Method, apparatus, and storage medium for determining a mask pattern

Abstract

According to example embodiments of the present disclosure, methods, devices and storage media for determining a mask pattern are provided. The method includes: dividing a target pattern into a plurality of regions; determining a mask pattern for each of the plurality of regions respectively based on the target pattern; determining a plurality of overlapping regions between the plurality of regions; and determining a target mask pattern corresponding to the target pattern by fusing pattern change information of the respective mask patterns in the plurality of overlapping regions. In this way, the quality of determining the mask pattern can be improved.

Description

Technical Field

[0001] The embodiments of this disclosure are primarily related to the field of integrated circuit technology, and more specifically, to methods, apparatus, and storage media for determining mask patterns. Background Technology

[0002] As feature sizes on wafers continue to shrink, traditional optical proximity correction (OPC) methods are increasingly unable to meet the requirements of advanced process nodes for lithography windows. Inverse lithography (ILT), a rigorous mathematical inverse solution method, can deduce the corresponding mask pattern based on the desired wafer imaging profile, thereby obtaining lithography results that more closely approximate the target. However, the inverse lithography calculation process typically involves large-scale iterations and complex numerical calculations, resulting in enormous computational overhead.

[0003] Therefore, how to ensure the imaging quality of the determined mask pattern while reducing computational complexity and resource consumption has become a noteworthy issue in photolithography. Summary of the Invention

[0004] In a first aspect of this disclosure, a method for determining a mask pattern is provided. The method includes: dividing a target pattern into multiple regions; determining a mask pattern for each region in the multiple regions based on the target pattern; determining multiple overlapping regions between the multiple regions; and determining a target mask pattern corresponding to the target pattern by fusing pattern variation information of the corresponding mask patterns in the multiple overlapping regions.

[0005] In a second aspect of this disclosure, a method for determining a mask pattern is provided. The method includes: for each overlapping region among multiple overlapping regions of a target pattern, calculating gradient information of each of the at least two regions intersecting in the overlapping region based on corresponding mask patterns of at least two regions intersecting in the overlapping region; determining fusion gradient information of the overlapping region based on the corresponding gradient information and weights of the at least two regions intersecting in the overlapping region; and determining a target mask pattern corresponding to the target pattern based on the fusion gradient information determined for each of the multiple overlapping regions.

[0006] In a third aspect of this disclosure, an electronic device is provided. The electronic device includes a processor and a memory coupled to the processor. The memory has instructions stored therein, which, when executed by the processor, cause the electronic device to perform a method according to a first or second aspect of this disclosure.

[0007] In a third aspect of this disclosure, a computer-readable storage medium is provided. A computer program is stored on the computer-readable storage medium. When executed by a processor, the computer program implements the method according to a first or second aspect of this disclosure.

[0008] As will be understood from the following description, according to embodiments of this disclosure, the target pattern can first be divided into multiple regions. Further, based on the target pattern, a mask pattern for each of the multiple regions can be determined. Then, multiple overlapping regions between the multiple regions can be determined. Further still, by fusing the pattern variation information of the corresponding mask patterns in the multiple overlapping regions, a target mask pattern corresponding to the target pattern can be determined. In this way, the pattern continuity at the boundaries can be restored to a greater extent, reducing misalignment, disconnection, or information loss problems that may occur during traditional block splicing processes.

[0009] It should be understood that the content described in this summary section 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

[0010] 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:

[0011] Figure 1 A schematic diagram of an example environment in which the various embodiments of this disclosure can be implemented is shown;

[0012] Figure 2 A schematic diagram of a target pattern according to some embodiments of the present disclosure is shown;

[0013] Figure 3A A schematic diagram of example areas according to some embodiments of the present disclosure is shown;

[0014] Figure 3B A schematic diagram of an example overlapping region according to some embodiments of the present disclosure is shown.

[0015] Figure 4 A flowchart illustrating a process for determining a mask pattern according to some embodiments of the present disclosure is shown;

[0016] Figure 5 A flowchart illustrating a process for determining a mask pattern according to some embodiments of the present disclosure is shown; and

[0017] Figure 6 A block diagram of an electronic device in which one or more embodiments of the present disclosure may be implemented is shown. Detailed Implementation

[0018] 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.

[0019] 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.

[0020] The following will describe in detail various example implementations of this scheme with reference to the accompanying drawings.

[0021] First see Figure 1 It illustrates a schematic diagram of an example environment 100 in which the various embodiments of this disclosure can be implemented. For example... Figure 1 As shown, the example environment 100 may generally include a layout processing device 110.

[0022] In some embodiments, the layout processing device 110 can interact with other devices or simulation systems (not shown in the figures). For example, the layout processing device 110 can receive input information from other devices and output feedback information to other devices. In some embodiments, the input message from other devices can be design layout data 120. The layout processing device 110 can perform corresponding processing on the design layout data (e.g., determine a corresponding mask pattern) and output the corresponding calculation result 130 to other devices. In some embodiments, the calculation result 130 can be an optimized mask pattern.

[0023] In example environment 100, layout processing device 110 can be any type of computing-capable device, including terminal devices or server devices. Terminal devices can be any type of mobile terminal, fixed terminal, or portable terminal, including mobile phones, desktop computers, laptop computers, notebook computers, netbook computers, tablet computers, media computers, multimedia tablets, personal communication system (PCS) devices, personal navigation devices, personal digital assistants (PDAs), audio / video players, digital cameras / camcorders, positioning devices, television receivers, radio receivers, e-book devices, gaming devices, or any combination of the foregoing, including accessories and peripherals of these devices or any combination thereof. Server devices can include, for example, computing systems / servers, such as mainframes, edge computing nodes, computing devices in cloud environments, and so on.

