodiments, when the third energy value is less than the second threshold, the distribution area corresponding to the third energy distribution range is defined as a non-graphical area, wherein the third energy value is the energy value in the exposure energy distribution map corresponding to the third energy distribution range. For example, in some embodiments, when the third energy value in the exposure energy distribution map corresponding to the third energy distribution range is less than 0.4, the distribution area corresponding to the third energy distribution range is defined as a non-graphical area.

[0048] In some embodiments, thresholds A and B can be determined by comparing the generated exposure energy distribution map with the original map. Different thresholds A and B will affect the sharpness of the boundaries of the subsequently generated pattern.

[0049] For example, in some embodiments, the threshold A can be determined by overlaying the exposure energy distribution map with the original layout and then analyzing the edges of the overlaid image. Since the exposure energy distribution map is a distribution matrix of energy values, the threshold A can be determined by observing the values ​​of the distribution matrix at the edges of the overlaid image. For example, if the energy value at the edge of the exposure energy distribution map corresponding to the original image 302 is 0.4 (generally, the energy value at the edge of the exposure energy distribution map corresponding to the edge of an original image is the same), then the threshold A can be determined empirically or by looking up a table, for example, to be 0.5. Of course, other values ​​are also possible, such as 0.6 and 0.65. For different thresholds A, the larger A is, the narrower the shape in the layout, such as a rectangle, will appear in the generated exposure energy distribution transformation map (e.g., an electron microscope image). Conversely, the smaller A is, the wider the shape in the layout, such as a rectangle, will appear in the generated exposure energy distribution transformation map. Different shapes of shapes can be obtained by setting different thresholds A.

[0050] In some embodiments, when comparing the exposure energy distribution map with the original map, a threshold B can be used to represent the blurred boundary area of ​​the overlapping region. Threshold B is mainly used to determine whether the boundary between the exposure energy and the original map needs additional blurring interference. The presence or absence of threshold B can be set as needed. If threshold B is absent, the contour boundary of the simulated image is unperturbed, and the boundary between the image feature area and the blank area is clear. If threshold B is present, it indicates that the boundary of the simulated image is blurred. The boundary between the image feature area and the blank area is not clear, and the boundary of the image contour is blurred.

[0051] At box 206, the exposure energy distribution maps corresponding to at least two sets of distribution areas are transformed. A transformed exposure energy distribution map is generated through this transformation. Since the energy values ​​of the exposure energy distribution map are floating-point values ​​between 0 and 1, the corresponding image brightness is very dark, making it difficult to clearly distinguish the individual graphics. Therefore, in some embodiments, the exposure energy distribution map is transformed.

[0052] In some embodiments, the energy values ​​of the exposure energy distribution maps corresponding to at least two sets of distribution areas are multiplied by corresponding coefficients to transform the exposure energy distribution maps into grayscale images.

[0053] In some embodiments, the exposure energy distribution maps corresponding to each distribution area can be transformed into grayscale images; the transformed grayscale images are used as the corresponding electron microscope images.

[0054] In some embodiments, the exposure energy is a floating-point value distributed between 0 and 1. Floating-point values ​​between 0 and 1 cannot be directly displayed as a grayscale image. Grayscale Figure 1The value is typically 0-255, so in some embodiments, floating-point values ​​between 0 and 1 can be converted to integer values ​​between 0 and 255, thereby enabling them to be displayed as grayscale images.

[0055] In some embodiments, the energy values ​​of the exposure energy distribution map are transformed from floating-point values ​​between 0 and 1 to integer values ​​between 0 and 255.

[0056] In some embodiments, multiplying a first energy value by a first coefficient (e.g., denoted by X1) transforms the first energy value to an integer value near 255 and less than 255. A second energy value can be multiplied by a second coefficient (e.g., denoted by X2), where X2 can be called a boundary blur parameter, and X2 is less than or equal to the first coefficient X1. When X2 = X1, it means the exposure pattern is relatively clear, and the feature map outline is relatively distinct. A contour abrupt change pattern can be generated; when X2 is less than X1, it indicates that the feature outline is blurred. A third energy value can be multiplied by a third coefficient (e.g., denoted by X3), where the third coefficient X3 is less than the second coefficient X2. Typically, X3 can take a very small value to transform the third energy value to an integer value near 0 and greater than 0.

