CN115470741B

CN115470741B - Method, electronic device and storage medium for light source mask co-optimization

Info

Publication number: CN115470741B
Application number: CN202211417124.0A
Authority: CN
Inventors: 请求不公布姓名
Original assignee: Advanced Manufacturing EDA Co Ltd
Current assignee: Advanced Manufacturing EDA Co Ltd
Priority date: 2022-11-14
Filing date: 2022-11-14
Publication date: 2023-02-10
Anticipated expiration: 2042-11-14
Also published as: CN115470741A

Abstract

Example embodiments of the present disclosure provide a method, electronic device, and storage medium for light source mask co-optimization. The method comprises the following steps: acquiring graph information in an original layout, wherein the graph information at least comprises the shape and the coordinates of a graph; determining distribution information of the graph in the original layout according to the search box and the graph information; determining the corresponding distribution proportion of the graph in the original layout based on the distribution information; determining at least one graph as a target graph based on the distribution proportion; and generating a light source mask gauge file of the target graph based on the coordinate information of the target graph so as to perform light source mask collaborative optimization on the target graph. The embodiment of the disclosure can quickly extract the target graph, improve the screening efficiency and accuracy of the target graph, and further contribute to obtaining accurate simulation and photoetching results.

Description

Method, electronic device and storage medium for light source mask co-optimization

Technical Field

Embodiments of the present disclosure relate generally to the field of semiconductors, and more particularly, to integrated circuits, and more particularly, to methods, electronic devices, and computer-readable storage media for light source mask co-optimization.

Background

With the gradual reduction of technical nodes of the integrated circuit manufacturing process, the distance between target patterns is smaller and smaller, so that the density of layout patterns on a mask plate is greatly increased. Optical Proximity Correction (OPC) is a lithography enhancement technique. Optical proximity correction compensates for diffraction-induced image errors by moving the edges of the pattern on the reticle or adding additional polygons. However, it becomes increasingly difficult to adjust the mask pattern solely by means of OPC. Therefore, source Mask Optimization (SMO) is on the stage and plays a significant role. SMO has a greater degree of freedom than conventional OPC and is one of the key technologies to further improve the lithographic resolution and process window.

However, in the flow of SMO, it is very important to select a sample pattern in the design pattern. In the known scheme, a large amount of manpower is consumed for selecting the sample graph, professional personnel are needed to analyze the graph in the original layout, typical graphs are screened out from the graph, and the time and the labor are consumed, and the accuracy and the rationality cannot be guaranteed.

Disclosure of Invention

In order to solve the problems of time and labor consumption and incapability of ensuring accuracy and reasonableness in the prior art of screening typical patterns from an original layout, according to an exemplary embodiment of the disclosure, a method, an electronic device and a computer-readable storage medium for light source mask collaborative optimization are provided.

In a first aspect of the present disclosure, there is provided a method for light source mask co-optimization, comprising: acquiring graph information in an original layout, wherein the graph information at least comprises the shape and the coordinates of a graph; determining distribution information of the graph in the original layout according to the search box and the graph information; determining the corresponding distribution proportion of the graph in the original layout based on the distribution information; determining at least one graph as a target graph based on the distribution proportion; and generating a light source mask gauge file of the target pattern based on the coordinate information of the target pattern so as to perform light source mask collaborative optimization on the target pattern.

In a second aspect of the disclosure, an electronic device is provided. The electronic device includes a processor; and a memory coupled with the processor, the memory having instructions stored therein that, when executed by the processor, cause the device to perform acts comprising: acquiring graph information in an original layout, wherein the graph information at least comprises the shape and the coordinates of a graph; determining distribution information of the graph in the original layout according to the search box and the graph information; determining the corresponding distribution proportion of the graph in the original layout based on the distribution information; determining at least one graph as a target graph based on the distribution proportion; and generating a light source mask gauge file of the target graph based on the coordinate information of the target graph so as to perform light source mask collaborative optimization on the target graph.

In some embodiments, determining distribution information of the graph in the original layout according to the search box and the graph information includes: and respectively determining the shape category and the number of the graphs in the search box at different positions in the layout in response to the traversal of the search box over the layout.

In some embodiments, determining, based on the distribution information, a corresponding distribution ratio of the graph in the original layout includes: determining the type and the number of graphs belonging to the graphs in the original layout and the total number of the graphs in the original layout based on the type and the number of the shapes; based on the number of the respective category patterns and the total number of the plurality of patterns, a respective distribution ratio of each pattern is determined.

