Layout defect detection method and device, storage medium and program product

Abstract

The invention discloses a layout defect detection method and device, a storage medium and a program product, and relates to the technical field of semiconductors. The method comprises the following steps: acquiring boundary information of M defect detection areas in a to-be-detected layout and position information of a plurality of measurement positions; based on the boundary information of the M defect detection areas and the position information of the multiple measurement positions, N target measurement positions are obtained through screening from the multiple measurement positions; based on the position information of each target measurement position in the N target measurement positions and a preset detection range parameter, determining to obtain N surrounding detection range areas; and performing defect detection on the target detection layer through the detection function to obtain a layout defect detection result of the to-be-detected layout, and determining the target detection layer based on the N surrounding detection range areas. According to the embodiment of the invention, layout defect detection of different users can be realized more comprehensively and accurately, so that the overall layout defect detection efficiency can be effectively improved, and the user satisfaction can be improved.

Description

Technical Field

[0001] The present application belongs to the field of semiconductor technology, and in particular, relates to a layout defect detection method, device, storage medium and program product. Background Art

[0002] At present, with the continuous development of semiconductor processes and technological breakthroughs, the impact of the optical proximity effect (OPE) on design graphics has become increasingly significant, and design layout distortion will directly affect device performance and batch yield. Therefore, simulating the design layout for defect detection and defect correction before actual production can effectively reduce production costs. In the actual defect detection link, LRC (Longitudinal Redundancy Check, lithography simulation detection) as a design inspection tool can realize contour inspection and output defect locations by matching different detectors to achieve the purpose of optimizing the design.

[0003] However, with the continuous improvement of the integration of integrated circuits, the layout area is gradually increasing, and the design graphics in the layout are becoming more and more complex, which puts higher requirements on the efficiency of layout defect detection. In the existing layout defect detection scheme, LRC detection is usually based on the whole picture, or a single sub-area cut out of the whole picture is written into a layer as a focus area for detection. Although the full-picture detection in this scheme is more comprehensive for defect detection, it is time-consuming; and a single sub-area cut out of the whole picture is written into a layer as a focus area for detection. Only a rectangular range can be framed each time, which can reduce the time consumption. However, if there is a need for multi-area detection in the layout, it is still necessary to run the detection program multiple times to detect multiple single sub-areas one by one. Therefore, there are problems such as cumbersome settings and low detection efficiency.

[0004] Based on this, the industry is still in urgent need of a new layout defect detection solution to meet the multi-area detection needs of the layout in a more flexible and efficient manner, so as to fully improve the LRC detection rate of the overall layout. Summary of the invention

[0005] The embodiments of the present application provide a layout defect detection method, device, storage medium and program product, which can more flexibly and efficiently meet the multi-area detection requirements of the layout, thereby fully improving the LRC detection rate of the overall layout and improving user satisfaction.

[0006] In a first aspect, an embodiment of the present application provides a layout defect detection method, the layout defect detection method comprising:

[0007] Obtain boundary information of M defect detection areas in the to-be-detected layout and position information of multiple measurement positions of the to-be-detected layout, where M is a positive integer;

[0008] Based on the boundary information of the M defect detection areas and the position information of the multiple measurement positions, N target measurement positions are obtained from the multiple measurement positions, where the N target measurement positions are measurement positions located in the M defect detection areas among the multiple measurement positions, and N is a positive integer;

[0009] Based on the position information of each target measurement position in the N target measurement positions and the preset detection range parameters, N surrounding detection range areas are determined, and the N surrounding detection range areas correspond to the N target measurement positions one by one;

[0010] Defect detection is performed on the target detection layer through the detection function to obtain the layout defect detection result of the layout to be detected. The target detection layer is determined based on N surrounding detection range areas.

[0011] In some possible implementations, the position information includes first coordinate values ​​of the start and end points of the measurement position in a first direction, and second coordinate values ​​in a second direction, and the first direction intersects the second direction; based on the boundary information of the M defect detection areas and the position information of the multiple measurement positions, N target measurement positions are obtained by screening from the multiple measurement positions, including:

[0012] Based on the boundary information of the M defect detection areas, determining a first coordinate range of the M defect detection areas in the first direction and a second coordinate range in the second direction;

[0013] Compare the first coordinate values ​​of the starting and ending points of the measurement positions of the plurality of measurement positions with the first coordinate ranges of at least some of the defect detection regions in the M defect detection regions, and compare the second coordinate values ​​of the starting and ending points of the measurement positions of the plurality of measurement positions with the second coordinate ranges of at least some of the defect detection regions in the M defect detection regions, to obtain a coordinate comparison result;

[0014] Based on the coordinate comparison result, N target measurement positions are screened out from the multiple measurement positions.

[0015] In some possible implementations, the measurement position start and end points include a measurement start point and a measurement end point, and the position information includes a first coordinate value and a second coordinate value of the measurement start point, and a first coordinate value and a second coordinate value of the measurement end point; based on the coordinate comparison result, N target measurement positions are obtained by screening from multiple measurement positions, including:

[0016] Determine a measurement position in which the first coordinate values ​​of the measurement start point and the measurement end point are both within the first coordinate range of the target defect detection area, and the second coordinate values ​​of the measurement start point and the measurement end point are both within the second coordinate range of the same target defect detection area as a target measurement position, so as to screen and obtain N target measurement positions from the multiple measurement positions;

[0017] The target defect detection area is any defect detection area among the M defect detection areas.

[0018] In some possible implementations, the preset detection range parameters include the shape of the detection range and the detection distance extended from the center point; based on the position information of each target measurement position in the N target measurement positions and the preset detection range parameters corresponding to the measurement position, N surrounding detection range areas are determined, including:

[0019] Based on the N target measurement positions, determine N center points corresponding to the N target measurement positions one by one;

[0020] N surrounding detection range areas are determined based on the position information of each of the N center points, the shape of the detection area, and the detection distance extended from the center point.

[0021] In some possible implementations, based on the position information of each target measurement position in the N target measurement positions and the preset detection range parameters corresponding to the measurement position, N surrounding detection range areas are determined, including:

[0022] Based on the position information of each target measurement position in the N target measurement positions, the preset detection range parameters and the boundary information of at least part of the defect detection areas in the M defect detection areas, N surrounding detection range areas are determined.

[0023] In some possible implementations, based on the position information of each target measurement position in the N target measurement positions, the preset detection range parameters, and the boundary information of at least part of the defect detection areas in the M defect detection areas, the N surrounding detection range areas are determined, including:

[0024] Determine a candidate detection range area corresponding to the first target measurement position based on the position information of the first target measurement position and the preset detection range parameters, where the first target measurement position is any target measurement position among the N target measurement positions;

[0025] Compare the candidate detection range area with the first defect detection area where the first target measurement position is located among the M defect detection areas to obtain an area comparison result;

[0026] When the area comparison result indicates that the candidate detection range area exceeds the first defect detection area, the candidate detection range area is cut based on the boundary information of the first defect detection area to obtain the surrounding detection range area corresponding to the first target measurement position;

[0027] When the area comparison result indicates that the candidate detection range areas are all within the first defect detection area, the candidate detection range areas are determined as the surrounding detection range areas corresponding to the first target measurement position.

[0028] In some possible implementations, before performing defect detection on the target detection layer by using the detection function to obtain a layout defect detection result of the layout to be detected, the layout defect detection method further includes:

[0029] Generate a target detection layer based on the detection range parameters corresponding to the N surrounding detection range areas;

[0030] The target detection layer is subjected to defect detection through the detection function to obtain the layout defect detection result of the layout to be detected, including:

[0031] The target detection layer is added to the region of interest, and the defect detection is performed on the region of interest through the detection function to obtain the layout defect detection result.