[0024] It should be understood that the structure and function of environment 100 are described for illustrative purposes only and do not imply any limitation on the scope of this disclosure. Exemplary embodiments according to this disclosure will now be described in detail with reference to the accompanying drawings.

[0025] As briefly mentioned above, in advanced lithography processes, auxiliary patterns are typically introduced to compensate for optical proximity effects and improve the imaging accuracy of mask patterns on wafers, thereby improving the imaging quality of pattern edges. Inversion lithography is a widely studied lithography optimization method. Based on the target imaging contour on the silicon wafer, inversion lithography can mathematically inversely calculate the required mask pattern layout and further derive the corresponding auxiliary patterns. Therefore, inversion lithography has significant advantages in optimizing critical dimensions, edge placement errors, and process windows, becoming an important means to supplement traditional optical proximity effect correction.

[0026] Inversion lithography typically relies on complex optical physical modeling and high-dimensional iterative optimization processes, resulting in massive computational demands and low efficiency. To alleviate computational overhead, the mask pattern can be divided into multiple blocks for parallel processing. However, this block-based processing strategy still has shortcomings. For example, the patterns of adjacent blocks in the boundary region cannot be accurately aligned, causing a spatial shift in the overall pattern. Furthermore, the continuity of the pattern at the boundary is disrupted, resulting in broken lines or incomplete edges. Additionally, the lack of comprehensive consideration of the mutual influence between adjacent blocks in the boundary region leads to deviations in the threshold determination of local patterns.

[0027] Therefore, with the continuous shrinking of wafer feature size and the increasing complexity of design rules, how to ensure the quality of the calculated mask pattern while maintaining the computational efficiency of inversion lithography has become a key problem that urgently needs to be solved in lithography technology.

[0028] Therefore, embodiments of this disclosure propose a scheme for determining a mask pattern. According to embodiments of this disclosure, the target pattern can first be divided into multiple regions. Further, based on the target pattern, a mask pattern for each of the multiple regions can be determined. Then, multiple overlapping regions between the multiple regions can be determined. Further still, a target mask pattern corresponding to the target pattern can be determined by fusing pattern change information of the corresponding mask patterns in the multiple overlapping regions.

[0029] In the embodiments of this disclosure, by comprehensively considering the pattern variation characteristics of adjacent regions in the overlapping areas, the pattern continuity at the boundary can be restored to a greater extent, reducing the problems of misalignment, disconnection, or information loss that may occur in the traditional block splicing process. In addition, the mask pattern determination of each region and the calculation process of the overlapping region can still be processed in parallel, which can ensure the efficiency of the overall mask pattern determination.

[0030] The following describes various example implementations of this scheme in further detail with reference to the accompanying drawings. In some embodiments, the process of determining the mask pattern described above can be performed by, for example... Figure 1 The layout processing device 110 shown is used for execution. The following is in conjunction with... Figure 1 Let me explain in detail.

[0031] In some embodiments, the layout processing apparatus 110 can divide a target pattern into multiple regions. A target pattern refers to a pattern structure that is expected to be imaged on a wafer using a photolithography process. The target pattern can correspond to the entire design pattern area, or it can be a local structure or a specific part of the pattern. To improve computational efficiency, the layout processing apparatus 110 can first perform block processing on the target pattern, thereby performing parallel computation and processing on each independent region.

[0032] As an example, Figure 2 A schematic diagram of a target pattern 200 according to some embodiments of the present disclosure is shown. For example... Figure 2 As shown, the layout processing device 110 can divide the target pattern 200 into multiple regions. Each region can be understood as a block of the target pattern 200. Each region (e.g., region 210) may include a core sub-region (e.g., core sub-region 205) and extended sub-regions. The core sub-regions of different regions do not overlap to ensure that each core sub-region independently corresponds to a specific part of the target pattern 200.

[0033] In some embodiments, the layout processing device 110 can divide a target pattern into multiple core sub-regions based on a predetermined length and width. A core sub-region can be understood as a basic unit region obtained after dividing the target pattern into blocks, with its size corresponding to the predetermined length and width. In some examples, the user can adjust the number and size of the sub-regions into which each target pattern is divided, based on the length and width set during the division. In this way, a balance can be achieved between overall computational load and parallel processing efficiency.

[0034] In some embodiments, for a core sub-region among multiple core sub-regions, the layout processing device 110 can determine an extended sub-region extending outward from the core sub-region based on a predetermined extension length. An extended sub-region refers to a region extending outward from the boundary of the core sub-region. The extended sub-region can be used to overlap with adjacent core sub-regions. By adjusting the extension length, the degree of overlap between different regions can be controlled, thereby affecting the fusion accuracy of the boundary regions and the continuity of the overall mask pattern.

[0035] Furthermore, for a region within multiple regions, the layout processing device 110 can determine the region by combining a core sub-region and a corresponding extended sub-region. For example, each region can consist of a core sub-region and its extended sub-regions. The core sub-regions of different regions do not overlap with each other, while the extended sub-regions of different regions can form overlapping regions for subsequent fusion calculations.