[0057] In some embodiments, converting the exposure energy distribution maps corresponding to each group of distribution areas into grayscale images may include:

[0058] Using the formula G1(k,j)= X1 I1(k,j) performs a first transformation on the first energy value. Here, I1(k,j) represents the first exposure value of the exposure energy point in the exposure energy distribution map, and the first exposure value belongs to the first energy value. In other words, the exposure energy point in the exposure energy distribution map is the exposure energy point located within the distribution area corresponding to the first energy distribution range. Since the exposure energy values ​​of all points within this range are called the first energy value, the first exposure value belongs to the first energy value. X1 is the first coefficient, with a value of 255 / Imax, where Imax is the maximum exposure energy value in the energy distribution map, and G1(k,j) is the value of the corresponding pixel point after the first transformation.

[0059] Using the formula G2(k,j)= X2 I2(k,j) transforms the second energy value, where I2(k,j) represents the second exposure value of the exposure energy point in the exposure energy distribution map, the second exposure value belongs to the second energy value, X2 is the second coefficient, which takes the value of 255 / A, A is the first threshold, wherein the second coefficient X2 is less than or equal to the first coefficient X1, and G2(k,j) is the value of the corresponding pixel point after the second transformation.

[0060] The third energy value can remain unchanged. In other words, the third coefficient can be 1. It should also be noted that since pixel values ​​are integers, the conversion result must be rounded down when converting to pixel values.

[0061] In some embodiments, after the above transformation, noise can be removed or added to the transformed image to give the generated image diversity. Various methods can be used to remove or add noise, which will not be described in detail here.

[0062] In some embodiments, graphic areas can be defined as white areas and non-graphic areas as black areas. In this case, the larger the transformed value, the more likely it is to correspond to a graphic area, and vice versa.

[0063] The image generated after the above transformation can be called an electron microscope image, or more precisely, a simulated electron microscope image. In some embodiments, these electron microscope images can be used to verify the performance of a lithography defect detection device. For example, it can verify whether the device can detect defects in the electron microscope image, and its detection sensitivity for electron microscope images of different shapes and sizes, etc. For example, assuming that the size of the pattern in the electron microscope image under normal circumstances should correspond to the size of the pattern in the electron microscope image generated when the threshold A is equal to 0.5, then the electron microscope image generated when the threshold A is 0.6 is provided to the lithography defect detection device, and the device should detect the anomaly in the electron microscope image. Otherwise, it can be concluded that the performance of the device is defective. The examples given herein are only for illustrating the embodiments of this disclosure and are not intended to limit the scope of the invention.

[0064] Some embodiments of this disclosure provide a method for processing a mask pattern. In this method, a simulated electron microscope (EM) image can be generated based on the mask pattern, and this simulated EEM image can be provided to a lithography defect detection device. For example, by simulating different EEM images, it can be verified whether the lithography defect detection device can inspect defects in the EEM image. For instance, if the critical dimension of the feature pattern in the EEM image is smaller than the minimum dimension required by the process, it indicates the presence of a lithography defect in the EEM image. Such an EEM image can be used to verify whether the lithography defect detection device can detect the location of the defect in the EEM image.

[0065] In some embodiments of this disclosure, electron micrographs are obtained by numerically transforming the exposure energy distribution map. The embodiments of this disclosure are not limited to this; in some embodiments, grayscale transformation can be directly applied to the original image. Grayscale transformation mainly processes individual pixels, changing the grayscale range occupied by the original image data to achieve a visually appealing change. Different grayscale transformation functions will produce different results for the same image. Therefore, the selection of a grayscale transformation function should be determined based on the nature of the image and the purpose of processing. Generally, the selection criterion is that after grayscale transformation, the dynamic range of pixels increases, the contrast of the image expands, and the image becomes clearer, more detailed, and easier to identify. In some embodiments of this disclosure, the grayscale value of each pixel in the original image can be changed point by point according to a certain transformation relationship. This can improve image quality and make the display effect of the image clearer. Electron micrographs can be used to verify the performance of photolithography defect detection equipment.

[0066] Those skilled in the art will understand that other methods can be used to obtain electron micrographs. For example, methods that add blurring algorithms to simulate feature map blurring. Histogram methods can also be used. These other methods are known to those skilled in the art and will not be described in detail.