[0032] Based on the same inventive concept, in a second aspect, an embodiment of the present application provides a layout defect detection device, the layout defect detection device comprising:

[0033] A first acquisition module is used to acquire boundary information of M defect detection areas in the to-be-detected layout and position information of multiple measurement positions in the to-be-detected layout, where M is a positive integer;

[0034] A first screening module is used to screen N target measurement positions from the multiple measurement positions based on the boundary information of the M defect detection areas and the position information of the multiple measurement positions, where the N target measurement positions are measurement positions located in the M defect detection areas among the multiple measurement positions, and N is a positive integer;

[0035] A first determination module is used to determine N surrounding detection range areas based on the position information of each target measurement position in the N target measurement positions and the preset detection range parameters, and the N surrounding detection range areas correspond to the N target measurement positions one by one;

[0036] The first detection module is used to perform defect detection on the target detection layer through the detection function to obtain the layout defect detection result of the layout to be detected. The target detection layer is determined based on N surrounding detection range areas.

[0037] In a third aspect, an embodiment of the present application provides a layout defect detection device, the layout defect detection device comprising:

[0038] a processor and a memory storing computer program instructions;

[0039] When the processor executes the computer program instructions, the layout defect detection method provided in any one of the above-mentioned embodiments of the present application is implemented.

[0040] In a fourth aspect, an embodiment of the present application provides a computer storage medium, on which computer readable storage medium is stored computer program instructions, and when the computer program instructions are executed by a processor, a layout defect detection method as provided in any one of the above-mentioned embodiments of the present application is implemented.

[0041] In a fifth aspect, an embodiment of the present application provides a computer program product. When the instructions in the computer program product are executed by a processor of an electronic device, the electronic device executes a layout defect detection method as provided in any one of the above-mentioned embodiments of the present application.

[0042] It can be seen from the above description that a layout defect detection method, device, storage medium and program product in an embodiment of the present application introduces a new multi-region detection method, which obtains the boundary information of multiple defect detection areas of particular concern in the layout to be detected, and then selects the target measurement position located in the above-mentioned defect detection area from multiple measurement positions of the layout according to the boundary information, so that the multi-region detection needs of the layout can be more flexibly and efficiently met based on the target measurement position. Furthermore, in order to ensure the accuracy of the defect detection results, the embodiment of the present application determines the peripheral detection range of each target measurement position by setting a preset detection range parameter, so as to obtain the surrounding detection range area corresponding to each measurement position in a targeted manner. Finally, by using the detection function to quickly and accurately locate the defects within the peripheral detection range of the target measurement position, it is possible to achieve accurate output of defects in the key areas of concern in the layout.

[0043] Overall, compared with the existing layout defect detection scheme in which a layer is written separately as a focus area for detection based on the entire image or a single sub-area cut out from the entire image, the layout defect detection method, device, storage medium and program product of the embodiment of the present application can more flexibly and efficiently meet the multi-area detection needs of the layout, thereby fully improving the LRC detection rate of the overall layout and improving user satisfaction, and can make the layout defect detection results more targeted while improving the layout defect detection speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solution of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0045] Figure 1 It is an existing LRC detection flow chart provided by an embodiment of the present application;

[0046] Figure 2 It is a flow chart of a layout defect detection method provided by an embodiment of the present application;

[0047] Figure 3 This is a schematic diagram of the distribution of defect detection areas in a layout to be detected provided by an embodiment of the present application;

[0048] Figure 4 It is a distribution diagram of the surrounding detection range area provided by an embodiment of the present application;

[0049] Figure 5 It is a scenario flow diagram of a layout defect detection method provided by an embodiment of the present application;

[0050] Figure 6 It is a structural schematic diagram of a layout defect detection device provided by an embodiment of the present application;

[0051] Figure 7 It is a structural schematic diagram of a layout defect detection device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0052] The features and exemplary embodiments of various aspects of the present application will be described in detail below. In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, rather than to limit the present application. For those skilled in the art, the present application can be implemented without the need for some of these specific details. The following description of the embodiments is only to provide a better understanding of the present application by illustrating the examples of the present application.

[0053] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "include..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0054] It should be noted that in the embodiments of the present application, certain software, components, models and other existing solutions in the industry may be mentioned, and they should be regarded as exemplary. Their purpose is only to illustrate the feasibility of implementing the technical solution of the present application, but it does not mean that the applicant has or will necessarily use the solution.

[0055] As mentioned in the background technology section, in existing layout defect detection solutions, LRC detection is usually based on the entire image, or a single sub-area cut out of the entire image is written into a single layer as a focus area for detection. Figure 1 , Figure 1 It is an existing LRC detection flow chart provided by an embodiment of the present application. The layout detection process in the current existing LRC detection technology mainly includes the following steps: Step 1: Configure the input file, including the model path (Model), GDS layout (Graphic Data System). Step 2: Configure the layer information, fill in the layer (layer) and parameter type (datatype) required for LRC detection. Step 3: Configure global parameters, including the boundary (boundary) required for running LRC detection, layout resolution (dbu), and parameters such as the switch that determines whether to detect the entire image. Step 4: Configure the detector, load the required detector according to demand, such as the edge placement error (Edge Placement Error) detector, the critical dimension variation (Critical Dimension Variation) detector, etc. Step 5: Configure the process window (Process Window Condition) information.

[0056] Compared with the time-consuming full-image LRC detection, in the above existing LRC detection process, users can effectively shorten the LRC test time by successively changing the detection sub-areas defined by the boundary. This solution can basically meet the needs of simple screening operations. However, this method of successive partition detection still has the following defects when facing large-scale screening:

[0057] Defect 1: Existing LRC detection technology only provides two methods: full-image detection and single boundary detection. Full-image detection is more comprehensive for defect detection, but it takes a long time. Through the boundary, a smaller area can be selected on the layout for detection, but only a rectangular range can be framed each time. If there is a need for multi-area detection, the detection program needs to be run multiple times, and the setting is more cumbersome.

[0058] Defect 2: The output of the existing LRC will expose all defect points that violate the detection rules in the detection area, and it is impossible to read the measurement position (gauge) information to observe the area around a specific position or specific pattern in a targeted manner. In this way, facing the layout generated by the test pattern design in the design exploration stage, the above LRC defect detection solution lacks sensitivity to changes in layout segmentation (pattern split) and targeting defects in key areas of concern.

[0059] In view of the above, in order to solve the problems of the prior art, the embodiments of the present application provide a layout defect detection method, device, storage medium and program product to improve the defect detection speed and make the defect detection results of the layout more targeted. It should be noted that the embodiments provided in this application are not intended to limit the scope of the disclosure of this application.

[0060] The following first introduces the layout defect detection method provided in the embodiment of the present application.

[0061] Figure 2 The schematic diagram of the process of the layout defect detection method provided by an embodiment of the present application is shown. The layout defect detection method is applied to an electronic device, which may include a server or a user terminal. Figure 2 As shown, the layout defect detection method includes the following steps:

[0062] S210, obtaining boundary information of M defect detection areas in the to-be-detected layout and position information of multiple measurement positions in the to-be-detected layout, where M is a positive integer;

[0063] S220, based on the boundary information of the M defect detection areas and the position information of the multiple measurement positions, N target measurement positions are obtained from the multiple measurement positions, where the N target measurement positions are measurement positions located in the M defect detection areas among the multiple measurement positions, and N is a positive integer;

[0064] S230, based on the position information of each target measurement position among the N target measurement positions and the preset detection range parameters corresponding to the measurement positions, determine N surrounding detection range areas, and the N surrounding detection range areas correspond to the N target measurement positions one by one;

[0065] S240, performing defect detection on the target detection layer through the detection function to obtain the layout defect detection result of the layout to be detected, and the target detection layer is determined based on N surrounding detection range areas.

