Error determination method, device, equipment, medium and product of photolithography simulation model

Abstract

The application discloses an error determination method, device and equipment of a photolithography simulation model, a medium and a product, and relates to the technical field of photolithography simulation. The error determination method of the photolithography simulation model comprises the following steps: obtaining a real photolithography profile of a target mask pattern and a simulation photolithography profile of the target mask pattern output by a photolithography simulation model; discretizing the real photolithography profile and the simulation photolithography profile respectively, and constructing a first quadtree; determining the shortest distance from each real discrete point to the simulation photolithography profile and the shortest distance from each simulation discrete point to the real photolithography profile according to the first quadtree; storing each shortest distance into an isomorphic index tree corresponding to the first quadtree respectively, and constructing a second quadtree; and determining the error of the photolithography simulation model according to the second quadtree. According to the application, the error determination efficiency of the photolithography simulation model can be improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of photolithography simulation, and particularly relates to a photolithography simulation model error determination method and device, equipment, medium and product. BACKGROUND

[0002] In a semiconductor manufacturing process, photolithography is a process of printing a pattern on a mask plate to a wafer through light exposure. Due to the existence of physical effects such as diffraction and interference, there is a deviation between the actual exposure area and the expected exposure area, so it is necessary to simulate and predict the photolithography process through a photolithography simulation model. However, due to the complexity of the photolithography process, the accuracy of the photolithography simulation model is difficult to guarantee. Therefore, the model needs to be continuously verified and optimized according to the error of the photolithography simulation model.

[0003] At present, the area difference or the average relative distance of pixels between the actual photolithography profile and the simulation photolithography profile is mainly calculated to measure the error of the photolithography simulation model.

[0004] However, the calculation amount of this method is large, and it can only be used to simply measure the global error of the photolithography simulation model, and cannot quickly locate the local error of the photolithography simulation model, resulting in low error determination efficiency of the photolithography simulation model. SUMMARY

[0005] The application embodiment provides a photolithography simulation model error determination method, device, equipment, medium and product, which can improve the error determination efficiency of the photolithography simulation model.

[0006] In an aspect of the application embodiment, a photolithography simulation model error determination method is provided, comprising:

[0007] obtaining a real photolithography profile of a target mask pattern and a simulation photolithography profile of the target mask pattern output by a photolithography simulation model;

[0008] discretizing the real photolithography profile and the simulation photolithography profile respectively, constructing a first quadtree, the parent node of the first quadtree being a matched profile area in the real photolithography profile and the simulation photolithography profile, and the child node of the first quadtree being the coordinates of a real discrete point on the real photolithography profile and the coordinates of a simulation discrete point on the simulation photolithography profile;

[0009] determining the shortest distance from each real discrete point to the simulation photolithography profile and the shortest distance from each simulation discrete point to the real photolithography profile according to the first quadtree;

[0010] store the shortest distances into the isomorphic index tree corresponding to the first quadtree respectively, construct a second quadtree, parent nodes of the second quadtree are the matched profile regions, child nodes of the second quadtree are the shortest distances from the real discrete points to the simulated lithography profile and the shortest distances from the simulated discrete points to the real lithography profile;

[0011] determine the error of the lithography simulation model according to the second quadtree.

[0012] An aspect of an embodiment of the present application provides an error determination method of a lithography simulation model, and the method comprises the following steps:

[0013] a profile obtaining module, configured to obtain a real lithography profile of a target mask pattern and a simulated lithography profile of the target mask pattern output by a lithography simulation model;

[0014] a quadtree constructing module, configured to discretize the real lithography profile and the simulated lithography profile respectively, and construct a first quadtree, parent nodes of the first quadtree are matched profile regions in the real lithography profile and the simulated lithography profile, and child nodes of the first quadtree are coordinates of real discrete points on the real lithography profile and coordinates of simulated discrete points on the simulated lithography profile;

[0015] a distance determining module, configured to determine, according to the first quadtree, the shortest distances from the real discrete points to the simulated lithography profile and the shortest distances from the simulated discrete points to the real lithography profile;

[0016] the quadtree constructing module is further configured to store the shortest distances into the isomorphic index tree corresponding to the first quadtree respectively, construct a second quadtree, parent nodes of the second quadtree are the matched profile regions, child nodes of the second quadtree are the shortest distances from the real discrete points to the simulated lithography profile and the shortest distances from the simulated discrete points to the real lithography profile;

[0017] an error determining module, configured to determine the error of the lithography simulation model according to the second quadtree.

[0018] An aspect of an embodiment of the present application provides an electronic device, which comprises a memory and a program or instructions stored in the memory and executable on a processor, and the program or instructions are executed by the processor to implement the error determination method of the lithography simulation model provided in any one of the aspects of the embodiments of the present application.

[0019] An aspect of an embodiment of the present application provides a readable storage medium, and the readable storage medium stores a program or instructions, and the program or instructions are executed by a processor to implement the error determination method of the lithography simulation model provided in any one of the aspects of the embodiments of the present application.

[0020] In an aspect of the embodiments of the present application, a computer program product is provided. Instructions in the computer program product, when executed by a processor of an electronic device, cause the electronic device to perform the method for determining error of a lithography simulation model according to any one of the above aspects of the embodiments of the present application.

[0021] In the method for determining error of a lithography simulation model provided by the embodiments of the present application, the real lithography profile and the simulation lithography profile are first discretized to obtain discrete points. In this way, only a plurality of representative position points are selected from the lithography profile for calculation, and each position does not need to be calculated, thereby reducing the calculation amount. Then, a first quadtree is constructed according to the discrete points, wherein a parent node of the first quadtree is used to represent a profile region, and a child node of the first quadtree is used to store the coordinates of the real discrete points and the coordinates of the simulation discrete points in the profile region. Each shortest distance is stored in an isomorphic index tree corresponding to the first quadtree, and a second quadtree is constructed. In this way, each shortest distance in each profile region can be quickly located through the second quadtree, and the local error of the lithography simulation model in each profile region can be quickly determined. In summary, the embodiments of the present application can reduce the calculation amount and quickly locate the local error of the lithography simulation model by discretization processing and quadtree construction, thereby improving the error determination efficiency of the lithography simulation model. BRIEF DESCRIPTION OF DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required to be used in the embodiments of the present application will be briefly introduced. Those skilled in the art can obtain other drawings according to these drawings without creating any creative labor.

