Mask manufacturing method, device, storage medium and electronic equipment

Abstract

The present application discloses a mask manufacturing method, device, storage medium and electronic device, wherein the mask manufacturing method includes determining a number of measurement points of an initial layout, and obtaining etching deviation values ​​of the measurement points and their corresponding initial feature information; using a manifold to correct the initial feature information to obtain target feature information; using the target feature information and etching deviation values ​​to perform model training to generate a deviation value prediction model; using the deviation value prediction model to obtain all etching deviation values ​​of the initial layout; generating a mask pattern based on all etching deviation values, and then manufacturing the mask. This solution can reduce etching deviations in the mask manufacturing process.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of semiconductor technology, and specifically to a mask manufacturing method, device, storage medium and electronic device. Background Art

[0002] In semiconductor manufacturing, mask manufacturing is a key step in achieving fine circuit patterns. As a core component of the photolithography process, the quality of the mask directly affects the performance and yield of the final product.

[0003] As integrated circuits continue to shrink and their functional integration increases, the etching deviation problem in the mask manufacturing process has become increasingly prominent. These deviations may not only cause circuit pattern distortion, but also cause a decline in electrical performance, thus affecting the reliability of the entire chip. Summary of the invention

[0004] The embodiments of the present application provide a mask manufacturing method, device, storage medium and electronic device, which can reduce etching deviation in the mask manufacturing process.

[0005] In a first aspect, an embodiment of the present application provides a mask manufacturing method, comprising:

[0006] Determine a number of measurement points of the initial layout, and obtain etching deviation values ​​of the measurement points and their corresponding initial feature information;

[0007] Using the manifold to correct the initial feature information to obtain target feature information;

[0008] Using the target feature information and the etching deviation value to perform model training to generate a deviation value prediction model;

[0009] Obtaining all etching deviation values ​​of the initial layout using the deviation value prediction model;

[0010] A mask pattern is generated based on all the etching deviation values, and mask manufacturing is then performed.

[0011] In the mask manufacturing method provided in the embodiment of the present application, the use of the manifold to correct the initial feature information to obtain the target feature information includes:

[0012] Screening the initial feature information to obtain secondary feature information;

[0013] The secondary feature information is corrected using the LLE algorithm to obtain target feature information.

[0014] In the mask manufacturing method provided in the embodiment of the present application, the use of the LLE algorithm to correct the secondary feature information to obtain the target feature information includes:

[0015] Using the LLE algorithm to perform dimensionality reduction processing on the secondary feature information;

[0016] Performing cluster analysis on the secondary feature information after dimensionality reduction processing to identify measurement points with similar features;

[0017] The measurement points with similar characteristics are weighted averaged to obtain the target feature information.

[0018] In the mask manufacturing method provided in the embodiment of the present application, the target feature information and the etching deviation value are used for model training to generate a deviation value prediction model, including:

[0019] Build a CNN model;

[0020] The CNN model is trained using the target feature information and the etching deviation value to generate a deviation value prediction model.

[0021] In the mask manufacturing method provided in the embodiment of the present application, the mask pattern is generated based on the total etching deviation values, and then the mask manufacturing is performed, including:

[0022] Performing etching compensation using all etching deviation values ​​to obtain a target pattern after etching;

[0023] A post-photolithography target pattern is acquired based on the post-etching target pattern, thereby generating a mask.

[0024] In the mask manufacturing method provided in the embodiment of the present application, the step of obtaining the target pattern after photolithography based on the target pattern after etching, and then generating a mask plate, includes:

[0025] Using a photolithography model or a photolithography deviation rule table on the post-etching target pattern to obtain a post-lithography target pattern;

[0026] An optical proximity correction program is run on the target pattern after photolithography to obtain a mask pattern.

[0027] In the mask manufacturing method provided in the embodiment of the present application, the step of obtaining all etching deviation values ​​of the initial layout by using the deviation value prediction model includes:

[0028] Obtaining target feature information of all measurement points of the initial layout;

[0029] The target feature information of all the measuring points is respectively input into the deviation value prediction model to obtain all the etching deviation values ​​of the initial layout.