[0036] In some embodiments, the extended sub-regions corresponding to the core sub-region of each region can be further subdivided into multiple levels. During block calculation and splicing, different boundary regions have different degrees of dependence on pattern information. By designing the extended sub-regions hierarchically, more effective pattern information can be retained in the area closer to the core sub-region for graphic repair and transition. In contrast, less pattern information can be retained in the outer regions far from the core sub-region, serving as a buffer for boundary conditions. In this way, differentiated processing of different boundary levels can be achieved, improving processing efficiency.

[0037] Figure 3A A schematic diagram of an example of region 210 according to some embodiments of the present disclosure is shown. Taking region 210 in a plurality of regions as an example, such as... Figure 3A As shown, the extended sub-regions include the first extended region 211, the second extended region 212, and the third extended region 213. These extended regions are located further away from the core sub-region in sequence.

[0038] The first extended region 211 is the region closest to the core sub-region 205 and is crucial in determining the pattern quality during subsequent fusion processing. The first extended region 211 directly inherits the boundary information of the core sub-region 205. The signals in the first extended region 211 can serve as an important data source for subsequent fusion calculations.

[0039] The second extension region 212 is the region closest to the core sub-region 205. The second extension region 212 is located outside the first extension region 211. Signals in the second extension region 212 can be used to help achieve a smooth transition between the boundaries of different sub-regions.

[0040] The third extension region 213 is located at the outermost edge of the core sub-region 205. During subsequent fusion processing, the third extension region 213 may not retain the pattern signal from region 210, but instead provide a reference for the core sub-region 205 and other extension sub-regions. This approach further improves the stability of subsequent fusion processing.

[0041] In some examples, the extended sub-region corresponding to each core sub-region may include at least one of the aforementioned first extended region, second extended region, and third extended region. Alternatively or additionally, the layout processing device 110 can adjust the size of the extended sub-region by adjusting the boundary lengths of different extended regions. In this way, it can adapt to target patterns of different complexities.

[0042] In some embodiments, the layout processing device 110 can determine the mask pattern for each of the multiple regions based on the target pattern. In other words, after the target pattern is divided into multiple regions, the layout processing device 110 can independently model and calculate for each region and generate the corresponding mask pattern for that region. In this way, the overall computational task of mask pattern generation can be decomposed, thereby improving computational efficiency.

[0043] In some embodiments, for each region, the layout processing device 110 can obtain the mask pattern for that region through parallel inversion lithography calculations. Inversion lithography calculations can solve for the optimal mask pattern under the constraints of a given target pattern. The mask pattern may include a main pattern and auxiliary patterns of the target pattern for that region. The auxiliary pattern can participate in the lithography process together with the main pattern to optimize the final imaging effect. For example, the functions of the auxiliary pattern include, but are not limited to: adjusting the local light intensity distribution, enhancing the definition of pattern edges, controlling pattern offset or deformation, and expanding the imageability of the pattern under different process conditions.

[0044] In some embodiments, the layout processing device 110 can determine multiple overlapping regions between multiple regions. By determining the overlapping regions, the layout processing device 110 can establish channels for information sharing and signal transmission at the boundaries of different regions. In this way, problems such as pattern misalignment, disconnection, or discontinuity caused by independent block calculation can be effectively avoided in subsequent processing.

[0045] In some embodiments, for each region, the layout processing device 110 can identify overlapping regions between extended sub-regions of that region and corresponding extended sub-regions of at least one adjacent region. In this way, the layout processing device 110 can establish interaction relationships of gradient information between different regions, so that the signals of the boundary regions can be fully preserved during fusion, thereby improving the boundary smoothness of the mask pattern.

[0046] In some examples, the overlapping area between blocks can be determined based on the first extension region. For instance, if the first extension regions of two adjacent regions A and B partially overlap spatially, then that overlapping portion is identified as the overlapping area of ​​region A and region B. The second and third extension regions may not be directly used to form the overlapping area, but rather serve as reference areas for subsequent signal smoothing and gradient calculation. In this way, it is ensured that during block stitching, overlapping relationships are established only within regions containing valid pattern signals. This avoids interference from other regions and helps improve the accuracy and efficiency of subsequent fusion calculations.

[0047] In some embodiments, the layout processing device 110 can determine a target mask pattern corresponding to a target pattern by fusing pattern variation information of corresponding mask patterns in multiple overlapping regions. The pattern variation information characterizes the local variation features of the mask pattern in spatial location. For example, the pattern variation information may include gradient information. By fusing pattern variation information from multiple regions, continuous pattern features can be effectively restored at region boundaries, avoiding misalignment or discontinuity, thereby improving the overall quality of the target mask pattern.

[0048] In some embodiments, the layout processing device 110 can calculate the gradient information of each region in the corresponding overlapping region based on the mask pattern of that region. The gradient information can be used to characterize the changes of the mask pattern in local space, providing a quantitative basis for subsequent multi-region fusion calculations. For example, the gradient information may include the gradient of the pattern signal in that region, as well as information such as the gradient distribution, magnitude, and direction.

[0049] In some embodiments, the layout processing device 110 can determine the gradient information of each region in the overlapping region by performing differential calculations on the signal values ​​of each pixel and its neighboring pixels in the overlapping region. For example, the layout processing device 110 can perform differential calculations on the signal values ​​of neighboring pixels in the first direction (X direction) and the second direction (Y direction) of the image to obtain the gradient components in the corresponding directions. This method can reflect the changes in signal intensity at the edges of the regions, thereby effectively characterizing the local edge features of the pattern.