[0023] Figure 1 FIG. 1 is a flowchart of a method for determining error of a lithography simulation model according to an embodiment of the present application;

[0024] Figure 2 FIG. 2 is a flowchart of S102 according to an embodiment of the present application;

[0025] Figure 3 FIG. 3 is a schematic diagram of a quadtree construction principle according to an embodiment of the present application;

[0026] Figure 4 FIG. 4 is a flowchart of S103 according to an embodiment of the present application;

[0027] Figure 5 FIG. 5 is a schematic diagram of the shortest distance from the real discrete point to the simulation lithography profile according to an embodiment of the present application;

[0028] Figure 6 FIG. 6 is a flowchart of S401 according to an embodiment of the present application;

[0029] Figure 7 is a first flowchart of S105 provided by an embodiment of the present application;

[0030] Figure 8 is a second flowchart of S105 provided by an embodiment of the present application;

[0031] Figure 9 is a flowchart of S101 provided by an embodiment of the present application;

[0032] Figure 10 is a structural diagram of an error determination device of a lithography simulation model provided by an embodiment of the present application;

[0033] Figure 11 is a structural diagram of an error determination device of a lithography simulation model provided by an embodiment of the present application. DETAILED DESCRIPTION

[0034] The features and exemplary embodiments of various aspects of the present application will be described in detail below with reference to the drawings. To make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, but not to limit the present application. The present application can be implemented without some of the specific details by those skilled in the art. The following description of the embodiments is only to provide a better understanding of the present application by showing examples of the present application.

[0035] It should be noted that, in this document, relational terms such as first and second and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without more limitations, an element defined by the phrase "comprising a" does not exclude the existence of additional identical elements in the process, method, article, or apparatus that includes the element.

[0036] It should be noted that the acquisition, storage, use, processing and the like of data in the technical solutions of the present application comply with the relevant provisions of national laws and regulations.

[0037] It should be noted that in the embodiments of the present application, some industry existing solutions such as certain software, components, models, etc. may be mentioned, which should be considered as exemplary, and the purpose is only to illustrate the feasibility in the implementation of the technical solutions of the present application, but it does not mean that the applicant has or will necessarily use the solutions.

[0038] In the semiconductor manufacturing process, the photolithography technology is a key step of printing the pattern on the mask plate to the wafer through light exposure. However, due to the existence of physical effects such as diffraction and interference, there is a deviation between the actual exposure area and the expected exposure area. In order to predict and compensate for these deviations, photolithography simulation models are widely used.

[0039] However, the existing photolithography simulation model error determination method has the problem of low efficiency. Specifically, these methods usually measure the error by calculating the area difference or the average relative distance of pixels between the real lithography profile and the simulated lithography profile. This method not only has a large amount of calculation, but also can only simply measure the global error and cannot quickly locate the local error, which leads to low efficiency of error determination of the photolithography simulation model.

[0040] The purpose of the present application is to provide a photolithography simulation model error determination method, device, equipment, medium and product. In the photolithography simulation model error determination method provided by the embodiments of the present application, the real lithography profile and the simulated lithography profile are first discretized to obtain discrete points. In this way, only a plurality of representative position points need to be selected from the lithography profile for calculation, without calculating each position, which can reduce the amount of calculation. Then, a first quadtree is constructed according to the discrete points, wherein the parent node of the first quadtree is used to represent the profile area, and the child node of the first quadtree is used to store the coordinates of the real discrete points and the coordinates of the simulated discrete points in the profile area. Each shortest distance is stored in a same structure index tree corresponding to the first quadtree, and a second quadtree is constructed. In this way, through the second quadtree, each shortest distance in each profile area can be quickly located, so as to quickly determine the local error of the photolithography simulation model in each profile area. In summary, through the discretization processing and the construction of the quadtree, the embodiments of the present application can reduce the amount of calculation and quickly locate the local error of the photolithography simulation model, thereby improving the error determination efficiency of the photolithography simulation model.

[0041] The specific embodiments of the photolithography simulation model error determination method, device, equipment, medium and product provided by the embodiments of the present application will be introduced below. First, the photolithography simulation model error determination method will be introduced.

[0042] Figure 1 A flowchart of a photolithography simulation model error determination method is provided, which can be applied to a server. The photolithography simulation model error determination method can include the following S101-S105.

[0043] S101, obtain the real lithographic contour of the target mask pattern, and the simulated lithographic contour of the target mask pattern output by the lithographic simulation model.

[0044] In this embodiment, the true lithographic profile is used to characterize the true wafer pattern profile obtained by printing the mask pattern on the mask onto the wafer through the exposure of light.

[0045] A lithography simulation model is a mathematical model used to simulate the lithography process. Specifically, it can be implemented using an optical approximation model or an electromagnetic field simulation model.

[0046] Simulated lithography profiles are used to characterize the simulated wafer pattern profiles obtained by simulating the lithography process using a lithography simulation model.

[0047] As an example, the server first acquires a real lithographic contour image of the target mask pattern using a scanning electron microscope, with a resolution of 1 nm / pixel. Simultaneously, a simulated lithographic contour image of the target mask pattern is generated using a lithography simulation model based on the Hopkins equation, also with a resolution of 1 nm / pixel.

