Method and device for accelerating photoetching hot spot detection

Abstract

The invention discloses a method and a device for accelerating photoetching hot spot detection. The method comprises the following steps: acquiring product layout data; performing photoetching hot spot inspection on the product layout data, positioning hot spots, and setting the radius of a hot spot pattern region; converting the hot spot region graph into a feature vector and mapping the feature vector into a node of the graph, and realizing formation and updating of a dynamic hot spot library based on the graph by using a greedy algorithm; carrying out product layout data localization and high-risk hot-spot layout area screening; and outputting photoetching analog simulation and photoetching hot spot pattern detection results. According to the method, through layout localized hot spot detection, photoetching process hot spot simulation can be converted from global processing to local processing, and the photoetching hot spot checking time is greatly shortened; an improved hot spot library hot spot matching method comprises a hot spot graph size selection method and a graph-based dynamic hot spot library establishment algorithm combined with a greedy algorithm, so that hot spot library graphs are matched more easily, and the training speed is increased.

Description

Technical Field

[0001] The present invention relates to the field of photolithography technology, and in particular to a method and device for accelerating photolithography hotspot detection. Background Art

[0002] The existing technologies mainly include hotspot matching mode based on known hotspot graphics and hotspot detection method based on machine learning. The first is hotspot matching based on known hotspot graphics. Manufacturers generally establish known process hotspot graphics into hotspot graphics libraries according to risk levels. When new design data is submitted, the design layout is matched with the hotspot graphics in the process hotspot library one by one to detect whether there are known photolithography process hotspots in the design layout. As can be seen from the process, the detection mode of known process hotspots includes two parts: analysis stage and application stage. The analysis stage mainly involves the establishment of hotspot libraries. A large number of wafer-level defect graphics will be accumulated in the long-term manufacturing process. These graphics are matched to the designer's layout graphics, and then the process hotspot graphics library is extracted according to the risk level of the hotspot graphics. When applied, the process hotspots are matched and detected one by one with the input layout. However, as the complexity of the layout increases, the complexity of the hotspot graphics will also increase exponentially, the amount of hotspot library accumulation will increase, and the hotspot library retrieval will become more and more complicated, time-consuming, and inefficient.

[0003] The algorithm flow of hotspot detection technology based on machine learning includes: in the stage of graphic training, it is necessary to first extract the relevant layout feature vectors, and then use the machine learning method to train the hotspots and finally establish the identifier model. In this process, the layout training set will be corrected for optical proximity effects and simulated by lithography simulation, and the simulation results will be used as the calibration basis for machine learning. In the final detection stage, the hotspot location will be found. This process mainly relies on the trained hotspot identifier model to check the new layout. There are many machine learning methods currently used for training, such as ANN (Artificial Neural Networks) and SVM (Support Vector Machines). Of course, the feature extraction method will have a great impact on the training efficiency and model accuracy to a large extent.

[0004] In short, the hotspot detection technology of machine learning is fast in execution and can also predict some hotspots that have not appeared in training. However, since the training set cannot cover more layout structures, the prediction of new hotspots is very limited. In addition, training models is very time-consuming and there are problems with model convergence. At the same time, the actual application of the trained models will also lead to omissions and misjudgments.

[0005] Graph-based hotspot detection is an important direction for fast lithography hotspot search. The establishment of a dynamic hotspot library based on a sparse hierarchical graph and the search for hotspot detection using a greedy algorithm can divide the hotspot matching search process into multiple stages, and each stage is sorted from low to high according to the search complexity, thereby improving the search efficiency. At the same time, the dynamic hotspot library detection based on the graph plus the greedy algorithm does not use model training but uses the graph node matching method. When a new hotspot appears, it can be mapped as a node and embedded in the hotspot library node graph, thereby improving the effect of judging and detecting new hotspots. Summary of the invention

[0006] The present invention aims to address the deficiencies of the prior art and to provide a method and device for accelerating photolithography hotspot detection.

