Photoetching control method, control device, photoetching system, storage medium and electronic equipment
Through machine learning models, the problem of insufficient accuracy of the lithography process is solved, and a higher-precision lithography process is achieved, and a smaller size and higher density semiconductor device manufacturing is adapted to.
Patent Information
- Application Number
- CN202510766739.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-09
- Publication Date
- 2025-07-18
AI Technical Summary
The existing lithography process has low accuracy and is difficult to meet the manufacturing needs of semiconductor device structures with smaller sizes and higher density.
Machine learning models are used to monitor lithography process data and adjust lithography parameters or equipment in real time to optimize lithography process accuracy.
It improves the accuracy of the lithography process, meets the manufacturing needs of semiconductor device structures with smaller sizes and higher density, reduces artificial errors, and improves efficiency.
Smart Images
Figure CN120335256A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of semiconductor technology, and particularly to a lithography control method, a control device, a lithography system, a storage medium, and an electronic device. Background Art
[0002] The lithography process is one of the key steps in semiconductor manufacturing. By transferring the pattern on the mask to the surface of the wafer coated with photoresist, the precise replication of micro-nano structures is achieved. The accuracy of the lithography process not only affects the minimum feature size of semiconductor devices, but also directly affects the performance, yield, and cost of the devices.
[0003] With the continuous progress of semiconductor device manufacturing processes, the demand for smaller-sized and higher-density semiconductor device structures is also increasing continuously.
[0004] However, the existing lithography process has low accuracy and is difficult to meet the manufacturing requirements of smaller-sized and higher-density semiconductor device structures. Summary of the Invention
[0005] The problem to be solved by the present invention is: how to improve the accuracy of the lithography process.
[0006] To solve the above problem, an embodiment of the present invention provides a lithography control method, which includes:
[0007] Using a first machine learning model, determining initial lithography parameters, and sending them to a lithography device to control the lithography device to perform a current lithography operation according to the initial lithography parameters;
[0008] Monitoring the lithography process data during the execution of the current lithography operation by the lithography device, and performing lithography accuracy analysis based on the monitored lithography process data;
[0009] When the lithography accuracy of the current lithography operation does not meet the preset lithography accuracy requirement, adjusting the initial lithography parameters or the lithography device to control the next lithography operation of the lithography device.
[0010] In a possible embodiment, the step of, when the lithography accuracy of the current lithography operation does not meet the preset lithography accuracy requirement, adjusting the initial lithography parameters or the lithography device to control the next lithography operation of the lithography device includes:
[0011] When the lithography accuracy of the current lithography operation does not meet the preset lithography accuracy requirement, adjusting the initial lithography parameters;
[0012] When the predicted result of the lithography accuracy corresponding to the adjusted lithography parameters does not meet the preset lithography accuracy requirement, and the adjusted lithography parameters reach the preset lithography parameter adjustment limit value, adjusting the lithography device to control the next lithography operation of the lithography device.
[0013] In a possible embodiment, a lithography accuracy prediction result corresponding to the adjusted lithography parameters is obtained by using the first machine learning model.
[0014] In a possible embodiment, when the lithography accuracy analysis result does not meet the preset lithography accuracy requirement, the initial lithography parameters are adjusted by using the first machine learning model.
[0015] In a possible embodiment, the adjustment of the lithography equipment includes: generating lithography equipment hardware adjustment instruction information and sending it to the lithography equipment to adjust the hardware configuration of the lithography equipment.
[0016] In a possible embodiment, the first machine learning model is obtained by learning the relationship between lithography process data and lithography accuracy.
[0017] In a possible embodiment, the lithography process data includes: in-chamber environment data, lithography parameters, wafer characteristic data, and photoresist response data.
[0018] In a possible embodiment, the lithography parameters include: exposure time, light source intensity, focal length, and photoresist concentration.
[0019] In a possible embodiment, the second machine learning model is a regression analysis model, a neural network model, a decision tree model, a reinforcement learning model, or a genetic algorithm model.
[0020] An embodiment of the present invention further provides a lithography control device, and the device includes:
[0021] A determination unit, adapted to use a first machine learning model to determine initial lithography parameters and send them to a lithography equipment to control the lithography equipment to perform a current lithography operation according to the initial lithography parameters;
[0022] A monitoring unit, adapted to monitor lithography process data during the execution of the current lithography operation by the lithography equipment and perform lithography accuracy analysis based on the monitored lithography process data;
[0023] An adjustment unit, adapted to adjust the initial lithography parameters or the lithography equipment when the lithography accuracy of the current lithography operation does not meet the preset lithography accuracy requirement, so as to control the next lithography operation of the lithography equipment.