[0030] In a second aspect, an embodiment of the present application provides a mask manufacturing device, comprising:

[0031] An acquisition unit, used to determine a number of measurement points of the initial layout, and to acquire etching deviation values ​​of the measurement points and their corresponding initial characteristic information;

[0032] A correction unit, used for correcting the initial feature information using a manifold to obtain target feature information;

[0033] A training unit, used to perform model training using the target feature information and the etching deviation value to generate a deviation value prediction model;

[0034] A prediction unit, used for obtaining all etching deviation values ​​of the initial layout by using the deviation value prediction model;

[0035] A generating unit is used to generate a mask pattern based on all etching deviation values, and then perform mask manufacturing.

[0036] In a third aspect, an embodiment of the present application provides a storage medium, wherein the storage medium stores a plurality of instructions, wherein the instructions are suitable for loading by a processor to execute any of the mask manufacturing methods described above.

[0037] In a fourth aspect, an embodiment of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements any of the above-described mask manufacturing methods when executing the computer program.

[0038] In summary, the mask manufacturing method provided by the embodiment of the present application includes determining several measurement points of the initial layout, and obtaining the etching deviation values ​​of the measurement points and their corresponding initial feature information; using the manifold to correct the initial feature information to obtain the target feature information; using the target feature information and the etching deviation value to perform model training to generate a deviation value prediction model; using the deviation value prediction model to obtain all etching deviation values ​​of the initial layout; generating a mask pattern based on all etching deviation values, and then manufacturing the mask. This solution can reduce the etching deviation in the mask manufacturing process. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0040] Figure 1 It is a schematic flow chart of the mask manufacturing method provided in an embodiment of the present application.

[0041] Figure 2It is a schematic diagram of the structure of a mask manufacturing device provided in an embodiment of the present application.

[0042] Figure 3 It is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0043] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0044] It should be noted that, in this article, the terms "include", "comprises" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element. In addition, components, features, and elements with the same name in different embodiments of the present application may have the same meaning or different meanings, and their specific meanings need to be determined by their explanation in the specific embodiment or further combined with the context of the specific embodiment.

[0045] It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0046] In the subsequent description, the suffixes such as "module", "component" or "unit" used to represent elements are only used to facilitate the description of the present application, and have no specific meanings. Therefore, "module", "component" or "unit" can be used in a mixed manner.

[0047] In the description of the present application, it should be noted that the terms "upper", "lower", "left", "right", "inner", "outer", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present application. In addition, terms such as "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0048] As integrated circuits continue to shrink and their functional integration increases, the etching deviation problem in the mask manufacturing process has become increasingly prominent. These deviations may not only cause circuit pattern distortion, but also cause a decline in electrical performance, thus affecting the reliability of the entire chip.

[0049] Based on this, the embodiments of the present application provide a mask manufacturing method, device, storage medium and electronic device. Specifically, the mask manufacturing device can be integrated in an electronic device, which can be a server or a terminal; wherein the terminal can include terminal devices such as a tablet computer, a laptop computer, and a personal computer (PC); the server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms.

[0050] The technical solutions shown in the present application will be described in detail below through specific embodiments. It should be noted that the description order of the following embodiments is not intended to limit the priority order of the embodiments.

[0051] See also Figure 1 , Figure 1 : is a schematic diagram of the process of the mask manufacturing method provided in the embodiment of the present application. The specific process of the mask manufacturing method can be as follows:

[0052] 101. Determine a number of measurement points of the initial layout, and obtain etching deviation values ​​of the measurement points and their corresponding initial feature information.

[0053] Among them, the initial layout refers to the physical layout of the chip generated after design, routing and verification in actual integrated circuit design, which includes all circuit elements, connections and compliance with design rules.

[0054] In some embodiments, several measurement points can be randomly selected in the initial layout. In another embodiment, important functional areas in the initial layout, such as contact points, wires, component edges, etc., can also be determined. Then, a uniform or non-uniform measurement grid is set according to the size of the initial layout to ensure that the entire initial layout is covered. Finally, the measurement points are selected according to the design rules of the manufacturing process to avoid selecting measurement points that are too close to the edge or sensitive area.