[0007] The object of the present invention is achieved through the following technical solution: a method for accelerating lithography hotspot detection, the method comprising:

[0008] S1. Obtain product layout data;

[0009] S2. Check the hot spots of the lithography process on the product layout data, locate the hot spots, and set the radius of the hot spot graphic area;

[0010] S3, convert the hotspot area graph into feature vectors and map them to nodes of the graph, and use the greedy algorithm to realize the formation and update of the dynamic hotspot library based on the graph;

[0011] S4. Localization of product map data and screening of high-risk hotspot map areas;

[0012] S5. Output of lithography simulation and lithography hotspot pattern detection results.

[0013] Furthermore, the radius of the hot spot pattern area is greater than 3 times the minimum design rule CD and less than 15 times the minimum design rule CD.

[0014] Furthermore, the process of converting the hotspot area graph into a feature vector and mapping it to a node of the graph includes: converting each hotspot area graph into a feature vector by calling a code library function, using the feature vector as an identifier of the hotspot and mapping the identifier to a node of the graph, wherein the nodes are connected to each other at different levels.

[0015] Furthermore, the use of a greedy algorithm to realize the formation of a graph-based dynamic hotspot library is specifically as follows:

[0016] The number of layers of the layout is preset according to the size of the hot spot area graph, the top layer is selected as the traversal layer, node J is selected and the nearest neighbor list, waiting list and visited list of the node are established in the traversal layer, wherein the capacity upper limit k is set for the nearest neighbor list, and the nearest neighbor list is initialized to any point in the traversal layer except the node J selected above;

[0017] Determine the distance relationship between each node in the waiting list and node J, and add the nodes with a distance less than the maximum distance in the nearest list to the nearest neighbor list, otherwise ignore them until all the nodes in the waiting list in this layer are traversed; if the number of nearest neighbor lists in this layer exceeds the upper limit k, select the k points closest to point J, if the number of nodes in this layer is less than k, then put all the nodes in this layer into the nearest neighbor list; all the traversed nodes are put into the visited list;

[0018] After all nodes in this layer are traversed, enter the next layer to establish a new nearest neighbor list, waiting list and visited list for traversal, and loop to the bottom layer. The nodes in the last bottom layer nearest neighbor list whose distance to point J is less than or equal to the threshold Dth are regarded as hot spots of the same type. The selection of Dth is determined according to the size of the hot spot graph; nodes of the same type keep the original node graph, if they are not of the same type, the nodes are inserted and the node graph is updated.

[0019] Furthermore, the number of layers of the layout preset according to the size of the hot spot area graphic is specifically:

[0020]

[0021] Among them, r is the radius of the intercepted figure, a represents the standard size, l is the fixed length value of the segmented edge, V i Represents the number of graphics vertices, d is the size of the polygon graphics in the layout; the radius of the hotspot graphics setting is set between 2.5a and a, and the corresponding Ly value is set to 10 to 20 layers.

[0022] Furthermore, the updating process of the hotspot library is as follows: a random number method is used to specify a layer number L for the node P that needs to be matched, and the P point will be embedded in all layers from the L layer to the bottom layer.

[0023] Then set the top layer as M, take all the nodes in the top layer as the starting point, and search the nearest neighbor list layer by layer according to the greedy algorithm, where each layer selects the nearest n points in the nearest neighbor list as the starting point for the next layer, until the Lth layer and the bottom layer, and search the nearest neighbor list layer by layer with the starting point of the Lth layer, select the nearest j nodes in the nearest neighbor list of each layer to connect with P, and then remove the edges connecting the nodes in the Lth to the 2nd layer that are not included in the j nodes connected to P in the bottom layer and point P, and update the node graph.

[0024] Furthermore, the product layout data localization and high-risk hotspot layout area screening are specifically as follows: the principle of screening processing is to select areas with a higher probability of hotspot occurrence and exclude areas with a lower probability of hotspot occurrence.