[0067] In some embodiments, SEM images can be used not only to validate lithography defect detection equipment but also in other fields requiring SEM images. For example, the SEM images generated through the above embodiments can also be used for machine learning, i.e., training the machine with the SEM images to generate new SEM images. The new SEM images can still be used to validate lithography defect detection equipment or for other applications.

[0068] Figure 3 A schematic diagram of the original mask layout 300 according to some embodiments of the present disclosure is shown. Figure 3 As shown, the original mask layout 300 contains multiple original patterns 302, such as... Figure 2 As shown, the original graphic 302 is a rectangle, but the embodiments of this disclosure are not limited to this, and can be of various shapes.

[0069] Figure 4 A schematic diagram of an electron microscope image 400 generated according to an embodiment of the present disclosure is shown. Specifically, as Figure 4 The electron microscope image shown is obtained by examining... Figure 3 The original mask pattern shown is generated through photolithography simulation. Figure 4 The electron microscope image 400 includes multiple elongated simulated patterns 404. The simulated patterns 404 are... Figure 3The image shown is an electron microscope image corresponding to pattern 302. It can be seen that the electron microscope image is generally the same shape as the original pattern 302. However, there are some differences at the edges, mainly that the end faces are rounded, and there are blurred areas at the edges of the rectangle. The width of the blurred area is determined by the second threshold B mentioned above. By setting thresholds A and B, images with different boundary sharpness can be obtained.

[0070] Figure 4 The electron microscope images shown are for illustrative purposes only and are not intended to limit the scope of this disclosure. The electron microscope images of embodiments of this disclosure can also be in various other forms.

[0071] Figure 5 A schematic block diagram of an example device 500 that can be used to implement embodiments of the present disclosure is shown. For example, Figure 1 The computing device 110 shown can be implemented by device 500. As shown, device 500 includes a central processing unit (CPU) 501, which can perform various appropriate actions and processes according to computer program instructions stored in read-only memory (ROM) 502 or loaded from storage unit 508 into random access memory (RAM) 503. RAM 503 may also store various programs and data required for the operation of device 500. CPU 501, ROM 502, and RAM 503 are interconnected via bus 504. Input / output (I / O) interface 505 is also connected to bus 504.

[0072] Multiple components in device 500 are connected to I / O interface 505, including: input unit 506, such as keyboard, mouse, etc.; output unit 507, such as various types of monitors, speakers, etc.; storage unit 508, such as disk, optical disk, etc.; and communication unit 509, such as network card, modem, wireless transceiver, etc. Communication unit 509 allows device 500 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0073] Processing unit 501 executes the various methods and processes described above, such as method 200. For example, in some embodiments, method 200 may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 508. In some embodiments, part or all of the computer program may be loaded and / or installed on device 500 via ROM 502 and / or communication unit 509. When the computer program is loaded into RAM 503 and executed by CPU 501, one or more steps of method 200 described above may be performed. Alternatively, in other embodiments, CPU 501 may be configured to execute method 200 by any other suitable means (e.g., by means of firmware).

[0074] The functions described above in this document can be performed at least in part by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: field programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload programmable logic devices (CPLDs), and so on.

[0075] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0076] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0077] Furthermore, although the operations are described in a specific order, this should be understood as requiring that such operations be performed in the specific order shown or in sequential order, or requiring that all illustrated operations be performed to achieve the desired result. In certain environments, multitasking and parallel processing may be advantageous. Similarly, although several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of this disclosure. Certain features described in the context of individual embodiments may also be implemented in combination in a single implementation. Conversely, various features described in the context of a single implementation may also be implemented individually or in any suitable sub-combination in multiple implementations.

[0078] Although the subject matter has been described using language specific to structural features and / or methodological logic, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are merely illustrative examples of implementing the claims.

Claims

1. A method for processing a mask layout, comprising: Obtain the exposure energy distribution map corresponding to the mask pattern; Based on the exposure energy distribution map, at least two sets of distribution areas representing the exposure energy distribution state in the mask pattern are determined; as well as The exposure energy distribution maps corresponding to the at least two sets of distribution areas are transformed to generate an exposure energy distribution transformation map corresponding to the mask pattern.

2. The method according to claim 1, wherein obtaining the exposure energy distribution map corresponding to the mask pattern comprises: Photolithography simulation is performed on the mask pattern to generate an exposure energy distribution map corresponding to the mask pattern.