[0055] In some embodiments, etching can be performed according to a predetermined process, ensuring that the etching parameters of each measurement point are recorded. Then, a scanning electron microscope (SEM) or a laser rangefinder is used to measure the feature size after etching. Then, the etching deviation value is calculated by comparing the measured value with the designed value.

[0056] In some embodiments, initial feature information corresponding to the etching deviation value may be collected by electronic design automation (EDA), wherein the initial feature information may include geometric feature values ​​(such as width, length, distance, and adjacent geometric features, etc.) and density feature values.

[0057] Afterwards, a data table can be constructed based on the etching deviation value of the measurement point and its corresponding initial feature information, and the data table can be used for subsequent model training. In some embodiments, the data table of a measurement point can be as follows:

[0058]

[0059] Among them, G1-G20 represents 20 geometric eigenvalues. D1-D10 represents 10 density eigenvalues. Float_value represents the etching deviation value, which is the calibration data of the model.

[0060] It can be understood that if n measurement points are collected, a two-dimensional data table with a dimension of n*(30+1) is formed, where 30 is the number of characteristic values ​​and 1 is the number of etching deviation values.

[0061] 102. The initial feature information is modified using the manifold to obtain the target feature information.

[0062] By using manifolds to modify the initial feature information, the redundancy and noise between features can be reduced, allowing the model to converge faster during training. By simplifying the data structure, the model can find the optimal solution faster. In addition, by removing unimportant features, the complexity of the model can be reduced, thereby reducing the risk of overfitting. Improve the generalization ability of the model. In addition, manifolds provide a visual low-dimensional representation, making the data structure easier to understand. Manifolds can better capture the intrinsic structure of the data and improve the prediction accuracy of the model. Especially in high-dimensional data, manifold correction can help the model better understand the relationship between features.

[0063] In the embodiment of the present application, the manifold used is the Locally Linear Embedding (LLE) algorithm. Specifically, the initial feature information can be first screened to obtain secondary feature information; then the secondary feature information can be corrected using the LLE algorithm to obtain the target feature information.

[0064] In some embodiments, a tree model (such as a random forest, a gradient boosted tree, etc.) can be used to calculate the importance of feature information. The importance of feature information can be obtained through a feature selection method of the model. A threshold can also be set to eliminate feature information below the threshold.

[0065] In some embodiments, the LLE algorithm can be used to perform dimensionality reduction processing on the secondary feature information; cluster analysis is performed on the secondary feature information after dimensionality reduction processing to identify measurement points with similar features; and weighted averaging is performed on the measurement points with similar features to obtain target feature information.

[0066] In some embodiments, a clustering algorithm such as K-means or DBSCAN may be selected to perform cluster analysis on the secondary feature information after dimensionality reduction processing to obtain a cluster label for each measurement point, thereby identifying measurement points with similar features.

[0067] 103. Use target feature information and etching deviation values ​​to perform model training and generate a deviation value prediction model.

[0068] In the embodiment of the present application, the model used is the Convolutional Neural Network (CNN) model of Google's tensorflow.

[0069] In some embodiments, a CNN model may be constructed first; then the CNN model may be trained using target feature information and etching deviation values ​​to generate a deviation value prediction model.

[0070] During the model training process, the two-dimensional data table obtained in step 101 can be used as a data set. Then, the data set is divided into a training set, a validation set, and a test set. For example, the division ratio is 70% training set, 15% validation set, and 15% test set. After that, the model is first trained with the training set data, and then the validation set is used to evaluate the performance of the model during the training process, and the hyperparameters are adjusted to prevent overfitting. Finally, the test set is used to evaluate the performance of the trained model, and the accuracy, mean square error and other indicators are calculated. The specific training process can refer to the existing model training implementation method, which will not be repeated here.

[0071] 104. Use the deviation value prediction model to obtain all etching deviation values ​​of the initial layout.

