A mask optimization method, a mask optimization device and an electronic device

Abstract

The present application relates to the field of integrated circuit mask design, and particularly relates to a mask optimization method, a mask optimization device and an electronic device, the method comprising the steps of: providing an initial mask plate pattern comprising a plurality of exposure patterns to be optimized; obtaining sub-resolution auxiliary patterns corresponding to each exposure pattern to be optimized based on a simulation lithography model; obtaining all sub-resolution auxiliary patterns of an exposure pattern to be optimized and associating based on a preset range, obtaining size information and distance information of each associated sub-resolution auxiliary pattern; analyzing the information based on a set classification rule to classify the plurality of exposure patterns to be optimized and calculating the information to obtain a setting rule for the sub-resolution auxiliary patterns of the exposure patterns to be optimized under the type; and optimizing other exposure patterns to be optimized of the same type based on the setting rule, the technical scheme provided by the present application has the characteristics of wide process window, fast optimization speed of the mask and good optimization effect.

Description

[Technical Field]

[0001] This invention relates to the field of integrated circuit mask design, and more particularly to a mask optimization method, a mask optimization device, and an electronic device. [Background Technology]

[0002] In integrated circuit manufacturing, the quality of mask design and optimization is crucial to the accuracy of photolithography imaging. Currently, there are various mask optimization methods, such as optimizing masks by adding sub-resolution auxiliary patterns. There are two main forms of adding sub-resolution auxiliary patterns: rule-based SBAR and model-based SBAR. For rule-based SBAR, the size and placement of the pattern need to be determined experimentally, and the resulting rules are closely related to the photolithography process. If the photolithography process changes, the corresponding rules need to be changed accordingly. Model-based SBAR, on the other hand, uses computational lithography technology to automatically generate the addition rules through model calculations. With model-based SBAR, if the model or process changes, only the corresponding computational model needs to be modified, which is more time-saving and labor-efficient than rule-based SBAR. However, even if the model remains unchanged, each optimization process requires recalculating and extracting the auxiliary pattern addition rules from the model. Excessive repetitive calculations waste time and computational resources, especially when dealing with the optimization of large batches of exposure patterns of different sizes. [Summary of the Invention]

[0003] To overcome the technical problems of low optimization efficiency and high optimization cost in the current mask optimization process, this invention provides a mask optimization method, a mask optimization device, and an electronic device.

[0004] To address the aforementioned technical problems, this invention provides a technical solution: a mask optimization method, comprising the following steps: providing an initial mask layout, the initial mask layout including multiple exposure patterns to be optimized; establishing a simulated lithography model; calculating sub-resolution auxiliary patterns corresponding to each exposure pattern to be optimized based on the simulated lithography model; acquiring all sub-resolution auxiliary patterns within a preset range for an exposure pattern to be optimized, and associating all sub-resolution auxiliary patterns with the exposure pattern to be optimized, obtaining the size information and distance information relative to the exposure pattern to be optimized for each associated sub-resolution auxiliary pattern; analyzing the size information and / or distance information based on a set classification rule to classify the multiple exposure patterns to be optimized, calculating the size information and distance information of all sub-resolution auxiliary patterns corresponding to the exposure patterns to be optimized under the same type to obtain setting rules for sub-resolution auxiliary patterns of the exposure patterns to be optimized under that type; adding sub-resolution auxiliary patterns to other masks to be optimized of the same type based on the setting rules for optimization.

[0005] Preferably, setting a size information threshold and / or a distance information threshold will group the sub-resolution auxiliary graphics to be optimized within the size information threshold range and / or the distance information threshold range into the same type.

[0006] Preferably, the setting rules for obtaining the sub-resolution auxiliary graphic of the exposure graphic to be optimized under the same type are specifically as follows: calculate the root mean square of all distance information and size information respectively, and the root mean square data of distance information and size information constitute the setting rules.

