CN117392197A - Block array selection method, system, computer equipment and storage medium - Google Patents
Block array selection method, system, computer equipment and storage medium Download PDFInfo
- Publication number
- CN117392197A CN117392197A CN202311184919.6A CN202311184919A CN117392197A CN 117392197 A CN117392197 A CN 117392197A CN 202311184919 A CN202311184919 A CN 202311184919A CN 117392197 A CN117392197 A CN 117392197A
- Authority
- CN
- China
- Prior art keywords
- square
- array
- patterns
- row
- graph
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000010187 selection method Methods 0.000 title claims abstract description 46
- 238000003860 storage Methods 0.000 title claims abstract description 17
- 238000000034 method Methods 0.000 claims description 35
- 238000004590 computer program Methods 0.000 claims description 14
- 238000012545 processing Methods 0.000 description 16
- 230000009286 beneficial effect Effects 0.000 description 8
- 230000008569 process Effects 0.000 description 8
- 238000010586 diagram Methods 0.000 description 6
- 238000005457 optimization Methods 0.000 description 5
- 238000004458 analytical method Methods 0.000 description 4
- 230000006870 function Effects 0.000 description 4
- 230000000694 effects Effects 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- XUIMIQQOPSSXEZ-UHFFFAOYSA-N Silicon Chemical compound [Si] XUIMIQQOPSSXEZ-UHFFFAOYSA-N 0.000 description 2
- 238000000206 photolithography Methods 0.000 description 2
- 229910052710 silicon Inorganic materials 0.000 description 2
- 239000010703 silicon Substances 0.000 description 2
- 241000153282 Theope Species 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/60—Analysis of geometric attributes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T11/00—2D [Two Dimensional] image generation
- G06T11/60—Editing figures and text; Combining figures or text
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Mathematical Optimization (AREA)
- Mathematical Analysis (AREA)
- Mathematical Physics (AREA)
- Pure & Applied Mathematics (AREA)
- Computational Mathematics (AREA)
- Geometry (AREA)
- Evolutionary Biology (AREA)
- Life Sciences & Earth Sciences (AREA)
- Image Analysis (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Probability & Statistics with Applications (AREA)
- Algebra (AREA)
- Operations Research (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
Abstract
The invention relates to the technical field of integrated circuits, in particular to a block array selection method, a system, computer equipment and a storage medium, which comprises the following steps: step S1: obtaining a square array and a square graph; step S2: obtaining a first auxiliary graph; step S3: adjusting the first auxiliary graph to obtain a second auxiliary graph overlapped with two rows of edges in the square array, and selecting each row of square graph based on the second auxiliary graph; step S4: adjusting the first auxiliary graph to obtain a third auxiliary graph overlapped with the square array, and selecting each row of square graph based on the third auxiliary graph; step S5: selecting and obtaining a square graph of each row and each column; compared with the traditional square array selection method which can not consider symmetrical square patterns, the square patterns of each row and each column can be accurately selected by utilizing the fact that two opposite sides of the auxiliary pattern are respectively overlapped with the edges of the square patterns.
Description
[ field of technology ]
The present invention relates to the field of integrated circuits, and in particular, to a method, a system, a computer device, and a storage medium for selecting a square array.
[ background Art ]
Photolithography is the most important process step in modern very large scale integrated circuit fabrication, and the design pattern of the integrated circuit on the mask is actually transferred to the silicon wafer by a photolithography machine. With the continuous evolution of technology nodes of integrated circuits, optical proximity effect (Optical Proximity Effect) is easily generated when exposure process is performed on high-density arranged mask patterns to transfer the patterns, and imaging of the mask on a silicon wafer will generate distortion and distortion.
In order to correct the OPE phenomenon, a computer system is generally used in the industry to perform OPC (Optical Proximity Correction ) processing on a circuit pattern of an integrated circuit in advance, and then to make a mask pattern according to the circuit pattern of the integrated circuit after OPC processing. DRAM (Dynamic Random Access Memory), dynamic random access memory, is a very typical semiconductor product and is commonly used in the system memory of mobile phones and computers. The DRAM adopts an array with row and column indication as a memory cell of a chip, which is the most important part of the DRAM chip, the memory cells are arranged in a matrix form, and the patterns arranged on the integrated circuit layout have the characteristic of high repeatability, wherein one typical layout pattern is a square array.