[0024] An embodiment of the present invention further provides a lithography system, and the system includes: the above-mentioned lithography control device and a lithography equipment; the lithography equipment is adapted to perform a lithography operation under the control of the lithography control device.
[0025] An embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored, and the computer program is executed by a processor to implement the steps of any one of the above methods.
[0026] An embodiment of the present invention also provides an electronic device, including a memory and a processor, where a computer program capable of running on the processor is stored on the memory, and when the processor runs the computer program, it executes the steps of any one of the above methods.
[0027] Compared with the prior art, the technical solution of the embodiment of the present invention has the following advantages:
[0028] Applying the solution of the present invention, by monitoring the lithography process data during the execution of the current lithography operation by the lithography equipment, and performing lithography accuracy analysis based on the monitored lithography process data, once the lithography accuracy analysis result does not meet the preset lithography accuracy requirement, the initial lithography parameters or the lithography equipment can be adjusted in real time, so as to control the next lithography operation of the lithography equipment. Thus, dynamic adjustment of the lithography process can be realized, and the lithography process can be continuously optimized, the lithography process accuracy can be improved, and the manufacturing requirements of semiconductor device structures with smaller sizes and higher densities can be met. In addition, using the first machine learning model to determine the initial lithography parameters can reduce the workload of manually determining the initial lithography parameters, improve the lithography efficiency, and reduce the influence of the deviation of manually determining the initial lithography parameters on the lithography accuracy, further improving the lithography accuracy. Description of the Drawings
[0029] Figure 1 is a flowchart of a method for obtaining a first machine learning model in an embodiment of the present invention;
[0030] Figure 2 is a flowchart of a lithography control method in an embodiment of the present invention;
[0031] Figure 3 is a schematic structural diagram of a lithography control device in an embodiment of the present invention;
[0032] Figure 4 is a schematic structural diagram of a lithography system in an embodiment of the present invention. Detailed Embodiments
[0033] The lithography process is a key technology for creating microstructures and patterns in the semiconductor manufacturing process. The lithography process coats a photosensitive material (such as photoresist) on the surface of a semiconductor wafer, and then uses a mask and a light source to transfer the required pattern onto the photoresist. Specifically, the lithography process may include the following steps:
[0034] 1) Prepare the wafer and clean the wafer, and evenly coat a layer of photoresist on the surface of the wafer;
[0035] 2) Heat the wafer coated with photoresist to remove the residual solvent in the photoresist and dry it evenly.
[0036] 3) Place the mask (the transparent part containing the required pattern) above the photoresist coating and ensure its alignment.
[0037] 4) Irradiate the mask with deep ultraviolet light. The light passes through the transparent part of the mask, causing a chemical change in the photoresist to complete the exposure of the wafer.
[0038] 5) Immerse the exposed wafer in the developer. The developer will dissolve the exposed part (positive photoresist) or the unexposed part (negative photoresist) of the photoresist, thereby forming the required pattern.
[0039] 6) Further heat the photoresist. After the development process, the wafer is heated again, which can make the photoresist more stable and enhance its tolerance to subsequent process steps.
[0040] 7) Transfer the pattern from the photoresist to the material layer on the wafer surface by dry etching (plasma etching) or wet etching.
[0041] 8) After the etching is completed, use a solvent or plasma treatment to remove the remaining photoresist, leaving the etched pattern.
[0042] However, in the existing lithography process, the lithography parameters are fixed and cannot be flexibly adjusted to meet new design and manufacturing requirements, resulting in low lithography accuracy.
[0043] To solve this problem, the present invention provides a lithography control method. By using this method, the next lithography operation of the lithography equipment can be dynamically adjusted by monitoring the lithography process data during the execution of the current lithography operation, thereby continuously optimizing the lithography process and improving the lithography accuracy.
[0044] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following detailed description of the specific embodiments of the present invention will be given with reference to the accompanying drawings.
[0045] Refer to Figure 1 , the embodiment of the present invention provides a method for obtaining a first machine learning model. Specifically, the method may include the following steps:
[0046] Step 11, data collection.