[0050] As an example, the layout processing device 110 can calculate the gradient information of the overlapping region of each region in the overlapping region according to the following formula:

[0051] (1)

[0052] Among them, ▽I k (x,y) represents the region. Gradient information at coordinates (x, y), where x represents the region. The coordinates in the first direction (e.g., horizontal) represent the region. The coordinates in a second direction (e.g., longitudinal). The layout processing device 110 can perform gradient calculation operations in parallel for multiple regions. For example, for each region... The layout processing device 110 can traverse each pixel (x, y) in the overlapping portion of the region and calculate the local gradient of the pixel based on the signal values ​​of its neighboring pixels. Wherein, I k (x,y) represents the region The signal value at coordinates (x, y). Through the above calculations, the layout processing device 110 can obtain the discrete gradient information of each region in the overlapping region. This gradient information can not only reflect the local change trend of the mask pattern in each region, but also provide a quantitative basis for subsequent cross-region pattern fusion.

[0053] In some embodiments, within the core sub-region and the first extended region, the layout processing device 110 can calculate corresponding gradient information based on the signals of the main pattern and the auxiliary pattern. The first extended region is the region closest to the core sub-region, directly inheriting the boundary information of the core sub-region 205. Therefore, the signals contained in the first extended region play a decisive role in the pattern quality during subsequent fusion processing and can serve as an important data source for gradient calculation.

[0054] In some embodiments, in the second extended region, the layout processing device 110 can calculate corresponding gradient information based on the signal of the main pattern. The second extended region is located outside the first extended region and serves to provide a transition between the boundaries of different regions. Therefore, in the second extended region, only the signal of the main pattern can be considered, thereby helping to reduce the risk of boundary discontinuities or misalignments during subsequent splicing and fusion.

[0055] In some embodiments, in the third extended region, the layout processing device 110 can calculate the corresponding gradient information based on the zero signal. The third extended region is located at the outermost edge of the core sub-region and may not retain the original pattern signal of this region. During the gradient calculation process, the gradient information corresponding to the third extended region can still be used as a reference. The gradient information corresponding to the third extended region can be used to constrain and correct the gradient calculation results of the core sub-region and other extended sub-regions, thereby improving the overall stability of the fusion process.

[0056] In some embodiments, for overlapping regions among multiple overlapping regions, the layout processing device 110 can determine the fusion gradient information of the overlapping region based on the corresponding gradient information and weights of at least two regions intersecting in the overlapping region. In this way, smooth stitching between different regions can be achieved while ensuring the accuracy of the boundaries of the overlapping regions.

[0057] In some embodiments, the layout processing device 110 can determine the fusion gradient information of the overlapping region by weighted summation of the gradient information of multiple regions that intersect in the overlapping region. The fusion gradient information obtained in this way can provide a balancing mechanism when there are conflicts in the gradients of multiple regions, so that the fusion result retains the important features of the edges while avoiding the excessive influence of a single region on the overall result.

[0058] As an example, the layout processing device 110 can determine the fusion gradient information of the overlapping region according to the following formula:

[0059] (2)

[0060] Among them, ▽I k (x,y) represents region I k The gradient information at coordinates (x, y), α k This represents the fusion weight for that region. The layout processing device 110 can traverse each grid point (x, y) in the overlapping region and perform gradient fusion operations in parallel to determine the fusion gradient information at coordinates (x, y) in the overlapping region. blend (x,y). The above calculations ensure that the information of adjacent blocks in the overlapping area can be smoothly transitioned, avoiding the seam problems that may occur in traditional splicing methods.

[0061] Alternatively or additionally, the layout processing apparatus 110 can also perform post-processing on the fusion result. For example, the layout processing apparatus 110 can process the fusion gradient information ▽I of the overlapping regions. blend (x,y) is smoothed and normalized using a pooling filter, thereby improving the stability and consistency of the fused gradient.

[0062] In some embodiments, the weight of each region can reflect its relative contribution to the corresponding overlapping region. The layout processing apparatus 110 can determine the weight of each region based on the signal sparsity of the mask patterns of the regions in at least two regions. For example, the density of the mask pattern can be quantified according to the proportion of pixels with signal strength exceeding a threshold within a block (i.e., sparsity). Regions with lower signal sparsity (i.e., denser information) can be assigned higher weights to ensure that more of their pattern features are preserved during fusion.

[0063] Alternatively or additionally, the layout processing device 110 can determine the weight of each region based on the weight parameters of regions in at least two regions specified by the user input. For example, the user can apply corresponding weights to the discrete gradient of each region according to the characteristics of different regions (e.g., pattern complexity, linewidth sensitivity, auxiliary graphic density, etc.), thereby improving the controllability of the fusion result.

[0064] As an example, Figure 3B A schematic diagram of an overlapping region 300 according to some embodiments of the present disclosure is shown. For example... Figure 3B As shown, the target pattern is divided into multiple regions (e.g., the multiple regions include a first region 301, a second region 302, a third region 303, and a fourth region 304). There are overlapping regions (i.e., the boundaries of adjacent regions) among these regions. Within these overlapping regions, the pattern variation information of different regions may differ. To ensure that the stitched mask pattern maintains continuity and consistency at these boundaries, the layout processing device 110 can perform gradient fusion calculations.

[0065] Continue to refer to Figure 3B The number of regions corresponding to different overlapping regions may be different. For example, the first overlapping region 311, where the first region 301 to the fourth region 304 intersect, and the second overlapping region 302, where the fourth region 304 intersects, are two overlapping regions. Determining the fusion gradient information of the first overlapping region 311 requires fusing the gradient information of all four regions from the first region 301 to the fourth region 304. Determining the fusion gradient information of the second overlapping region 312 requires fusing the gradient information of the second region 302 and the fourth region 304.