Before OPC processing is performed on the circuit pattern of the integrated circuit of the DRAM chip, the position of each block pattern on the block array is selected. For the square array, the arrangement of the patterns has symmetry and repeatability, each square pattern (i.e. the memory unit) in the array has a unique position index, and the existing square array selection method screens and selects all square patterns according to specific integration attributes, so that the selection data with high repeatability is easy to obtain, and the accurate selection of any square pattern of two square patterns with the same shape and symmetrical positions in the square array cannot be realized.
[ invention ]
In order to solve the problem that the existing square array selection method cannot accurately select any square graph in a high-repeatability square array, the invention provides a square array selection method, a system, computer equipment and a storage medium.
The invention provides the following technical scheme for solving the technical problems: a block array selection method comprises the following steps: step S1: obtaining a square array, wherein the square array comprises a plurality of square patterns which are arranged in an array manner and are spaced apart; step S2: adjusting all the square patterns to enable gaps among the patterns to be free of gaps, and obtaining a first auxiliary pattern; step S3: adjusting the edges of the first auxiliary graph to obtain a second auxiliary graph with two edges overlapped with two edges in the square array, and selecting each row of square graph in the square array based on the second auxiliary graph and the square array; step S4: adjusting the edges of the first auxiliary graph to obtain a third auxiliary graph with two edges overlapped with two rows of edges in the square array, and selecting each row of square graph in the square array based on the third auxiliary graph and the square array; step S5: based on the selected square patterns of each row and each column, the data characteristics of the square array are obtained through statistics.
Preferably, the step S2 further includes the steps of:
step S21: measuring the distance between the square patterns;
step S22: according to the distance between the square patterns, the size of the edges of the square patterns is adjusted, and gaps between the square patterns are filled by moving the edges of all the square patterns;
step S23: and combining all the square patterns filled in the gaps into a large pattern to obtain a first auxiliary pattern.
Preferably, each edge of the square graph is determined by identifying the coordinate parameters of the four vertices of each square graph.
Preferably, the step S3 further includes the steps of:
step S31: adjusting the opposite sides of the first auxiliary graph to coincide with the first row and the last row of square graphs in the square array to obtain a second auxiliary graph;
step S32: and selecting a first row and a last row of square patterns which are overlapped with two opposite sides of the second auxiliary pattern, and obtaining the data characteristics of the first row and the last row of square patterns.
Preferably, the step S32 further includes the following steps: step S33: judging whether a square graph is unselected; step S34: if yes, removing the first row and the last row of square patterns obtained in the step S32 from the square array to obtain a new square array; and returns to step S2 until each row of square patterns is selected.
Preferably, the step S4 further includes the steps of:
step S41: adjusting the opposite sides of the first auxiliary graph to coincide with the first row and the last row of square graphs in the square array to obtain a third auxiliary graph;
step S42: and selecting a first row square pattern and a last row square pattern which are overlapped with two opposite sides of the third auxiliary pattern, and obtaining the data characteristics of the first column square pattern and the last column square pattern.
Preferably, the step S42 further includes the following steps: step S43: judging whether a square graph is unselected; step S44: if yes, removing the first row and the last row of square patterns obtained in the step S42 from the square array to obtain a new square array; and returns to step S2 until each row of block patterns is selected.
The invention provides another technical scheme for solving the technical problems as follows: a block array selection system is used for realizing the block array selection method, and comprises the following modules: the acquisition module is used for: obtaining a square array, wherein the square array comprises a plurality of square patterns which are arranged in an array manner and are spaced apart;
and a graph adjustment module: adjusting all the square patterns to enable gaps among the patterns to be free of gaps, and obtaining a first auxiliary pattern;
column selection module: adjusting the edges of the first auxiliary graph to obtain a second auxiliary graph with two edges overlapped with two edges in the square array, and selecting each row of square graph in the square array based on the second auxiliary graph and the square array;
and a row selection module: adjusting the edges of the first auxiliary graph to obtain a third auxiliary graph with two edges overlapped with two rows of edges in the square array, and selecting each row of square graph in the square array based on the third auxiliary graph and the square array;
and a statistics module: based on the selected square patterns of each row and each column, the data characteristics of the square array are obtained through statistics.