[0072] Specifically, the target feature information of all measurement points of the initial layout can be obtained; then the target feature information of all measurement points is respectively input into the deviation value prediction model to obtain all etching deviation values ​​of the initial layout.

[0073] 105. Generate a mask pattern based on all etching deviation values, and then perform mask manufacturing.

[0074] Specifically, the entire etching deviation values ​​may be used to perform etching compensation to obtain a target pattern after etching; a target pattern after photolithography may be acquired based on the target pattern after etching, and then a mask may be generated.

[0075] In some embodiments, a photolithography model or a photolithography deviation rule table may be used for the post-etching target pattern to obtain the post-lithography target pattern; and an optical proximity correction program may be run on the post-lithography target pattern to obtain a mask pattern.

[0076] In summary, the mask manufacturing method provided by the embodiment of the present application includes determining several measurement points of the initial layout, and obtaining the etching deviation values ​​of the measurement points and their corresponding initial feature information; using the manifold to correct the initial feature information to obtain the target feature information; using the target feature information and the etching deviation value to train the model and generate a deviation value prediction model; using the deviation value prediction model to obtain all etching deviation values ​​of the initial layout; generating a mask pattern based on all etching deviation values, and then manufacturing the mask. By combining the manifold and the CNN model, this solution can improve the prediction efficiency and accuracy of the etching deviation value, thereby reducing the etching deviation in the mask manufacturing process, and thus improving the reliability of the chip.

[0077] In order to better implement the mask manufacturing method provided in the embodiment of the present application, the embodiment of the present application also provides a mask manufacturing device, wherein the meanings of the terms are the same as those in the above-mentioned mask manufacturing method, and the specific implementation details can refer to the description in the method embodiment.

[0078] See also Figure 2 , Figure 2 20 is a schematic diagram of the structure of a mask manufacturing device provided in an embodiment of the present application. The mask manufacturing device may include an acquisition unit 201, a correction unit 202, a training unit 203, a prediction unit 204 and a generation unit 205.

[0079] An acquisition unit 201 is used to determine a number of measurement points of the initial layout, and to acquire etching deviation values ​​of the measurement points and their corresponding initial feature information;

[0080] A correction unit 202, used to correct the initial feature information using the manifold to obtain target feature information;

[0081] A training unit 203 is used to perform model training using target feature information and etching deviation values ​​to generate a deviation value prediction model;

[0082] A prediction unit 204, configured to obtain all etching deviation values ​​of the initial layout using a deviation value prediction model;

[0083] The generating unit 205 is used to generate a mask pattern based on all etching deviation values, and then perform mask manufacturing.

[0084] The specific implementation of each of the above units can refer to the above-mentioned embodiment of the mask manufacturing method, which will not be described one by one here.

[0085] In summary, the mask manufacturing device provided in the embodiment of the present application determines several measurement points of the initial layout through the acquisition unit 201, and obtains the etching deviation values ​​of the measurement points and their corresponding initial feature information; the correction unit 202 uses the manifold to correct the initial feature information to obtain the target feature information; the training unit 203 uses the target feature information and the etching deviation value to perform model training to generate a deviation value prediction model; the prediction unit 204 uses the deviation value prediction model to obtain all etching deviation values ​​of the initial layout; the generation unit 205 generates a mask pattern based on all etching deviation values, and then performs mask manufacturing. This solution can improve the prediction efficiency and accuracy of the etching deviation value by combining the manifold and the CNN model, thereby reducing the etching deviation in the mask manufacturing process and improving the reliability of the chip.