[0025] Furthermore, in the lithography simulation, the simulation area is divided according to the results of product layout data localization and high-risk hotspot layout area screening, including

[0026] After removing the simulated IP library from the low-risk area, the intersection with the high-risk area is taken to obtain the area R that needs to be simulated. The edge area of ​​R is extended outward by a distance d to form an annular reference area D, where the value of d is used to expand all the edges in the hotspot to 0.1 to 0.5 times the minimum critical dimension CD value of the layout design; the lithography simulation area is the union of the lithography hotspot simulation area R and the annular reference area D.

[0027] According to another aspect of the specification, there is provided an accelerated lithography hotspot detection device, comprising a memory and one or more processors, wherein the memory stores executable code, and when the processor executes the executable code, the accelerated lithography hotspot detection method is implemented.

[0028] According to another aspect of the specification, a computer-readable storage medium is provided, on which a program is stored, and when the program is executed by a processor, the method for accelerating lithography hotspot detection is implemented.

[0029] Beneficial effects of the present invention:

[0030] 1) Localized hotspot detection of the layout can transform the hotspot simulation of the lithography process from global to local processing, greatly reducing the time for lithography hotspot inspection;

[0031] 2) Improved hotspot library hotspot matching method, including hotspot graph size selection method and graph-based dynamic hotspot library establishment algorithm combined with greedy algorithm, effectively solves the following problems:

[0032] 1. The hotspot size is too large, which makes the hotspot map information complicated and the hotspot library graphics difficult to match, which is not conducive to the simplification and compression of hotspot graphics;

[0033] 2. The hotspot size is too small, which greatly increases the time for hotspot checking and matching;

[0034] 3. The imperfect matching of hotspot classification in machine learning causes a large number of graphics of the same type to be divided into different hotspot graphics, increasing the complexity of the hotspot library and its matching; or causes a large number of graphics of different types to be divided into the same hotspot graphics, resulting in misjudgment;

[0035] 4. The machine learning hotspot detection model is slow to train. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 A flow chart of a method for accelerating lithography hotspot detection provided by an embodiment of the present invention;

[0037] Figure 2 A schematic diagram of selecting the size of a hotspot graphic provided in an embodiment of the present invention;

[0038] Figure 3 A graph showing the relationship between the size selection and the number of vertices included in a heat map provided in an embodiment of the present invention;

[0039] Figure 4 A flow chart of layout localized lithography hotspot detection provided by an embodiment of the present invention;

[0040] Figure 5 A flowchart for establishing a graph-based dynamic hotspot library combined with a greedy algorithm provided in an embodiment of the present invention;

[0041] Figure 6 A flowchart of inserting new graphics into a hotspot library and updating the hotspot library based on a graph provided by an embodiment of the present invention;

[0042] Figure 7 A schematic diagram of matching and searching a hotspot graph node map provided by an embodiment of the present invention;

[0043] Figure 8 A schematic diagram of localizing a lithography hotspot area provided by an embodiment of the present invention;

[0044] Fig. 9 A schematic diagram of simulation area division provided by an embodiment of the present invention;

[0045] Fig.10 A schematic diagram of an accelerated lithography hotspot detection device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0046] The specific implementation modes of the present invention are further described in detail below with reference to the accompanying drawings.

[0047] The present invention focuses on the simulation of lithography process hotspots based on hotspot library matching and layout localization. On the basis of traditional hotspot library matching to detect hotspots, it is equivalent to appropriately processing the original design layout. Targeted lithography process hotspot simulation will greatly shorten the lithography simulation time.

[0048] like Figure 1 As shown, the present invention provides a method for accelerating lithography hotspot detection, comprising:

[0049] S1. Get data from the product map;

[0050] S2. Check the hot spots on the layout (the hot spots are determined for reference in establishing a hot spot library), select the appropriate hot spot size (hot spot graphic area radius) according to the specific hot spot situation, and collect hot spot graphic data;

[0051] S3, convert the hotspot area graph into a feature vector and map it to a node of the graph, then use a graph-based algorithm combined with a greedy algorithm to establish a dynamic hotspot library, and put the collected hotspot data into the hotspot library;

[0052] S4. Localize the layout data of new products that need to check hot spots, that is, focus on the key areas of the layout, ignore the unimportant parts of the edge of the layout, and then match the hot spot graphic areas from the hot spot library. The obtained hot spot graphic areas are screened as high-risk hot spot layout areas;

[0053] S5. Perform lithography simulation on high-risk hotspot layout areas, and finally output lithography hotspot graphic detection results.