[0048] Then, edge detection and contour extraction were performed on the real and simulated lithographic contour images. The Canny edge detection algorithm was used, with thresholds set to a low threshold of 50 and a high threshold of 150, to extract the real and simulated lithographic contours.

[0049] S102, Discretize the real lithography contour and the simulated lithography contour respectively, and construct the first quadtree. The parent node of the first quadtree is the contour region that matches the real lithography contour and the simulated lithography contour. The child nodes of the first quadtree are the coordinates of the real discrete points on the real lithography contour and the coordinates of the simulated discrete points on the simulated lithography contour.

[0050] In this embodiment, the discretization process is used to characterize the process of converting a continuous contour curve into a series of discrete points.

[0051] A quadtree is a spatial index tree used to store a set of points in two-dimensional Euclidean space. Each parent node of a quadtree represents a contour region that matches the actual lithographic contour and the simulated lithographic contour. Each parent node has at most four child nodes, where each child node typically stores the coordinates of a real discrete point on the actual lithographic contour and a simulated discrete point on the simulated lithographic contour within the matching contour region represented by its parent node. Quadtrees recursively divide the two-dimensional space into smaller regions to facilitate fast querying and management of point sets in the space.

[0052] As an example, the server first takes the entire lithographic contour area as the root node, and then recursively divides the real lithographic contour and the simulated lithographic contour into multiple contour areas according to the same division method. The contour area that matches the real lithographic contour and the simulated lithographic contour is taken as a parent node of the first quadtree.

[0053] Simultaneously, a Cartesian coordinate system is constructed with any point in the lithographic contour region as the origin. Then, the coordinates of the real discrete points on the real lithographic contour and the simulated discrete points on the simulated lithographic contour in the recursively partitioned contour region space are stored in the Cartesian coordinate system in the child nodes under the corresponding parent nodes.

[0054] S103, based on the first quadtree, determine the shortest distance from each real discrete point to the simulated lithography profile, and the shortest distance from each simulated discrete point to the real lithography profile.

[0055] In this embodiment, the shortest distance refers to the shortest distance from a point to a curve, which can be achieved by using the method for calculating the distance from a point to a line segment.

[0056] As an example, for each real discrete point of the real lithography profile, the server quickly finds the nearest simulated discrete point in the first quadtree, then calculates the distance between the real discrete point and the adjacent line segments of the nearest simulated discrete point, and takes the minimum value as the shortest distance from the real discrete point to the simulated lithography profile.

[0057] Meanwhile, for each simulated discrete point of the simulated lithography profile, the shortest distance from the simulated discrete point to the actual lithography profile is calculated in the same way as described above.

[0058] S104, store each shortest distance into the isomorphic index tree corresponding to the first quadtree, and construct the second quadtree. The parent node of the second quadtree is the matching contour region, and the child nodes of the second quadtree are the shortest distance from each real discrete point to the simulated lithographic contour, and the shortest distance from each simulated discrete point to the real lithographic contour.

[0059] In this embodiment, the isomorphic index tree is used to represent index trees with the same structure, that is, the second quadtree is isomorphic to the first quadtree. However, the child nodes of the first quadtree are used to store the coordinates of real discrete points and simulated discrete points in the contour region, and the child nodes of the second quadtree are used to store the shortest distance from each real discrete point in the contour region to the simulated lithographic contour, as well as the shortest distance from each simulated discrete point to the real lithographic contour.

[0060] As an example, the server constructs a homogeneous index tree corresponding to the first quadtree based on its structure. Then, the shortest distances calculated above are stored in the corresponding child nodes of the homogeneous index tree, thus obtaining the second quadtree.

[0061] Table 1 below provides a second quadtree data structure table. Each row corresponds to a discrete point. The first column represents the discrete point's number, the second column represents the contour identification information to which the discrete point belongs, where 1 represents the actual lithographic contour and 2 represents the simulated lithographic contour. The third column represents the parent node number, the fourth and fifth columns represent the X-axis and Y-axis coordinates of the discrete point, respectively, and the sixth column represents the shortest distance corresponding to the discrete point.

[0062]

[0063] Table 1. Data Structure of the Second Quadtree

[0064] S105, determine the error of the lithography simulation model based on the second quadtree.

[0065] In this embodiment, the server analyzes the errors of the lithography simulation model based on the second quadtree. Specifically, it calculates the mean of the shortest distances included in each parent node as the local error of the contour region corresponding to that parent node. At the same time, it calculates the global mean of all shortest distances as the overall error of the lithography simulation model.

[0066] In this embodiment, the actual and simulated lithographic contours are first discretized to obtain discrete points. This allows for calculation only of a few representative points selected from the lithographic contours, eliminating the need to calculate every single point and reducing computational load. Then, a first quadtree is constructed based on the discrete points. The parent node of the first quadtree represents the contour region, and the child nodes store the coordinates of the actual and simulated discrete points within the contour region. Each shortest distance is then stored in a corresponding isomorphic index tree to construct a second quadtree. This second quadtree allows for rapid location of the shortest distances within each contour region, enabling quick determination of local errors in the lithographic simulation model within each contour region. In summary, this embodiment, through discretization and quadtree construction, reduces computational load and quickly locates local errors in the lithographic simulation model, thereby improving the efficiency of error determination in lithographic simulation models.

[0067] As an optional embodiment, such as Figure 2 As shown, S102 may specifically include the following S201 to S205:

[0068] S201, the real lithography contour and the simulated lithography contour are divided into contour regions respectively to obtain multiple first contour regions corresponding to the real lithography contour and multiple second contour regions corresponding to the simulated lithography contour.

[0069] S202, Based on the matching results of the first contour region and the second contour region, construct the parent node of the spatial index tree;

[0070] S203, according to the preset interval distance, the first contour region and the second contour region are discretized respectively to obtain the real discrete points of the first contour region and the simulated discrete points of the second contour region.