In some embodiments, determining at least one of the patterns as a target pattern based on the distribution ratio comprises: acquiring each type of graph or the weight of each graph; and for each type of graph or each graph, obtaining the product of the weight and the corresponding distribution proportion; and acquiring the graph category or graph with the product exceeding a first preset threshold value, and determining a target graph from the graph category or the graph.

In some embodiments, obtaining the category of graphics or graphics whose product exceeds a first preset threshold, and determining the target graphics therefrom comprises: acquiring a graph of which the product exceeds a first preset threshold value, and determining the graph as a target graph; and acquiring the graph category of which the product exceeds a first preset threshold value, and selecting at least one graph in the graph category as a target graph.

In some embodiments, determining at least one of the graphs as a target graph based on the distribution ratio comprises: determining graphs or graph categories with area ratios smaller than a second preset value in the original layout; and taking the determined figure as a target figure.

In some embodiments, determining the weight of the graph comprises: determining the photoetching difficulty of the graph based on the shape and the size of the graph in the layout; and endowing the graph with higher photoetching difficulty in the layout with higher weight.

In some embodiments, further comprising: and determining the size of the search box based on the process size of the layout and the light source mask collaborative optimization simulation range box.

In some embodiments, determining the size of the search box comprises: determining the sum of the process dimension and the side length of the light source mask collaborative optimization simulation range frame; and determining the size of the search box to be larger than or equal to the side length of the light source mask collaborative optimization simulation range box, and smaller than or equal to the sum of the two.

In some embodiments, the actions further comprise: determining the size of the layout and the position information of a plurality of graphs; and determining a search path of the search box based on the size of the layout and the position information of the plurality of graphs.

In some embodiments, further comprising: and outputting the coordinates of at least one graph according to the sequence of the distribution proportion from high to low.

In a third aspect of the present disclosure, a computer-readable storage medium is provided, having stored thereon a computer program which, when executed by a processor, implements a method according to the first aspect of the present disclosure.

According to the scheme of the embodiment of the disclosure, the target graph in the layout can be extracted quickly, the screening efficiency and quality of the target graph are improved, and therefore a satisfactory graph can be formed on the wafer. It should be understood that what is described in this summary section is not intended to define key or essential features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following description.

Drawings

The above and other features, advantages and aspects of embodiments of the present disclosure will become more apparent upon consideration of the following detailed description, taken in conjunction with the accompanying drawings. The same or similar reference numbers in the drawings identify the same or similar elements, of which:

fig. 1 shows a flow diagram of an SMO operation according to the related art;

FIG. 2 illustrates a schematic diagram of an example environment in which some embodiments of the present disclosure can be implemented;

FIG. 3 illustrates a flow diagram of a method for light source mask co-optimization according to some embodiments of the present disclosure;

FIG. 4 illustrates a flow of SMO operations to select a representative graph, in accordance with some embodiments of the present disclosure; and

FIG. 5 illustrates a block diagram of a computing device capable of implementing various embodiments of the present disclosure.

Detailed Description

Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but rather are provided for a more complete and thorough understanding of the present disclosure. It should be understood that the drawings and embodiments of the disclosure are for illustration purposes only and are not intended to limit the scope of the disclosure.

In describing embodiments of the present disclosure, the terms "include" and its derivatives should be interpreted as being inclusive, i.e., "including but not limited to. The term "based on" should be understood as "based at least in part on". The term "one embodiment" or "the embodiment" should be understood as "at least one embodiment". The terms "first," "second," and the like may refer to different or the same object. Other explicit and implicit definitions are also possible below.

As mentioned previously, SMO is one of the key technologies to further improve the lithographic resolution and process window. SMO considers both the source illumination mode and the mask pattern, and compared to conventional resolution enhancement techniques, SMO has a greater degree of freedom and is one of the key techniques to further improve the lithographic resolution and process window. The basic principle of light source mask co-optimization simulation calculation is similar to model-based proximity effect correction. The edge of the mask pattern is moved and its deviation from the target pattern on the wafer, i.e., edge placement error, is calculated. Disturbance of exposure dose, focusing power and pattern size on the mask plate is deliberately introduced into the model during optimization, and edge placement errors of images on the wafer caused by the disturbance are calculated. Both the merit function and the optimization are based on edge placement errors. The light source mask co-optimizes the calculated result to include not only a pixelated light source but also proximity effect corrections to the input design. Since the illumination parameters and the pattern on the mask may be varied simultaneously, the results of the optimization calculations may not be unique.