The invention provides another technical scheme for solving the technical problems as follows: a computer device comprising a memory storing a computer program and a processor implementing the steps of a method of selecting a block array as described above when the computer program is executed.
The invention provides another technical scheme for solving the technical problems as follows: a computer storage medium having stored thereon a computer program which when executed by a processor performs the steps of a method of square array selection as described above.
Compared with the prior art, the block array selection method, the system, the computer equipment and the storage medium provided by the invention have the following beneficial effects:
1. compared with the traditional square array selection method which can not consider symmetrical square patterns, the square array selection method provided by the invention can accurately describe and match the shape of the square patterns and specifically select the square patterns of each row and each line by utilizing the opposite sides of the auxiliary patterns to be respectively overlapped with the edges of the square patterns, thereby greatly improving the selection efficiency; the edges of the auxiliary patterns are adjusted, so that only two edges are overlapped with the edges of the square patterns in two columns or two rows of the edges of the square array, the selection can be accurately aligned, the missing selection or repeated selection is avoided, and the accuracy of the selection is improved; after the block patterns of each row and each column are selected, the subsequent data feature statistics can be performed on the block array according to the selected structure so as to facilitate subsequent analysis, processing and optimization, and further the information value of the block array is utilized.
2. The square array selection method provided by the invention can accurately determine the distance between the square graphs by calculating the distance between the adjacent edges, expands the square array to an array containing more square graphs, is suitable for square graphs with different sizes and shapes, is effective for arranging the square graphs tightly or dispersedly, ensures no gap between the square graphs by accurate adjustment and movement, and avoids incomplete or overlapping conditions.
3. The square array selection method provided by the invention can select and obtain specific square patterns of each row and each column, form the collective coordinates of the square patterns in the square array, extract the key characteristics and the integral description of the square patterns, and provide a basis for the next analysis and optimization.
4. The square array selection method provided by the invention is suitable for a plurality of square patterns which are arranged in an array and are spaced apart, so that the square array selection method has certain universality and flexibility, and the square patterns can be selected according to actual requirements.
5. The invention provides a square array selection method, which provides an iterative method for processing unselected square graphs and continuously executing the square array selection process, wherein the iterative processing mode can continuously process unselected square graphs until the square graphs of each row and each column are selected, thereby improving the integrity and the accuracy of a square array selection algorithm.
6. The embodiment of the invention also provides a square array selection system, which has the same beneficial effects as the square array selection method, and the description is omitted herein.
7. The embodiment of the invention also provides a computer device, which comprises a memory and a processor, wherein the memory stores a computer program, and has the same beneficial effects as the block array selection method, and the description is omitted herein.
8. The embodiment of the invention also provides a computer storage medium, which has the same beneficial effects as the above-mentioned square array selection method and is not described herein.
[ description of the drawings ]
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart illustrating a block array selection method according to a first embodiment of the present invention.
Fig. 2 is a flowchart illustrating a block array selection method according to a first embodiment of the present invention.
Fig. 3 is a flowchart illustrating a block array selection method according to a first embodiment of the present invention.
Fig. 4 is a flowchart illustrating steps after step S32 of a block array selection method according to a first embodiment of the present invention.
Fig. 5 is a flowchart illustrating a block array selection method according to a first embodiment of the present invention.
Fig. 6 is a flowchart illustrating a block array selection method according to a first embodiment of the present invention after step S42.
Fig. 7 is a diagram illustrating a block array selection method according to a first embodiment of the present invention.
Fig. 8 is a block diagram of a block array selection system according to a second embodiment of the present invention.
[ detailed description ] of the invention
For the purpose of making the technical solution and advantages of the present invention more apparent, the present invention will be further described in detail below with reference to the accompanying drawings and examples of implementation. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Referring to fig. 1, a first embodiment of the present invention provides a block array selection method, which includes the following steps:
step S1: obtaining a square array, wherein the square array comprises a plurality of square patterns which are arranged in an array manner and are spaced apart;
step S2: adjusting all the square patterns to enable gaps among the patterns to be free of gaps, and obtaining a first auxiliary pattern;
step S3: adjusting the edges of the first auxiliary graph to obtain a second auxiliary graph with two edges overlapped with two edges in the square array, and selecting each row of square graph in the square array based on the second auxiliary graph and the square array;
step S4: adjusting the edges of the first auxiliary graph to obtain a third auxiliary graph with two edges overlapped with two rows of edges in the square array, and selecting each row of square graph in the square array based on the third auxiliary graph and the square array;
step S5: based on the selected square patterns of each row and each column, the data characteristics of the square array are obtained through statistics.