[0071] S204, store the coordinates of each real discrete point and the coordinates of each simulated discrete point into the child nodes under the corresponding parent node of the spatial index tree;

[0072] S205, the spatial index tree storing the coordinates of real discrete points and simulated discrete points is determined as the first quadtree.

[0073] In this embodiment, the problem of how to efficiently organize and manage contour data is solved by discretizing the real lithographic contour and the simulated lithographic contour and constructing a quadtree structure.

[0074] The segmentation of the real and simulated lithographic contours can be achieved in various ways, such as equal-space segmentation, adaptive segmentation, or feature-point-based segmentation. Equal-space segmentation divides the entire lithographic contour area into several sub-regions of equal size. Adaptive segmentation dynamically adjusts the granularity of the segmentation based on the complexity of the lithographic contour, with finer segmentation in complex areas and coarser segmentation in simple areas. Feature-point-based segmentation determines the segmentation boundaries based on key points on the lithographic contour (such as corners, points where curvature changes exceed a preset curvature change threshold, etc.).

[0075] The parent nodes of the spatial index tree are constructed to establish the correspondence between the outline regions and the data structures. Each parent node can contain boundary information, region identifiers, etc., for that region. This structural design makes subsequent data storage and retrieval more efficient.

[0076] Discretization at preset intervals transforms a continuous contour into a set of discrete points. The selection of the preset interval requires a trade-off between accuracy and computational efficiency. Too small an interval increases computational load, while too large an interval may lose detailed contour information. In practical applications, a suitable interval can be determined based on the accuracy requirements of the photolithography process. Specifically, real discrete points are obtained by discretizing the real photolithography contour, while simulated discrete points are obtained by discretizing the simulated photolithography contour.

[0077] Storing the coordinates of discrete points into child nodes of the spatial index tree is the core step in data organization. Each child node stores the coordinates of a discrete point while maintaining its association with its parent node. This hierarchical storage structure enables rapid location of relevant regions during spatial queries, improving the efficiency of data retrieval.

[0078] As an example, such as Figure 3 The diagram illustrates the principle of quadtree construction. It assumes that both the real and simulated lithographic contours are approximately circular, and a Cartesian coordinate system is constructed with the center of the circle as the origin. According to the quadrants of the Cartesian coordinate system, the real lithographic contour is divided into first contour regions 310, 320, 330, and 340, and the simulated lithographic contour is divided into second contour regions 350, 360, 370, and 380.

[0079] Wherein, the first contour region 310 and the second contour region 350 are matching contour regions, and the first contour region 310 and the second contour region 350 are constructed as a parent node of the spatial index tree. At the same time, the coordinates of the real discrete points 311 and 312 in the first contour region 310 in the Cartesian coordinate system are stored in the child nodes under the corresponding parent node; the coordinates of the simulated discrete points 351 and 352 in the second contour region 350 in the Cartesian coordinate system are also stored in the child nodes under the corresponding parent node.

[0080] In this embodiment, the real and simulated lithographic contours are discretized to construct a first quadtree. This allows for rapid location of the corresponding quadtree node when searching for discrete points within a specific region, without traversing the entire dataset, thus significantly improving the processing efficiency of lithographic contour data.

[0081] As an optional embodiment, such as Figure 4 As shown, S103 may specifically include:

[0082] For each real discrete point, execute the following steps S401 to S403 respectively:

[0083] S401, through the first quadtree, search for the target simulation discrete point that is closest to the target real discrete point, where the target real discrete point is any real discrete point;

[0084] S402, obtain the forward discrete point and the backward discrete point of the target simulation discrete point. The forward discrete point is the simulation discrete point that is one position before the target simulation discrete point in a preset order, and the backward discrete point is the simulation discrete point that is one position after the target simulation discrete point in a preset order.

[0085] S403, the minimum value between the distance from the target real discrete point to the forward line segment and the distance from the target real discrete point to the backward line segment is determined as the shortest distance from the target real discrete point to the simulated lithography profile. The forward line segment is the line connecting the forward discrete point and the target simulated discrete point, and the backward line segment is the line connecting the backward discrete point and the target simulated discrete point.

[0086] In this embodiment, the target simulated discrete point is the simulated discrete point that is closest to the target real discrete point, obtained through a first quadtree search. The search process can employ nearest neighbor search algorithms, such as kd-trees or ball trees, to optimize search efficiency.

[0087] Forward and backward discrete points are adjacent points relative to the target simulation discrete point. Assuming the preset order is clockwise, the forward discrete point is the simulation discrete point that precedes the target simulation discrete point in clockwise order, and the backward discrete point is the simulation discrete point that follows the target simulation discrete point in clockwise order.

[0088] Forward and backward line segments are key elements used to calculate the actual shortest distance from the real discrete point of the target to the simulated lithographic profile. The forward line segment connects the forward discrete point to the simulated discrete point of the target, and the backward line segment connects the backward discrete point to the simulated discrete point of the target.

[0089] As an example, such as Figure 5 The diagram illustrates the shortest distance from a real discrete point to a simulated lithographic profile. Specifically, for a target real discrete point 503 within the real lithographic profile 502, assuming its coordinates are (100.5nm, 200.3nm), the server first searches for the nearest target simulated discrete point 504 within the simulated lithographic profile 501 using a first quadtree. The coordinates of the found nearest target simulated discrete point 504 are assumed to be (101.2nm, 199.8nm).

[0090] Furthermore, assuming the preset order is clockwise, obtain the forward discrete point 506 of the target simulation discrete point 504, with coordinates (100.8nm, 199.5nm); and obtain the backward discrete point 505 of the target simulation discrete point 504, with coordinates (101.5nm, 200.2nm).