[0086] The present application also provides an electronic device, in which the mask manufacturing device of the present application can be integrated, such as Figure 3 As shown, it shows a schematic diagram of the structure of the electronic device involved in the embodiment of the present application, specifically:

[0087] The electronic device may include a radio frequency (RF) circuit 301, a memory 302 including one or more computer-readable storage media, an input unit 303, a display unit 304, a sensor 305, an audio circuit 306, a wireless fidelity (WiFi) module 307, a processor 308 including one or more processing cores, and a power supply 309. Those skilled in the art will appreciate that Figure 3 The electronic device structure shown in the figure does not constitute a limitation on the electronic device, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0088] The RF circuit 301 can be used for receiving and sending signals during the process of sending and receiving information or making calls. In particular, after receiving the downlink information of the base station, it is handed over to one or more processors 308 for processing; in addition, the data related to the uplink is sent to the base station. Usually, the RF circuit 301 includes but is not limited to an antenna, at least one amplifier, a tuner, one or more oscillators, a subscriber identity module (Subscriber Identity Module, SIM) card, a transceiver, a coupler, a low noise amplifier (LowNoiseAmplifier, LNA), a duplexer, etc. In addition, the RF circuit 301 can also communicate with the network and other devices through wireless communication. Wireless communication can use any communication standard or protocol, including but not limited to Global System of Mobile communication (Global System of Mobile communication, GSM), General Packet Radio Service (General PacketRadio Service, GPRS), Code Division Multiple Access (Code Division Multiple Access, CDMA), Wideband Code Division Multiple Access (Wideband Code Division Multiple Access, WCDMA), Long Term Evolution (Long Term Evolution, LTE), email, Short Messaging Service (SMS), etc.

[0089] The memory 302 can be used to store software programs and modules. The processor 308 executes various functional applications and information processing by running the software programs and modules stored in the memory 302. The memory 302 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area may store data created according to the use of the electronic device (such as audio data, a phone book, etc.), etc. In addition, the memory 302 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices. Accordingly, the memory 302 may also include a memory controller to provide the processor 308 and the input unit 303 with access to the memory 302.

[0090] The input unit 303 can be used to receive input digital or character information, and generate keyboard, mouse, joystick, optical or trackball signal input related to user settings and function control. Specifically, in a specific embodiment, the input unit 303 may include a touch-sensitive surface and other input devices. The touch-sensitive surface, also known as a touch display screen or a touch pad, can collect the user's touch operations on or near it (such as the user's operation on or near the touch-sensitive surface using any suitable object or accessory such as a finger, a stylus, etc.), and drive the corresponding connection device according to a pre-set program. Optionally, the touch-sensitive surface may include a touch detection device and a touch controller. Among them, the touch detection device detects the user's touch orientation, detects the signal brought by the touch operation, and transmits the signal to the touch controller; the touch controller receives the touch information from the touch detection device, converts it into the touch point coordinates, and then sends it to the processor 308, and can receive and execute the command sent by the processor 308. In addition, the touch-sensitive surface can be implemented using multiple types such as resistive, capacitive, infrared and surface acoustic wave. In addition to the touch-sensitive surface, the input unit 303 may also include other input devices. Specifically, other input devices may include, but are not limited to, one or more of a physical keyboard, function keys (such as a volume control key, a switch key, etc.), a trackball, a mouse, a joystick, and the like.

[0091] The display unit 304 can be used to display information input by the user or information provided to the user, as well as various graphical user interfaces of the electronic device, which can be composed of graphics, text, icons, videos, and any combination thereof. The display unit 304 may include a display panel. Optionally, the display panel may be configured in the form of a liquid crystal display (LCD), an organic light-emitting diode (OLED), or the like. Furthermore, the touch-sensitive surface may cover the display panel, and when the touch-sensitive surface detects a touch operation on or near it, it is transmitted to the processor 308 to determine the type of touch event, and then the processor 308 provides a corresponding visual output on the display panel according to the type of touch event. Although in Figure 3 In the embodiment, the touch-sensitive surface and the display panel are used as two independent components to realize input and output functions, but in some embodiments, the touch-sensitive surface and the display panel can be integrated to realize input and output functions.