Specifically, as a specific embodiment, the square array in step S1 may be extracted from the image by using an image processing technology, or directly obtained by using a hardware device such as a sensor, and the square array is obtained by selecting a suitable method according to a specific application scenario.
In step S2, all the block patterns are adjusted to be arranged without gaps, so that no gaps exist between the blocks, and the block patterns filling the gaps are combined into a large pattern, thereby obtaining a first auxiliary pattern, which has an important effect on the selection of the subsequent block patterns.
Step S3, adjusting the edge of the first auxiliary graph to be overlapped with two rows of the edge of the square array, controlling the overlapping degree of the second auxiliary graph and the square array by adjusting the first auxiliary graph, adjusting according to the requirement and the actual situation to obtain the best matching result, and accurately describing and matching the shape of each square graph in the square array and selecting the square graph of each row and each column after selecting the row of the array graph in step S4, thereby completing step S5.
Referring to fig. 2, further, step S2 further includes the following steps: step S21: measuring the distance between the square patterns; step S22: adjusting the size of the edges of the patterns according to the distance between the square patterns, and filling gaps between the square patterns by moving the edges of all the square patterns; step S23: and combining all the square patterns filled in the gaps into a large pattern to obtain a first auxiliary pattern.
Specifically, based on the relative positions of the square patterns, the distance between the square patterns can be measured, the size of edges of the square patterns is adjusted according to the measured distance, gaps between the square patterns are filled by moving the edges of all the square patterns, all the square patterns filled with the gaps are combined into a large pattern, a first auxiliary pattern is obtained, the shape and arrangement of the original square patterns are reserved to a certain extent by the first auxiliary pattern, but a continuous and compact pattern structure is formed by the edges of all the square patterns after adjustment and filling, so that edge connection is compact, and matching accuracy is improved.
Further, each edge of the square graph is determined by identifying coordinate parameters of four vertices of each square graph.
Specifically, the coordinate parameters of four vertices may be obtained by using an image processing technique or a feature extraction technique, and four sides of the square graph may be determined by connecting adjacent vertices, and the sides may be associated with the vertex coordinate parameters of the square graph for subsequent optimization and adjustment.
It will be appreciated that each edge of the square pattern is determined by an algorithm, and extends to an array comprising more square patterns, suitable for square patterns of different sizes and shapes, and effective for closely or dispersedly arranged square patterns, accurate adjustment and movement ensures that there is no gap between square patterns, avoiding incomplete or misplacement of the overlapping.
Further, referring to fig. 3-4, step S3 further includes the following steps: step S31: adjusting two opposite auxiliary patterns to be overlapped with the first row and the last row of square patterns in the square array to obtain a second auxiliary pattern; step S32: and selecting a first row and a last row of square patterns which are overlapped with two opposite sides of the second auxiliary pattern, and obtaining the data characteristics of the first row and the last row of square patterns.
Specifically, the first auxiliary pattern is adjusted to obtain a second auxiliary pattern, so that the edge of the second auxiliary pattern coincides with the first row and the last row of square patterns in the square array on the edge or the inner space, and the adjustment can be the edge of the first auxiliary pattern or the whole shape of the first auxiliary pattern, in this embodiment, the specific adjustment behavior is not limited, as long as the second auxiliary pattern obtained by adjustment can coincide with the first row and the last row of square patterns in the square array in edge-to-edge or the purpose of pattern coincidence can be achieved.
It will be appreciated that the geometric characteristics and data characteristics are used to select a square pattern that has an overlap with the second auxiliary pattern on opposite sides or that has an overlap with the pattern, thereby enabling accurate selection of the square pattern.
Further, please continue to refer to fig. 3-4, the step S32 further includes the following steps: step S33: judging whether a square graph is unselected; step S34: if yes, removing the first row and the last row of square patterns obtained in the step S32 from the square array to obtain a new square array; and returns to step S2 until each row of square patterns is selected.