[0007] Preferably, the root mean square data of the distance information are arranged from small to large according to their magnitude, and when adding sub-resolution auxiliary graphics to optimize the mask to be optimized, they are added from near to far.

[0008] Preferably, obtaining the size and distance information of each sub-resolution auxiliary graphic includes the following steps: obtaining the coordinate information, size information, and coordinate information and size information of each of the exposure graphics to be optimized and each of the associated sub-resolution auxiliary graphics; calculating the distance information between the exposure graphics to be optimized and each of the associated sub-resolution auxiliary graphics based on the coordinate information, size information, and coordinate information and size information of the exposure graphics to be optimized and each of the associated sub-resolution auxiliary graphics.

[0009] Preferably, a Python algorithm is written based on the Python interface of the klayout tool to obtain the coordinate information, size information, and coordinate information and size information of each of the exposure patterns to be optimized, as well as the sub-resolution auxiliary patterns; to obtain the sub-resolution auxiliary patterns associated with any exposure pattern to be optimized, and to obtain the distance information of each sub-resolution auxiliary pattern associated with the exposure pattern to be optimized.

[0010] Preferably, the exposure pattern to be optimized includes a periodically arranged main pattern or a periodically arranged overprinted mark.

[0011] Preferably, the setting rules formed by the root mean square data of distance and size information are associated with the periodic data and line width size of the exposure pattern to be optimized and stored.

[0012] To address the aforementioned technical problems, this invention also provides a mask optimization device for optimizing a mask to be optimized. The device includes a mask design module for providing an initial mask layout, the initial mask layout including multiple exposure patterns to be optimized; a model building module for building a simulated lithography model; a calculation module for calculating sub-resolution auxiliary patterns corresponding to each exposure pattern to be optimized based on the simulated lithography model; an acquisition module for acquiring all sub-resolution auxiliary patterns of an exposure pattern to be optimized within a preset range, and associating all sub-resolution auxiliary patterns with the exposure pattern to be optimized, obtaining the size information and distance information relative to the exposure pattern to be optimized for each associated sub-resolution auxiliary pattern; a classification module for analyzing the size information and / or distance information based on set classification rules to classify the multiple exposure patterns to be optimized, and calculating the size information and distance information of all sub-resolution auxiliary patterns corresponding to the exposure patterns to be optimized of the same type to obtain setting rules for the sub-resolution auxiliary patterns of that type of exposure pattern to be optimized; and an optimization module for adding sub-resolution auxiliary patterns to the mask to be optimized based on the setting rules for optimization.

[0013] To address the aforementioned technical problems, the present invention also provides an electronic device comprising one or more processors and a storage device, wherein the storage device is used to store one or more programs, which, when executed by the one or more processors, cause the one or more processors to implement the mask optimization method as described above.

[0014] Compared with the prior art, the technical solution provided by the present invention has the following beneficial effects:

[0015] 1. First, based on the established simulated lithography model, calculations are performed to obtain the rules for adding sub-resolution auxiliary patterns to the exposure pattern to be optimized. Then, the sub-resolution auxiliary patterns added based on the model are analyzed according to classification rules. Based on the analysis results, the exposure pattern to be optimized is classified. Based on the size and distance information of the sub-resolution auxiliary patterns associated with the same category of exposure pattern to be optimized, fixed setting rules for a specific type of exposure pattern to be optimized are calculated and directly applied to the optimization of the same type of mask. That is, without changing the simulated lithography model, there is no need to recalculate the model or summarize rules based on experience and experiments. This effectively combines traditional single model-based auxiliary pattern placement rules with experience-based auxiliary pattern placement rules, greatly saving mask optimization time and obtaining a wider process window to ensure exposure accuracy.

[0016] 2. Classification rules based on size information thresholds and / or distance information thresholds result in better performance for the exposure images to be optimized, improving classification accuracy.

[0017] 3. Calculate the root mean square of all distance and size information separately. The root mean square data of distance and size information constitute the setting rules, which can more accurately represent all sub-resolution auxiliary graphic information of the exposure graphic to be optimized under this type, thus improving the accuracy of the setting rules.