[0054] Specifically, for Figure 1 Step S2 in the process performs hotspot location and hotspot radius selection. Hotspot radius selection is related to how to set the appropriate hotspot graphic size. Setting the appropriate hotspot graphic size can achieve excellent hotspot search results. Specifically, the general radius value setting needs to be greater than 3 times the minimum design rule CD, otherwise the established hotspot library graphic has no matching meaning. However, a radius that is too large will include more areas for lithography process hotspot inspection, so the upper limit of the radius value of the present invention is set to 15 times the minimum design rule CD.

[0055] The size of the hotspot graphics in the hotspot library directly affects the matching of the design layout graphics. The larger the size of the hotspot library graphics, the richer the layout information content, and the fewer graphics that can be matched, which is not conducive to subsequent graphics compression; while the smaller the size of the hotspot graphics library, the more graphics can be matched, and the time for hotspot checking will increase significantly. It is necessary to further explore how to set the appropriate hotspot library graphics size.

[0056] Figure 2 The method of selecting the size of the hot spot pattern is shown in the figure. The left half of Figure 2 is a SEM image of the defect pattern found during the tape-out process, and it is obvious that pinch-off occurs in the central area. Figure 2 The right half of the figure is the development simulation image generated by the photolithography process inspection simulation software package for the design layout of the hotspot pattern. The defect pattern selection method is to take the process defect as the center, expand the length r in four directions, and finally form a square area of ​​size 2r*2r. This area is finally stored in the hotspot library for hotspot pattern matching.

[0057] In the actual layout production process, as long as the electrical performance meets the design requirements, the line width of the layout pattern will always be arranged according to the minimum design rule size. This method can obtain more effective integrated circuit devices within a certain area. Based on the area A of a single polygonal pattern in this layout i and the polygon size d and perimeter C iThe relationship can be approximated as formula 1-1,

[0058] (1-1)

[0059] Due to the requirements of etching process and chemical mechanical polishing process for pattern uniformity, dummy patterns will be added in the actual process production process to make the area density of the layout pattern basically consistent, and the edge segmentation is kept consistent as much as possible. Let the size radius of the intercepted pattern be represented by r, a is a common constant, and E i represents the number of edges in the intercepted graph, l is the fixed length of the segmented edge, which conforms to the following formulas 1-2 and 1-3:

[0060] (1-2)

[0061] (1-3)

[0062] In the plane, all graphics satisfy Euler's formula, V i Indicates the number of graph vertices, F i Indicates the number of polygonal faces. Figure 1 Generally, it is a simple single-sided graph without involving complex topological graphs. In this case, F i Always equal to l, E i represents the number of polygon edges, thus we get formula 1-4,

[0063] (1-4)

[0064] In summary, under the above-mentioned constraints, after simple processing, we can get formula 1-5:

[0065] (1-5)

[0066] So far, we can find that if the radius r of the intercepted figure is relatively large, both the number of vertices and the number of edges of the design layout will become larger and larger, and the complexity of the figure will become higher and higher.

[0067] Figure 3 The following is the verification result, for Figure 2 The hotspot graphs are set to have radii of 5a, 2.5a, 1.5a, a and 0.7a to generate the corresponding number of vertices V. It can be clearly found that the number of graphs and the number of vertices decrease in turn, and the complexity of the graph decreases. Based on experience and experimental results, the radius is usually set between 2.5a and a, depending on the specific situation.