As mentioned briefly above, in the flow of SMO, it is very important to select a sample pattern in a design pattern, so that the sample pattern can cover the pattern type of the mask plate to a greater extent, and thus the optimized result is better and meets the process requirements.

Therefore, the choice of the sample needs to cover all relevant types of the original pattern as much as possible, otherwise, the optimized light source has problems, which may result in that part of the pattern cannot be etched correctly, thereby affecting the final chip yield.

The flow of SMO operations is described in detail below with reference to fig. 1. Fig. 1 shows a flow diagram of an SMO operation according to the related art. As shown in FIG. 1, in the method 100, at block 102, an original layout is provided. At block 104, a typical graphic is selected. At block 106, SMO is performed on the selected representative graph. At block 108, an initial light source is set. At block 110, SMO iteration operations are performed. At block 112, lighting conditions are set. At block 114, a mask of a typical pattern is set. At block 116, an OPC model is built to perform OPC on the overall layout based on the lighting conditions. At block 118, it is verified whether the result of the OPC meets the requirements, and in the case of a non-meeting requirement (N), the flow returns to block 110 to continue the SMO iteration. In the case of compliance (Y), at block 120, the mask layout is output.

In the known solutions, a great deal of manpower is usually required when selecting a typical figure. Specifically, a professional is required to analyze the patterns in the original layout and screen out typical patterns. The professional must have a very substantial business experience, such as considering the chip manufacturing process, the sparse pattern density, the chip design application field, etc. But also may cause the selection of typical patterns to be unreasonable or may miss typical patterns due to operator error. So that the finally determined light source is not optimized or better.

Therefore, it is desirable to select typical graphs more intelligently, i.e., to automatically screen out a part of typical graphs from the graphs in the original layout, so as to improve SMO efficiency and accuracy.

In view of the above, the present disclosure aims to provide an improved method for light source mask co-optimization.

According to an embodiment of the present disclosure, a method for light source mask co-optimization is presented. In the method, graphic information in an original layout is obtained, wherein the graphic information at least comprises the shape and the coordinates of a graphic. According to the method, distribution information of the graph in the original layout is determined according to the search box and the graph information. In the method, the corresponding distribution proportion of the graph in the original layout is determined based on the distribution information. In the method, at least one graph is determined as a target graph based on the distribution proportion. In the method, a light source mask gauge file of the target graph is generated based on the coordinates of the target graph so as to carry out light source mask collaborative optimization on the target graph.

In the scheme, the target graph (typical graph) in the layout can be quickly extracted by optimizing the selection method of the typical graph, and the screening efficiency and quality of the target graph are improved. Therefore, the scheme of the embodiment of the present disclosure can form a satisfactory pattern on a wafer.

Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings. Referring to fig. 2, there is shown a schematic diagram of an example environment 200 in which various embodiments of the present disclosure can be implemented. As shown in FIG. 2, the example environment 200 includes a computing device 210, a client 220.

In some embodiments, computing device 210 may interact with client 220. For example, computing device 210 may receive input messages from client 220 and output feedback messages to client 220. In some embodiments, the input message from the client 220 may specify the needs of the user. For example, for some parts with a small number of distributions but with special or important graphic designs, the user can selectively add the parts to the collection of typical graphics, so that the special graphics can be covered in the SMO simulation process to generate more reasonable lithography light sources. Computing device 210 may process the incoming message. For example, computing device 210 may add a user-specified graphic to a typical graphic collection and SMO the typical graphic collection in response to an input message from client 220.

In some embodiments, computing device 210 may include, but is not limited to, a personal computer, a server computer, a handheld or laptop device, a mobile device (such as a mobile phone, a personal digital assistant, PDA, a media player, etc.), a consumer electronic product, a minicomputer, a mainframe computer, a cloud computing resource, and the like.

It should be understood that the description of the structure and functionality of the example environment 200 is for illustrative purposes only and is not intended to limit the scope of the subject matter described herein. The subject matter described herein may be implemented in various structures and/or functions.

The technical solutions described above are only used for illustration and do not limit the invention. It is to be understood that the example environment 200 may have other various implementations.

To more clearly explain the principles of the disclosed solution, it will be described in more detail below with reference to fig. 3. It is to be understood that the method 300 may also include additional blocks not shown and/or may omit certain blocks shown. The scope of the present disclosure is not limited in this respect.