[0018] 4. Further, based on the magnitude of the root mean square data of the distance information, the root mean square data is arranged from small to large. When adding sub-resolution auxiliary graphics to optimize the mask to be optimized, they are added from near to far. This can effectively refine the setting rules and further ensure that the addition of sub-resolution auxiliary graphics is more accurate.

[0019] 5. The mask optimization device and electronic device provided by the present invention have the same beneficial effects as the mask optimization method. [Attached Image Description]

[0020] Figure 1 This is a flowchart illustrating the mask optimization method provided in the first embodiment of the present invention;

[0021] Figure 2 This is a schematic diagram of the structure of the initial mask pattern provided in the first embodiment of the present invention;

[0022] Figure 3 This is a schematic diagram of another initial mask layout provided in the first embodiment of the present invention;

[0023] Figure 4 This is a schematic diagram of the sub-resolution auxiliary pattern added around the exposure pattern to be optimized, obtained based on a simulated lithography model.

[0024] Figure 5 This is a schematic diagram of the structure of two layers of sub-resolution auxiliary graphics added around the image to be optimized for exposure;

[0025] Figure 6 After executing step S4, each dashed box represents a sub-resolution auxiliary graphic and its corresponding exposure-to-optimize graphic, forming a correlation.

[0026] Figure 7 This is a schematic diagram showing the result after adding sub-resolution auxiliary graphics to other masks of the same type for optimization based on the setting rules during step S6.

[0027] Figure 8 This is a schematic diagram of the process window curves after optimizing the mask using different optimization methods;

[0028] Figure 9 This is a schematic diagram of the mask optimization device provided in the second embodiment of the present invention;

[0029] Figure 10 This is a schematic diagram of the modules of the electronic device provided in the third embodiment of the present invention;

[0030] Figure 11 This is a schematic diagram of the structure of a computer system suitable for implementing the server of the present invention.

Detailed Implementation Methods

[0031] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0032] Please see Figure 1 The first embodiment of the present invention provides a mask optimization method for optimizing a mask to be optimized, comprising the following steps:

[0033] S1. Provide an initial mask layout, which includes multiple exposure patterns to be optimized;

[0034] S2. Establish a simulated photolithography model;

[0035] S3. Calculate the sub-resolution auxiliary pattern corresponding to each exposure pattern to be optimized based on the simulated lithography model;

[0036] S4. Based on a preset range, obtain all sub-resolution auxiliary graphics of the exposure image to be optimized within the preset range, and associate all sub-resolution auxiliary graphics with the exposure image to be optimized to obtain the size information and distance information of each associated sub-resolution auxiliary graphic relative to the exposure image to be optimized.

[0037] S5. Based on the set classification rules, analyze the size information and / or distance information to classify the multiple exposure patterns to be optimized; calculate the size information and distance information of all sub-resolution auxiliary patterns corresponding to the exposure patterns to be optimized of the same type to obtain the setting rules for the sub-resolution auxiliary patterns of the exposure patterns to be optimized of that type; and

[0038] S6. Based on the setting rules, add sub-resolution auxiliary graphics to other masks of the same type to be optimized for optimization.

[0039] In step S1 above, the initial mask layout provided can be understood as a sample mask, which is mainly used to provide services for the mask optimization method proposed in this invention. The type of the initial mask layout is roughly the same as the type of the mask to be optimized. For example, the arrangement pattern or periodicity of the exposure patterns to be optimized distributed on the two masks are similar, so that the mask optimization method provided by this invention can be applied to the optimization of the mask to be optimized.

[0040] Please see Figure 2 The initial mask layout includes a regularly arranged main pattern 10. When optimizing the regularly arranged main pattern 10, the sub-resolution auxiliary patterns added also exhibit a certain regularity, making them relatively easy to summarize into empirical rules for application to the optimization of similar masks. The main pattern 10 can be understood as the pattern that will be exposed and imaged on the photoresist, while the sub-resolution auxiliary patterns will not be imaged on the photoresist.