[0047] The lithography control device can collect a large amount of lithography process data during the lithography process. The lithography process data may include: in-chamber environment data, lithography parameters, wafer characteristic data, and photoresist response data. Among them, the in-chamber environment data may include data such as in-chamber temperature and humidity. The lithography parameters may include: exposure time, light source intensity, focal length, photoresist concentration, etc., and may also include exposure wavelength, exposure dose, numerical aperture (NA), illumination mode, partial coherence coefficient σ, etc. Among them, the numerical aperture determines the trade-off between resolution and depth of focus. The illumination mode includes annular, dipole, quasar / c-quad, etc. The partial coherence coefficient σ is used to adjust the light source focusing characteristics and affects the imaging quality and depth of focus. The wafer characteristic data may include the physical characteristic data and process characteristic data of the wafer, etc. Among them, the physical characteristic data of the wafer includes the size, crystal structure, and surface roughness of the wafer, etc. The process characteristic data of the wafer may include the thickness of the film layer, etc. The photoresist response data may include data such as the sensitivity, contrast, and thermal fluidity of the photoresist.
[0048] In a specific implementation, the lithography control device may also collect lithography accuracy data corresponding to different lithography processes. Among them, the lithography accuracy data may include at least one of post-lithography critical dimension (CD) data, overlay accuracy data, and line width uniformity data.
[0049] Step 12, data preprocessing.
[0050] In a specific implementation, the lithography control device may preprocess the collected data, including data cleaning, denoising, and normalization, etc.
[0051] Step 13, model architecture selection.
[0052] In a specific implementation, the first machine learning model can be obtained by using a variety of machine learning (ML) algorithms. The machine learning algorithms include but are not limited to regression analysis algorithms, neural network algorithms, decision tree algorithms, reinforcement learning algorithms, genetic algorithms, particle swarm optimization algorithms, etc. Correspondingly, the first machine learning model can be a regression analysis model, a neural network model, a decision tree model, a reinforcement learning model, a genetic algorithm model, or a particle swarm optimization model, etc. The model architectures corresponding to different machine learning algorithms are also different.
[0053] Step 14, model training.
[0054] In a specific implementation, the initial machine learning model can be trained using lithography process data and corresponding lithography accuracy data to obtain a first machine learning model.
[0055] Step 15, parameter optimization.
[0056] In a specific implementation, test sample data can be set to optimize the parameters of the trained first machine learning model, so that the lithography accuracy prediction result of the first machine learning model is closer to the actual lithography accuracy.
[0057] Step 16, model evaluation.
[0058] In a specific implementation, the first machine learning model after parameter optimization can be evaluated to confirm that the final first machine learning model can meet the requirements of model accuracy.
[0059] In one embodiment, the first machine learning model can be a multi-input multi-output machine learning model, that is, a machine learning model with more than two lithography process data as independent variables and more than two lithography accuracy data as dependent variables. Using the first machine learning model, more than two lithography accuracy data can be predicted, and thus the lithography accuracy can be evaluated more accurately based on the prediction results. For example, using the first machine learning model, post-lithography critical dimension (CD) data and overlay data can be predicted.
[0060] Refer to Figure 2 , an embodiment of the present invention provides a lithography control method, and the method may include the following steps:
[0061] Step 21, use the first machine learning model to determine the initial lithography parameters and send them to the lithography equipment to control the lithography equipment to perform the current lithography operation according to the initial lithography parameters.
[0062] In a specific implementation, as described in steps 11 to 16, the first machine learning model is obtained by learning the relationship between lithography process data and lithography accuracy.
[0063] In a specific implementation, the lithography equipment is a device for performing lithography operations on wafers. Among them, each lithography operation can be a lithography operation on a single wafer, or a lithography operation on a batch of wafers, or a lithography operation on more than two batches of wafers, which can be specifically set according to actual needs.
[0064] In a specific implementation, before controlling the lithography equipment to perform the current lithography operation, the initial lithography parameters can be determined by using a first machine learning model. Specifically, the lithography parameters can be adjusted, and the adjusted lithography parameters are substituted into the first machine learning model to obtain a lithography accuracy prediction result. It is judged whether the lithography accuracy prediction result reaches the desired lithography accuracy. Thus, the lithography parameters are repeatedly adjusted until the lithography accuracy prediction result reaches the desired lithography accuracy, and the lithography parameters corresponding to when the desired lithography accuracy is reached are used as the initial lithography parameters.