[0066] To fully utilize computing resources, the layout processing device 110 can employ a multi-core parallel computing strategy and prioritize processing overlapping regions where multiple regions intersect. In some embodiments, the layout processing device 110 can determine the priority of overlapping regions among multiple overlapping regions based on the number of at least two regions intersecting at the same point. Based on this priority, the layout processing device 110 can determine the fusion gradient information of the overlapping regions. In these overlapping regions, the number of gradient information points requiring fusion is greater, making their impact on overall pattern continuity and stitching accuracy more critical. The layout processing device 110 can prioritize determining the fusion gradient information of these types of overlapping regions. In this way, the overall computational efficiency can be improved while ensuring the pattern quality of boundary regions.

[0067] In some embodiments, the layout processing device 110 can determine a target mask pattern corresponding to the target pattern based on the fusion gradient information determined for each of the multiple overlapping regions. For example, the layout processing device 110 can use the fusion gradient in each overlapping region as an internal constraint, and combine it with boundary constraints to construct and solve the constraint conditions, thereby obtaining a target mask pattern that satisfies boundary continuity and local feature fidelity.

[0068] In some embodiments, the layout processing device 110 can determine a source map region and at least one background region of an overlapping region among a plurality of overlapping regions, based on the signal sparsity of at least two regions intersecting in the overlapping region. The at least one background region is the remaining region outside the source map region in the at least two regions.

[0069] The source region is the area with lower signal sparsity among at least two regions. The source region is the area with lower signal sparsity (i.e., richer information and denser structure) among all regions intersecting with and overlapping with the source region. The background region is the remaining intersecting region (region with even higher signal sparsity and less information) excluding the source region. Therefore, the layout processing device 110 can use the source region with higher information content as the primary feature source and the background region as a supplement to improve the fidelity and boundary continuity of the fused pattern.

[0070] In some embodiments, the layout processing device 110 can construct boundary constraints for overlapping regions based on the source region of the overlapping region and at least one background region. For example, the layout processing device 110 can utilize the main boundary information provided by the source region and the supplementary boundary information provided by the background region to construct corresponding boundary constraints, so as to ensure smooth transition of the overlapping regions and maintain the continuity of the overall pattern.

[0071] Furthermore, in some embodiments, the layout processing device 110 can determine the target mask pattern by performing constraint solving based on the fused gradient information of multiple overlapping regions and boundary constraints. For example, the layout processing device 110 can use the fused gradient in each overlapping region as an internal consistency constraint, and combine it with the boundary constraints to construct and solve the Poisson equation, thereby obtaining a target mask pattern that satisfies boundary continuity and local feature fidelity.

[0072] In some embodiments, the layout processing device 110 can construct and solve the Poisson equation based on the fusion gradient information determined in multiple overlapping regions, thereby determining the target mask pattern corresponding to the target pattern. For example, the layout processing device 110 can use the fusion gradient in each overlapping region as an internal consistency constraint, and at the same time construct a constraint solution model that satisfies boundary continuity and local feature fidelity based on the boundaries of at least two regions that intersect in the overlapping region.

[0073] In some embodiments, the layout processing device 110 can determine the target mask pattern by solving the Poisson equation. For example, the layout processing device 110 uses the fused gradient information of multiple overlapping regions as internal constraints, and forms a constraint equation by integrating boundary constraints. Further, the layout processing device 110 can solve this equation to further determine the target mask pattern. The target mask pattern has boundary continuity and effectively preserves the local features of each region, ensuring a natural transition and consistent detail in the image stitching effect.

[0074] As an example, the layout processing device 110 can determine the target mask pattern by constructing and solving the Poisson equation according to the following formula:

[0075] (3)

[0076] (4)

[0077] in, and The boundary between at least one background region and one source image region in the overlapping region is represented. The Poisson Equation is used to construct constraints based on the boundaries between the background and source image regions, thereby maintaining continuity and smoothness between different regions during the fusion process. This indicates the process of constructing constraints, i.e., establishing the Poisson equation. In other words, the layout processing device 110 can construct a Poisson equation based on the constraints of the background region and the source map region boundary. This indicates that a Poisson fusion solution is performed based on the fusion gradient information of multiple overlapping regions to obtain the fusion result of the overlapping regions. This represents the boundaries of multiple overlapping regions. Furthermore, the layout processing device 110 can stitch the fusion result together with the mask patterns of the non-overlapping regions to determine the target mask pattern I corresponding to the target pattern. blend Through the above processing, the overall imaging quality and stability of the generated target mask pattern can be improved while ensuring computational efficiency.

[0078] In summary, the mask pattern determination scheme of the embodiments of this disclosure fully utilizes boundary information by dividing the target pattern into core sub-regions and extended sub-regions, and introducing a fusion mechanism in the overlapping areas of adjacent sub-regions. This approach not only reduces boundary errors and stitching overhead in inversion lithography calculations but also significantly improves the imaging consistency and stability of the mask pattern design. While ensuring computational efficiency, it enhances the accuracy and robustness of the mask pattern in the wafer imaging process, thereby strengthening the engineering practicality of the scheme.