[0066] It can be seen from the above description that a layout defect detection method in an embodiment of the present application introduces a new multi-region detection method, which obtains the boundary information of multiple defect detection areas of particular concern in the layout to be detected, and then selects the target measurement position located in the above-mentioned defect detection area from multiple measurement positions of the layout according to the boundary information, so that the multi-region detection needs of the layout can be more flexibly and efficiently met based on the target measurement position. Furthermore, in order to ensure the accuracy of the defect detection results, the embodiment of the present application determines the peripheral detection range of each target measurement position by setting a preset detection range parameter, so as to obtain the surrounding detection range area corresponding to each measurement position in a targeted manner. Finally, by using the detection function to quickly and accurately locate the defects within the peripheral detection range of the target measurement position, it is possible to achieve accurate output of defects in the key areas of concern in the layout.

[0067] Overall, compared with the existing layout defect detection scheme in which a layer is written separately as a focus area for detection based on the entire image or a single sub-area cut out from the entire image, a layout defect detection method in an embodiment of the present application can more flexibly and efficiently meet the multi-area detection needs of the layout, thereby fully improving the LRC detection rate of the overall layout and improving user satisfaction, and can make the layout defect detection results more targeted while improving the layout defect detection speed.

[0068] The specific implementation of the above steps 210 to 240 is described in detail below.

[0069] In S210, during specific implementation, boundary information of M defect detection areas in the to-be-detected layout and position information of multiple measurement positions of the to-be-detected layout are obtained, where M is a positive integer.

[0070] The M defect detection areas may be local areas in the layout to be detected where attention should be paid to whether there are defects, which may be set based on the user's actual defect detection experience or production process limitations. The defect detection areas may be, for example, pattern areas, critical dimension areas, periodic graphic areas, etc. Detection of the defect detection areas is crucial to ensuring the accuracy of lithography simulation defect detection and improving lithography yield.

[0071] Taking the pattern area as an example, in lithography simulation defect detection, the pattern area often directly affects the generation of lithography hot spots, so it can be used as the focus of layout defect detection. The critical dimension area is more conducive to paying attention to whether there are defects such as etching residues, grooves and bridges in the layout, so as to facilitate more efficient OPC correction of the layout.

[0072] The shape of the defect detection area can be, for example, a rectangle, a circle or other geometric shapes. Accordingly, the boundary information of the defect detection area is parameter information that characterizes the location of the defect detection area. For example, taking the defect detection area as a rectangle as an example, its boundary information can be the vertex coordinates of the rectangular area. Alternatively, taking the defect detection area as a circle as an example, its boundary information can be the midpoint coordinates of the circular area and the radius length information.

[0073] It should be added that, in order to facilitate improving the defect detection rate, the shape of the above-mentioned defect detection area can be determined as a rectangle in this embodiment, and the boundary information of the above-mentioned defect detection area can specifically be the coordinate parameters of the two vertices of the rectangle in the diagonal direction, such as the coordinate information of the two vertices of the lower left corner and the upper right corner, which is not strictly limited here.

[0074] The position information of the multiple measurement positions of the above-mentioned layout to be inspected can be determined by photographing and processing the layout to be inspected by a measuring machine in advance, by setting up a marking system for multi-layer overlay accuracy measurement, or by calculating according to an empirical model, and no strict restrictions are made here.

[0075] The above-mentioned multiple measurement positions are the key measurement positions of this layout in the lithography process, and can be used to evaluate the alignment accuracy and interlayer offset between lithography layers in subsequent lithography simulation. Therefore, this application can mainly focus on the above-mentioned multiple measurement positions for more efficient defect detection, so that it can be quickly corrected according to the defects around the measurement positions. The above-mentioned measurement position can be a measurement line segment including a measurement start point and a measurement end point, which is not strictly limited here.

[0076] In S220, based on the boundary information of the M defect detection areas and the position information of the multiple measurement positions, N target measurement positions are screened from the multiple measurement positions, where the N target measurement positions are measurement positions located within the M defect detection areas among the multiple measurement positions, and N is a positive integer.

[0077] In a specific implementation, for example, after obtaining the boundary information of the above-mentioned M defect detection areas and the position information of multiple measurement positions, it is possible to traverse and determine which of the above-mentioned multiple measurement positions fall into the defect detection area of ​​the layout to be inspected based on the position information of the multiple measurement positions, so as to finally determine the N target measurement positions in the layout that are located in the above-mentioned M defect detection areas.

[0078] It should be noted that each of the N target measurement positions falls within a defect detection area among the M defect detection areas. Also, each of the M defect detection areas may have at least one target measurement position, or there may be a defect detection area that does not fall within the target measurement position. The specific situation may depend on the actual distribution of measurement positions and the boundary division of the defect detection area, and is not strictly limited here.

[0079] Optionally, in some feasible embodiments of the present application, in combination with the actual layout detection scenario, in order to more reasonably screen the N target measurement positions included in the above-mentioned M defect detection areas, the above-mentioned position information may include the first coordinate values ​​of the start and end points of the measurement position in the first direction, and the second coordinate values ​​in the second direction, and the first direction intersects with the second direction. The above-mentioned N target measurement positions are screened from multiple measurement positions based on the boundary information of the M defect detection areas and the position information of multiple measurement positions, including:

[0080] Based on the boundary information of the M defect detection areas, determining a first coordinate range of the M defect detection areas in the first direction and a second coordinate range in the second direction;

[0081] Compare the first coordinate values ​​of the starting and ending points of the measurement positions of the plurality of measurement positions with the first coordinate ranges of at least some of the defect detection regions in the M defect detection regions, and compare the second coordinate values ​​of the starting and ending points of the measurement positions of the plurality of measurement positions with the second coordinate ranges of at least some of the defect detection regions in the M defect detection regions, to obtain a coordinate comparison result;

[0082] Based on the coordinate comparison result, N target measurement positions are screened out from the multiple measurement positions.

[0083] In this embodiment, in a two-dimensional coordinate system, the first direction may be, for example, a horizontal direction X, and the second direction may be, for example, a vertical direction Y. The boundary information of the M defect detection areas may specifically be coordinate parameters of two vertices of the defect detection areas in a diagonal direction, such as coordinate information of two vertices at the lower left corner and the upper right corner, which is not strictly limited here.

[0084] To facilitate understanding of the boundary information of the defect detection area in this application, please refer to the following Figure 3 , Figure 3 Schematic diagram of the distribution of defect detection areas in the to-be-detected layout provided by an embodiment of the present application. Figure 3 As shown, the physical meanings of PATCH_BOUNDARY1 and PATCH_BOUNDARY2 are defect detection area 1 and defect detection area 2 in the above-mentioned M defect detection areas. Among them, in the XY coordinate system, the boundary information of defect detection area 1 is the coordinates of the lower left corner (1.1, 1.1) and the coordinates of the upper right corner (3, 3). The boundary information of defect detection area 2 is the coordinates of the lower left corner (5.1, 5.1) and the coordinates of the upper right corner (8, 8). It should be supplemented that, in some other embodiments, the boundary information of the above-mentioned defect detection area can also be the coordinate information of the two boundary points of the lower right corner and the upper left corner in the diagonal direction, which is not strictly limited here.

[0085] After understanding the boundary information of the defect detection area in this implementation, based on the boundary information, the M defect detection areas are respectively within a first coordinate range in the first direction and a second coordinate range in the second direction. Figure 3 It can be seen that the first coordinate range of defect detection area 1 in the first direction is [1.1, 3], and the second coordinate range of defect detection area 1 in the second direction is [1.1, 3]. By analogy, the first coordinate range and the second coordinate range of each defect detection area in the above M defect detection areas can be determined.

[0086] Next, by comparing the first coordinate values ​​of the starting and ending points of the measurement positions of the multiple measurement positions on the to-be-detected layout with the first coordinate ranges of different defect detection areas, it is possible to determine whether the measurement position falls within the first coordinate range of a defect detection area among the M defect detection areas. And, by comparing the second coordinate values ​​of the starting and ending points of the measurement positions of the multiple measurement positions on the to-be-detected layout with the second coordinate ranges of different defect detection areas, it is possible to determine whether the measurement position falls within the second coordinate range of a defect detection area among the M defect detection areas.