[0091] Then, the distance from the target real discrete point 503 to the forward line segment formed by the target simulated discrete point 504 and the forward discrete point 506 is calculated, as is the distance from the target real discrete point 503 to the backward line segment formed by the target simulated discrete point 504 and the backward discrete point 505. Assume the calculated distances are 0.8 nm and 0.6 nm, respectively. The minimum value of 0.6 nm is then taken as the shortest distance from the target real discrete point 503 to the simulated lithographic contour 501.

[0092] Repeat this process until all real discrete points have been processed to obtain the shortest distance from each real discrete point of the real lithographic profile to the simulated lithographic profile.

[0093] This embodiment combines the efficient search capabilities of quadtrees with accurate distance calculation based on line segments. This significantly reduces the computational load compared to traditional point-by-point comparison methods. Furthermore, by considering the line segments formed by adjacent discrete points, the accuracy of distance calculation is improved, better reflecting the local features of the contour.

[0094] As an optional embodiment, such as Figure 6 As shown, S401 may specifically include the following S601 to S602:

[0095] S601, in the first quadtree, select the target parent node. The target parent node is the parent node to which the child node that stores the coordinates of the target's real discrete point belongs.

[0096] S602, determine the simulated discrete point in the target parent node that is closest to the target's real discrete point as the target's simulated discrete point.

[0097] In this embodiment, the selection process for the target parent node can be implemented by traversing the quadtree. Starting from the root node, the corresponding contour region is determined based on the coordinates of the target's actual discrete points, and then the corresponding target parent node is determined based on this contour region.

[0098] When determining the target simulation discrete points, Euclidean distance can be used as a distance metric. Specifically, all simulation discrete points in the target parent node can be traversed, the Euclidean distance between each simulation discrete point and the target real discrete point can be calculated, and the simulation discrete point corresponding to the minimum distance can be recorded as the target simulation discrete point.

[0099] As an example, suppose the coordinates of the target's true discrete point are (100.5, 200.7). The server then starts from the root node of the quadtree and, based on the coordinates (100.5, 200.7), continues until it finds a sub-region containing that point. The node corresponding to this sub-region is the target's parent node.

[0100] Then, examine the simulation discrete points included in the target parent node. Assuming there are 20 simulation discrete points, these points are identified as candidate simulation discrete points.

[0101] Finally, the Euclidean distances from these 20 candidate simulation discrete points to the target real discrete point (100.5, 200.7) are calculated, and the point with the smallest distance is found. Assuming the nearest point's coordinates are (100.6, 200.8), this point is determined as the target simulation discrete point.

[0102] In this embodiment, the first quadtree serves as a spatial index structure to store the coordinates of discrete points on both the real and simulated lithographic contours. Filtering the target parent node allows for rapid localization of the region containing the target real discrete point, narrowing the search range. Finally, by comparing the distances between each simulated discrete point in the target parent node and the target real discrete point, the closest point is determined as the target simulated discrete point. Thus, by utilizing the hierarchical structure and spatial partitioning characteristics of the quadtree, the goal of quickly locating and searching for the nearest point is achieved. Compared to traversing all simulated discrete points, this significantly reduces computational load and improves search efficiency.

[0103] As an optional embodiment, the error includes local errors;

[0104] like Figure 7 As shown, S105 may specifically include the following S701 to S702:

[0105] S701, determine the maximum value among the shortest distances included in the parent node of the second quadtree as the target distance of the parent node;

[0106] S702 determines the target distance of each parent node as the local error of the lithography simulation model in the contour region corresponding to each parent node.

[0107] In this embodiment, by determining the maximum value among the shortest distances included in the parent node of the second quadtree as the target distance, the most significant error within each contour region can be captured.

[0108] Specifically, the execution logic of this method is as follows: First, traverse each parent node of the second quadtree, where each parent node represents a different contour region. Then, for each parent node, examine all the shortest distance values ​​it contains and find the maximum value. This maximum value is defined as the target distance of the parent node, representing the maximum error within that contour region. Finally, use the target distance of each parent node as the local error of the corresponding contour region. In this way, within each local region, the maximum error value that best reflects the model performance of the region is selected as the local error. By focusing on the maximum error, this method can effectively identify regions that need to be optimized, without being obscured by average errors or smaller errors.

[0109] As an example, for each sub-region, the shortest distance between the real and simulated lithographic contours is calculated and stored in a second quadtree. For instance, for one of the sub-regions of the real and simulated lithographic contours, suppose the following shortest distance values ​​are stored: [2.1, 1.8, 3.5, 2.7, 1.9, 4.2, 2.3, 3.1]. In this case, the target distance (i.e., local error) for this sub-region will be determined to be 4.2, which is the maximum error value within this sub-region.

[0110] This embodiment analyzes the maximum error in each sub-region, effectively locating and quantifying local errors, providing more precise guidance for model optimization. This allows for the effective identification of areas requiring focused optimization, avoiding being masked by average or smaller errors.

[0111] As an optional embodiment, the error includes overall error;

[0112] like Figure 8 As shown, S105 may specifically include the following S801 to S803:

[0113] S801, determine the maximum value among the shortest distances included in the parent node of the second quadtree as the target distance of the parent node;

[0114] S802, perform statistical processing on the target distance of each parent node of the second quadtree to obtain the target statistical value, which is the median or average of the target distance;

[0115] S803 determines the target statistical value as the overall error of the lithography simulation model.

[0116] In this embodiment, by performing statistical processing on the shortest distances in each contour region stored in the second quadtree, a statistical value that can represent the overall error level is obtained.

[0117] Specifically, the target distances of each parent node representing different regions are first obtained. Then, these distance values ​​are statistically analyzed, and either the median or the average can be calculated. This approach considers the error situation across all regions, avoiding the influence of extreme values ​​while reflecting the overall error level. By defining this statistical value as the overall error, a quantitative evaluation of the overall performance of the lithography simulation model is achieved.