[0079] Figure 4 A flowchart of a process 400 for determining a mask pattern according to some embodiments of the present disclosure is shown. In some embodiments, process 400 may be performed by, for example... Figure 1 The layout processing device 110 shown performs the operation. It should be understood that process 400 may also include additional boxes not shown and / or some (or more) of the boxes shown may be omitted; the scope of this disclosure is not limited in this respect. The following is in conjunction with... Figure 1 The process 400 is described in detail.

[0080] like Figure 4 As shown in box 410, the layout processing device 110 divides the target pattern into multiple regions.

[0081] In frame 420, layout processing device 110 determines the mask pattern for each region in multiple regions based on the target pattern.

[0082] In frame 430, layout processing device 110 defines multiple overlapping regions between multiple regions.

[0083] In frame 440, the layout processing device 110 determines the target mask pattern corresponding to the target pattern by fusing pattern change information of corresponding mask patterns in multiple overlapping regions.

[0084] In some embodiments, dividing the target pattern into multiple regions includes: dividing the target pattern into multiple core sub-regions based on a predetermined length and width; and for a core sub-region among the multiple core sub-regions, determining an extended sub-region extending outward from the core sub-region based on a predetermined extension length; and obtaining one of the multiple regions by combining the core sub-region and the extended sub-region.

[0085] In some embodiments, determining the mask pattern for each of the multiple regions includes: calculating the mask pattern for each region by inversion lithography, wherein the mask pattern includes a main pattern and an auxiliary pattern for the region.

[0086] In some embodiments, determining multiple overlapping regions among multiple regions includes: for each region, identifying overlapping regions between extended sub-regions of that region and corresponding extended sub-regions of at least one adjacent region.

[0087] In some embodiments, the pattern change information includes gradient information, and fusing the pattern change information of the mask patterns corresponding to multiple overlapping regions includes: for each region, calculating the gradient information of the region in the corresponding overlapping region based on the mask pattern of the region; for the overlapping regions among the multiple overlapping regions, determining the fused gradient information of the overlapping region based on the corresponding gradient information and weights of at least two regions intersecting in the overlapping region; and determining the target mask pattern corresponding to the target pattern based on the fused gradient information determined for the multiple overlapping regions respectively.

[0088] In some embodiments, the region includes extended sub-regions, which include a first extended region, a second extended region, and a third extended region. Calculating gradient information includes: calculating corresponding gradient information based on the signals of the main pattern and the auxiliary pattern in the core sub-region and the first extended region; calculating corresponding gradient information based on the signal of the main pattern in the second extended region; and calculating corresponding gradient information based on the zero signal in the third extended region.

[0089] In some embodiments, the weights are determined by at least one of the following: the signal sparsity of the mask pattern of the regions in at least two regions, or the weight parameters of the regions in at least two regions specified by user input.

[0090] In some embodiments, determining the fusion gradient information of the overlapping region further includes: determining the priority of the overlapping region among multiple overlapping regions based on the number of at least two regions that intersect in the overlapping region; and determining the fusion gradient information of the overlapping region based on the priority.

[0091] In some embodiments, determining the target mask pattern corresponding to the target pattern includes: for overlapping regions among multiple overlapping regions, determining a source image region and at least one background region of the overlapping region based on the signal sparsity of at least two regions intersecting in the overlapping region, wherein the source image region is the region with lower signal sparsity among the at least two regions, and the at least one background region is the remaining region outside the source image region among the at least two regions; for the overlapping region, constructing boundary constraints based on the source image region and at least one background region of the overlapping region; and determining the target mask pattern by performing constraint solving based on the fused gradient information of multiple overlapping regions and the boundary constraints.

[0092] Figure 5 A flowchart of a process 500 for determining a mask pattern according to some embodiments of the present disclosure is shown. In some embodiments, process 500 may be performed by, for example... Figure 1 The layout processing apparatus 110 shown performs the operation. It should be understood that process 500 may also include additional boxes not shown and / or some (or more) of the boxes shown may be omitted; the scope of this disclosure is not limited in this respect. The following is in conjunction with... Figure 1 The process 500 is described in detail.

[0093] like Figure 5 As shown in block 510, the layout processing device 110 calculates gradient information of each region in the overlapping region of at least two regions intersecting in the overlapping region for each overlapping region among multiple regions of the target pattern, based on the corresponding mask patterns of at least two regions intersecting in the overlapping region.

[0094] In frame 520, the layout processing device 110 determines the fusion gradient information of each overlapping region among multiple overlapping regions of the target pattern, based on the corresponding gradient information and weights of at least two regions intersecting in the overlapping region.

[0095] In frame 530, the layout processing device 110 determines the target mask pattern corresponding to the target pattern based on the fusion gradient information determined for multiple overlapping regions respectively.

[0096] In some embodiments, the gradient information of each region in the overlapping region is determined by differential calculation of the signal values ​​of each pixel and its neighboring pixels in the overlapping region.

[0097] In some embodiments, the gradient information of each region in the overlapping region is calculated according to the following formula:

[0098] ;

[0099] Among them, ▽I k (x,y) represents the region. The gradient information at coordinates (x, y), I k (x,y) represents the region. The signal value at coordinates (x, y), where x represents the region. The coordinates in the first direction, y represents the region. Coordinates in the second direction.

[0100] In some embodiments, the fusion gradient information of the overlapping region is determined by weighted summation of the gradient information of multiple regions that intersect in the overlapping region.

[0101] In some embodiments, the fusion gradient information of the overlapping region is determined according to the following formula: ;

[0102] Among them, ▽I blend( x,y) represents the fusion gradient information at coordinates (x,y) in the overlapping region, ▽I k (x,y) represents the region. The gradient information at coordinates (x, y), α k This area The weight.