[0087] In this way, the above-mentioned coordinate comparison result can be determined. This coordinate comparison result may include the defect detection area where the measurement position falls in the first direction and the defect detection area where the measurement position falls in the second direction. Finally, based on the coordinate comparison result, N target measurement positions are screened from multiple measurement positions.

[0088] In this embodiment, by comparing the boundary information of M defect detection areas with the position information of the measurement positions in the layout, the target measurement positions located in the specified area can be screened out, and these target measurement positions are subsequently used for targeted multi-region defect detection. As for the measurement positions beyond the boundary of the defect detection area, this application will not pay attention to them in the future. Therefore, compared with the traditional solution for outputting all defects in the entire layout or a single sub-layout, this embodiment fully realizes the targeted detection of layout defects.

[0089] Optionally, in a more specific embodiment, in order to fully ensure that the N target measurement positions obtained by the above screening are the measurement positions included in the above M defect detection areas, the above measurement position start and end points include a measurement start point and a measurement end point, and the position information includes a first coordinate value and a second coordinate value of the measurement start point, and a first coordinate value and a second coordinate value of the measurement end point; the above-mentioned N target measurement positions are obtained by screening from multiple measurement positions based on the coordinate comparison result, including:

[0090] Determine a measurement position in which the first coordinate values ​​of the measurement start point and the measurement end point are both within the first coordinate range of the target defect detection area, and the second coordinate values ​​of the measurement start point and the measurement end point are both within the second coordinate range of the same target defect detection area as a target measurement position, so as to screen and obtain N target measurement positions from the multiple measurement positions;

[0091] The target defect detection area is any defect detection area among the M defect detection areas.

[0092] In this embodiment, if the first coordinate values ​​of the measurement start point and the measurement end point of the measurement position are within the first coordinate range of the target defect detection area, and the second coordinate values ​​of the measurement start point and the measurement end point of the measurement position are within the second coordinate range of the same target defect detection area, it can be proved that the measurement position falls within the above-mentioned target defect detection area, and thus it can be determined as the target measurement position. Similarly, it is judged whether the coordinates of the measurement start point and the measurement end point of the above-mentioned multiple measurement positions meet the above-mentioned falling conditions, so as to finally select N target measurement positions that fall within the above-mentioned M defect detection areas from the multiple measurement positions.

[0093] In one example, Figure 3For example, there are measurement position 1 and measurement position 2 in the layout to be inspected. The coordinates of the measurement starting point of measurement position 1 are (1.5, 1.5), and the coordinates of the measurement end point are (2, 2); the coordinates of the measurement starting point of measurement position 2 are (2, 1), and the coordinates of the measurement end point are (2, 2). The first coordinate values ​​of the measurement starting point and the measurement end point of the measurement position 1 are both located in the first coordinate range of the defect detection area 1 in the first direction, which is [1.1, 3], and the second coordinate values ​​2 of the measurement starting point and the measurement end point are also located in the second coordinate range of the defect detection area 1 in the second direction, which is [1.1, 3]. Therefore, the measurement position 1 falls into the defect detection area 1, and the measurement position 1 is determined as the target measurement position.

[0094] Correspondingly, the determination method of the measurement position 2 is the same as above, and the second coordinate value 1 of the measurement starting point of the measurement position 2 does not fall into the second coordinate range [1.1,3] of the defect detection area 1. Therefore, the measurement position 2 does not fall into the defect detection area 1.

[0095] It should also be noted that in the specific software implementation stage of this embodiment, each of the above-mentioned M defect detection areas corresponds to a process. In this way, through the parallel script setting of multiple processes, the synchronous screening of measurement positions (gauges) located in multiple areas and the determination of the detection areas around the gauges can be realized, thereby facilitating the synchronous LRC detection of multiple areas.

[0096] In S230, during specific implementation, based on the position information of each target measurement position among the N target measurement positions and the preset detection range parameters, N surrounding detection range areas are determined, and the N surrounding detection range areas correspond to the N target measurement positions one by one.

[0097] In this embodiment, the above-mentioned preset detection range parameter may specifically indicate the surrounding area range corresponding to the target measurement position to be detected.

[0098] Exemplarily, the above-mentioned preset detection range parameters may include the shape of the detection range, such as a circle or a rectangle, etc. The above-mentioned preset detection range parameters may also include specific geometric parameter settings of the shape, such as the radius of the circle, the side length of the rectangle, the deflection angle, etc., which are not strictly limited here.

[0099] Optionally, in some feasible embodiments of the present application, in combination with actual layout defect detection scenarios, in order to more quickly and accurately realize the reasonable positioning of the above-mentioned N surrounding detection range areas, so as to subsequently perform targeted detection of layout defects based on the N surrounding detection range areas, the above-mentioned preset detection range parameters include the shape of the detection range and the detection distance extended from the center point; based on the position information of each target measurement position in the N target measurement positions and the preset detection range parameters corresponding to the measurement position, the N surrounding detection range areas are determined, including:

[0100] Based on the N target measurement positions, determine N center points corresponding to the N target measurement positions one by one;

[0101] N surrounding detection range areas are determined based on the position information of each of the N center points, the shape of the detection area, and the detection distance extended from the center point.

[0102] In a specific implementation, taking the coordinate information of the measurement start point and the measurement end point as an example, the center point may be the midpoint between the measurement start point and the measurement end point of the target measurement position. For example, if the measurement start point and the measurement end point are (x1, y1) (x2, y2) respectively, then the center point is ((x1+x2) / 2, (y1+y2) / 2).

[0103] After the N center points are determined, the surrounding detection range areas corresponding to the N center points can be determined according to the shape of the detection range specified in the preset detection range parameters and the detection distance extended from the center point.

[0104] It should be added that the target measurement position in this application can be understood in physical terms as a line segment including a pair of the aforementioned measurement start point and measurement end point. In order to ensure the subsequent accurate and effective detection of defects in the surrounding area of ​​the target measurement position, the above-mentioned detection distance extending from the center point can be much greater than the length of the line segment corresponding to the target measurement position.

[0105] In order to facilitate understanding of the division method of the surrounding detection range area in this application, please refer to the following Figure 4 , Figure 4 : is a schematic diagram of the distribution of the surrounding detection range area provided by an embodiment of the present application. Figure 4 As shown, the shape of the detection range provided in this embodiment is specifically a square (Square), and the detection distance (Length) extending from the center point is 1 / 2 of the side length of the square.

[0106] It should be added that, in addition to Figure 4In addition to the surrounding detection range area shown, the shape of the above detection range can also be, for example, a circle, and the detection distance extending from the center point can be the radius of the circle, which is not strictly limited in the embodiments of the present application.

[0107] In this embodiment, after obtaining N target measurement positions within the region through screening the boundary information of the aforementioned M defect detection regions, the map within a certain range around the N target measurement positions is divided according to the N center points corresponding to the N target measurement positions, thereby obtaining the aforementioned N surrounding detection range areas, so that targeted multi-region defect detection can be performed based on the surrounding areas around the target measurement positions in the future. For positions outside the aforementioned N surrounding detection range areas, even if they are located in the defect detection area, they will not be detected.

[0108] Compared with the traditional solution of full-area defect detection and defect output within the regional boundary, this embodiment focuses the defect detection on the target measurement position that is more sensitive to the layout pattern segmentation and more representative. In addition, in order to fully guarantee the accurate detection and output of defects, this embodiment pre-sets the above-mentioned preset detection range parameters, and takes the center point of the target measurement position as the center to circle the corresponding surrounding detection range area for subsequent actual defect detection, so that it can achieve fast and accurate positioning and output of defects within a certain range around the target measurement position.

[0109] Optionally, in some feasible embodiments of the present application, in combination with the aforementioned solution, if there are some target measurement positions that are located exactly at the edge of the defect detection area, then it is possible that the range area selected with the center point of the target measurement position as the center will exceed the above-mentioned defect detection area.