Specifically, after the selection of the first column and the last column of the original square array is completed, determining whether there is any unselected square pattern through an algorithm, if so, continuing to execute the next step S34; otherwise, the algorithm is ended. After judging that the unselected block patterns exist, removing the block patterns which are already selected from the block array to form a new block array, preparing for the next iteration selection, returning to the step S2, continuing iteration until the block patterns of each row are selected, and judging that the unselected block patterns do not exist. In the present invention, the steps S33-S34 ensure that the method can be applied to the selection of a square array comprising two or more square patterns, and in general, the number of memory cells of a DRAM chip can be very large, ranging from several millions to several billions.
It can be understood that the square array has repeatability and symmetry, and the number of squares to be selected is huge, and the square graphics can be ensured to be selected by gradually processing the square graphics in an iterative mode, and the method can automatically adjust and generate new auxiliary graphics when processing the square array, thereby adapting to square graphics arrays with different shapes and layouts or dynamically changing.
Further, referring to fig. 5-6, the step S4 further includes the following steps:
step S41: adjusting two opposite first auxiliary patterns to coincide with the first and last rows of square patterns in the square array to obtain a third auxiliary pattern; step S42: and selecting a first row square pattern and a last row square pattern which are overlapped with two opposite sides of the third auxiliary pattern, and obtaining the data characteristics of the first column square pattern and the last column square pattern.
It can be understood that, similar to step S3, the first auxiliary pattern is adjusted to obtain a third auxiliary pattern, so that the third auxiliary pattern overlaps with the first column and the last column of the square patterns in the square array on some sides or some internal spaces; the third auxiliary pattern is similar to the second auxiliary pattern, and is obtained by adjusting the first auxiliary pattern formed by filling gaps between the square patterns by moving edges of all the square patterns, and is used for assisting in selecting the square patterns in the square array, and the second auxiliary pattern is used for selecting each row of square patterns, so that overlapping parts with each row of square patterns are ensured; the third auxiliary pattern is used for selecting the square patterns of each row, so that the overlapping part of each row of square patterns is ensured, and as an implementation mode, the shapes of the first auxiliary pattern, the second auxiliary pattern and the third auxiliary pattern can be rectangular or square.
Further, with continued reference to fig. 5-6, the step S42 further includes the following steps: step S43: judging whether a square graph is unselected; step S44: if yes, removing the first row and the last row of square patterns obtained in the step S42 from the square array to obtain a new square array; and returns to step S2 until each row of block patterns is selected.
It will be appreciated that, like step S3, the block patterns are processed step by step in an iterative manner, ensuring that the block patterns of each row can be selected using the third auxiliary pattern that is iterated continuously. In one embodiment, the order of the steps S3 and S4 may be reversed, and the selection of the row of the square array is performed first, followed by the selection of the column of the square array.
Referring to fig. 7, in conjunction with a specific example method, the operational flow steps are as follows: step S1, obtaining a square array a, wherein the square array a is a 3*3 array example and comprises a plurality of square patterns which are arranged in an array manner and are spaced apart;
step S2, measuring the distance between the square patterns, adjusting the size of edges of the square patterns according to the distance between the square patterns, filling gaps between the square patterns by moving the edges of all the square patterns to obtain a pattern b, and obtaining a first auxiliary pattern c by combining the large pattern b formed by all the square patterns filling the gaps;
step S3 includes adjusting the edges of the first auxiliary pattern c in step S31 to obtain a second auxiliary pattern d with only two edges overlapping with two edges of the square array;
in step S32, a first row and a last row of square patterns overlapping two opposite sides of the second auxiliary pattern can be selected, so as to obtain data features of the first row and the last row of square patterns;
after judging that the unselected square patterns exist in the step S33, the step S34 can acquire the column data of the square array by adjusting the auxiliary pattern d and the square array and returning to the step S2;
step S4 is similar to step S3, the edges of the first auxiliary graph c are adjusted to obtain a third auxiliary graph e, only two edges of the third auxiliary graph e are overlapped with two edges of the square array, the third auxiliary graph e is selected to obtain the first row and the last row of the square array, and the row data of the square array can be obtained by adjusting the auxiliary graph e and the square array and returning to step S2, so that the unique position index of each storage unit in the array is obtained.