[0041] Please see Figure 3 In other embodiments, the exposure pattern to be optimized can also be periodically arranged overlay markers. Overlay marker type masks are typically chosen when multi-layer exposure images are required. The shape and size of the overlay markers themselves are relatively simple and fixed, while their distribution also exhibits a certain regularity and periodicity. For example... Figure 3 As shown, the overlay markers include periodically arranged markers in the X and Y directions. In the X direction, there are five vertically arranged markers and five horizontally arranged markers; in the Y direction, there are five vertically arranged markers and five horizontally arranged markers. The sub-resolution auxiliary graphics added when optimizing these overlay markers also exhibit certain regularity, making them relatively easy to summarize into empirical rules for application to the optimization of similar masks. The overlay markers can be understood as patterns that will be exposed and imaged on the photoresist, while the sub-resolution auxiliary graphics will not be imaged on the photoresist. It should be noted that... Figure 3The periodic arrangement of the overprinted markings shown, or the specific number of overprinted markings, is merely an example and not a limitation. Other periodic arrangements of overprinted markings exist and are diverse.

[0042] In some specific embodiments, an initial mask layout can be designed and drawn based on the klayout tool. Once the layout is completed, the position and size information of the designed exposure pattern to be optimized can be directly read using the klayout tool.

[0043] In the following description, the exposure pattern to be optimized is further illustrated by the example of a periodically distributed overlay mark.

[0044] In step S2 above, establishing the simulated lithography model mainly involves creating a model of the lithography parameters related to the light source, photoresist, etc., involved in the lithography process. This model is used when adding sub-resolution auxiliary patterns to the initial mask pattern. The specific creation process is the same as the existing methods and will not be described in detail here.

[0045] In step S3, sub-resolution auxiliary patterns corresponding to each exposure pattern to be optimized are calculated based on the simulated lithography model. Through calculations using the simulated lithography model, sub-resolution auxiliary patterns are inserted around the exposure pattern to be optimized. Furthermore, the imaging contrast of the exposure pattern to be optimized is calculated based on the size and insertion position of the sub-resolution auxiliary patterns. Then, these parameters, such as the size and insertion position of the sub-resolution auxiliary patterns, are continuously adjusted until the maximum imaging contrast is obtained. It is considered that the inserted sub-resolution auxiliary patterns have effectively optimized the exposure pattern to be optimized.

[0046] Please see Figure 4 In one implementation, the exposure pattern 20 to be optimized is a periodically distributed elongated overlay mark. A sub-resolution auxiliary pattern 10 is calculated and added around the exposure pattern 20 based on a simulated lithography model. For example, the period is 800nm, 900nm, or other values. As an illustration, in the appendix... Figure 4 The image shown is only a partial screenshot. Figure 4 As can be seen, the sub-resolution auxiliary pattern 10 is also elongated and distributed at approximately equal intervals on both sides of the exposure pattern 20 to be optimized. For example, the distance L between them is 148nm-151nm. Simultaneously, the length and width dimensions of the sub-resolution auxiliary pattern 10 are also relatively the same. For example, its width N ranges from 20-40nm, and its length M ranges from 1-40μm.

[0047] Please see Figure 5In some complex exposure patterns 20 to be optimized, the patterns themselves are relatively intricate. Therefore, during the optimization process, multiple sub-resolution auxiliary patterns 10 need to be set. These multiple sub-resolution auxiliary patterns 10 are divided according to their distance from the exposure pattern 20 to be optimized. For example... Figure 5 As shown, each exposure pattern 20 to be optimized includes two layers of sub-resolution auxiliary patterns 10, which are equally spaced on both sides of the exposure pattern 20 to be optimized.