[0068] Figure 4The figure shows the flow chart of localized lithography hotspot detection, which shows the basic process of regionalized (localized) lithography hotspot inspection. The lithography hotspot library is compared with the layout data of the new product, and the matching parts are positively screened out as high-risk areas. At the same time, the simulation IP library with existing simulation experience is used to compare with the layout data of the new product. The data in the simulation IP library is a low-risk lithography layout graphic without hotspots, and the matching parts are reversely filtered out as low-risk areas. The processed area is then simulated by the lithography process friendliness inspection software package for lithography hotspot simulation, and finally the lithography process hotspot is obtained.

[0069] Figure 5 The figure shows the specific process of forming a dynamic hotspot library based on a graph combined with a greedy algorithm and matching it with high-risk areas (hotspot areas), corresponding to Figure 1 The S3 dynamic hotspot database forms part of the S4 hotspot area (high-risk area). Greedy algorithm matches the corresponding Figure 4 The existing simulation IP library is formed by using the same algorithm process as the hotspot library, except that the hotspot graphics are replaced with certain non-hotspot (low-risk) area patterns.

[0070] The hotspot library establishment and matching process first converts the hotspot area graphics with the selected radius size into feature nodes. The conversion method is to convert each hotspot area graphic into a feature vector μ by calling the code library function h H = [y 1， y 2， y 3，.......， y n ], where h represents a single hotspot graph, H represents the hotspot graph set of the entire map, and the feature vector is used as a unique identifier of the hotspot, which is mapped to the node of the graph. Nodes are connected to each other at different levels, and points existing in the high-level layers are embedded in all lower layers, thus forming a hotspot library node graph from high-level to low-level, from sparse to dense. The traversal of the graph must pass through a connection from one node to another.

[0071] Specifically, the graph has Ly layers, Ly is a predefined variable, Ly is selected according to the radius size, and the radius size 2.5a to a corresponds to the Ly value set to 10 to 20 layers. First select the top layer (Ly layer is the top layer, 1 layer is the bottom layer) as the P layer, select a node J, and establish the nearest neighbor list C, waiting list W, and visited list V of J point in the P layer. There is no specific limit on the capacity of lists W and V, and the upper limit value of the capacity of list C is k. The k value ranges from 50 to 2000 according to different layout conditions. C is initialized to any point in the P layer except J, and W is initialized to all points in the top layer except J. Determine the relationship between the distance between the node in W and the point J and the distance between the node in C and the point J. The nodes in W that are less than the maximum distance in C are placed in C, otherwise they are ignored. This cycle continues until all points in the P layer C are selected.

[0072] The meaning of "selected" is to traverse all W points in this layer. If the number of points in this layer is greater than or equal to k, all k points closest to point J will be selected, or if the number of nodes in this layer is less than k, all points in this layer will be put into the C list. The browsed nodes in W and C will be put into the V list, and the nodes in the V list will not be traversed again to prevent repeated access and improve efficiency. Specifically, the distance between nodes is defined by the Euclidean distance d: , where J = [j 1， j 2，.......， j n ], Q = [q 1， q 2，.......， q n ] represents the characteristic vector of the hot spot graph corresponding to the two nodes. Then select the next layer as the P layer, and establish the nearest neighbor list C, waiting list W, and visited list V of point J in the new P layer. The V list of the new P layer will save the V list data of the upper layer. The new W list is updated to all points in the current P layer except point J and the nodes in the C list. The C list remains the same as the upper layer, and then repeats the node distance judgment steps of the previous layer. This cycle continues until the bottom layer is traversed. Finally, the nodes in the bottom C list whose distance from point J is less than or equal to a certain threshold Dth are regarded as similar hot spots. The selection of Dth is determined according to the size of the hot spot graph. The range of Dth is [1e-6, 1e-4]. The larger the graph size, the larger the Dth, and vice versa. If there is a similar hot spot, keep the original node graph unchanged. Otherwise, execute it for point J in the hot spot library node graph. Figure 6 The insertion algorithm flow shown in the figure updates the node graph. If all the nodes corresponding to the graph on the product layout are traversed, the hotspot library is established.