Referring to fig. 8, a second embodiment of the present invention provides a block array selection system for implementing the block array selection method, which includes the following modules: the acquisition module 10: obtaining a square array, wherein the square array comprises a plurality of square patterns which are arranged in an array manner and are spaced apart;
the graphic adjustment module 20: adjusting all the square patterns to enable gaps among the patterns to be free of gaps, and obtaining a first auxiliary pattern;
column selection module 30: adjusting the edges of the first auxiliary graph to obtain a second auxiliary graph with two edges overlapped with two edges in the square array, and selecting each row of square graph in the square array based on the second auxiliary graph and the square array;
row selection module 40: adjusting the edges of the first auxiliary graph to obtain a third auxiliary graph with two edges overlapped with two rows of edges in the square array, and selecting each row of square graph in the square array based on the third auxiliary graph and the square array;
statistics module 50: based on the selected square patterns of each row and each column, the data characteristics of the square array are obtained through statistics.
It may be appreciated that, the acquiring module 10 is configured to acquire a square graph from the square array, which may be implemented by an image processing algorithm, a sensor, or a user input, so as to accurately acquire the square graph and provide a data base for subsequent processing; the graph adjustment module 20 generates a corresponding first auxiliary graph according to the acquired data characteristics of the square graph, and the method for generating the first auxiliary graph can be designed according to a specific selection method and the actual situation of the square array, so that a basis is provided for the selection of the subsequent square graph; the column selecting module 30 and the row selecting module 40 are configured to adjust the features of the first auxiliary pattern to obtain a second auxiliary pattern and a third auxiliary pattern, where the obtained second auxiliary pattern and the obtained third auxiliary pattern are coincident with edges or patterns of the square patterns, and each column or each row of patterns that are coincident with the auxiliary patterns is selected according to the coincidence of the second auxiliary pattern and the third auxiliary pattern with the square array, and in a specific embodiment, the selecting order of the columns or rows in the column selecting module 30 and the row selecting module 40 may be reversed. The statistics module 50 calculates the data features of the square array based on the selected square patterns of each row and each column, including the data features of each specific row or each column of the square array, such as the number of square patterns, specific coordinates of each square pattern, etc., and based on the specific data features, can obtain the feature representation of the square patterns for subsequent OPC processing.
A third embodiment of the present invention provides a computer storage device, including a memory and a processor, where the memory stores a computer program, and the processor implements steps of a block array selection method as described above when executing the computer program, and the computer storage device has the same advantages as those of the method described above, and is not described herein.
A fourth embodiment of the present invention provides a computer storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of a block array selection method as described above, and the computer storage medium has the same advantages as the method described above, and will not be described herein.
In the embodiments provided herein, it should be understood that "B corresponding to a" means that B is associated with a, from which B can be determined. It should also be understood that determining B from a does not mean determining B from a alone, but may also determine B from a and/or other information.
It should be appreciated that reference throughout this specification to "one embodiment" or "an embodiment" means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. Those skilled in the art will also appreciate that the embodiments described in the specification are alternative embodiments and that the acts and modules referred to are not necessarily required for the present invention.
In various embodiments of the present invention, it should be understood that the sequence numbers of the foregoing processes do not imply that the execution sequences of the processes should be determined by the functions and internal logic of the processes, and should not be construed as limiting the implementation of the embodiments of the present invention.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, with the determination being made based upon the functionality involved. It will be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Compared with the prior art, the block array selection method, the system, the computer equipment and the storage medium provided by the invention have the following beneficial effects:
1. compared with the traditional square array selection method which can not consider symmetrical square patterns, the square array selection method provided by the invention can accurately describe and match the shape of the square patterns and specifically select the square patterns of each row and each line by utilizing the opposite sides of the auxiliary patterns to be respectively overlapped with the edges of the square patterns, thereby greatly improving the selection efficiency; the edges of the auxiliary patterns are adjusted, so that only two edges are overlapped with the edges of the square patterns in two columns or two rows of the edges of the square array, the selection can be accurately aligned, the missing selection or repeated selection is avoided, and the accuracy of the selection is improved; after the block patterns of each row and each column are selected, the subsequent data feature statistics can be performed on the block array according to the selected structure so as to facilitate subsequent analysis, processing and optimization, and further the information value of the block array is utilized.
2. The square array selection method provided by the invention can accurately determine the distance between the square graphs by calculating the distance between the adjacent edges, expands the square array to an array containing more square graphs, is suitable for square graphs with different sizes and shapes, is effective for arranging the square graphs tightly or dispersedly, ensures no gap between the square graphs by accurate adjustment and movement, and avoids incomplete or overlapping conditions.