[0048] In step S4, based on a preset range, all sub-resolution auxiliary images within this preset range are acquired, and all sub-resolution auxiliary images are associated with the image to be optimized, obtaining the size information and distance information relative to the image to be optimized for each associated sub-resolution auxiliary image. In this step, the preset range can be set based on human experience or based on the user's requirements for imaging accuracy. Alternatively, the sub-resolution auxiliary images corresponding to each image to be optimized, calculated based on the simulated lithography model in step S3, can be considered to have a correlation with the image to be optimized.

[0049] Please see Figure 6 The preset range is 0-200nm. Using this preset range, a search can find an exposure pattern to be optimized that has a sub-resolution auxiliary pattern on both sides. This auxiliary pattern is then associated with the exposure pattern to be optimized. Specifically, in step S4, a Python algorithm is written based on the Python interface of the klayout tool to obtain the coordinate information, size information, and coordinate information and size information of each exposure pattern to be optimized, as well as the coordinate information and size information of each sub-resolution auxiliary pattern; the sub-resolution auxiliary pattern associated with any exposure pattern to be optimized is obtained, and the distance information of each sub-resolution auxiliary pattern associated with the exposure pattern to be optimized is obtained.

[0050] In step S5, the size information and / or distance information are analyzed based on the set classification rules to classify the multiple exposure patterns to be optimized. The size and distance information of all sub-resolution auxiliary graphics corresponding to the exposure patterns to be optimized of the same type are calculated to obtain the setting rules for the sub-resolution auxiliary graphics of that type of exposure pattern to be optimized. In this step, the classification of the multiple exposure patterns to be optimized is mainly based on the simultaneous analysis of the size and distance information of the sub-resolution auxiliary graphics.

[0051] The classification rules include: setting size information thresholds and / or distance information thresholds, and grouping sub-resolution auxiliary graphics associated with each other within the size information threshold range and / or distance information threshold range into the same type of exposure graphics to be optimized. Since the exposure graphics to be optimized have a certain regularity and periodicity, the size and distance information of the sub-resolution auxiliary graphics can be analyzed to classify the exposure graphics to be optimized, and certain setting rules can be summarized and applied to the optimization of the same type of exposure graphics.

[0052] The preset size and distance information thresholds can be set manually or by combining them with simulated lithography. For example, the size information threshold can be set to a maximum length of 50nm and a maximum width of 5nm for the sub-resolution auxiliary pattern. The distance information threshold can be set to a maximum distance of 10nm between the sub-resolution auxiliary pattern and the related exposure pattern to be optimized. These values ​​are just examples and are not specific limitations. In this step, a Python algorithm is written using the klayout tool's Python interface to classify the exposure patterns to be optimized that meet the classification rules. Furthermore, the size and distance information of all sub-resolution auxiliary patterns corresponding to the exposure patterns to be optimized under the same type are calculated to obtain the setting rules for the sub-resolution auxiliary patterns of that type of exposure pattern to be optimized. That is, the root mean square data of the distance and size information constitute the setting rules. Once the size and distance information of the sub-resolution auxiliary pattern of an exposure pattern to be optimized are known, sub-resolution auxiliary pattern information can be added around it.

[0053] The specific calculation method is as follows: calculate the root mean square (RMS) of all distance and dimension information separately, and use the RMS data of distance and dimension information to establish the rules. It should be noted that calculating the RMS is only one method; it can also be done by calculating the average or other methods.

[0054] In some specific embodiments, to better summarize and form the setting rules, the root mean square data of the distance information is further arranged from smallest to largest, and added from near to far when adding sub-resolution auxiliary graphics to optimize the mask to be optimized. This allows for the orderly addition of sub-resolution auxiliary graphics, avoiding omissions or deviations in the addition position. Furthermore, the inner layer of sub-resolution auxiliary graphics can be used as a reference for the relative outer layer of sub-resolution auxiliary graphics.