At block 302, graph information in an original layout is obtained, wherein the graph information includes at least a shape and coordinates of a graph. In some embodiments, the graph information is obtained by reading the original layout, for example, the total number of graphs, the positions of the graphs, the shapes of the graphs, and the like in the original layout can be obtained. Wherein the figure position information may include figure coordinates, a width size of the figure, a distance between adjacent figures, etc. In some embodiments, parameters such as the width dimension of the patterns, the distance between adjacent patterns, the shapes and the dimensions of the patterns can be obtained through geometric calculation by using the coordinate information of the patterns. The graphic coordinate information is a key information.

With respect to reading the original layout, in some embodiments, the layout file may be opened and displayed by a corresponding tool commonly used in the lithography industry. Similar tools can be embedded in the SMO tool, a layout file can be read and written, information such as coordinates and shapes of graphs in the layout can be recorded, and graph information in a certain region (a freely set range) can be counted.

At block 304, distribution information for the graph in the original layout may be determined in accordance with the search box and the graph information. In some embodiments, the layout may be searched through a search box. Specifically, the layout can be searched in regions by selecting a search box with a predetermined shape and size. In some embodiments, the size of the search box may be determined based on the process dimensions of the layout and the range of the light source mask co-optimization simulation. For example, different size search boxes may be selected for different chip processes, e.g., 28nm, 14nm, 7nm, etc. In some embodiments, the search box may be a fixed size rectangle, square, or the like.

Generally, the light source mask co-optimization simulation run is performed within a set size range (which may be referred to as a box). The size of the frame can be set according to the requirements of the customer, and a default value can be automatically set under the condition that the user does not set the size of the frame. Typically the frame is rectangular or square. The side length of the square can be 2048nm/3072nm/4096nm, etc.

The width of the minimum pattern is typically determined by the process. For example, for a 28nm chip, the width of the chip design pattern is usually about 30nm at the minimum, and a relatively safe distance is kept between adjacent patterns to prevent adjacent lines from being etched together.

In some embodiments, assuming that the side length of the simulated range box of the SMO is X, and the side length of the rectangle of the final search box is Y, then generally, Y = X + r process dimension. Where r is a parameter factor and can range from 0 to 1. In other words, the minimum size of the search box is the size of the side length of the SMO simulation range box, and the maximum size is the sum of the side length of the SMO simulation range box and the process size. It can be seen that, generally, the side length Y of the rectangle will be larger than the SMO simulation range, i.e. larger than the range of the simulation range box. In other words, the size of the search box will typically be larger than the SMO simulation range. This may try to make the search box cover the complete graph within the simulation. In this way, even if it cannot be fully guaranteed that each position of the search box in the layout covers the complete pattern, and occasionally covers a part of the pattern, the accuracy of the result is not substantially affected due to the small number.

In addition, the purpose of setting the simulation range box is to determine how large area of the graph in the layout is optimized each time the SMO simulation is optimized. It should be noted that although the scope of each SMO simulation is that of one block, the simulation may be performed in parallel, and the simulations of multiple blocks may be performed simultaneously in multiple threads.

The above embodiments mention the way of selecting a search box, however, it will be understood by those skilled in the art that embodiments of the present disclosure are not limited to the above way of selecting a search box, but may be variously changed as needed. For example, the shape, size, and manner in which the search box is determined may all vary.

In some embodiments, after the search box is selected, a search may be performed within the layout to determine a target graphic (a representative graphic). In some embodiments, dimensions of the layout and position information of the graphics may be determined. As mentioned earlier, information of the layout may be read, which may include the size of the layout, and position information of each pattern in the layout, the total number of patterns, pattern position information, the shape, size, and the like. The search path of the search box may be determined based on the size of the layout and the positional information of each graphic. In some embodiments, an initial position of the search box may first be determined based on the positional information of the determined layout. For example, the initial position of the search box may be selected as the upper left corner of the layout. And placing a search box at the upper left corner of the layout, and counting graph information, such as the number of graphs, the category of the graphs and the like, within the range of the search box. In some embodiments, for example, a total of 10 patterns may be determined, including 5 rectangles, 2L shapes, and 3T shapes. The position of the search box may then be moved, for example, in a horizontal direction to the next position, covering the next SMO simulation range, and counting the graphical information within the search box range at that position. And by analogy, gradually moving the position of the search box, searching different positions of the layout, and determining the graphic information in the range of the search box at different positions. In other words, the search box traverses the entire layout, and may determine at least the shape class and the number of graphics within the search box at different locations in the layout, respectively.