[0103] In some embodiments, determining the target mask pattern includes: for each overlapping region, constructing a Poisson equation based on the boundaries of at least two regions intersecting in the overlapping region; and determining the target mask pattern by solving the Poisson equation.

[0104] In some embodiments, the Poisson equation is constructed according to the following formula:

[0105] ;

[0106] in, This indicates the establishment of the Poisson equation. Represents the Poisson equation. and The boundary of at least one background region and the boundary of the source image region are represented by the overlapping region. The source image region is the region with lower signal sparsity among at least two regions that intersect in the overlapping region. The at least one background region is the remaining region outside the source image region among at least two regions.

[0107] In some embodiments, the Poisson equation is solved according to the following formula:

[0108] ;

[0109] Among them, I blend Represents the target mask pattern, ΔI blend This represents the fused gradient information of multiple overlapping regions. It represents the boundary of multiple overlapping regions.

[0110] Figure 6 A block diagram is shown of an electronic device 600 in which one or more embodiments of the present disclosure may be implemented. The electronic device 600 may, for example, be used to implement... Figure 1 The layout processing device 110 shown. It should be understood that... Figure 6 The electronic device 600 shown is merely exemplary and should not be construed as limiting the functionality and scope of the embodiments described herein.

[0111] like Figure 6 As shown, electronic device 600 is in the form of a general-purpose electronic device. Components of electronic device 600 may include, but are not limited to, one or more processors 610 or processing units, memory 620, storage device 630, one or more communication units 640, one or more input devices 650, and one or more output devices 660. The processing unit may be a physical or virtual processor and is capable of performing various processes according to programs stored in memory 620. In a multiprocessor system, multiple processing units execute computer-executable instructions in parallel to improve the parallel processing capability of electronic device 600.

[0112] Electronic device 600 typically includes multiple computer storage media. Such media can be any available media accessible to electronic device 600, including but not limited to volatile and non-volatile media, removable and non-removable media. Memory 620 can be volatile memory (e.g., registers, cache, random access memory (RAM)), non-volatile memory (e.g., read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory), or some combination thereof. Storage device 630 can be a removable or non-removable medium and can include machine-readable media, such as flash drives, disks, or any other media that can be used to store information and / or data (e.g., training data for training) and can be accessed within electronic device 600.

[0113] Electronic device 600 may further include additional removable / non-removable, volatile / non-volatile storage media. Although not explicitly stated... Figure 6 As shown, disk drives for reading from or writing to removable, non-volatile disks (e.g., "floppy disks") and optical disk drives for reading from or writing to removable, non-volatile optical disks can be provided. In these cases, each drive can be connected to a bus (not shown) via one or more data media interfaces. Memory 620 may include computer program product 625 having one or more program modules configured to perform various methods or actions of various embodiments of this disclosure.

[0114] The communication unit 640 enables communication with other electronic devices via a communication medium. Additionally, the functionality of the components of the electronic device 600 can be implemented using a single computing cluster or multiple computing machines capable of communicating via communication connections. Therefore, the electronic device 600 can operate in a networked environment using logical connections to one or more other servers, networked personal computers (PCs), or another network node.

[0115] Input device 650 can be one or more input devices, such as a mouse, keyboard, trackball, etc. Output device 660 can be one or more output devices, such as a monitor, speaker, printer, etc. Electronic device 600 can also communicate with one or more external devices (not shown) via communication unit 640 as needed. These external devices include storage devices, display devices, etc., and can communicate with one or more devices that enable user interaction with electronic device 600, or with any device that enables electronic device 600 to communicate with one or more other electronic devices (e.g., network card, modem, etc.). Such communication can be performed via input / output (I / O) interfaces (not shown).

[0116] According to an exemplary implementation of this disclosure, a computer-readable storage medium is provided that stores one or more computer instructions, wherein one or more computer instructions are executed by a processor to implement the methods described above.

[0117] Various aspects of this disclosure are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products implemented according to this disclosure. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0118] These computer-readable program instructions can be provided to a processing unit of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processing unit of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner. Thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0119] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions that execute on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0120] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction, which contains one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0121] Various implementations of this disclosure have been described above. The foregoing description is exemplary and not exhaustive, nor is it limited to the disclosed implementations. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described implementations. The terminology used herein is chosen to best explain the principles, practical applications, or improvements to technology in the market, or to enable others skilled in the art to understand the implementations disclosed herein.

Claims

1. A method for determining a mask pattern, characterized in that, include: Divide the target pattern into multiple regions; Based on the target pattern, a mask pattern is determined for each of the plurality of regions; Identify multiple overlapping regions among the multiple regions; as well as By fusing the pattern change information of the corresponding mask patterns in the multiple overlapping regions, a target mask pattern corresponding to the target pattern is determined.

2. The method for determining a mask pattern according to claim 1, characterized in that, Dividing the target pattern into multiple regions includes: The target pattern is divided into multiple core sub-regions based on a predetermined length and width; and For each core sub-region, The expansion sub-regions extending outward from the core sub-region are determined based on the predetermined expansion length; and One of the plurality of regions is obtained by combining the core sub-region and the extended sub-region.

3. The method for determining a mask pattern according to claim 1, characterized in that, Determining the mask pattern for each of the plurality of regions includes: For each region, a mask pattern for that region is obtained through inversion lithography calculations. The mask pattern includes a main pattern and an auxiliary pattern for that region.