[0055] In some other embodiments, since the exposure pattern to be optimized is a periodically arranged main pattern or a periodically arranged overlay mark, the period data and line width dimensions of the corresponding exposure pattern to be optimized are roughly the same under the same setting rules. When storing the setting rules, they are further associated with the period data and line width dimensions to facilitate quickly finding the corresponding setting rules when optimizing exposure patterns with the same period later, thus improving work efficiency. The specific setting rules can be roughly as shown in the table below:

[0056]

[0057]

[0058] Where Pitch represents the period of the exposure pattern to be optimized, cd represents the linewidth of the sub-resolution auxiliary pattern; space1 represents the distance between the first sub-resolution auxiliary pattern and the exposure pattern to be optimized, width1 represents the width of the first sub-resolution auxiliary pattern, space2 represents the distance between the second sub-resolution auxiliary pattern and the first sub-resolution auxiliary pattern, width2 represents the width of the second sub-resolution auxiliary pattern, and Length represents the length information of the sub-resolution auxiliary pattern.

[0059] Please see Figure 7 In step S6, based on the setting rules, sub-resolution auxiliary patterns are added to other masks of the same type to be optimized to create the optimized image. The width of the sub-resolution auxiliary pattern 20 is 21 nm, and the distance between the sub-resolution auxiliary pattern 20 and the exposure pattern 10 to be optimized is 145 nm. Figure 4 The width of the sub-resolution auxiliary pattern 20, which optimizes the exposure pattern to be optimized based on the simulation lithography model, is 30nm, and the distance between the sub-resolution auxiliary pattern 20 and the exposure pattern to be optimized is 149.3nm.

[0060] Further combine with the appendix Figure 8 The graph shows the process window values ​​for the exposure pattern to be optimized (CD, nm) on the horizontal axis and the depth of focus (DOF, 5% EL) on the vertical axis. Curve 01 represents the process window value curve for the unoptimized mask during exposure. Curve 02 represents the process window value curve for the mask optimized based on the simulated lithography model during exposure. Curve 03 represents the process window value curve for the mask optimized based on the setting rules obtained in step S5 during exposure. It can be directly seen that the process window obtained after optimizing the exposure pattern using the setting rules is quite similar to the process window obtained after optimization based on the simulated lithography model.

[0061] Please see Figure 9 The second embodiment of the present invention provides a mask optimization device 100 for optimizing a mask to be optimized, which includes a mask design module 30, a model building module 40, a calculation module 50, an acquisition module 60, a classification module 70 and an optimization module 90.

[0062] Mask design module 30: used to provide an initial mask layout, the initial mask layout including multiple exposure patterns to be optimized;

[0063] Model building module 40: Building a simulated lithography model;

[0064] Calculation module 50: Calculates sub-resolution auxiliary patterns corresponding to each exposure pattern to be optimized based on the simulated lithography model;

[0065] Acquisition module 60: Based on a preset range, acquire all sub-resolution auxiliary graphics of an exposure graphic to be optimized within the preset range, and associate all sub-resolution auxiliary graphics with the exposure graphic to be optimized to obtain the size information and distance information of each associated sub-resolution auxiliary graphic relative to the exposure graphic to be optimized;

[0066] Classification module 70: Analyzes the size information and / or distance information based on the set classification rules to classify the multiple exposure patterns to be optimized, and calculates the size information and distance information of all sub-resolution auxiliary patterns corresponding to the exposure patterns to be optimized under the same type to obtain the setting rules for the sub-resolution auxiliary patterns of the exposure patterns to be optimized under that type;

[0067] Optimization module 90: Adds sub-resolution auxiliary graphics to the mask to be optimized based on the setting rules for optimization.

[0068] Please see Figure 10 The third embodiment of the present invention provides an electronic device 200, which includes one or more processors 201 and storage devices 202.

[0069] Storage device 202 is used to store one or more programs, which, when executed by one or more processors 201, enable the one or more processors 201 to implement the mask optimization method provided in the first embodiment.