Therefore, by searching the search box, the distribution information of the graphic in the layout can be determined by combining the acquired graphic information (such as the shape, the coordinates and the like of the graphic) in the original layout. For example, it is possible to know how many patterns are in the layout, what pattern shapes are included, the distribution of patterns in various positions, and the like. Thereby helping to accurately determine the distribution ratio of each graph in the subsequent selection process.

At block 306, based on the distribution information, a corresponding distribution ratio of the graph in the original layout is determined. In some embodiments, the category and number of graphics to which the graphics in the original layout belong and the total number of graphics within the original layout may be determined based on the previously determined category and number of graphics shapes in the search box at each location. The respective distribution ratio of each type of pattern may be determined based on the number of patterns of each category and the total number of the plurality of patterns. In other words, by determining the category and number of graphics in a single search box, the total category and total number of graphics may be determined. And then, based on the number of the graphs of each category and the total number of the multiple graphs, determining the corresponding distribution proportion of the multiple graphs, that is, determining the proportion of the graphs of each category in all the graphs in the layout. For example, the rectangular shape is 60%, the L shape is 20%, the T shape is 30%, and the rest is 10%.

At block 308, at least one graph is determined to be a target graph based on the distribution scale of the graphs. In some embodiments, at least one of the patterns whose distribution ratio exceeds a ratio threshold may be determined as a target pattern. In some embodiments, assuming that the ratio threshold is 15%, for the aforementioned case that the ratio of the rectangle is 60%, the ratio of the L-shape is 20%, the ratio of the T-shape is 30%, and the remaining ratio is 10%, a certain number of figures can be selected from the rectangle, the L-shape, and the T-shape as target figures (typical figures), respectively. Specifically, one or more of the first type of graphics may be selected as a target graphic, and the target graphic is selected according to actual needs. As can be seen from the above description, an appropriate threshold value can be determined based on the distribution proportion of the pattern, so that a typical pattern can be reasonably determined, and the distribution condition of the pattern in the layout can be sufficiently reflected, thereby being helpful for obtaining accurate simulation and lithography results.

As will be appreciated by those skilled in the art, there are often more than one type of pattern, and the patterns in a chip design layout are very complex and of many types, many of which are the same pattern, but placed in different locations, combined with different patterns, with different effects. Therefore, the typical pattern to be finally selected is usually many. For example, in some embodiments, 90% of the rectangles are similar in shape, one of which may be optimized, and the other substantially the same, and optimization calculations may not be needed. The results of photolithography for the same pattern and the same light source are usually not very different. In some embodiments, the number of typical graphics may be selected from these based on their similarity. For example, substantially the same, only one may be selected. If the difference is slightly large, more than one can be selected.

In some embodiments, the target graphic may be selected taking into account both the distribution aspect ratio of the graphics and the weight of each type of graphic or each graphic. In some embodiments, each type of graph or weight of each graph may be obtained. And acquiring the product of the weight and the corresponding distribution proportion of each type of graph or each graph. And acquiring the graph category or graph with the product exceeding a first preset threshold value, and determining a target graph from the graph category or the graph. In some embodiments, obtaining the category of graphics or graphics whose product exceeds the first preset threshold, and determining the target graphics therefrom may comprise: acquiring a graph of which the product exceeds a first preset threshold value, and determining the graph as a target graph; and acquiring the graph category of which the product exceeds a first preset threshold value, and selecting at least one graph in the graph category as a target graph. In other words, a single graph whose product exceeds the first preset threshold may be used as the target graph, or one or more graphs may be selected as the target graphs from a certain category of graphs whose product exceeds the first preset threshold.

In some embodiments, the lithography difficulty of the pattern may be determined based on the shape and size of the pattern in the layout; and endowing a graph with higher photoetching difficulty in the layout with higher weight. In this way, the finally calculated light source can give consideration to the graph with higher photoetching difficulty, and finally the photoetching result meeting the requirement is obtained.