[0092] The electronic device may also include at least one sensor 305, such as a light sensor, a motion sensor, and other sensors. Specifically, the light sensor may include an ambient light sensor and a proximity sensor, wherein the ambient light sensor may adjust the brightness of the display panel according to the brightness of the ambient light, and the proximity sensor may turn off the display panel and / or backlight when the electronic device is moved to the ear. As a type of motion sensor, the gravity acceleration sensor can detect the magnitude of acceleration in each direction (generally three axes), and can detect the magnitude and direction of gravity when stationary. It can be used for applications that identify the posture of the mobile phone (such as horizontal and vertical screen switching, related games, magnetometer posture calibration), vibration recognition related functions (such as pedometer, tapping), etc.; As for other sensors that can be configured in electronic devices, such as gyroscopes, barometers, hygrometers, thermometers, infrared sensors, etc., they will not be repeated here.

[0093] The audio circuit 306, the speaker, and the microphone can provide an audio interface between the user and the electronic device. The audio circuit 306 can transmit the electrical signal converted from the received audio data to the speaker, which is converted into a sound signal for output; on the other hand, the microphone converts the collected sound signal into an electrical signal, which is received by the audio circuit 306 and converted into audio data, and then the audio data is output to the processor 308 for processing, and then sent to another electronic device through the RF circuit 301, or the audio data is output to the memory 302 for further processing. The audio circuit 306 may also include an earplug jack to provide communication between an external headset and the electronic device.

[0094] WiFi is a short-range wireless transmission technology. Electronic devices can help users send and receive emails, browse web pages, and access streaming media through WiFi module 307. It provides users with wireless broadband Internet access. Figure 3 , a WiFi module 307 is shown, but it is understandable that it is not a necessary component of the electronic device and can be omitted as needed without changing the essence of the invention.

[0095] The processor 308 is the control center of the electronic device. It uses various interfaces and lines to connect various parts of the entire mobile phone. By running or executing software programs and / or modules stored in the memory 302 and calling data stored in the memory 302, it executes various functions of the electronic device and processes data, thereby monitoring the mobile phone as a whole. Optionally, the processor 308 may include one or more processing cores; preferably, the processor 308 may integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user interface, and application programs, and the modem processor mainly processes wireless communications. It is understandable that the above-mentioned modem processor may not be integrated into the processor 308.

[0096] The electronic device also includes a power supply 309 (such as a battery) for supplying power to each component. Preferably, the power supply can be logically connected to the processor 308 through a power management system, so that the power management system can manage charging, discharging, and power consumption management. The power supply 309 can also include one or more DC or AC power supplies, recharging systems, power failure detection circuits, power converters or inverters, power status indicators, and other arbitrary components.

[0097] Although not shown, the electronic device may also include a camera, a Bluetooth module, etc., which will not be described in detail herein. Specifically in this embodiment, the processor 308 in the electronic device will load the executable files corresponding to the processes of one or more application programs into the memory 302 according to the following instructions, and the processor 308 will run the application programs stored in the memory 302, thereby realizing various functions, such as:

[0098] Determine a number of measurement points of the initial layout, and obtain etching deviation values ​​of the measurement points and their corresponding initial feature information;

[0099] The initial feature information is modified using the manifold to obtain the target feature information;

[0100] The target feature information and etching deviation value are used for model training to generate a deviation value prediction model;

[0101] Using the deviation value prediction model to obtain all etching deviation values ​​of the initial layout;

[0102] A mask pattern is generated based on all etching deviation values, and mask manufacturing is then performed.

[0103] In summary, the electronic device provided in the embodiment of the present application can determine several measurement points of the initial layout, and obtain the etching deviation values ​​of the measurement points and their corresponding initial feature information; use the manifold to correct the initial feature information to obtain the target feature information; use the target feature information and the etching deviation value to perform model training to generate a deviation value prediction model; use the deviation value prediction model to obtain all etching deviation values ​​of the initial layout; generate a mask pattern based on all etching deviation values, and then perform mask manufacturing. This solution can improve the prediction efficiency and accuracy of the etching deviation value by combining the manifold and the CNN model, thereby reducing the etching deviation in the mask manufacturing process and improving the reliability of the chip.

[0104] In the above embodiments, the description of each embodiment has its own emphasis. For the part that is not described in detail in a certain embodiment, please refer to the detailed description of the mask manufacturing method above, and will not be repeated here.