[0070] The following is for reference. Figure 11 It shows a schematic diagram of the structure of a computer system 800 suitable for implementing terminal devices / servers of the present invention. Figure 11 The terminal device / server shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0071] like Figure 11As shown, the computer system 800 includes a central processing unit (CPU) 801, which can perform various appropriate actions and processes based on programs stored in read-only memory (ROM) 802 or programs loaded from storage section 808 into random access memory (RAM) 803. The RAM 803 also stores various programs and data required for the operation of the system 800. The CPU 801, ROM 802, and RAM 803 are interconnected via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0072] The following components are connected to the I / O interface 805: an input section 806 including a keyboard, mouse, etc.; an output section 807 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 808 including a hard disk, etc.; and a communication section 809 including a network interface card such as a LAN card, modem, etc. The communication section 809 performs communication processing via a network such as the Internet. A drive 810 is also connected to the I / O interface 805 as needed. A removable medium 811, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 810 as needed so that computer programs read from it can be installed into the storage section 808 as needed.

[0073] According to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 809, and / or installed from removable medium 811. When the computer program is executed by central processing unit (CPU) 801, it performs the functions defined in the methods of this invention. It should be noted that the computer-readable medium described in this invention can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. The computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0074] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0075] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0076] The units described in the embodiments of the present invention can be implemented in software or in hardware. As another aspect, the present invention also provides a computer-readable medium, which may be included in the apparatus described in the above embodiments; or it may exist independently and not assembled into the apparatus.

[0077] Compared with the prior art, the technical solution provided by the present invention has the following beneficial effects:

[0078] 1. First, based on the established simulated lithography model, calculations are performed to obtain the rules for adding sub-resolution auxiliary patterns to the exposure pattern to be optimized. Then, the sub-resolution auxiliary patterns added based on the model are analyzed according to classification rules. Based on the analysis results, the exposure pattern to be optimized is classified. Based on the size and distance information of the sub-resolution auxiliary patterns associated with the same category of exposure pattern to be optimized, fixed setting rules for a specific type of exposure pattern to be optimized are calculated and directly applied to the optimization of the same type of mask. That is, without changing the simulated lithography model, there is no need to recalculate the model or summarize rules based on experience and experiments. This effectively combines traditional single model-based auxiliary pattern placement rules with experience-based auxiliary pattern placement rules, greatly saving mask optimization time and obtaining a wider process window to ensure exposure accuracy.

[0079] 2. Classification rules based on size information thresholds and / or distance information thresholds result in better performance for the exposure images to be optimized, improving classification accuracy.

[0080] 3. Calculate the root mean square of all distance and size information separately. The root mean square data of distance and size information constitute the setting rules, which can more accurately represent all sub-resolution auxiliary graphic information of the exposure graphic to be optimized under this type, thus improving the accuracy of the setting rules.

[0081] 4. Further, based on the magnitude of the root mean square data of the distance information, the root mean square data is arranged from small to large. When adding sub-resolution auxiliary graphics to optimize the mask to be optimized, they are added from near to far. This can effectively refine the setting rules and further ensure that the addition of sub-resolution auxiliary graphics is more accurate.

[0082] 5. The mask optimization device and electronic device provided by the present invention have the same beneficial effects as the mask optimization method.

[0083] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A mask optimization method for optimizing a mask to be optimized, characterized in that... Includes the following steps: An initial mask pattern is provided, which includes multiple exposure patterns to be optimized; Establish a simulated photolithography model; Based on the simulated lithography model, sub-resolution auxiliary patterns corresponding to each exposure pattern to be optimized are calculated; Based on a preset range, acquire all sub-resolution auxiliary graphics within the preset range of an exposure graphic to be optimized, and associate all sub-resolution auxiliary graphics with the exposure graphic to be optimized to obtain the size information and distance information relative to the exposure graphic to be optimized for each associated sub-resolution auxiliary graphic. The size information and / or distance information are analyzed based on the set classification rules to classify the multiple exposure patterns to be optimized. The size information and distance information of all sub-resolution auxiliary patterns corresponding to the exposure patterns to be optimized under the same type are calculated to obtain the setting rules for the sub-resolution auxiliary patterns of the exposure patterns to be optimized under that type. Based on the aforementioned setting rules, sub-resolution auxiliary graphics are added to other masks of the same type to be optimized for further optimization. The classification rules include: setting size information thresholds and / or distance information thresholds, and classifying sub-resolution auxiliary graphics associated with size information thresholds and / or distance information thresholds into the same type; The specific rules for calculating the sub-resolution auxiliary graphic for the same type of exposure graphic to be optimized are as follows: calculate the root mean square of all distance and size information respectively, and the root mean square data of distance and size information constitute the setting rules.