Generally, similar patterns, one optimization is sufficient. Because of the similar pattern, the final photo-etched pattern is similar. In theory it is sufficient to perform a simulation calculation with one of them. Of course, multiple numbers may be selected as desired, since the purpose of SMO is to simulate a relatively good luminaire that can tolerate all of these typical patterns. In other words, the light source on the final lithography machine is not optimal for all patterns. The light source ensures that all patterns can meet the use requirements of the chip after being carved out (for example, no wire breakage and no place where adhesion occurs). For example, in consideration of the high ratio of the rectangles, two or three more rectangles may be selected as a typical pattern in order to ensure better lithography effect of the rectangles. Assuming that two similar graphs are selected, the second graph is optimized on the basis of the first graph. I.e. the light source will eventually benefit the pattern more, but it will not have a great influence on other patterns. For example, after optimization of the light source, the error is 1nm for class A patterns and 0.9nm for class B patterns. If several class a patterns are chosen as the typical pattern, the final optimized light source may vary somewhat, but the variation should be small. After the variation, the post-lithography error of the A-type pattern was changed from 1nm to 0.98nm at most. The post-lithography error for class B patterns changed from 0.9 to 0.91nm. In summary, the proportion of the graphs is large, and some graphs can be properly selected to be added into the set of typical graphs, because each typical graph affects the final result, but the influence possibly caused to other graphs needs to be considered. In this way, the selected graph can reflect the real condition of the graph in the layout as much as possible, so that accurate simulation and photoetching results are obtained.

In some embodiments, for some parts with a small distribution number but with special or important figure shapes, the user can selectively add the parts into the collection of typical figures, so that the special figures can be covered in the SMO simulation process to generate more reasonable lithography light sources.

In the embodiment of the invention, after the shape and coordinate information of the graph is obtained, the area of the graph can be calculated according to the information, further, after the class induction processing is carried out on the graph, the area of a plurality of class graphs (if the graph is a single-shape graph, the class of graphs has only one graph) can be obtained, namely the area is the distribution information of the graph in the layout, and as the total area of the layout is known, the area ratio of each class of graphs can be calculated, namely the corresponding distribution proportion of the graph is obtained.

For example, in some embodiments, determining at least one graph as a target graph based on the distribution ratio may include: determining graphs or graph categories with the area ratio smaller than a second preset value in the original layout; and taking the determined graph as a target graph. In other words, a pattern or a pattern category having an area ratio smaller than the second preset value is also determined as the target pattern. For a pattern with a small area and a small distribution ratio, the pattern does not need to be selected as a target pattern if only the distribution ratio is considered. However, the small area is difficult to handle in the chip manufacturing process, and therefore needs to be added to the target pattern set. In this way, it can be ensured that the selected typical pattern contains information of various shapes in the layout as comprehensive as possible, thereby obtaining accurate simulation and lithography results.

Generally, in principle, the more graphics types selected, the better. But the more categories, the longer the computation time, since the computation of SMO itself is usually slow. Therefore, a typical graph needs to be selected for parameter optimization and finally reapplied to all graphs. For conventional patterns, such as squares or rectangles, it is easier to lithographically apply and the weights may be relatively small. For special shaped patterns, the difficulty of lithography is greater, and the selected weight may be greater. In this way, appropriate weights can be determined for different patterns according to actual requirements to take account of the representativeness of the selected pattern and the speed of SMO simulation calculations to obtain satisfactory simulation and lithography results.

In some embodiments, the user may be allowed to make additional or fewer adjustments to the representative image as desired. In other words, some user-selected special graphics are added, or some unimportant images are deleted. In this way, the screening cost of the typical graph is greatly reduced, and the rationality of the typical graph can be better ensured.

As can be seen from the above description of the embodiments, the typical pattern may be selected based on one or more of the scale threshold of the pattern distribution, the specificity of the pattern shape, the similarity of the pattern shapes, the difficulty of lithography, the actual requirements of the user, and the like.

At block 310, a light source mask gauge file of the target pattern is generated based on the coordinate information of the target pattern to perform light source mask co-optimization on the target pattern. In some embodiments, the coordinate information of the at least one graph may be output in order of distribution scale from high to low. In this way, the user can easily see which graphics are added to the typical graphics set based on the coordinate information of the graphics output in order, and thus can further manually adjust (increase or decrease) the scale threshold that needs to be added to the typical graphics set. The coordinate information of the at least one pattern may be used to generate a light source mask gauge file for use in a post-SMO simulation process. In some embodiments, coordinate information for one or several groups of graphics in the same category may be given for generating the gauge file. This file is a typical graphical key information file used to generate the post SMO simulation process. This file records some key information in a typical pattern, such as pattern coordinates, width, spacing between adjacent patterns, etc. That is, the selected typical pattern may record its information in the gauge file, and the recorded information may be used to find the position of the typical pattern in the whole chip design drawing during SMO simulation.

In some embodiments of the present disclosure, methods for light source mask co-optimization are shown. The above manner is merely illustrative, and embodiments of the present disclosure are not limited to the above manner. One skilled in the art, based on the teachings of the present disclosure, will appreciate that light source mask co-optimization may also be improved by other suitable methods.