[0110] Based on this, in order to fully meet the user's targeted detection needs within the specified defect detection area, the above-mentioned N surrounding detection range areas are determined based on the position information of each target measurement position in the N target measurement positions and the preset detection range parameters corresponding to the measurement position, including:

[0111] Based on the position information of each target measurement position in the N target measurement positions, the preset detection range parameters and the boundary information of at least part of the defect detection areas in the M defect detection areas, N surrounding detection range areas are determined.

[0112] In this embodiment, in the process of determining the above-mentioned N surrounding detection range areas, the boundary information of M defect detection areas will be further introduced to fully ensure that the final N surrounding detection range areas are all located within the M defect detection areas, avoiding redundant detection of the range outside the defect detection area, thereby effectively improving the efficiency and specificity of subsequent defect detection.

[0113] More specifically, in order to more fully and reasonably realize the user's targeted detection in the designated defect detection area, so as to quickly and accurately output defects in a certain area around the target measurement position in the defect detection area, the above-mentioned determination of N surrounding detection range areas based on the position information of each target measurement position in the N target measurement positions, the preset detection range parameters and the boundary information of at least part of the defect detection areas in the M defect detection areas includes:

[0114] Determine a candidate detection range area corresponding to the first target measurement position based on the position information of the first target measurement position and the preset detection range parameters, where the first target measurement position is any target measurement position among the N target measurement positions;

[0115] Compare the candidate detection range area with the first defect detection area where the first target measurement position is located among the M defect detection areas to obtain an area comparison result;

[0116] When the area comparison result indicates that the candidate detection range area exceeds the first defect detection area, the candidate detection range area is cut based on the boundary information of the first defect detection area to obtain the surrounding detection range area corresponding to the first target measurement position;

[0117] When the area comparison result indicates that the candidate detection range areas are all within the first defect detection area, the candidate detection range areas are determined as the surrounding detection range areas corresponding to the first target measurement position.

[0118] In the specific implementation, the first target measurement position among the N target measurement positions is taken as an example for explanation, and the defect detection area where the first target measurement position is located is the first defect detection area. First, according to the position information of the first target measurement position and the preset detection range parameters, the corresponding candidate detection range area is determined. The candidate detection range area is compared with the first defect detection area to determine whether the candidate detection range area exceeds the boundary of the first defect detection area.

[0119] If it is determined that the candidate detection range area exceeds the boundary of the first defect detection area, the candidate detection range area is cut according to the first defect detection area, and only the part of the candidate detection range area located inside the first defect detection area is retained and used as the surrounding detection range area corresponding to the first target measurement position.

[0120] On the contrary, if it is determined that the candidate detection range areas are all located within the first defect detection area, the candidate detection range areas are directly used as the surrounding detection range areas corresponding to the first target measurement position.

[0121] It should be added that, in this embodiment, in order to improve the efficiency of determining the surrounding detection range area corresponding to the above-mentioned target measurement position, the above-mentioned first target measurement position can be a target measurement position whose distance from the boundary of the first defect detection area is less than a preset distance, and the above-mentioned boundary range comparison may not be performed for the measurement position located in the center range of the first defect detection area. In this way, the overall defect detection efficiency of the layout is improved.

[0122] In S240, defect detection is performed on the target detection layer through the detection function to obtain a layout defect detection result of the layout to be detected, and the target detection layer is determined based on N surrounding detection range areas.

[0123] In specific implementation, in lithography simulation defect detection, the detection function can be used to detect pattern edges in the layout, critical dimension changes, etc. The above detection function can be written based on deep learning detection algorithms, machine learning algorithms, or geometric verification algorithms, etc., and can be determined based on defect detection experience in actual scenarios and the differences in detection effects between different methods.

[0124] The target detection layer may specifically be a detection layer including the N surrounding detection range areas. By using the detection function to detect the N surrounding detection range areas in the target detection layer, defect detection can be performed only on the focused areas, thereby outputting the final layout defect detection result.

[0125] It should be added that, in this embodiment, when outputting the above-mentioned layout defect detection results, only the defect results corresponding to the surrounding detection range areas where defects exist or the defects are more serious can be output. For example, the above-mentioned N surrounding detection range areas include area 1, area 2 and area 3. If the defects in area 3 do not affect the final yield of the product, or no defects are detected in area 3, only the defects in areas 1 and 2 can be output to reduce interference with engineers in data analysis and defect resolution.

[0126] Optionally, in some feasible embodiments of the present application, in order to fully improve the accuracy and efficiency of lithography simulation defect detection, in combination with an actual semiconductor defect detection method, before performing defect detection on the target detection layer through the detection function to obtain a layout defect detection result of the layout to be detected, the layout defect detection method further includes:

[0127] Generate a target detection layer based on the detection range parameters corresponding to the N surrounding detection range areas;

[0128] The target detection layer is subjected to defect detection through the detection function to obtain the layout defect detection result of the layout to be detected, including:

[0129] The target detection layer is added to the region of interest, and the defect detection is performed on the region of interest through the detection function to obtain the layout defect detection result.

[0130] In a specific implementation, the detection range parameters corresponding to N surrounding detection range areas are packaged and integrated into the above-mentioned target detection layer, and the target detection layer is the detection range that needs to be detected for defects in the finalized layout.

[0131] Then, the target detection layer is added to the care area. The care area is an area of ​​special concern and key monitoring in semiconductor defect detection, which has an important impact on the performance and yield of semiconductor devices. In this embodiment, after the target detection layer is added to the care area, the care area is defect detected by the detection function to obtain the final layout defect detection result.

[0132] It should be supplemented that the above-mentioned method of adding the target detection layer to the focus region may be, for example, assigning the target detection layer to the focus region.

[0133] In this embodiment, defect detection is performed by setting the above-mentioned target detection layer and importing and adding the target detection layer to the area of ​​interest, which helps to improve the accuracy and efficiency of lithography simulation defect detection and ensure the quality and reliability of the semiconductor manufacturing process.

[0134] In order to facilitate understanding of the layout defect detection method provided in the above embodiment, the above method is described below using a specific scenario embodiment. Figure 5 A schematic diagram of a scenario flow of a layout defect detection method provided in one embodiment of the present application.

[0135] In the introduction Figure 5 Before that, the application scenario and parameter configuration of this scenario embodiment are introduced. In this scenario embodiment, the division of the LRC detection area, the reading of the gauge (measurement position) and the setting of the detection range around the gauge are realized by introducing the following multiple parameters. Therefore, in this solution, the corresponding parameter values ​​need to be configured in the script to realize the LRC defect detection of the area around the selected gauge. Among them, in the field of lithography simulation defect detection, the gauge, as a measurement position, may involve the measurement of the size, shape or other key parameters of a specific position on the layout to ensure the accuracy and quality control during the layout design and manufacturing process.

[0136] In the actual early code stage, there are 25 interruption parameters in this technical solution. For details, please refer to Table 1, which is a parameter configuration table provided by this scenario embodiment. Among them, the parameter names are shown in Table 1 below:

[0137] Table 1

[0138]

[0139] The detailed meaning and usage of the LRC detection setting parameters in Table 1 are as follows:

[0140] The first part is global parameters.

[0141] The global parameters are used to read the global variables required for the subsequent LRC detection script, where INPUT_GDS is the layout to be detected; GAUGE is used to receive the gauge that needs to be used for LRC defect detection in the layout to be detected. In physical terms, the gauge can be understood as a line segment containing a pair of start and end points (the aforementioned measurement start and end points). The coordinate information corresponding to the measurement start and end points of the gauge and the corresponding polygon should be located in INPUT_GDS. The variable format is a string read by the np.loadtxt function, for example:

[0142] GAUGE=np.loadtxt('. / A.txt').

[0143] And, the physical meaning of PATCH_BOUNDARY1 and PATCH_BOUNDARY2 is the defect detection area to be detected, and the format is a list of four floating point numbers, representing the coordinates of the two boundary points of the lower left corner and the upper right corner, for example:

[0144] PATCH_BOUNDARY1=[1.1,1.1,3,3];

[0145] PATCH_BOUNDARY2=[5.1,5.1,8,8].