[0105] It should be noted that, for the mask manufacturing method in the embodiment of the present application, those skilled in the art can understand that all or part of the process of implementing the mask manufacturing method in the embodiment of the present application can be completed by controlling the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, such as stored in the memory of the terminal, and executed by at least one processor in the terminal. During the execution process, it can include the process of the embodiment of the mask manufacturing method.

[0106] For the mask manufacturing device of the embodiment of the present application, each functional module can be integrated into a processing chip, or each module can exist physically separately, or two or more modules can be integrated into one module. The above-mentioned integrated module can be implemented in the form of hardware or in the form of software functional modules. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0107] To this end, the present application provides a storage medium in which a plurality of instructions are stored, and the instructions can be loaded by a processor to execute the steps in any of the mask manufacturing methods provided in the present application. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), etc.

[0108] The mask manufacturing method, device, storage medium and electronic device provided by the present application are respectively introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the core idea of ​​the present application; at the same time, for technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A mask manufacturing method, characterized in that: include: Determine a number of measurement points of the initial layout, and obtain etching deviation values ​​of the measurement points and their corresponding initial feature information; Screening the initial feature information to obtain secondary feature information; Using the LLE algorithm to perform dimensionality reduction processing on the secondary feature information; Performing cluster analysis on the secondary feature information after dimensionality reduction processing to identify measurement points with similar features; Perform weighted averaging on measurement points with similar features to obtain target feature information; Using the target feature information and the etching deviation value to perform model training to generate a deviation value prediction model; Obtaining all etching deviation values ​​of the initial layout using the deviation value prediction model; A mask pattern is generated based on all the etching deviation values, and mask manufacturing is then performed.

2. The mask manufacturing method according to claim 1, characterized in that: The method of using the target feature information and the etching deviation value to perform model training and generate a deviation value prediction model includes: Build a CNN model; The CNN model is trained using the target feature information and the etching deviation value to generate a deviation value prediction model.

3. The mask manufacturing method according to claim 1, characterized in that: The step of generating a mask pattern based on all etching deviation values ​​and then manufacturing the mask comprises: Performing etching compensation using all etching deviation values ​​to obtain a target pattern after etching; A post-photolithography target pattern is acquired based on the post-etching target pattern, thereby generating a mask.

4. The mask manufacturing method according to claim 3, characterized in that: The step of acquiring a post-photolithography target pattern based on the post-etching target pattern and then generating a mask plate comprises: Using a photolithography model or a photolithography deviation rule table on the post-etching target pattern to obtain a post-lithography target pattern; An optical proximity correction program is run on the target pattern after photolithography to obtain a mask pattern.

5. The mask manufacturing method according to claim 1, characterized in that: The step of obtaining all etching deviation values ​​of the initial layout by using the deviation value prediction model includes: Obtaining target feature information of all measurement points of the initial layout; The target feature information of all the measuring points is respectively input into the deviation value prediction model to obtain all the etching deviation values ​​of the initial layout.

6. A mask manufacturing device, characterized in that: include: An acquisition unit, used to determine a number of measurement points of the initial layout, and to acquire etching deviation values ​​of the measurement points and their corresponding initial characteristic information; A correction unit, used for screening the initial feature information to obtain secondary feature information; Using the LLE algorithm to perform dimensionality reduction processing on the secondary feature information; Performing cluster analysis on the secondary feature information after dimensionality reduction processing to identify measurement points with similar features; Perform weighted averaging on measurement points with similar features to obtain target feature information; A training unit, used to perform model training using the target feature information and the etching deviation value to generate a deviation value prediction model; A prediction unit, used for obtaining all etching deviation values ​​of the initial layout by using the deviation value prediction model; A generating unit is used to generate a mask pattern based on all etching deviation values, and then perform mask manufacturing.

7. A storage medium, characterized in that: The storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor to execute the mask manufacturing method according to any one of claims 1 to 5.

8. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the mask manufacturing method according to any one of claims 1 to 5 is implemented.

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