Fig. 4 illustrates a flow diagram of an SMO operation to select a representative graph according to some embodiments of the present disclosure. In the method 400:

at block 402, an original layout is provided.

At block 404, layout information is automatically searched and all categories of graphics are analyzed. As mentioned in the previous embodiment, the layout may be searched using a search box to determine the category of graphics in the layout.

At block 406, the distribution fraction of an individual graph or a group of graphs in the global is calculated. For example, the proportion of an individual L-shape of a graph in the global layout, or the proportion of a set of rectangles in the global layout, is calculated.

At block 408, it is determined whether the graphical proportion is greater than a user-set threshold. When the occupancy is larger than a threshold set by the user (Y), the graphics are added to the set of typical graphics, and information such as position coordinates is output. The output information such as position coordinates can be used for accurately determining the position of the selected typical graph in the subsequent SMO simulation process. In the event that the duty is not greater than the user-set threshold (N), proceed to block 410.

At block 410, the occupancy is not greater than the user-set threshold, and the user decides to supplement the added 0 or few graphics at his or her discretion, i.e., not add or add a few graphics.

At block 412, the selected graphic is added to the collection of representative graphics and information such as location coordinates is output.

At block 414, SMO is performed on the selected representative graph.

At block 416, an initial light source is set. After the SMO is performed on the typical graph, the initial light source starts to work and participates in SMO iterative operation. After the typical pattern is screened, as an initial parameter of the SMO iteration, the light source parameter is continuously optimized and changed in the subsequent iteration process.

At block 418, an SMO iteration operation is performed.

At block 420, lighting conditions are set.

At block 422, a mask of a typical pattern is set. After SMO iteration, a reasonable light source and a graph result after SMO optimization of a typical graph are generated. The result of this pattern is a shape pattern that will be engraved on the reticle in the future.

At block 424, an OPC model is built to perform OPC on the overall layout based on the lighting conditions.

At block 426, the OPC results are verified for compliance, and in the event of compliance, return is made to block 418 to continue the SMO iteration.

In the iterative operation process, optimization of light source parameters can be performed firstly according to a theoretical algorithm formula of photoetching to obtain a light source src1, photoetching simulation is performed based on the src1, and OPC repair is performed on a typical pattern to obtain a mask1. The mask1 is subjected to verification of the lithography rule. If the mask1 does not meet the photoetching rule, the system can optimize the light source again based on the current mask1 to obtain a light source src2, then carry out OPC repair on the mask1 based on the light source src2 to obtain a mask2, and if the mask2 verification does not meet the requirement, carry out a third round of \8230;, and repeat iteration until the final mask _ n meets the rule verification.

At block 428, the mask layout is output if desired.

The aspects of the present disclosure have been described above with respect to specific embodiments, and the aspects of the present disclosure are not limited thereto but may be variously modified.

The scheme of the embodiment of the disclosure improves the screening of typical patterns. The graph information in the original layout is analyzed, the distribution proportion information of the graph with higher distribution proportion is counted, the typical graph is extracted more quickly and intelligently to serve as a sample graph, and the screening efficiency of the typical graph can be well improved. Meanwhile, the user is supported to autonomously decide to supplement the distribution to less and necessary graphs or delete part of graphs which are not much in the meaning of the user, and the reasonability and the integrity of the typical graph are better ensured. According to the scheme of the embodiment of the disclosure, a part of typical graphs can be rapidly screened out from the graphs in the original layout, the screening efficiency is improved, the SMO efficiency and accuracy are improved, and accurate simulation and photoetching results are obtained.

Fig. 5 illustrates a schematic block diagram of an example device 500 that may be used to implement embodiments of the present disclosure. For example, the computing device 210 shown in fig. 2 may be implemented by the apparatus 500. As shown, device 500 includes a Central Processing Unit (CPU) 501 that may perform various appropriate actions and processes in accordance with computer program instructions stored in a Read Only Memory (ROM) 502 or loaded from a storage unit 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the device 500 can also be stored. The CPU 501, ROM 502, and RAM 503 are connected to each other through a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.

A number of components in the device 500 are connected to the I/O interface 505, including: an input unit 506 such as a keyboard, a mouse, or the like; an output unit 507 such as various types of displays, speakers, and the like; a storage unit 508, such as a magnetic disk, optical disk, or the like; and a communication unit 509 such as a network card, modem, wireless communication transceiver, etc. The communication unit 509 allows the device 500 to exchange information/data with other devices through a computer network such as the internet and/or various telecommunication networks.