[0073] Figure 6The figure shows a flow chart of the insertion of new graphs into the graph-based hotspot library and the update of the hotspot library. Through the algorithm, a random number method is used to specify a layer number L for the node P that needs to be matched. Point P will be embedded in all layers from layer L to the bottom layer. This mechanism ensures that the bottom layer graph is more complex than the high-level graph, and achieves a hierarchical node graph structure from sparse to dense, and all nodes corresponding to the traversed hotspot graphs will be embedded in the bottom layer graph to ensure that the matching results appear at the bottom layer. Specifically, the top layer is set to M layer, and the initial starting point O is selected. Point O is selected as all nodes in the top layer because the number of nodes in the top layer is small. Then in the next layer, that is, the M+1 layer, search for O's nearest neighbor list C, and use Figure 5 The greedy algorithm is used to search and sort the n points closest to point P in C as the starting point O for the next layer of search, until the L layer is found. The value of n is selected from 3 to 5 based on experience according to the different hot spot radius sizes. From the L layer to the bottom layer, the starting point O of the L layer is used to search for the nearest neighbor list C of P, and the j closest nodes are selected in each layer C to connect to P. Then remove the edges connecting the nodes in the L layer to the 2nd layer (the layer above the lowest layer) that are not included in the j nodes connected to P in the bottom layer, and then update the node graph. It should be additionally explained that for building a dynamic node graph hot spot library from scratch, the initial node will be directly embedded in the graph.

[0074] Figure 7 The figure shows a matching search diagram of the hotspot graph node mapping diagram. 701 corresponds to the graph to be searched, 705 corresponds to the matching graph, 702 corresponds to the top layer of the hotspot library node graph, 703 corresponds to several layers in the middle, and 704 represents the bottom layer. The required graph is first mapped to a node, and the node searches for neighboring points from the high layer to the low layer in the multi-layer graph. The hotspot graph with the lowest layer neighboring point whose distance to the required search node is less than the specified threshold is a successfully matched hotspot graph.

[0075] Figure 8 The following is a schematic diagram of layout data localization. Figure 8 , Fig. 9 correspond Figure 1 The layout data of step S4 in the process is localized. The principle of screening is to select areas with a higher probability of hotspots as much as possible and exclude areas with a lower probability of hotspots. Assuming that the lithography test layout contains global area A, local area B is screened as a high-risk hotspot area (left figure). In this case, the area of ​​hotspot simulation is reduced and the time consumption of hotspot inspection is reduced. Similarly, in global area A, area B is found to be a lower-risk hotspot area (right figure). In this way, area B does not need to be inspected for lithography process hotspots, which can also reduce the simulation time of lithography process hotspots.

[0076] Fig. 9The following is the specific process rule for simulation area division: Assuming the original layout data is A, the high-risk hotspot area screened is B and the IP library that has been simulated is C, after the data area is processed, the area that needs to be simulated is R, and R represents the graphic part in A that matches the simulated IP library C. The logic is shown in the following formula 2-1,

[0077] (2-1)

[0078] During the photolithography process simulation, it should be noted that the entire R region cannot be directly used as a photolithography process hotspot simulation. The optical proximity correction effect of the region edge should also be considered to prevent the region edge from affecting the photolithography simulation of the photolithography hotspot simulation region R. The specific method is to extend the edge region of R outward by a distance d to form a ring reference region D. During the optical proximity correction process, the logical operation of the photolithography simulation region T is as follows:

[0079] (2-2)

[0080] The final process simulation check shows that the hotspot area of ​​the lithography process is still the R area. The expansion distance d of the hotspot library is mainly to expand all the edges in the hotspot to 0.1 to 0.5 times the minimum critical dimension CD value of the layout design, that is, the minimum spacing of the layout pattern outline.

[0081] Corresponding to the aforementioned embodiment of a method for accelerating photolithography hotspot detection, the present invention also provides an embodiment of a device for accelerating photolithography hotspot detection.

[0082] See also Fig.10 An accelerated lithography hotspot detection device provided in an embodiment of the present invention includes a memory and one or more processors. The memory stores executable code. When the processor executes the executable code, it is used to implement an accelerated lithography hotspot detection method in the above embodiment.