[0065] After receiving the initial lithography parameters sent by the lithography control device, the lithography equipment can perform the current lithography operation according to the initial lithography parameters (including exposure time, light source intensity, focal length, photoresist concentration, partial coherence coefficient σ, etc.).
[0066] Step 22, monitor the lithography process data during the execution of the current lithography operation by the lithography equipment, and perform lithography accuracy analysis based on the monitored lithography process data.
[0067] In a specific implementation, multiple sensors and monitoring devices can be arranged in the chamber. The sensors and monitoring devices can be communicatively connected to the lithography control device, and thus the sensors and monitoring devices are used to collect the lithography process data during the current lithography operation in real time.
[0068] The lithography control device can also obtain the lithography accuracy of the current lithography operation by using the monitoring device. When performing lithography accuracy analysis based on the monitored lithography process data, the lithography control device can compare the lithography accuracy of the current lithography operation with the desired lithography accuracy to judge whether the lithography accuracy of the current lithography operation meets the preset lithography accuracy requirement, that is, to judge whether the lithography accuracy of the current lithography operation reaches the desired lithography accuracy.
[0069] Step 23, when the lithography accuracy of the current lithography operation does not meet the preset lithography accuracy requirement, adjust the initial lithography parameters or the lithography equipment to control the next lithography operation of the lithography equipment.
[0070] In a specific implementation, if the lithography accuracy of the current lithography operation meets the preset lithography accuracy requirement, the lithography equipment can be controlled to perform the next lithography operation according to the lithography parameters of the current lithography operation. If the lithography accuracy of the current lithography operation does not meet the preset lithography accuracy requirement, the initial lithography parameters or the lithography equipment can be adjusted so that the next lithography operation of the lithography equipment meets the preset lithography accuracy requirement.
[0071] In a specific implementation, when the lithography accuracy of the current lithography operation does not meet the preset lithography accuracy requirement, the lithography control device can first adjust the lithography parameters of the current lithography operation, that is, adjust the initial lithography parameters.
[0072] Specifically, various methods can be adopted to adjust the lithography parameters of the current lithography operation. For example, when it is detected that the light source intensity is uneven, the exposure time can be increased in a certain step. The adjusted lithography parameters can be input into the first machine learning model for lithography accuracy prediction until the lithography accuracy prediction result meets the preset lithography accuracy requirement, and the corresponding lithography parameters at this time are used as the initial lithography parameters for the next lithography operation.
[0073] In a specific implementation, when the lithography accuracy prediction result corresponding to the adjusted lithography parameters does not meet the preset lithography accuracy requirement and the adjusted lithography parameters reach the preset lithography parameter adjustment limit value, the lithography control device can control the next lithography operation of the lithography equipment by adjusting the lithography equipment.
[0074] Specifically, the lithography control device can generate lithography equipment hardware adjustment instruction information and send it to the lithography equipment to adjust the hardware configuration of the lithography equipment. After receiving this lithography equipment hardware adjustment instruction information, the lithography equipment can automatically adjust its own hardware configuration according to this information. Among them, the hardware configuration that the lithography equipment can adjust includes: the configuration information of the exposure system, the coating and developing system, and other auxiliary hardware. Among them, the configuration information of the exposure system can include: aberration compensation information, automatic alignment information, focal length information, wafer stage movement accuracy information, mask temperature control information, etc. The configuration information of the coating and developing system can include: the spin coating speed and time of the photoresist, the pre-bake (Soft Bake) temperature and time, the post-exposure bake (PEB) temperature and time, the type and concentration of the developer, the developing time and stirring method, the cooling parameters, etc. The configuration information of other auxiliary hardware can include: the automatic feedback correction of the alignment error of the on-line overlay correction system, the multi-machine matching parameters, and the environmental control information, etc.
[0075] In some embodiments, the lithography equipment hardware adjustment instruction information can also include adjustment suggestion information. Thus, the lithography equipment can adjust the hardware configuration according to this information. For example, the adjustment suggestion information can be to adjust the position of the hardware in the lithography equipment that affects alignment, thereby compensating for any deviation of the lithography parameters caused by environmental changes.
[0076] In some embodiments, the lithography control device can also analyze the state of the lithography equipment by monitoring the lithography process data in real time during the execution of the current lithography operation by the lithography equipment, and based on the monitored lithography process data, it can predict possible problems or failures of the lithography equipment, which is convenient for early adjustment and maintenance of the lithography equipment, avoiding situations that affect lithography accuracy, and reducing the downtime and scrap rate in production.