[0146] CHIP_BOUNDARY is numerically equal to PATCH_BOUNDARY, that is, PATCH_BOUNDARY is assigned to CHIP_BOUNDARY, for example:

[0147] CHIP_BOUNDARY1=PATCH_BOUNDARY1

[0148] CHIP_BOUNDARY2=PATCH_BOUNDARY2

[0149] It should be added that this scenario embodiment takes the LRC defect detection of two defect detection areas at the same time as an example, but in some other embodiments, if necessary, the detection range and corresponding parameters can be further increased, such as PATCH_BOUNDARY3..., and no strict restrictions are made here.

[0150] In this scenario, each PATCH_BOUNDARY corresponds to a process, in which the gauges in the current defect detection area are screened and the LRC defect detection is performed on the area around the internal gauges. In this way, the synchronous LRC detection of multiple areas is achieved through the script setting of multiple processes in parallel.

[0151] The second part is the preprocessing parameters.

[0152] Preprocessing parameters are used to receive global variables and define variables required by the process.

[0153] Each PATCH_BOUNDARY corresponds to a process, and gauge is used to receive the global variable GAUGE to this process, and the expression is: gauge = GAUGE.

[0154] gauge_num uses the len function to calculate the total amount of gauge read for subsequent loops. The format is: gauge.num = len(gauge[:,0]).

[0155] polygon_property is used to define a new empty data frame dataframe, which is used to store the newly generated detection range parameters. Its format is: polygon_property = [].

[0156] The third part, coordinates.

[0157] In this scenario, the position information of each gauge in the current layout to be inspected and the boundary information of the CHIP_BOUNDARY defect inspection area are obtained in advance. The coordinates x and y are the center point coordinates of the gauge, respectively, and the format is:

[0158] x=float(gauge[i,0]); y=float(gauge[i,1])

[0159] x1, y1, x2, y2 are used to read the four floating point numbers in CHIP_BOUNDARY, representing the coordinates of the two boundary points in the lower left corner and upper right corner of the detection area. The format is:

[0160] x1=CHIP_BOUNDARY[0]; y1=CHIP_BOUNDARY[1];

[0161] x2=CHIP_BOUNDARY[2]; y2=CHIP_BOUNDARY[3].

[0162] lx, rx, by, and ty are the minimum and maximum values ​​of x and y respectively, and the format is:

[0163] lx = min([x1, x2]) * 1000; rx = max([x1, x2]) * 1000;

[0164] by = min([y1, y2]) * 1000; ty = max([y1, y2]) * 1000.

[0165] It should be added that the function of multiplying by 1000 in the above example is for unit conversion. Since the coordinate unit of boundary is micrometers, it needs to be converted to nanometers.

[0166] Based on the above coordinate information and the boundary range of the defect detection area, the read gauges can be traversed one by one using a for loop. When the coordinates of the measurement start point and the measurement end point of the gauge simultaneously satisfy lx < x < rx and by < y < ty, it can be proved that the currently traversed gauge is within this defect detection area, and subsequent LRC defect detection can be performed on the surrounding area of the gauge.

[0167] Part Four, Detection Range.

[0168] First, create a polygon set, which is used to store the detection range parameters corresponding to the surrounding detection range area applied to the current gauge, and its format is: Polygon1 = {}.

[0169] The shape, center, and length in the polygon respectively represent the shape, center point of the detection area, and the detection distance extended from the center point, and the format is:

[0170] polygon1['shape'] = 'SQUARE'; polygon1['center'] = [x, y];

[0171] polygon1['length'] = XXX.

[0172] Among them, specifically, the x and y coordinates of the center point of the gauge can be used as the coordinates of the center point of the surrounding detection range area, that is, in the foregoing embodiment, the midpoint coordinates between the start and end points of the measurement position of the target measurement position are used as the center point to determine the surrounding detection range area.

[0173] The detection range parameters corresponding to the above-mentioned surrounding detection range area will be written into the preprocessing parameter polygon_property through the append function, and defined as layer_check_sbar through pu.generate_polygon, so as to facilitate the subsequent call of care_area. The format is: layer_check_sbar=pu.generate_polygon(polygon_property).

[0174] In this way, when performing specific LRC defect detection later, when a defect occurs within the detection range of the guage determined by the length, the defect output within the range will be performed. For locations outside the detection range, even if they are in the boundary (defect detection area), no detection will be performed.

[0175] Part 5: Focus Area.

[0176] First, define care_area: care_area = pu.CareArea().

[0177] Then, use add_include_region to apply layer_check_sbar to care_area:

[0178] care_area.add_include_region(layer_check_sbar).

[0179] In this way, the detection layer layer_eval is generated based on the detection range parameters corresponding to the surrounding detection range area of ​​the determined gauge, and then layer_eval is added to the focus area. Next, the detection function pu.execute_detector can be used to read the layer_eval added in the focus area to realize LRC defect detection of the surrounding area of ​​the gauge in the boundary.

[0180] After introducing the above parameter configuration and parameter functions in this scenario embodiment, please refer to Figure 5 The specific defect detection process of this scenario embodiment is as follows:

[0181] Step 1: Set global parameters, read GAUGE and INPUT_GDS required for LRC detection, and set different boundary areas through PATCH_BOUNDARY1, CHIP_BOUNDARY1, PATCH_BOUNDARY2, and CHIP_BOUNDARY2. Each boundary area corresponds to a process. If and only if the gauge in the layout is in the boundary, the area around the gauge will be tested for LRC defects later.

[0182] Step 2: Set the process parameter gauge to receive the global parameter GAUGE, as well as gauge_number for the for loop and polygon_property for storing the detection range parameter.

[0183] Step 3: Use a for loop to traverse the gauges one by one and assign the coordinates of the gauge center point to x and y. At the same time, use x1, y1, x2, and y2 to receive the boundary coordinates, and after sorting by lx, rx, by, and ty, determine whether the gauge is within the current boundary range.

[0184] Step 4: For the gauge that falls within the boundary range after step 3, determine the surrounding detection range area corresponding to the gauge according to its coordinates. The detection range parameters corresponding to the surrounding detection range area corresponding to the above gauge are determined by polygon1['shape'], polygon1['center'], and polygon1['length'] representing the detection area shape, center point coordinates (gauge coordinates) and detection area range, respectively, and the detection range parameters corresponding to the surrounding detection range area are defined as layer_check_sbar.

[0185] Step 5: Generate a target detection layer based on layer_check_sbar defined in step 4, and then add the target detection layer to the care_area area. By using the detection function to call the target detection layer added to the care_area area, the defects in the focus area of ​​the layout can be quickly and accurately output.

[0186] In this scenario embodiment, on the basis of the existing LRC detection based on the whole map or a single boundary area, a new multi-area detection technology is introduced in LRC by modifying and adding script code. Multiple detection areas are set by configuring several parameters and compared with the read gauge coordinate parameters. When the gauge coordinates are in the detection area, LRC detection is performed on the layout within a certain range around it. In this way, the defects within a certain range around the gauge in multiple detection areas of focus in the layout are quickly and accurately located, which can realize highly targeted defect detection more quickly and conveniently.

[0187] Compared with the traditional detection method, if you want to realize multi-boundary detection, you need to reconfigure the solution after the LRC script determined by a boundary is completed. This application can realize LRC defect detection within multiple boundaries in one script by configuring multiple boundary parameters, thereby improving detection efficiency.

[0188] Moreover, in traditional detection methods, whether it is full-image detection or sub-region detection by setting a boundary, the results are all output for all defects within the boundary range, which lacks specificity. However, this application can read the gauge coordinates and compare them with the boundary range, and define the corresponding surrounding detection range area based on the gauge. Subsequently, it can focus on defect detection in a certain range around the gauge in multiple detection areas of the layout, thereby achieving more accurate output of defects, and making the layout defect detection results more targeted while improving the layout defect detection speed.