2. The mask optimization method as described in claim 1, characterized in that: Based on the magnitude of the root mean square data of the distance information, the root mean square data are arranged from smallest to largest, and when adding sub-resolution auxiliary graphics to optimize the mask to be optimized, they are added from near to far.

3. The mask optimization method as described in claim 1, characterized in that: Obtaining the size and distance information of each sub-resolution auxiliary graphic involves the following steps: Obtain the coordinate information and size information of each of the exposure-to-optimize graphics, and the coordinate information and size information of each associated sub-resolution auxiliary graphic; Based on the coordinate and size information of the exposure pattern to be optimized, and the coordinate and size information of each associated sub-resolution auxiliary pattern, the distance information between the exposure pattern to be optimized and each associated sub-resolution auxiliary pattern is calculated.

4. The mask optimization method as described in claim 3, characterized in that: A Python algorithm was written based on the klayout tool's Python interface to obtain the coordinate and size information of each exposure pattern to be optimized, as well as the coordinate and size information of each sub-resolution auxiliary pattern; Obtain sub-resolution auxiliary graphics associated with any exposure pattern to be optimized, and obtain distance information about each sub-resolution auxiliary graphic associated with the exposure pattern to be optimized.

5. The mask optimization method as described in any one of claims 1-4, characterized in that: The exposure pattern to be optimized includes a periodically arranged main pattern or a periodically arranged overprinted mark.

6. The mask optimization method as described in claim 5, characterized in that, The setting rules formed by the root mean square data of distance and size information are associated with the periodic data and line width dimensions of the exposure pattern to be optimized and stored.

7. A mask optimization apparatus for optimizing a mask to be optimized, characterized in that... It includes: Mask design module: used to provide an initial mask layout, which includes multiple exposure patterns to be optimized; Model building module: Builds a simulated lithography model; Calculation module: Calculates sub-resolution auxiliary patterns corresponding to each exposure pattern to be optimized based on the simulated lithography model; Acquisition module: Based on a preset range, acquire all sub-resolution auxiliary graphics of an image to be optimized within the preset range, and associate all sub-resolution auxiliary graphics with the image to be optimized to obtain the size information and distance information of each associated sub-resolution auxiliary graphic relative to the image to be optimized; The classification module analyzes the size information and / or distance information based on the set classification rules to classify the multiple exposure patterns to be optimized. It calculates the size and distance information of all sub-resolution auxiliary patterns corresponding to the exposure patterns to be optimized of the same type to obtain the setting rules for the sub-resolution auxiliary patterns of that type. The classification rules include: setting size information thresholds and / or distance information thresholds, and classifying sub-resolution auxiliary graphics associated with size information thresholds and / or distance information thresholds into the same type; The specific rules for calculating the sub-resolution auxiliary graphic for the same type of exposure graphic to be optimized are as follows: calculate the root mean square of all distance and size information respectively, and the root mean square data of distance and size information constitute the setting rules; Optimization module: Based on the settings rules, add sub-resolution auxiliary graphics to the mask to be optimized for further optimization.

8. An electronic device, characterized in that: It includes one or more processors and storage devices. A storage device for storing one or more programs, which, when executed by one or more processors, cause the one or more processors to implement the mask optimization method as described in any one of claims 1-6.

Images