[0077] Specifically, the lithography process data may include: key lithography process data for advanced process control (APC), lithography equipment process data, and post-litho feedback data, etc. Among them, the key lithography process data for APC may include: pre-litho input data, wafer thickness data, wafer roughness data, overlay deviation of the previous layer, wafer temperature data, etc. The lithography equipment process data may include: exposure energy, alignment error, focus offset data, etc. The post-litho feedback data may include: critical dimension (CD) data, etc.
[0078] Based on the lithography process data, various methods can be used to predict whether the lithography equipment fails. For example, a time series modeling device's "health status" can be utilized, and the established model can be used to predict whether the equipment fails. Another example is that abnormal detection of the lithography equipment can be performed based on statistical results, including establishing distribution mechanical energy statistics for key indicators of each batch / each wafer, etc., to achieve abnormal detection of the lithography equipment.
[0079] As can be seen from the above, in the lithography control method of the embodiment of the present invention, a machine learning algorithm can process and analyze large-scale real-time data, so that the changes and unstable factors in the lithography process can still be better understood, and intelligent adjustment of the lithography process can be achieved. In addition, by applying the solution in the embodiment of the present invention, potential manufacturing defects and optimization parameters can also be identified to ensure the consistency and accuracy of the lithography process.
[0080] To enable those skilled in the art to better understand and implement the present invention, the corresponding devices, test systems, electronic devices, and computer-readable storage media of the above methods are described in detail below.
[0081] Refer to Figure 3 , the embodiment of the present invention further provides a lithography control device 30, and the device 30 may include: a determination unit 31, a monitoring unit 32, and an adjustment unit 33. Among them:
[0082] The determination unit 31 is adapted to use a first machine learning model to determine initial lithography parameters and send them to the lithography equipment to control the lithography equipment to perform the current lithography operation according to the initial lithography parameters;
[0083] The monitoring unit 32 is adapted to monitor the lithography process data during the execution of the current lithography operation by the lithography equipment and perform lithography accuracy analysis based on the monitored lithography process data;
[0084] The adjustment unit 33 is adapted to adjust the initial lithography parameters or the lithography equipment when the lithography accuracy of the current lithography operation does not meet the preset lithography accuracy requirement, so as to control the next lithography operation of the lithography equipment.
[0085] Regarding the determination unit 31, the monitoring unit 32, and the adjustment unit 33, specific implementation can refer to the above description of steps 21 to 23, and will not be elaborated here.
[0086] Refer to Figure 4 , an embodiment of the present invention further provides a lithography system, which includes the above-mentioned lithography control device 30 and a lithography equipment 40, and the lithography equipment 40 is adapted to perform a lithography operation under the control of the lithography control device 30.
[0087] Specifically, the lithography control device 30 can provide initial lithography parameters for each lithography operation of the lithography equipment 40. The lithography equipment 40 can perform a lithography operation according to the initial lithography parameters provided by the lithography control device 30. The lithography control device 30 can also obtain lithography process data during each lithography operation through sensors and other monitoring devices, and use the monitored lithography process data to perform lithography accuracy analysis, so as to optimize the lithography parameters. Thus, the lithography parameters in the entire lithography process can be flexibly adjusted to meet the lithography accuracy requirement, which is beneficial to the manufacture of semiconductor device structures with smaller size and higher density.
[0088] An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, and the computer program is executed by a processor to implement the steps of any one of the above methods.
[0089] In specific implementation, the computer-readable storage medium may include: ROM, RAM, a magnetic disk, an optical disc, etc.
[0090] An embodiment of the present invention further provides an electronic device, which includes a memory and a processor. A computer program capable of running on the processor is stored on the memory, and when the processor runs the computer program, it executes the steps of any one of the above methods.