The processing unit 501 performs the various methods and processes described above, such as the method 300. For example, in some embodiments, the method 300 may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as the storage unit 508. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 500 via the ROM 502 and/or the communication unit 509. When the computer program is loaded into RAM 503 and executed by CPU 501, one or more steps of method 300 described above may be performed. Alternatively, in other embodiments, CPU 501 may be configured to perform method 300 by any other suitable means (e.g., by way of firmware).

The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a load programmable logic device (CPLD), and the like.

Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program code, when executed by the processor or controller, causes the functions/acts specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

Further, while operations are depicted in a particular order, this should be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. Under certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limitations on the scope of the disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single implementation. Conversely, various features that are described in the context of a single implementation can also be implemented in multiple implementations separately or in any suitable subcombination.

Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.

Claims

1. A method for light source mask co-optimization, comprising:

acquiring graph information in an original layout, wherein the graph information at least comprises the shape and the coordinates of a graph;

determining distribution information of the graph in the original layout according to a search box and the graph information, wherein the search box has a preset shape and size so as to perform regional search on the original layout, and the distribution information comprises the shape type and the number of the graph in the original layout;

determining the corresponding distribution proportion of the graph in the original layout based on the distribution information;

determining at least one graph as a target graph based on the distribution proportion; and

and generating a light source mask gauge file of the target graph based on the coordinates of the target graph so as to perform light source mask collaborative optimization on the target graph.

2. The method for light source mask collaborative optimization according to claim 1, wherein determining distribution information of a graph in an original layout according to a search box and the graph information includes:

and responding to the traversal of the original layout by the search box, and respectively determining the shape category and the number of the graphs in the search box at different positions in the original layout.

3. The method for light source mask collaborative optimization according to claim 2, wherein determining, based on the distribution information, a corresponding distribution ratio of the graph in the original layout includes:

determining the type and the number of the graphs belonging to the graphs in the original layout and the total number of the graphs in the original layout based on the type and the number of the shapes;

determining a respective distribution ratio for each class of graphics based on the number of graphics and the total number of graphics for each class.

4. The method for light source mask co-optimization according to any one of claims 1-3, wherein determining at least one pattern as a target pattern based on the distribution ratio comprises:

acquiring each type of graph or the weight of each graph;

aiming at each type of graph or each graph, acquiring the product of the weight and the corresponding distribution proportion; and

and acquiring the graph category or graph with the product exceeding a first preset threshold value, and determining a target graph from the graph category or the graph.

5. The method of claim 4, wherein obtaining the pattern class or pattern whose product exceeds the first predetermined threshold and determining the target pattern therefrom comprises:

acquiring a graph of which the product exceeds a first preset threshold value, and determining the graph as a target graph; and

and acquiring the graph category of which the product exceeds a first preset threshold value, and selecting at least one graph in the graph category as a target graph.

6. The method for light source mask co-optimization according to any one of claims 1-3, wherein determining at least one pattern as a target pattern based on the distribution ratio comprises:

determining graphs or graph categories with area ratios smaller than a second preset value in the original layout; and

and taking the determined graph as a target graph.

7. The method of claim 4, wherein obtaining the weight for each type of pattern or each pattern comprises:

determining the photoetching difficulty of the graph based on the shape and the size of the graph in the layout; and

and giving greater weight to the graph with higher photoetching difficulty in the layout.

8. The method for light source mask co-optimization of any one of claims 1-3, further comprising:

and determining the size of the search box based on the process size of the layout and the light source mask collaborative optimization simulation range box.

9. The method for light source mask co-optimization of claim 8, wherein determining the size of the search box comprises:

determining the sum of the process dimension and the side length of the light source mask collaborative optimization simulation range frame; and

and determining the size of the search box to be larger than or equal to the side length of the light source mask collaborative optimization simulation range box, and smaller than or equal to the sum of the two.

10. The method for light source mask co-optimization of any one of claims 1-3, further comprising:

determining the size of the layout and the position information of the graph; and

and determining a search path of the search box based on the size of the layout and the position information of the graph.

11. The method for light source mask co-optimization of claim 1, further comprising:

and outputting the coordinates of the at least one graph according to the sequence from high to low of the distribution proportion.

12. An electronic device, comprising:

a processor; and

a memory coupled with the processor, the memory having instructions stored therein that, when executed by the processor, cause the electronic device to perform the method of any of claims 1-11.

13. A computer-readable storage medium, characterized in that a computer program is stored thereon, which program, when being executed by a processor, carries out the method according to any one of claims 1-11.