[0118] In this application, the statistical processing of the target distances of each parent node in the second quadtree can be performed in various ways. For example, the median can be calculated, which effectively avoids the influence of extreme values ​​and provides a more robust overall error estimate. Specifically, the target distances of all parent nodes can be arranged in ascending order, and the value at the middle position can be selected as the median. If the number of parent nodes is odd, the middle value is directly selected; if it is even, the average of the two middle values ​​is taken.

[0119] Another optional statistical method is to calculate the average. This method takes into account the error across all regions and provides a comprehensive overall error assessment. Specifically, the target distances of all parent nodes can be summed and then divided by the total number of parent nodes to obtain the average.

[0120] Both statistical methods have their advantages, and the appropriate method can be chosen based on the specific application scenario. For example, when the data distribution is relatively uniform, the mean may be more suitable; while when there are some outliers, the median may provide more stable results.

[0121] As a specific embodiment, assume that the second quadtree of a lithography simulation model contains 16 parent nodes, each representing a contour region. The target distances of these parent nodes are: 2.5, 3.1, 2.8, 3.3, 2.9, 3.0, 3.2, 2.7, 3.4, 2.6, 3.5, 2.9, 3.1, 3.0, 2.8, 3.2.

[0122] First, we can choose to calculate the median as the target statistic. After sorting these values, we get: 2.5, 2.6, 2.7, 2.8, 2.8, 2.9, 2.9, 3.0, 3.0, 3.1, 3.1, 3.2, 3.2, 3.3, 3.4, 3.5. Since there are 16 values ​​(an even number), we take the average of the two middle values ​​(3.0 and 3.0), resulting in a median of 3.0.

[0123] Alternatively, you can choose to calculate the average. Adding all the values ​​together gives 48.0, which, when divided by the number of parent nodes (16), yields an average of 3.0 nanometers.

[0124] In this embodiment, the shortest distances of each contour region stored in the second quadtree are statistically processed to obtain a statistical value that represents the overall error level. This method considers the error situation of all regions, avoiding the influence of extreme values ​​while reflecting the overall error level. By defining this statistical value as the overall error, a quantitative evaluation of the overall performance of the lithography simulation model is achieved.

[0125] As an optional embodiment, such as Figure 9As shown, S101 may specifically include the following S901 to S903:

[0126] S901, based on the target mask pattern, performs photolithography on the wafer to be processed to obtain the real photolithography outline;

[0127] S902, input the target mask pattern into the lithography simulation model to perform lithography simulation and obtain the simulated lithography profile.

[0128] In this embodiment, the target mask pattern is a mask pattern used for photolithography processing and photolithography simulation. The target mask pattern can be various complex integrated circuit design patterns, such as logic circuits, memory cells, etc. These patterns can be generated by computer-aided design (CAD) software and saved in an appropriate format, such as GDSII or OASIS format.

[0129] Photolithography is used to characterize the actual photolithography operations performed on the wafer to be processed according to the target mask pattern, thereby obtaining the true photolithographic profile. This step can be performed on actual photolithography equipment, such as deep ultraviolet (DUV) or extreme ultraviolet (EUV) lithography machines. Parameters in the photolithography process, such as exposure dose, focal length, and numerical aperture, need to be precisely controlled according to specific process requirements.

[0130] The actual lithographic profile is the profile obtained through actual lithographic processing and serves as a reference standard. These profiles can be obtained using high-precision measurement equipment such as scanning electron microscopes (SEM) or atomic force microscopes (AFM).

[0131] Simulated lithographic profiles are profiles output from lithographic simulation models and used for comparison with real profiles. These profiles typically exist in digital form, which can be pixelated images or vectorized profile data.

[0132] As an example, the first step is to prepare a target mask pattern. This can be a test pattern containing various typical features, such as lines, angles, and holes, to comprehensively evaluate the performance of the photolithography simulation model. Next, based on the target mask pattern, the wafer to be processed is subjected to photolithography using actual photolithography equipment. During this process, photolithography parameters, such as exposure dose and focal length, need to be strictly controlled to ensure a high-quality, realistic photolithography profile. After photolithography is completed, a high-precision measuring device, such as a scanning electron microscope, is used to scan the pattern on the processed wafer to obtain accurate data of the realistic photolithography profile.

[0133] Simultaneously, the same target mask pattern is input into the photolithography simulation model. This model already includes relevant parameters such as the optical system and photoresist properties. By running the photolithography simulation, data of the simulated photolithography profile is obtained. This data is usually digitized and can be directly used for subsequent comparison and analysis.

[0134] This embodiment enables the acquisition of fully comparable real and simulated lithography profiles. Therefore, this direct comparison method, compared to traditional methods of calculating area difference or average pixel distance, more intuitively and accurately reflects the differences between the lithography simulation model and the actual lithography process.

[0135] An error determination method based on a photolithography simulation model. Accordingly, this application also provides specific embodiments of an error determination device based on a photolithography simulation model.

[0136] like Figure 10 As shown, the error determination device 1000 for the lithography simulation model provided in this application embodiment includes a contour acquisition module 1010, a quadtree construction module 1020, a distance determination module 1030, and an error determination module 1040.

[0137] The contour acquisition module 1010 is used to acquire the real lithographic contour of the target mask pattern and the simulated lithographic contour of the target mask pattern output by the lithographic simulation model.

[0138] The quadtree construction module 1020 is used to discretize the real lithography contour and the simulated lithography contour respectively, and construct the first quadtree. The parent node of the first quadtree is the contour region that matches the real lithography contour and the simulated lithography contour. The child nodes of the first quadtree are the coordinates of the real discrete points on the real lithography contour and the coordinates of the simulated discrete points on the simulated lithography contour.

[0139] The distance determination module 1030 is used to determine the shortest distance from each real discrete point to the simulated lithography profile, and the shortest distance from each simulated discrete point to the real lithography profile, based on the first quadtree.