[0083] An embodiment of an accelerated lithography hotspot detection device provided by the present invention can be applied to any device with data processing capabilities, and the device with data processing capabilities can be a device or apparatus such as a computer. The device embodiment can be implemented through software, or through hardware or a combination of software and hardware. Taking software implementation as an example, as a device in a logical sense, it is formed by the processor of any device with data processing capabilities in which it is located reading the corresponding computer program instructions in the non-volatile memory into the internal memory for execution. From a hardware perspective, if Fig.10 As shown, it is a hardware structure diagram of any device with data processing capability where an accelerated lithography hot spot detection device provided by the present invention is located, except Fig.10 In addition to the processor, memory, network interface, and non-volatile memory shown, any device with data processing capabilities in which the apparatus in the embodiments is located may also include other hardware, generally based on the actual functions of the device with data processing capabilities, which will not be described in detail.

[0084] The implementation process of the functions and effects of each unit in the above-mentioned device is specifically described in the implementation process of the corresponding steps in the above-mentioned method, and will not be repeated here.

[0085] For the device embodiment, since it basically corresponds to the method embodiment, the relevant parts can refer to the partial description of the method embodiment. The device embodiment described above is only schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of the present invention. Ordinary technicians in this field can understand and implement it without paying creative work.

[0086] An embodiment of the present invention further provides a computer-readable storage medium on which a program is stored. When the program is executed by a processor, a method for accelerating lithography hotspot detection in the above embodiment is implemented.

[0087] The computer-readable storage medium may be an internal storage unit of any device with data processing capability described in any of the aforementioned embodiments, such as a hard disk or a memory. The computer-readable storage medium may also be an external storage device of any device with data processing capability, such as a plug-in hard disk, a smart media card (SMC), an SD card, a flash card, etc. equipped on the device. Furthermore, the computer-readable storage medium may also include both an internal storage unit and an external storage device of any device with data processing capability. The computer-readable storage medium is used to store the computer program and other programs and data required by any device with data processing capability, and may also be used to temporarily store data that has been output or is to be output.

[0088] The present invention also provides a computer program product, including a computer program, and when the computer program is executed by a processor, the method for accelerating lithography hotspot detection is implemented.

[0089] Those skilled in the art will readily appreciate other embodiments of the present application after considering the description and practicing the contents disclosed herein. The present application is intended to cover any modification, use or adaptation of the present application, which follows the general principles of the present application and includes common knowledge or customary techniques in the art that are not disclosed in the present application. The description and examples are intended to be exemplary only, and the true scope and spirit of the present application are indicated by the claims.

[0090] It should be understood that the above general description and the detailed description below are only exemplary and explanatory and cannot limit the present application. The present application is not limited to the precise structure described above and shown in the drawings, and various modifications and changes can be made without departing from the scope thereof. The scope of the present application is limited only by the attached claims.

Claims

1. A method for accelerating photolithography hotspot detection, characterized in that: The method includes: S1. Obtain product layout data; S2. Check the hot spots of the lithography process on the product layout data, locate the hot spots, and set the radius of the hot spot graphic area; S3, convert the hotspot area graph into feature vectors and map them to nodes of the graph, and use the greedy algorithm to realize the formation and update of the dynamic hotspot library based on the graph; S4. Localization of product map data and screening of high-risk hotspot map areas; S5. Output of lithography simulation and lithography hotspot pattern detection results.

2. The method for accelerating photolithography hotspot detection according to claim 1, characterized in that: The radius of the hot spot pattern area is greater than 3 times the minimum design rule CD and less than 15 times the minimum design rule CD.

3. The method for accelerating lithography hotspot detection according to claim 1, characterized in that: The method of converting the hotspot area graph into a feature vector and mapping it into a node of the graph includes: converting each hotspot area graph into a feature vector by calling a code library function, using the feature vector as an identifier of the hotspot and mapping the identifier into a node of the graph, wherein the nodes are connected to each other at different levels.