3. The square array selection method provided by the invention can select and obtain specific square patterns of each row and each column, form the collective coordinates of the square patterns in the square array, extract the key characteristics and the integral description of the square patterns, and provide a basis for the next analysis and optimization.
4. The square array selection method provided by the invention is suitable for a plurality of square patterns which are arranged in an array and are spaced apart, so that the square array selection method has certain universality and flexibility, and the square patterns can be selected according to actual requirements.
5. The invention provides a square array selection method, which provides an iterative method for processing unselected square graphs and continuously executing the square array selection process, wherein the iterative processing mode can continuously process unselected square graphs until the square graphs of each row and each column are selected, thereby improving the integrity and the accuracy of a square array selection algorithm.
6. The embodiment of the invention also provides a square array selection system, which has the same beneficial effects as the square array selection method, and the description is omitted herein.
7. The embodiment of the invention also provides a computer device, which comprises a memory and a processor, wherein the memory stores a computer program, and has the same beneficial effects as the block array selection method, and the description is omitted herein.
8. The embodiment of the invention also provides a computer storage medium, which has the same beneficial effects as the above-mentioned square array selection method and is not described herein.
The above describes in detail a block array selection method, system, computer device and storage medium disclosed in the embodiments of the present invention, and specific examples are applied to illustrate the principles and embodiments of the present invention, where the above description of the embodiments is only for helping to understand the method and core idea of the present invention; meanwhile, as for those skilled in the art, according to the idea of the present invention, there are changes in the specific embodiments and the application scope, and in summary, the present disclosure should not be construed as limiting the present invention, and any modifications, equivalent substitutions and improvements made within the principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A block array selection method is characterized in that: the method comprises the following steps:
step S1: obtaining a square array, wherein the square array comprises a plurality of square patterns which are arranged in an array manner and are spaced apart;
step S2: adjusting all the square patterns to enable gaps among the patterns to be free of gaps, and obtaining a first auxiliary pattern;
step S3: adjusting the edges of the first auxiliary graph to obtain a second auxiliary graph with two edges overlapped with two edges in the square array, and selecting each row of square graph in the square array based on the second auxiliary graph and the square array;
step S4: adjusting the edges of the first auxiliary graph to obtain a third auxiliary graph with two edges overlapped with two rows of edges in the square array, and selecting each row of square graph in the square array based on the third auxiliary graph and the square array;
step S5: based on the selected square patterns of each row and each column, the data characteristics of the square array are obtained through statistics.
2. The method for selecting a tile array as claimed in claim 1, wherein: the step S2 further comprises the steps of:
step S21: measuring the distance between the square patterns;
step S22: according to the distance between the square patterns, the size of the edges of the square patterns is adjusted, and gaps between the square patterns are filled by moving the edges of all the square patterns;
step S23: and combining all the square patterns filled in the gaps into a large pattern to obtain a first auxiliary pattern.
3. The method for selecting a tile array as claimed in claim 2, wherein: each edge of the square graph is determined by identifying the coordinate parameters of the four vertices of each square graph.
4. The method for selecting a tile array as claimed in claim 1, wherein: the step S3 further includes the steps of:
step S31: adjusting the opposite sides of the first auxiliary graph to coincide with the first row and the last row of square graphs in the square array to obtain a second auxiliary graph;
step S32: and selecting a first row and a last row of square patterns which are overlapped with two opposite sides of the second auxiliary pattern, and obtaining the data characteristics of the first row and the last row of square patterns.
5. The method of selecting a tile array as claimed in claim 4, wherein: the step S32 further includes the following steps:
step S33: judging whether a square graph is unselected;
step S34: if yes, removing the first row and the last row of square patterns obtained in the step S32 from the square array to obtain a new square array; and returns to step S2 until each row of square patterns is selected.
6. The method for selecting a tile array as claimed in claim 1, wherein: the step S4 further includes the steps of:
step S41: adjusting the opposite sides of the first auxiliary graph to coincide with the first row and the last row of square graphs in the square array to obtain a third auxiliary graph;
step S42: and selecting a first row and a last row of square patterns which are overlapped with two opposite sides of the third auxiliary pattern, and obtaining the data characteristics of the first row and the last row of square patterns.