[0140] The quadtree construction module 1020 is also used to store each shortest distance into a homogeneous index tree corresponding to the first quadtree to construct a second quadtree. The parent node of the second quadtree is the matching contour region, and the child nodes of the second quadtree are the shortest distance from each real discrete point to the simulated lithographic contour, and the shortest distance from each simulated discrete point to the real lithographic contour.

[0141] The error determination module 1040 is used to determine the error of the photolithography simulation model based on the second quadtree.

[0142] In the error determination device for the lithography simulation model provided in this application embodiment, the real lithography contour and the simulated lithography contour are first discretized to obtain discrete points. This way, only a few representative position points need to be selected from the lithography contour for calculation, eliminating the need to calculate every position, thus reducing the computational load. Then, a first quadtree is constructed based on the discrete points. The parent node of the first quadtree represents the contour region, and the child nodes store the coordinates of the real discrete points and the coordinates of the simulated discrete points within the contour region. Each shortest distance is then stored in a homogeneous index tree corresponding to the first quadtree, constructing a second quadtree. Thus, the second quadtree allows for rapid location of the shortest distances within each contour region, thereby quickly determining the local errors of the lithography simulation model in each contour region. In summary, this application embodiment, through discretization and quadtree construction, reduces the computational load and quickly locates the local errors of the lithography simulation model, thereby improving the efficiency of error determination for the lithography simulation model.

[0143] As an optional embodiment, the quadtree construction module 1020 is specifically used for:

[0144] The real lithography contour and the simulated lithography contour are divided into contour regions to obtain multiple first contour regions corresponding to the real lithography contour and multiple second contour regions corresponding to the simulated lithography contour.

[0145] Based on the matching results of the first contour region and the second contour region, construct the parent node of the spatial index tree;

[0146] According to the preset interval distance, the first contour region and the second contour region are discretized respectively to obtain the real discrete points of the first contour region and the simulated discrete points of the second contour region.

[0147] Store the coordinates of each real discrete point and the coordinates of each simulated discrete point in the child nodes under the corresponding parent node of the spatial index tree;

[0148] The spatial index tree storing the coordinates of real discrete points and simulated discrete points is determined as the first quadtree.

[0149] As an optional embodiment, the distance determination module 1030 specifically includes the following units:

[0150] The discrete point search unit is used to search through the first quadtree to find the target simulated discrete point that is closest to the target real discrete point. The target real discrete point can be any real discrete point.

[0151] The discrete point acquisition unit is used to acquire the forward discrete point and the backward discrete point of the target simulation discrete point. The forward discrete point is the simulation discrete point that is one position before the target simulation discrete point in a preset order, and the backward discrete point is the simulation discrete point that is one position after the target simulation discrete point in a preset order.

[0152] The distance determination unit is used to determine the minimum value between the distance from the target's real discrete point to the forward line segment and the distance from the target's real discrete point to the backward line segment as the shortest distance from the target's real discrete point to the simulated lithographic profile. The forward line segment is the line connecting the forward discrete point and the target's simulated discrete point, and the backward line segment is the line connecting the backward discrete point and the target's simulated discrete point.

[0153] As an optional embodiment, the discrete point search unit is specifically used for:

[0154] In the first quadtree, the target parent node is selected. The target parent node is the parent node to which the child node that stores the coordinates of the target's real discrete point belongs.

[0155] The simulated discrete point that is closest to the actual discrete point of the target in the target parent node is determined as the target simulated discrete point.

[0156] As an optional embodiment, the error includes local errors;

[0157] Error determination module 1040 is specifically used for:

[0158] The maximum value among all the shortest distances included in the parent node of the second quadtree is determined as the target distance of the parent node;

[0159] The target distance of each parent node is determined as the local error of the lithography simulation model in the contour region corresponding to each parent node.

[0160] As an optional embodiment, the error includes overall error;

[0161] The error determination module 1040 is also used for:

[0162] The maximum value among all the shortest distances included in the parent node of the second quadtree is determined as the target distance of the parent node;

[0163] The target distances of each parent node in the second quadtree are statistically processed to obtain the target statistical values, which are the median or average of the target distances.

[0164] The target statistical value is determined as the overall error of the lithography simulation model.

[0165] As an optional embodiment, the contour acquisition module 1010 is specifically used for:

[0166] Based on the target mask pattern, photolithography is performed on the wafer to be processed to obtain the real photolithographic outline;

[0167] The target mask pattern is input into the lithography simulation model for lithography simulation to obtain the simulated lithography profile.

[0168] An error determination method based on a photolithography simulation model. Accordingly, this application also provides specific embodiments of an error determination device based on a photolithography simulation model.

[0169] Figure 11 A schematic diagram of the hardware structure of the error determination device for the photolithography simulation model provided in the embodiments of this application is shown.

[0170] The error determination device for the photolithography simulation model may include a processor 1101 and a memory 1102 storing computer program instructions.

[0171] Specifically, the processor 1101 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0172] Memory 1102 may include mass storage for data or instructions. For example, and not limitingly, memory 1102 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 1102 may include removable or non-removable (or fixed) media. Where appropriate, memory 1102 may be internal or external to the integrated gateway disaster recovery device. In a particular embodiment, memory 1102 is non-volatile solid-state memory.

[0173] The processor 1101 reads and executes computer program instructions stored in the memory 1102 to implement any of the error determination methods for the lithography simulation model in the above embodiments.

[0174] In one example, the error determination device for the photolithography simulation model may further include a communication interface 1103 and a bus 1110. Wherein, as Figure 11 As shown, the processor 1101, memory 1102, and communication interface 1103 are connected through bus 1110 and complete communication with each other.

[0175] The communication interface 1103 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.

[0176] Bus 1110 includes hardware, software, or both, that couples components of a device for determining the error of a lithography simulation model together. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 1110 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, any suitable bus or interconnect is contemplated herein.