[0189] Based on the layout defect detection method provided in the above embodiment, for the same inventive concept, the present application also provides a layout defect detection device corresponding to the above layout defect detection method. Figure 6 A detailed introduction to the layout defect detection device is given.

[0190] Figure 6 A schematic diagram of the structure of a layout defect detection device provided in one embodiment of the present application is shown.

[0191] Figure 6 The layout defect detection apparatus 600 shown includes:

[0192] A first acquisition module 610 is used to acquire boundary information of M defect detection areas in the to-be-detected layout and position information of multiple measurement positions in the to-be-detected layout, where M is a positive integer;

[0193] A first screening module 620 is used to screen N target measurement positions from the multiple measurement positions based on the boundary information of the M defect detection areas and the position information of the multiple measurement positions, where the N target measurement positions are measurement positions located in the M defect detection areas among the multiple measurement positions, and N is a positive integer;

[0194] A first determination module 630 is used to determine N surrounding detection range areas based on the position information of each target measurement position in the N target measurement positions and the preset detection range parameters, and the N surrounding detection range areas correspond to the N target measurement positions one by one;

[0195] The first detection module 640 is used to perform defect detection on the target detection layer through the detection function to obtain the layout defect detection result of the layout to be detected. The target detection layer is determined based on N surrounding detection range areas.

[0196] A layout defect detection device provided by an embodiment of the present application, by setting corresponding functional modules, obtains the boundary information of multiple defect detection areas that are focused on in the layout to be detected, and then selects the target measurement position located in the above-mentioned defect detection area from multiple measurement positions of the layout according to the boundary information, so that the multi-region detection requirements of the layout can be met more flexibly and efficiently based on the target measurement position. Further, in order to ensure the accuracy of the defect detection results, the embodiment of the present application determines the peripheral detection range of each target measurement position by setting a preset detection range parameter, so as to obtain the surrounding detection range area corresponding to each measurement position in a targeted manner. Finally, by using the detection function to quickly and accurately locate the defects within the peripheral detection range of the target measurement position, it is possible to achieve accurate output of the defects in the focus area of ​​the layout. A layout defect detection method of an embodiment of the present application can meet the multi-region detection requirements of the layout more flexibly and efficiently, so as to fully improve the LRC detection rate of the overall layout and improve user satisfaction, and can make the layout defect detection results more targeted while improving the layout defect detection speed.

[0197] Optionally, in some feasible embodiments of the present application, the position information includes first coordinate values ​​of the start and end points of the measurement position in the first direction, and second coordinate values ​​in the second direction, and the first direction intersects the second direction; the first screening module 620, based on the boundary information of the M defect detection areas and the position information of the multiple measurement positions, screens N target measurement positions from the multiple measurement positions, including:

[0198] The first determination submodule may be used to determine, based on boundary information of the M defect detection areas, a first coordinate range of the M defect detection areas in the first direction and a second coordinate range in the second direction;

[0199] The first comparison submodule may be used to compare the first coordinate values ​​of the starting and ending points of the measurement positions of the multiple measurement positions with the first coordinate ranges of at least some of the defect detection regions in the M defect detection regions, and to compare the second coordinate values ​​of the starting and ending points of the measurement positions of the multiple measurement positions with the second coordinate ranges of at least some of the defect detection regions in the M defect detection regions, to obtain a coordinate comparison result;

[0200] The first screening submodule can be used to screen and obtain N target measurement positions from multiple measurement positions based on the coordinate comparison result.

[0201] Optionally, in some feasible embodiments of the present application, the starting and ending points of the measurement position include a measurement starting point and a measurement end point, and the position information includes a first coordinate value and a second coordinate value of the measurement starting point, and a first coordinate value and a second coordinate value of the measurement end point; the first screening submodule, based on the coordinate comparison result, screens N target measurement positions from multiple measurement positions, including:

[0202] Determine a measurement position in which the first coordinate values ​​of the measurement start point and the measurement end point are both within the first coordinate range of the target defect detection area, and the second coordinate values ​​of the measurement start point and the measurement end point are both within the second coordinate range of the same target defect detection area as a target measurement position, so as to screen and obtain N target measurement positions from the multiple measurement positions;

[0203] The target defect detection area is any defect detection area among the M defect detection areas.

[0204] Optionally, in some feasible embodiments of the present application, the preset detection range parameters include the shape of the detection range and the detection distance extended from the center point; the first determination module 630, based on the position information of each target measurement position in the N target measurement positions and the preset detection range parameters corresponding to the measurement position, determines N surrounding detection range areas, including:

[0205] The second determination submodule may be used to determine N center points corresponding to the N target measurement positions one by one based on the N target measurement positions;

[0206] The third determination submodule can be used to determine N surrounding detection range areas according to the position information of each center point among the N center points, the shape of the detection area, and the detection distance extended from the center point.

[0207] Optionally, in some feasible embodiments of the present application, the first determination module 630 determines N surrounding detection range areas based on the position information of each target measurement position in the N target measurement positions and the preset detection range parameters corresponding to the measurement position, including:

[0208] Based on the position information of each target measurement position in the N target measurement positions, the preset detection range parameters and the boundary information of at least part of the defect detection areas in the M defect detection areas, N surrounding detection range areas are determined.

[0209] Optionally, in some feasible embodiments of the present application, the above-mentioned determination of N surrounding detection range areas based on the position information of each target measurement position in the N target measurement positions, the preset detection range parameters and the boundary information of at least part of the defect detection areas in the M defect detection areas includes:

[0210] A fourth determination submodule may be used to determine a candidate detection range area corresponding to the first target measurement position based on the position information of the first target measurement position and the preset detection range parameter, wherein the first target measurement position is any target measurement position among the N target measurement positions;

[0211] The second comparison submodule may be used to compare the candidate detection range area with the first defect detection area where the first target measurement position is located among the M defect detection areas to obtain an area comparison result;

[0212] The first processing submodule may be used to cut the candidate detection range area based on the boundary information of the first defect detection area to obtain the surrounding detection range area corresponding to the first target measurement position when the area comparison result indicates that the candidate detection range area exceeds the first defect detection area;

[0213] The fifth determination submodule may be used to determine the candidate detection range area as the surrounding detection range area corresponding to the first target measurement position when the area comparison result indicates that the candidate detection range areas are all within the first defect detection area.

[0214] Optionally, in some feasible embodiments of the present application, before performing defect detection on the target detection layer by using the detection function to obtain a layout defect detection result of the layout to be detected, the layout defect detection device further includes:

[0215] A first setting module may be used to pre-set an initial detection layer, wherein the initial detection layer includes a pre-defined area of ​​interest;

[0216] The target detection layer is subjected to defect detection through the detection function to obtain the layout defect detection result of the layout to be detected, including:

[0217] The target detection layer is added to the region of interest, and the defect detection is performed on the region of interest through the detection function to obtain the layout defect detection result.

[0218] Based on the layout defect detection method provided in the above embodiment, for the same inventive concept, the present application also provides a layout defect detection device corresponding to the above layout defect detection method. Figure 7 A detailed introduction to layout defect detection equipment is given.

[0219] See below Figure 7 , Figure 7 It is a structural schematic diagram of a layout defect detection device provided in one embodiment of the present application.

[0220] The layout defect detection device may include a processor 701 and a memory 702 storing computer program instructions.

[0221] Specifically, the processor 701 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0222] The memory 702 may include a large capacity memory for data or instructions. By way of example and not limitation, the memory 702 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive or a combination of two or more of these. In appropriate cases, the memory 702 may include a removable or non-removable (or fixed) medium. In appropriate cases, the memory 702 may be inside or outside the integrated gateway disaster recovery device. In a specific embodiment, the memory 702 is a non-volatile solid-state memory.