4. The method for accelerating photolithography hotspot detection according to claim 1, characterized in that: The method of using the greedy algorithm to realize the formation of a dynamic hotspot library based on a graph is specifically as follows: The number of layers of the layout is preset according to the size of the hot spot area graph, the top layer is selected as the traversal layer, node J is selected and the nearest neighbor list, waiting list and visited list of the node are established in the traversal layer, wherein the capacity upper limit k is set for the nearest neighbor list, and the nearest neighbor list is initialized to any point in the traversal layer except the node J selected above; Determine the distance relationship between each node in the waiting list and node J, and add the nodes with a distance less than the maximum distance in the nearest list to the nearest neighbor list, otherwise ignore them until all the nodes in the waiting list in this layer are traversed; if the number of nearest neighbor lists in this layer exceeds the upper limit k, select the k points closest to point J, if the number of nodes in this layer is less than k, then put all the nodes in this layer into the nearest neighbor list; all the traversed nodes are put into the visited list; After all nodes in this layer are traversed, enter the next layer to establish a new nearest neighbor list, waiting list and visited list for traversal, and loop to the bottom layer. The nodes in the last bottom layer nearest neighbor list whose distance to point J is less than or equal to the threshold Dth are regarded as hot spots of the same type. The selection of Dth is determined according to the size of the hot spot graph; nodes of the same type keep the original node graph, if they are not of the same type, the nodes are inserted and the node graph is updated.

5. The method for accelerating photolithography hotspot detection according to claim 4, characterized in that: The number of layers of the layout preset according to the size of the hot spot area graphic is specifically: ； Among them, r is the radius of the intercepted figure, a represents the standard size, l is the fixed length value of the segmented edge, V i Represents the number of graphics vertices, d is the size of the polygon graphics in the layout; the radius of the hotspot graphics setting is set between 2.5a and a, and the corresponding Ly value is set to 10 to 20 layers.

6. The method for accelerating photolithography hotspot detection according to claim 4, characterized in that: The updating process of the hotspot database is as follows: a random number method is used to specify a layer number L for the node P that needs to be matched, and the P point will be embedded in all layers from the L layer to the bottom layer. Then set the top layer as M, take all the nodes in the top layer as the starting point, and search the nearest neighbor list layer by layer according to the greedy algorithm, where each layer selects the nearest n points in the nearest neighbor list as the starting point for the next layer, until the Lth layer and the bottom layer, and search the nearest neighbor list layer by layer with the starting point of the Lth layer, select the nearest j nodes in the nearest neighbor list of each layer to connect with P, and then remove the edges connecting the nodes in the Lth to the 2nd layer that are not included in the j nodes connected to P in the bottom layer and point P, and update the node graph.

7. The method for accelerating photolithography hotspot detection according to claim 1, characterized in that: The product layout data localization and high-risk hotspot layout area screening are specifically as follows: the principle of screening processing is to select areas with a higher probability of hotspot occurrence and exclude areas with a lower probability of hotspot occurrence.

8. The method for accelerating lithography hotspot detection according to claim 1, characterized in that: In the lithography simulation, the simulation area is divided according to the results of product layout data localization and high-risk hotspot layout area screening, including After removing the simulated IP library from the low-risk area, the intersection with the high-risk area is taken to obtain the area R that needs to be simulated. The edge area of ​​R is extended outward by a distance d to form an annular reference area D, where the value of d is used to expand all the edges in the hotspot to 0.1 to 0.5 times the minimum critical dimension CD value of the layout design; the lithography simulation area is the union of the lithography hotspot simulation area R and the annular reference area D.

9. An accelerated lithography hotspot detection device, comprising a memory and one or more processors, wherein the memory stores executable code, characterized in that: When the processor executes the executable code, a method for accelerating lithography hotspot detection as described in any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium having a program stored thereon, characterized in that: When the program is executed by a processor, a method for accelerating lithography hotspot detection as described in any one of claims 1 to 8 is implemented.

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