[0177] Furthermore, in conjunction with the error determination method for the photolithography simulation model in the above embodiments, this application embodiment can provide a computer storage medium for implementation. This computer storage medium stores computer program instructions; when these computer program instructions are executed by a processor, they implement any of the error determination methods for the photolithography simulation model in the above embodiments.

[0178] In addition, in conjunction with the error determination method of the lithography simulation model in the above embodiments, this application embodiment can provide a computer program product to implement it. When the instructions in the computer program product are executed by the processor of the electronic device, the electronic device executes the error determination method of the lithography simulation model as provided in any aspect of the above embodiments of this application.

[0179] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0180] The functional blocks shown in the above-described structural diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0181] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0182] The aspects of this disclosure have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by special-purpose hardware performing the specified functions or actions, or can be implemented by a combination of special-purpose hardware and computer instructions.

[0183] The above description is merely a specific implementation of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application.

Claims

1. A method for determining the error of a photolithography simulation model, characterized in that, The method comprises the following steps: obtaining a real lithography profile of a target mask pattern and a simulation lithography profile of the target mask pattern output by a lithography simulation model; discretizing the real lithography profile and the simulation lithography profile respectively to construct a first quadtree, wherein a parent node of the first quadtree is a matched profile region in the real lithography profile and the simulation lithography profile, and a child node of the first quadtree is a coordinate of a real discrete point on the real lithography profile and a coordinate of a simulation discrete point on the simulation lithography profile; determining, according to the first quadtree, a shortest distance from each real discrete point to the simulation lithography profile and a shortest distance from each simulation discrete point to the real lithography profile; storing each shortest distance into a same structure index tree corresponding to the first quadtree respectively to construct a second quadtree, wherein a parent node of the second quadtree is the matched profile region, and a child node of the second quadtree is the shortest distance from each real discrete point to the simulation lithography profile and the shortest distance from each simulation discrete point to the real lithography profile; determining, according to the second quadtree, an error of the lithography simulation model.

2. The method of claim 1, wherein, The discretizing the real lithography profile and the simulation lithography profile respectively to construct a first quadtree comprises the following steps: dividing profile regions of the real lithography profile and the simulation lithography profile respectively to obtain a plurality of first profile regions corresponding to the real lithography profile and a plurality of second profile regions corresponding to the simulation lithography profile; constructing a parent node of a space index tree based on a matching result of the first profile regions and the second profile regions; discretizing the first profile regions and the second profile regions respectively according to a preset interval distance to obtain real discrete points of the first profile regions and simulation discrete points of the second profile regions; storing coordinates of each real discrete point and coordinates of each simulation discrete point into child nodes under the parent node of the space index tree; determining the space index tree storing the coordinates of the real discrete points and the simulation discrete points as the first quadtree.

3. The method of claim 1, wherein, The determining, according to the first quadtree, the shortest distance from each real discrete point to the simulation lithography profile comprises the following steps: for each real discrete point, the following steps are performed respectively: searching, through the first quadtree, to obtain a target simulation discrete point closest to a target real discrete point, wherein the target real discrete point is any one of the real discrete points; obtaining a forward discrete point and a backward discrete point of the target simulation discrete point, wherein the forward discrete point is a simulation discrete point in front of the target simulation discrete point in a preset order, and the backward discrete point is a simulation discrete point behind the target simulation discrete point in the preset order; The minimum value of a distance of the target real discrete point to a forward line segment and a distance of the target real discrete point to a backward line segment is determined as a shortest distance of the target real discrete point to the simulation lithography profile, the forward line segment being a line connecting the forward discrete point and the target simulation discrete point, and the backward line segment being a line connecting the backward discrete point and the target simulation discrete point.

4. The method of claim 3, wherein, The searching of the target simulation discrete point closest to the target real discrete point from the first quadtree comprises: In the first quadtree, a target parent node is screened out, the target parent node being a parent node to which a child node storing coordinates of the target real discrete point belongs; The simulation discrete point closest to the target real discrete point in the target parent node is determined as the target simulation discrete point.

5. The method according to any one of claims 1 to 4, characterized in that, The error comprises a local error; The determining of the error of the lithography simulation model according to the second quadtree comprises: A maximum value of each of the shortest distances included in a parent node of the second quadtree is determined as a target distance of the parent node; The target distance of each of the parent nodes is determined as a local error of the lithography simulation model in a profile region corresponding to each of the parent nodes.

6. The method according to any one of claims 1 to 4, characterized in that, The error comprises an overall error; The determining of the error of the lithography simulation model according to the second quadtree comprises: A maximum value of each of the shortest distances included in a parent node of the second quadtree is determined as a target distance of the parent node; A target statistical value is obtained by statistical processing of the target distances of each of the parent nodes of the second quadtree, the target statistical value being a median or an average of the target distances; The target statistical value is determined as an overall error of the lithography simulation model.

7. The method according to any one of claims 1 to 4, characterized in that, The obtaining of a real lithography profile of a target mask pattern and a simulation lithography profile of the target mask pattern output by a lithography simulation model comprises: Performing lithography processing on a wafer to be processed based on a target mask pattern to obtain the real lithography profile; Inputting the target mask pattern into the lithography simulation model to perform lithography simulation to obtain the simulation lithography profile.

8. An electronic device, comprising: The device comprises a processor and a memory storing computer program instructions; The processor executes the computer program instructions to implement the error determination method of the lithography simulation model according to any one of claims 1-7.

9. A computer-readable storage medium, characterized in that, The computer program instructions are stored on the computer readable storage medium and are executed by the processor to implement the error determination method of the lithography simulation model according to any one of claims 1-7.

10. A computer program product, characterised in that, The instructions in the computer program product are executed by the processor of the electronic device to cause the electronic device to perform the error determination method of the lithography simulation model according to any one of claims 1-7.

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