[0091] Regarding each device and product described in the above embodiments, each module / unit included therein can be a software module / unit, a hardware module / unit, or can be partially a software module / unit and partially a hardware module / unit. For example, for each device and product applied to or integrated into a chip, each module / unit included therein can be implemented in the form of hardware such as circuits. Or, at least some of the modules / units can be implemented in the form of a software program that runs on a processor integrated inside the chip, and the remaining (if any) part of the modules / units can be implemented in the form of hardware such as circuits. For each device and product applied to or integrated into a chip module, each module / unit included therein can be implemented in the form of hardware such as circuits. Different modules / units can be located in the same component (such as a chip, a circuit module, etc.) or different components of the chip module. Or, at least some of the modules / units can be implemented in the form of a software program that runs on a processor integrated inside the chip module, and the remaining (if any) part of the modules / units can be implemented in the form of hardware such as circuits. For each device and product applied to or integrated into a terminal, each module / unit included therein can be implemented in the form of hardware such as circuits. Different modules / units can be located in the same component (such as a chip, a circuit module, etc.) or different components inside the terminal. Or, at least some of the modules / units can be implemented in the form of a software program that runs on a processor integrated inside the terminal, and the remaining (if any) part of the modules / units can be implemented in the form of hardware such as circuits.
[0092] Although the present invention is disclosed as above, the present invention is not limited thereto. Any person skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, the protection scope of the present invention should be subject to the scope defined by the claims.
Claims
1. A lithography control method, characterized in that, Including: Using a first machine learning model to determine initial lithography parameters and sending them to a lithography device to control the lithography device to perform a current lithography operation according to the initial lithography parameters; Monitoring lithography process data during the execution of the current lithography operation by the lithography device and performing lithography accuracy analysis based on the monitored lithography process data; When the lithography accuracy of the current lithography operation does not meet the preset lithography accuracy requirement, adjusting the initial lithography parameters or the lithography device to control the next lithography operation of the lithography device.
2. The lithography control method according to claim 1, wherein The step of adjusting the initial lithography parameters or the lithography device to control the next lithography operation of the lithography device when the lithography accuracy of the current lithography operation does not meet the preset lithography accuracy requirement includes: When the lithography accuracy of the current lithography operation does not meet the preset lithography accuracy requirement, adjusting the initial lithography parameters; When the predicted lithography accuracy result corresponding to the adjusted lithography parameters does not meet the preset lithography accuracy requirement and the adjusted lithography parameters reach a preset lithography parameter adjustment limit value, adjusting the lithography device to control the next lithography operation of the lithography device.
3. The lithography control method according to claim 2, wherein Obtaining the predicted lithography accuracy result corresponding to the adjusted lithography parameters by using the first machine learning model.
4. The lithography control method according to claim 2, characterized in that When the lithography accuracy analysis result does not meet the preset lithography accuracy requirement, using the first machine learning model to adjust the initial lithography parameters.
5. The lithography control method according to claim 2, characterized in that, Adjusting the lithography device includes: generating lithography device hardware adjustment instruction information and sending it to the lithography device to adjust the hardware configuration of the lithography device.
6. The lithography control method according to claim 1, wherein The first machine learning model is obtained by learning the relationship between lithography process data and lithography accuracy.
7. The lithography control method according to claim 6, wherein The lithography process data includes: in-chamber environment data, lithography parameters, wafer characteristic data, and photoresist response data.
8. The lithography control method according to claim 7, wherein The lithography parameters include: exposure time, light source intensity, focal length, photoresist concentration.
9. The lithography control method according to claim 6, wherein The second machine learning model is a regression analysis model, a neural network model, a decision tree model, a reinforcement learning model, or a genetic algorithm model.
10. A lithography control device, characterized in that, Including: A determination unit adapted to use a first machine learning model to determine initial lithography parameters and send them to a lithography device to control the lithography device to perform a current lithography operation according to the initial lithography parameters; A monitoring unit adapted to monitor lithography process data during the execution of the current lithography operation by the lithography device and perform lithography accuracy analysis based on the monitored lithography process data; An adjustment unit adapted to adjust the initial lithography parameters or the lithography device when the lithography accuracy of the current lithography operation does not meet the preset lithography accuracy requirement to control the next lithography operation of the lithography device.
11. A lithography system, characterized in that, Including: The lithography control device according to claim 10, and a lithography device; the lithography device is adapted to perform a lithography operation under the control of the lithography control device.
12. A computer-readable storage medium having a computer program stored thereon, characterized in that, The computer program is executed by a processor to implement the steps of the method according to any one of claims 1 to 9.
13. An electronic device, comprising a memory and a processor, wherein a computer program capable of running on the processor is stored on the memory, characterized in that, When the processor runs the computer program, it executes the steps of the method according to any one of claims 1 to 9.
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