3. The method according to claim 2, wherein performing photolithographic simulation on the mask pattern to generate an exposure energy distribution map corresponding to the mask pattern comprises: Set the parameters of the photolithography simulation model, wherein the parameters include at least the light source and the photoresist; as well as The photolithography simulation model is used to perform photolithography simulation on the mask pattern to generate the exposure energy distribution map corresponding to the mask pattern; The exposure energy distribution map is an energy distribution matrix composed of the energy values ​​of each exposure point in the mask pattern.

4. The method according to any one of claims 1-3, wherein determining at least two sets of distribution regions representing the distribution state of exposure energy in the mask pattern based on the exposure energy distribution map comprises: In response to the comparison of the energy values ​​of the exposure energy distribution map with at least one predetermined threshold, at least two sets of exposure energy distribution ranges are determined; as well as The regions in the mask pattern corresponding to each group of exposure energy distribution ranges are defined as the distribution areas of the exposure energy distribution state.

5. The method according to claim 4, wherein determining the region in the mask pattern corresponding to each group of exposure energy distribution range as the distribution region of the exposure energy distribution state comprises: When the first energy value is greater than the first threshold, the distribution area corresponding to the first energy distribution range is determined as the graphic area, wherein the first energy value is the energy value in the exposure energy distribution map that corresponds to the first energy distribution range; When the second energy value is greater than the second threshold and less than the first threshold, the distribution area corresponding to the second energy distribution range is determined as a blurred region of the graphic boundary, wherein the second energy value is the energy value in the exposure energy distribution map corresponding to the second energy distribution range; and When the third energy value is less than the second threshold, the distribution area corresponding to the third energy distribution range is determined as a non-graphic area, wherein the third energy value is the energy value in the exposure energy distribution map corresponding to the third energy distribution range.

6. The method of claim 5, wherein transforming the exposure energy distribution maps corresponding to the at least two sets of distribution regions comprises: The exposure energy distribution map corresponding to each distribution area is transformed into a grayscale image; as well as The transformed grayscale image is used as the corresponding electron microscope image.

7. The method according to claim 6, wherein converting the exposure energy distribution map corresponding to each group of distribution areas into a grayscale image comprises: The energy values ​​in the exposure energy distribution map are transformed from floating-point values ​​between 0 and 1 to integer values ​​between 0 and 255.

8. The method according to claim 6, wherein converting the exposure energy distribution map corresponding to each group of distribution areas into a grayscale image comprises: Multiply the first energy value by the first coefficient to transform the first energy value to an integer value that is near 255 and less than 255; Multiply the second energy value by a second coefficient, wherein the second coefficient is less than or equal to the first coefficient; and The third energy value is multiplied by a third coefficient to transform the third energy value to an integer value near 0 and greater than 0, wherein the third coefficient is less than the second coefficient.

9. The method according to claim 6, wherein converting the exposure energy distribution map corresponding to each group of distribution areas into a grayscale image comprises: Using the formula G1(k,j)= X1 I1(k,j) performs a first transformation on the first energy value, where I1(k,j) represents the first exposure value of the exposure energy point in the exposure energy distribution map, the first exposure value belongs to the first energy value, X1 is a first coefficient with a value of 255 / Imax, Imax is the maximum exposure energy value in the energy distribution map, and G1(k,j) is the value of the corresponding pixel point after the first transformation; and Using the formula G2(k,j)= X2 I2(k,j) performs a second transformation on the second energy value, where I2(k,j) represents the second exposure value of the exposure energy point in the exposure energy distribution map, the second exposure value belongs to the second energy value, X2 is a second coefficient with a value of 255 / A, A is the first threshold, wherein the second coefficient X2 is less than or equal to the first coefficient X1, and G2(k,j) is the value of the corresponding pixel point after the second transformation; and The third energy value remains unchanged.

10. The method of claim 6, further comprising: The electron microscope images are trained using machine learning methods to generate new electron microscope images for verifying the photolithography defect detection equipment.

11. An electronic device, comprising: processor; as well as A memory coupled to a processor, containing instructions stored therein, which, when executed by the processor, cause the device to perform actions, including: Obtain the exposure energy distribution map corresponding to the mask pattern; Based on the exposure energy distribution map, at least two sets of distribution regions representing the distribution state of exposure energy in the mask pattern are determined; and The exposure energy distribution maps corresponding to the at least two sets of distribution areas are transformed to generate an exposure energy distribution transformation map corresponding to the mask pattern.

12. A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method according to any one of claims 1-10.

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