4. The method for determining a mask pattern according to claim 1, characterized in that, Determining multiple overlapping regions among the multiple regions includes: For each region, the overlapping region between the extended sub-region of that region and the corresponding extended sub-region of at least one adjacent region is identified.

5. The method for determining a mask pattern according to claim 1, characterized in that, The pattern change information includes gradient information, and fusing the pattern change information of the mask pattern corresponding to the plurality of overlapping regions includes: For each region, gradient information of that region in the corresponding overlapping region is calculated based on the mask pattern of that region; For each of the multiple overlapping regions, based on the corresponding gradient information and weights of at least two regions intersecting within that overlapping region, the fusion gradient information of that overlapping region is determined; and Based on the fusion gradient information determined for the multiple overlapping regions respectively, a target mask pattern corresponding to the target pattern is determined.

6. The method for determining a mask pattern according to claim 5, characterized in that, The region includes extended sub-regions, which include a first extended region, a second extended region, and a third extended region. The calculation of the gradient information includes: In the first extended region, corresponding gradient information is calculated based on the signals of the main pattern and the auxiliary pattern; In the second extended region, corresponding gradient information is calculated based on the signal of the main pattern; and In the third extended region, the corresponding gradient information is calculated based on the zero signal.

7. The method for determining a mask pattern according to claim 5, characterized in that, The weights are determined by at least one of the following: The signal sparsity of the mask pattern in the at least two regions, or The user inputs the weight parameters of the regions in the specified at least two regions.

8. The method for determining a mask pattern according to claim 5, characterized in that, Determining the fusion gradient information of the overlapping region also includes: Based on the number of at least two regions intersecting in the overlapping region, the priority of the overlapping region among the plurality of overlapping regions is determined; and Based on the priority, the fusion gradient information of the overlapping region is determined.

9. The method for determining a mask pattern according to claim 5, characterized in that, Determining the target mask pattern corresponding to the target pattern includes: For the overlapping regions among the plurality of overlapping regions, based on the signal sparsity of at least two regions that intersect in the overlapping region, a source image region and at least one background region of the overlapping region are determined. The source image region is the region with lower signal sparsity among the at least two regions, and the at least one background region is the remaining region outside the source image region among the at least two regions. For the overlapping region, boundary constraints are constructed based on the source image region and at least one background region of the overlapping region; and The target mask pattern is determined by performing constraint solving based on the fused gradient information and boundary constraints of the multiple overlapping regions.

10. A method for determining a mask pattern, characterized in that, include: For each overlapping region among multiple overlapping regions of the target pattern, Based on the corresponding mask patterns of at least two regions that intersect in the overlapping region, the gradient information of each of the at least two regions in the overlapping region is calculated; Based on the corresponding gradient information and weights of at least two regions that intersect in the overlapping region, the fusion gradient information of the overlapping region is determined; as well as Based on the fusion gradient information determined for the multiple overlapping regions respectively, a target mask pattern corresponding to the target pattern is determined.

11. The method for determining a mask pattern according to claim 10, characterized in that, The gradient information of each region in the overlapping region is determined by differential calculation of the signal values ​​of each pixel and its neighboring pixels in the overlapping region.

12. The method for determining a mask pattern according to claim 11, characterized in that, The gradient information is calculated according to the following formula. Among them, ▽I k (x,y) represents the region. The gradient information at coordinates (x, y), I k (x,y) represents the region. The signal value at coordinates (x, y), where x represents the region. The coordinates in the first direction, y represents the region. Coordinates in the second direction.

13. The method for determining a mask pattern according to claim 10, characterized in that, The fusion gradient information of the overlapping region is determined by weighted summation of the gradient information of multiple regions that intersect in the overlapping region.

14. The method for determining a mask pattern according to claim 13, characterized in that, The fusion gradient information is determined according to the following formula: Among them, ▽I blend( x,y) represents the fusion gradient information at coordinates (x,y) in the overlapping region, ▽I k (x,y) represents the region. The gradient information at coordinates (x, y), α k This area The weight.

15. The method for determining a mask pattern according to claim 10, characterized in that, Determining the target mask pattern includes: For each overlapping region, a Poisson equation is constructed based on the boundaries of at least two regions intersecting within that overlapping region; and The target mask pattern is determined by solving the Poisson equation.

16. The method for determining a mask pattern according to claim 15, characterized in that, The Poisson equation is constructed based on the following formula: in, This indicates the establishment of the Poisson equation. This represents the Poisson equation. and The boundary of at least one background region and the boundary of the source image region are represented by the overlapping region. The source image region is the region with lower signal sparsity among at least two regions that intersect in the overlapping region. The at least one background region is the remaining region outside the source image region among the at least two regions.

17. The method for determining a mask pattern according to claim 15, characterized in that, The Poisson equation is solved according to the following formula: Among them, I blend The target mask pattern is represented by ΔI. blend This represents the fusion gradient information of the multiple overlapping regions. This indicates the boundary of the multiple overlapping regions.

18. An electronic device, characterized in that, include: At least one processing unit; as well as At least one memory, coupled to the at least one processing unit and storing instructions for execution by the at least one processing unit, the instructions causing the electronic device to perform the method according to any one of claims 1 to 9 or 10 to 17 when executed by the at least one processing unit.

19. A computer-readable storage medium, characterized in that, It stores a computer program thereon, characterized in that the computer program can be executed by a processor to implement the method according to any one of claims 1 to 9 or claims 10 to 17.

Images