[0223] The memory may include read-only memory (ROM), random access memory (RAM), magnetic disk storage media devices, optical storage media devices, flash memory devices, electrical, optical or other physical / tangible memory storage devices. Thus, typically, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to an aspect of the present disclosure.

[0224] The processor 701 implements any one of the layout defect detection methods in the above embodiments by reading and executing computer program instructions stored in the memory 702 .

[0225] In one example, the data layout defect detection device may further include a communication interface 703 and a bus 710. Figure 7 As shown, the processor 701, the memory 702, and the communication interface 703 are connected via a bus 710 and communicate with each other.

[0226] The communication interface 703 is mainly used to implement communication between various modules, devices, units and / or equipment in the embodiments of the present application.

[0227] Bus 710 includes hardware, software or both, and couples the components of the layout defect detection device to each other. For example, but not limitation, the bus may include an accelerated graphics port (AGP) or other graphics bus, an enhanced industrial standard architecture (EISA) bus, a front-end bus (FSB), a hypertransport (HT) interconnect, an industrial 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 standard association local (VLB) bus or other suitable bus or a combination of two or more of these. Where appropriate, bus 710 may include one or more buses. Although the present application embodiment describes and illustrates a specific bus, the present application considers any suitable bus or interconnect.

[0228] The layout defect detection device executes the layout defect detection method in the embodiment of the present application, thereby realizing the layout defect detection method described in the embodiment of the present application.

[0229] In addition, in combination with the layout defect detection method in the above embodiment, the embodiment of the present application can provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when the computer program instructions are executed by a processor, any one of the layout defect detection methods in the above embodiment is implemented.

[0230] Based on the layout defect detection method in the above-mentioned embodiment, an embodiment of the present application provides a computer program product. When the instructions in the computer program product are executed by a processor of an electronic device, the electronic device executes the layout defect detection method provided in any one of the above-mentioned embodiments of the present application.

[0231] It should be clear that the present application is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present application is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present application.

[0232] The functional blocks shown in the above-described block diagram can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present application are programs or code segments that are used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link by a data signal carried in 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, optical fiber media, radio frequency (RF) links, etc. The code segment can be downloaded via a computer network such as the Internet, an intranet, etc.

[0233] It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. 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 embodiment, or in a different order from the embodiment, or several steps can be performed simultaneously.

[0234] Aspects of the present disclosure are described above with reference to the flowchart and / or block diagram of the method, device (system) and computer program product according to the embodiment of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram 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 device to produce a machine so that these instructions executed by the processor of the computer or other programmable data processing device enable the implementation of the function / action specified in one or more boxes of the flowchart and / or block diagram. 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 can also be understood that each box in the block diagram and / or flowchart and the combination of boxes in the block diagram and / or flowchart can also be implemented by dedicated hardware that performs a specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions.

[0235] The above is only a specific implementation of the present application. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. It should be understood that the protection scope of the present application is not limited to this. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in this application, and these modifications or replacements should be included in the protection scope of this application.

Claims

1. A layout defect detection method, characterized in that: The method comprises: Obtain boundary information of M defect detection areas in the to-be-detected layout and position information of multiple measurement positions of the to-be-detected layout, where M is a positive integer; Based on the boundary information of the M defect detection areas and the position information of the multiple measurement positions, N target measurement positions are obtained by screening from the multiple measurement positions, where the N target measurement positions are measurement positions located within the M defect detection areas among the multiple measurement positions, and N is a positive integer; Based on the position information of each target measurement position in the N target measurement positions and the preset detection range parameters, N surrounding detection range areas are determined, and the N surrounding detection range areas correspond to the N target measurement positions one by one; Defect detection is performed on the target detection layer through the detection function to obtain the layout defect detection result of the layout to be detected, and the target detection layer is determined based on the N surrounding detection range areas.

2. The method according to claim 1, characterized in that The position information includes first coordinate values ​​of the start and end points of the measurement position in a first direction, and second coordinate values ​​in a second direction, and the first direction intersects with the second direction; the screening of N target measurement positions from the multiple measurement positions based on the boundary information of the M defect detection areas and the position information of the multiple measurement positions includes: Based on the boundary information of the M defect detection areas, determining first coordinate ranges of the M defect detection areas in the first direction and second coordinate ranges in the second direction respectively; Comparing first coordinate values ​​of the starting and ending points of the multiple measurement positions with first coordinate ranges of at least some of the M defect detection areas, and comparing second coordinate values ​​of the starting and ending points of the multiple measurement positions with second coordinate ranges of at least some of the M defect detection areas, to obtain coordinate comparison results; Based on the coordinate comparison result, the N target measurement positions are obtained by screening from the multiple measurement positions.

3. The method according to claim 2, characterized in that The measurement position start and end points include a measurement start point and a measurement end point, the position information includes a first coordinate value and a second coordinate value of the measurement start point, and a first coordinate value and a second coordinate value of the measurement end point; the N target measurement positions are obtained by screening from the multiple measurement positions based on the coordinate comparison result, including: Determine, as the target measurement position, a measurement position in which the first coordinate values ​​of the measurement starting point and the measurement end point are both within a first coordinate range of a target defect detection region, and the second coordinate values ​​of the measurement starting point and the measurement end point are both within a second coordinate range of the same target defect detection region, so as to screen the N target measurement positions from the multiple measurement positions; Wherein, the target defect detection area is any defect detection area among the M defect detection areas.

4. The method according to claim 1, characterized in that The preset detection range parameters include the shape of the detection range and the detection distance extended from the center point; the N surrounding detection range areas are determined based on the position information of each target measurement position in the N target measurement positions and the preset detection range parameters corresponding to the measurement position, including: Based on the N target measurement positions, determining N center points corresponding to the N target measurement positions one by one; The N surrounding detection range areas are determined according to the position information of each center point of the N center points, the shape of the detection area, and the detection distance extended from the center point.

5. The method according to claim 1, characterized in that The determining of N surrounding detection range areas based on the position information of each target measurement position in the N target measurement positions and the preset detection range parameters corresponding to the measurement positions includes: The N surrounding detection range areas are determined based on the position information of each target measurement position in the N target measurement positions, the preset detection range parameters, and the boundary information of at least part of the defect detection areas in the M defect detection areas.

6. The method according to claim 5, characterized in that The determining of the N surrounding detection range areas based on the position information of each target measurement position in the N target measurement positions, the preset detection range parameters, and the boundary information of at least part of the defect detection areas in the M defect detection areas includes: Determine a candidate detection range area corresponding to the first target measurement position based on the position information of the first target measurement position and the preset detection range parameter, wherein the first target measurement position is any target measurement position among the N target measurement positions; Compare the candidate detection range area with the first defect detection area where the first target measurement position is located among the M defect detection areas to obtain an area comparison result; When the area comparison result indicates that the candidate detection range area exceeds the first defect detection area, the candidate detection range area is cut based on the boundary information of the first defect detection area to obtain the surrounding detection range area corresponding to the first target measurement position; When the area comparison result indicates that the candidate detection range areas are all within the first defect detection area, the candidate detection range areas are determined as the surrounding detection range areas corresponding to the first target measurement position.

7. The method according to claim 1, characterized in that Before performing defect detection on the target detection layer by using the detection function to obtain the layout defect detection result of the layout to be detected, the method further includes: Generating the target detection layer based on the detection range parameters corresponding to the N surrounding detection range areas; The method of performing defect detection on the target detection layer by using the detection function to obtain the layout defect detection result of the layout to be detected includes: The target detection layer is added to the focus area, and defect detection is performed on the focus area using the detection function to obtain the layout defect detection result.

8. A layout defect detection device, characterized in that: The device comprises: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, the layout defect detection method as described in any one of claims 1-7 is implemented.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer program instructions, and when the computer program instructions are executed by a processor, the layout defect detection method according to any one of claims 1 to 7 is implemented.

10. A computer program product, characterized in that When the instructions in the computer program product are executed by a processor of an electronic device, the electronic device executes the layout defect detection method as described in any one of claims 1-7.

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