7. The method of claim 6, wherein: the step S42 further includes the following steps:
step S43: judging whether a square graph is unselected;
step S44: if yes, removing the first row and the last row of square patterns obtained in the step S42 from the square array to obtain a new square array; and returns to step S2 until each row of block patterns is selected.
8. A block array selection system for implementing a block array selection method as claimed in any one of claims 1 to 7, wherein: the method comprises the following modules:
the acquisition module is used for: obtaining a square array, wherein the square array comprises a plurality of square patterns which are arranged in an array manner and are spaced apart;
and a graph adjustment module: adjusting all the square patterns to enable gaps among the patterns to be free of gaps, and obtaining a first auxiliary pattern;
column selection module: adjusting the edges of the first auxiliary graph to obtain a second auxiliary graph with two edges overlapped with two edges in the square array, and selecting each row of square graph in the square array based on the second auxiliary graph and the square array;
and a row selection module: adjusting the edges of the first auxiliary graph to obtain a third auxiliary graph with two edges overlapped with two rows of edges in the square array, and selecting each row of square graph in the square array based on the third auxiliary graph and the square array;
and a statistics module: based on the selected square patterns of each row and each column, the data characteristics of the square array are obtained through statistics.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any one of claims 1 to 8 when the computer program is executed.
10. A computer storage medium having a computer program stored thereon, characterized by: the computer program implementing the steps of the method of any of claims 1 to 7 when executed by a processor.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311184919.6A CN117392197A (en) | 2023-09-13 | 2023-09-13 | Block array selection method, system, computer equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311184919.6A CN117392197A (en) | 2023-09-13 | 2023-09-13 | Block array selection method, system, computer equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117392197A true CN117392197A (en) | 2024-01-12 |
Family
ID=89469122
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311184919.6A Pending CN117392197A (en) | 2023-09-13 | 2023-09-13 | Block array selection method, system, computer equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117392197A (en) |
-
2023
- 2023-09-13 CN CN202311184919.6A patent/CN117392197A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US8347246B2 (en) | Placement and optimization of process dummy cells | |
| US6243855B1 (en) | Mask data design method | |
| US8392854B2 (en) | Method of manufacturing semiconductor device by using uniform optical proximity correction | |
| US9311443B2 (en) | Correcting for stress induced pattern shifts in semiconductor manufacturing | |
| TW201802712A (en) | Method for integrated circuit design | |
| US20160071261A1 (en) | Pattern analysis method of a semiconductor device | |
| US10747916B2 (en) | Parameter generation for modeling of process-induced semiconductor device variation | |
| CN115863203B (en) | Test pattern acquisition method, system, device, computer equipment and medium | |
| CN105223771B (en) | A kind of optimization method of optical proximity effect correction model | |
| CN112818632B (en) | Analysis method and device for pattern density of chip and electronic equipment | |
| CN106294935B (en) | A kind of process modeling modeling and modification method based on pattern density | |
| CN117392197A (en) | Block array selection method, system, computer equipment and storage medium | |
| US20070240089A1 (en) | Apparatus and method for correcting layout pattern data | |
| CN105278234B (en) | Method for enhancing precision of OPC model on graphic deviation | |
| CN115826349A (en) | Optical proximity correction method, system, electronic device and storage medium | |
| US20190392100A1 (en) | Storage medium which stores instructions for a simulation method in a semiconductor design process, semiconductor design system that performs the simulation method in the semiconductor design process, and simulation method in the semiconductor design process | |
| US20120050728A1 (en) | Identifying defects | |
| US11733603B2 (en) | Proximity correction methods for semiconductor manufacturing processes | |
| CN114662443A (en) | Integrated circuit layout design method, device and readable storage medium | |
| CN116500851B (en) | 3D imaging system, calibration method, device, equipment and storage medium thereof | |
| US20230314957A1 (en) | Process proximity correction method and computing device for the same | |
| CN116736075B (en) | Transistor matching precision detection method, controller and storage medium | |
| CN104752251B (en) | Using the method for CDSEM resolution charts | |
| CN110068986B (en) | Method of trimming pattern, medium, server, and method of manufacturing photomask | |
| Shim et al. | Lithography defect probability and its application to physical design optimization |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |