Control method and system for variable slit of lithography machine and lithography machine

Abstract

The present application provides a control method, system, and lithography machine for a variable slit of a lithography machine, wherein the method comprises: a global controller obtains current state data of each motor in a driving device, the global controller determines at least one motor group based on the correlation between the motors in the driving device, the global controller performs collaborative prediction based on the current state data of each motor in the target motor group and a pre-trained behavior prediction model, obtains behavior prediction data of each motor in the target motor group in at least one future time step, and sends each behavior prediction data to a distributed collaborative control network, which controls the operation of each motor according to each behavior prediction data and via each motor controller. The present application realizes the collaborative control of multiple motors, improves the real-time performance of motor control, and improves the anti-interference capability of the system.

Description

Technical Field

[0001] The present application relates to the field of semiconductor technology, and in particular to a control method and system for a variable slit of a lithography machine, and a lithography machine. Background Art

[0002] In semiconductor lithography, the variable slit is a key component of the illumination system. It primarily consists of blades, a drive mechanism, and a control device. Four independent linear motors typically drive the four blades, creating a rectangular aperture with controllable size and position. The motors adjust the position of the blades, enabling dynamic and precise control of the exposure area. As integrated circuit manufacturing processes continue to shrink, the requirements for variable slit control accuracy, speed, and synchronization are becoming increasingly stringent.

[0003] In the existing technology, the variable slit control method mainly controls the motor individually through an independent controller. This method has problems such as insufficient coordination, limited real-time performance, weak anti-disturbance ability, and difficulty in taking into account multiple performance indicators. Summary of the Invention

[0004] The purpose of this application is to address the deficiencies in the above-mentioned prior art and provide a control method, system and lithography machine for a variable slit of a lithography machine, so as to solve the problems in the prior art of insufficient coordination, limited real-time performance, weak anti-disturbance capability and difficulty in taking into account multiple performance indicators in the variable slit control process.

[0005] To achieve the above objectives, the technical solutions adopted in this application are as follows:

[0006] In a first aspect, the present application provides a control method for a variable slit of a lithography machine, which is applied to a control system for a variable slit of a lithography machine. The system includes: a global controller, a distributed collaborative control network, and a drive device. The drive device includes: multiple motors and a motor controller corresponding to each motor. The method includes:

[0007] The global controller acquires current state data of each motor in the driving device, wherein the current state data includes at least one of the following: speed, position, and external disturbance information of each motor;

[0008] The global controller determines at least one motor group according to the association relationship between the motors in the driving device, each motor group includes a plurality of the motors, and the movement information of the first motor in the same motor group is affected by the second motor;

[0009] The global controller performs collaborative prediction based on the current state data of each motor in the target motor group and a pre-trained behavior prediction model to obtain behavior prediction data of each motor in the target motor group in at least one future time step, and sends each behavior prediction data to the distributed collaborative control network, wherein the behavior prediction data includes: predicted speed and predicted position of each motor in the target motor group;

[0010] The distributed cooperative control network controls the operation of each motor according to each behavior prediction data via each motor controller.

[0011] Optionally, the global controller performs collaborative prediction based on the current state data of each motor in the target motor group and a pre-trained behavior prediction model to obtain behavior prediction data of each motor in the target motor group in at least one future time step, including:

[0012] The global controller inputs the current state data of each motor in the target motor group into the behavior prediction model, and the behavior prediction model predicts and obtains the initial prediction data of each motor in the target motor group for at least one future time step;

[0013] The global controller optimizes the initial prediction data based on a multi-objective optimization function and constraints to obtain behavior prediction data of each motor in the target motor group in multiple future time steps. The objective function of the multi-objective optimization function includes at least one of the following: motor positioning accuracy, motor response speed and motor energy consumption. The constraints include: motor physical limitation information and control boundary information.

[0014] Optionally, the method further includes:

[0015] Acquire a current working mode of the driving device, where the current working mode includes: high-precision mode, high-speed mode, and energy-saving mode;

[0016] The weights of corresponding objective functions in the multi-objective optimization function are adjusted according to the current working mode.

[0017] Optionally, the system further comprises: an adaptive controller, a parameter identification module, and a disturbance observation module, wherein the parameter identification module and the disturbance observation module are communicatively connected to the adaptive controller, and the adaptive controller is communicatively connected to the global controller;

[0018] The method further comprises:

[0019] The disturbance observation module obtains external disturbance information of each of the motors, where the external disturbance information is used to represent the difference between the actual movement information and the target movement information of the motor;

[0020] The parameter identification module identifies and obtains motor parameter information of each motor in the drive device, wherein the motor parameter information includes at least one of the following: motor inductance, motor resistance, and friction coefficient;

[0021] The adaptive controller sends the external disturbance information and the motor parameter information to the global controller, so that the global controller updates the motor parameters of each motor stored in the global controller based on the motor parameter information.

[0022] Optionally, the disturbance observation module obtains external disturbance information of each of the motors, including:

[0023] After each of the motor controllers executes the control instruction, the disturbance observation module obtains the actual position and actual speed of each motor;

[0024] The disturbance observation module determines the external disturbance information according to the actual position, actual speed, predicted position and predicted speed of each motor.

[0025] Optionally, the parameter identification module identifies and obtains motor parameter information of each motor in the drive device, including:

[0026] The parameter identification module collects real-time data of each motor, and the real-time data includes: input voltage, input current, output speed, and motor position;

[0027] The parameter identification module performs parameter estimation on the real-time data of each motor based on the recursive least square method to obtain motor parameter information of each motor.

[0028] Optionally, the distributed collaborative control network controls the movement of each motor according to each behavior prediction data and via each motor controller, including:

[0029] The distributed collaborative control network obtains behavior prediction data of each motor according to the identification of each motor;

[0030] The distributed collaborative control network generates control instructions for each motor based on the behavior prediction data of each motor;

[0031] The distributed collaborative control network sends control instructions of each motor to the corresponding motor.

[0032] In the second aspect, the present application provides a control system for a variable slit of a lithography machine, the system comprising the global controller described in the first aspect, a distributed collaborative control network, a drive device, and a plurality of blades correspondingly connected to the drive device, the drive device comprising: a plurality of motors and a motor controller correspondingly connected to each motor, each of the motors being connected to a blade and used to drive the blade to move.

[0033] Optionally, the system further comprises the adaptive controller, parameter identification module, and disturbance observation module described in the first aspect, wherein the parameter identification module and the disturbance observation module are communicatively connected to the adaptive controller, and the adaptive controller is communicatively connected to the global controller.

[0034] In a third aspect, an embodiment of the present application further provides a lithography machine, comprising: a control system for the variable slit of the lithography machine as described in the second aspect above.

[0035] The beneficial effects of this application are: by grouping related motors and predicting the behavior of future time steps based on the current state data of the same motor group, collaborative prediction and collaborative control of related motors are achieved. By predicting the behavior data of future time steps, the subsequent tracking capability of rapidly changing trajectories can be improved, thereby improving the real-time performance of motor control. By combining external disturbance information for behavior prediction, the adaptability to external disturbances such as friction changes and load fluctuations can also be improved, thereby improving the anti-disturbance capability during the motor control process.

[0036] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work.

[0038] Figure 1 A schematic diagram of a variable slit provided in an embodiment of the present application is shown;

[0039] Figure 2 A schematic diagram of the architecture of a control system provided by an embodiment of the present application is shown;

[0040] Figure 3 A flow chart of a method for controlling a variable slit of a lithography machine provided in an embodiment of the present application is shown;

[0041] Figure 4 A flowchart of determining behavior prediction data provided by an embodiment of the present application is shown;

[0042] Figure 5 A flowchart of adjusting the weight of an objective function provided by an embodiment of the present application is shown;

[0043] Figure 6 A schematic diagram of the architecture of another control system provided in an embodiment of the present application is shown;

[0044] Figure 7 A flow chart of performing feedback correction provided by an embodiment of the present application is shown;

[0045] Figure 8 A flowchart of determining external disturbance information provided by an embodiment of the present application is shown;

[0046] Figure 9 A flow chart for determining motor parameter information provided by an embodiment of the present application is shown;

[0047] Figure 10 A flow chart showing a distributed collaborative control network issuing control instructions provided by an embodiment of the present application is shown;

[0048] Figure 11 A structural diagram of a global controller provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of this application.

[0050] It should be noted that the term "comprising" will be used in the embodiments of the present application to indicate the existence of the features declared thereafter, but does not exclude the addition of other features.

[0051] A variable slit is a mechanical device in the illumination system of a lithography machine. It typically consists of four movable blades, two of which can move in the X direction and the other two in the Y direction. Its function is to control the size and center position of the mask's illuminated field of view by adjusting the blade positions, preventing the imaging beam from exposing areas outside the exposure zone.

[0052] by Figure 1For example, assuming that the four motors in the figure are respectively connected to a blade to form a variable slit, the shape of the formed exposure area and the size of the exposure area can be changed by driving the blade to move by the motor in the figure, thereby improving the lithography accuracy, efficiency and mask utilization.

[0053] In the prior art, the motor is generally controlled by an independent motor controller, such as Figure 1 Each motor in the system is connected to a motor controller, and each motor controller drives the motor independently.

[0054] However, independently controlling the four motors makes it difficult to guarantee the shape and positional accuracy of the resulting exposure area, resulting in a lack of coordination. Traditional controller control methods cannot effectively predict the system's future state, suffer from insufficient tracking capabilities for rapidly changing trajectories, and experience limited real-time performance. Existing variable slit control methods are poorly adaptable to disturbances such as friction changes and load fluctuations, which can also affect positioning accuracy. Furthermore, existing methods struggle to simultaneously address multiple performance metrics, including positioning accuracy, response speed, and energy consumption.

[0055] Based on this, this application proposes a control method for a variable slit in a photolithography machine. This method groups strongly correlated motors and collaboratively predicts the motor behavior over a period of time based on the current state data of each motor in the group. This method then controls the motors based on their collaborative relationships, improving the coordination of motor control. Furthermore, by predicting motor behavior over a period of time, it also improves trajectory tracking capabilities and enhances the real-time performance of motor control.

[0056] Furthermore, this application also effectively improves the anti-disturbance capability during variable slit control by comparing the actual motor movement information with the predicted movement information and adjusting the motor controller parameters based on the comparison results. Furthermore, by performing multi-objective optimization on the predicted motor behavior, multiple performance indicators are considered and optimized.

[0057] Figure 2 This is a schematic diagram of the architecture of a control system for a variable slit in a lithography machine provided by this application. Figure 2 The system includes a global controller, a distributed collaborative network, and a drive device. The global controller is connected to a host computer and the distributed collaborative control network, and is used to receive process requirement data from the host computer, as well as motor data from the drive device via the distributed collaborative control network. The distributed collaborative network is connected to the drive device, which includes multiple motors and motor controllers connected to each motor. Each motor is connected to a blade, and the motor controller can control the motor to drive the blade to move.

[0058] The global controller can be a global MPC (Model Predictive Controller) implemented on an FPGA (Field-Programmable Gate Array), using a Xilinx Zynq UltraScale+ MPSoC chip with an integrated ARM Cortex-A53 processor and programmable logic unit. For example, the global controller runs at a 1kHz frequency and performs system state estimation, multi-step prediction, and optimization calculations within each control cycle.

[0059] The distributed collaborative control network communicates with the global controller via the high-speed EtherCAT bus and receives behavior prediction data from the global controller; at the same time, it interacts with the drive device through a dedicated real-time communication network to send control instructions and receive status feedback.

[0060] The drive unit can be composed of four distributed, coordinated motor controllers, each responsible for the precise control of a linear motor. For example, the motor controllers can be implemented based on high-performance DSP chips, running at a frequency of 10kHz, ensuring fast response and precise execution. The drive unit controllers not only receive instructions from the distributed coordinated control network but also exchange status information with each other via a high-speed communication network with a ring topology, achieving coordinated control.

[0061] Next, combine Figure 3 , the control method of the variable slit of the lithography machine of the present application is described, and the method can be applied to Figure 2 In the control system of the variable slit of the lithography machine shown in FIG. Figure 3 As shown, the method includes:

[0062] S301. The global controller obtains current state data of each motor in the driving device. The current state data includes at least one of the following: speed, position, and external disturbance information of each motor.

[0063] Optionally, a global controller coordinates and manages the operation of all motors in the control system, serving as the control system's central control unit. The global controller can connect to a host computer, receive process requirement data from the host computer, and determine the behavior of each motor based on this data and the current status of each motor. This process requirement data can include exposure dose, spot shape requirements, and scanning speed.

[0064] The global controller can periodically obtain the current state data of each motor in the drive device. The current state data can be discrete data. In each cycle, each motor can report the current state data to Figure 2The distributed cooperative control network in the system sends the current state data of each motor to the global controller.

[0065] In another possible implementation, the global controller can also send data acquisition instructions to each motor via the distributed collaborative control network according to a preset period. After receiving the data acquisition instruction, each motor sends the current state data to the global controller via the distributed collaborative control network.

[0066] The driving device includes a combination of multiple motors and motor controllers. Each motor controller is used to control a motor, and the motor is used to drive the movement of the slit blade to adjust the size of the slit.

[0067] Optionally, the motor speed may be the current rotational speed of the motor, and the motor position may be the current rotational position or linear position of the motor. The global controller may also obtain parameters of each motor, including parameters such as the inductance, resistance, and friction of the motor.

[0068] Optionally, the external disturbance information includes: friction change information, load fluctuation information, and electromagnetic interference information. The global controller can combine the external disturbance information to predict the motor behavior, thereby improving the anti-disturbance capability during the variable gap control process.

[0069] External disturbance information can be observed by a disturbance observer module, which can be a sensor connected to the motor to detect disturbances such as friction changes, load fluctuations, and electromagnetic interference during motor operation. As a possible implementation, external disturbance information can be estimated based on the difference between the actual movement information and the target movement information of each motor after the previous adjacent cycle.

[0070] S302: The global controller determines at least one motor group according to the association relationship between the motors in the driving device, where each motor group includes multiple motors, and movement information of a first motor in the same motor group is affected by a second motor.

[0071] The relationship between the motors can be pre-set. In a lithography machine variable slit system, multiple blades typically need to move simultaneously to adjust the size and shape of the slit. These blades are driven by different motors. In the embodiment of the present application, motors that physically or functionally affect each other can be grouped into a motor group. For example, the motors connected to blades that need to be synchronously controlled can be grouped into a motor group to ensure that the motors within the same motor group can share data and perform synchronous control.

[0072] For example, Figure 2Motor 1 and motor 2 need to ensure simultaneous movement during movement, and motor 3 and motor 4 need to ensure synchronous movement. Therefore, an association relationship can be established between motor 1 and motor 2, and an association relationship can be established between motor 3 and motor 4 to obtain two motor groups.

[0073] The global controller can pre-save the association relationship between motors and divide the motors with the association relationship into a motor group. After receiving the current status data of each motor, the current status data of the motors in the same motor group can be used as a group of data to be processed based on the pre-saved association relationship.

[0074] In another implementation, the current state data of a motor may contain an association identifier, which indicates all motors in at least one motor group that are associated with the current motor. After receiving the current state data, the global controller may treat the current state data of the same motor group as a set of data to be processed based on the association identifier in the current state data.

[0075] S303. The global controller performs collaborative prediction based on the current state data of each motor in the target motor group and the pre-trained behavior prediction model to obtain the behavior prediction data of each motor in the target motor group in at least one future time step, and sends each behavior prediction data to the distributed collaborative control network. The behavior prediction data includes: the predicted speed and predicted position of each motor in the target motor group.

[0076] Optionally, the target motor group can be any motor group in the above step S302, and the target motor group includes multiple motors. The global controller can input the current state data of all motors in the target motor group as a set of data to be processed into the behavior prediction model.

[0077] The behavior prediction model can predict the behavior prediction data of the motor at multiple future time steps based on the current state data of the motor and the system model of the drive device. The system model of the drive device can be a dynamic model obtained by modeling all motors in the drive device.

[0078] Alternatively, all motors can be pre-modeled as a system to generate a system model. This system model can then provide the dynamic coupling relationships between motors for subsequent prediction steps. After the current state data of the motor group is input into the behavior prediction model, the behavior prediction model can combine the current state data of the target motor group with the dynamic coupling relationships between the target motor groups in the system model to predict the behavior of the target motor group at multiple future time steps.

[0079] For example, the dynamic model of a single motor and the kinematic model of the blade can be established first, and then the dynamic models of each motor and the kinematic model of the blade can be integrated together based on the coupling relationship between the electrodes to form a system model that describes the entire variable slit system.

[0080] Optionally, the behavior prediction data includes a predicted speed and a predicted position of each motor in the target motor group in at least one future time step.

[0081] It should be noted that during the behavior prediction process, the behavior prediction model can first generate initial prediction data, and then the global controller can optimize the initial prediction data to obtain behavior prediction data. During the data optimization process, a particle swarm optimization algorithm can be combined with a multi-objective optimization function to perform optimization processing to ensure that the obtained behavior prediction data can simultaneously meet multiple indicators.

[0082] S304 , the distributed collaborative control network controls the operation of each motor according to each behavior prediction data and through each motor controller.

[0083] Optionally, the distributed collaborative control network can make decisions based on the behavior prediction data of each motor group, generate control instructions for each motor, and send the control instructions for each motor to the motor controller. After receiving the control instructions, the motor controller executes the control instructions and controls the speed, position, etc. of the motor, thereby driving the blade connected to the motor to move along the trajectory indicated by the control instructions to change the size, shape, etc. of the exposure area.

[0084] As a possible implementation method, the distributed collaborative control network can be a network connected to each motor controller, each motor controller is connected to the distributed collaborative control network, and uploads current status data to the global controller through the distributed collaborative control network. In addition, the distributed collaborative control network can make decisions based on the behavior prediction data of each motor group, generate instructions for each motor controller, and distribute the instructions to each motor controller.

[0085] As another possible implementation method, the distributed collaborative control network can also be a network composed of motor controllers. The global controller sends behavior prediction data to each motor controller in the distributed collaborative control network. After receiving the behavior prediction data, each motor controller can filter out the behavior prediction data of the motors in the same motor group as itself, and make decisions based on the behavior prediction data of the same group, generate control instructions, and control the operation of the motor based on the control instructions.

[0086] In the embodiments of the present application, by grouping related motors and predicting their behavior in future time steps based on the current state data of the same motor group, collaborative prediction and control of related motors are achieved. Predicting the behavior data for future time steps also improves the ability to track rapidly changing trajectories, thereby enhancing the real-time performance of motor control. By combining behavior prediction with external disturbance information, adaptability to external disturbances such as friction changes and load fluctuations can also be improved, thereby enhancing the anti-disturbance capability of the motor control process.

[0087] The following is a further explanation of how the global controller performs collaborative prediction based on the pre-trained behavior prediction model according to the current state data of each motor in the target motor group to obtain the behavior prediction data of each motor in the target motor group in at least one future time step, such as Figure 4 As shown, the above step S303 includes:

[0088] S401: The global controller inputs the current state data of each motor in the target motor group into the behavior prediction model, and the behavior prediction model predicts and obtains the initial prediction data of each motor in the target motor group for at least one future time step.

[0089] Optionally, the behavior prediction model can combine the input current state data and a pre-established system model to perform predictions to obtain initial prediction data for each motor in the target motor group for at least one future time step.

[0090] The initial prediction data includes: the initial predicted position and initial predicted speed of each motor in the target motor group in at least one future time step.

[0091] S402. The global controller optimizes the initial prediction data based on the multi-objective optimization function and the constraints to obtain the behavior prediction data of each motor in the target motor group in multiple time steps in the future. The objective function of the multi-objective optimization function includes at least one of the following: motor positioning accuracy, motor response speed, and motor energy consumption. The constraints include: motor physical limitation information and control boundary information.

[0092] As a possible implementation method, the multi-objective optimization function can be used as the objective function of the particle swarm optimization algorithm, and the particle swarm optimization algorithm is used to optimize the initial prediction data based on the multi-objective optimization function and constraints to obtain behavior prediction data.

[0093] Optionally, the multi-objective optimization function can be a weighted objective function composed of multiple objective functions as shown in the following formula (1). Where J represents the objective function, Represents time k in the future The predicted value of the system output at time , is the reference input value at time k+i, It is at the moment , ε is the control input increment, Q, R, and S are weight matrices used to balance the importance of different terms. Q is the weight matrix for the motor positioning accuracy objective function, R is the weight matrix for the motor response speed objective function, and S is the weight matrix for the motor energy consumption objective function.

[0094] (1)

[0095] Constraints are used to constrain the physical limitations and control boundaries of the motor during the optimization process. The motor physical limitation information includes the maximum torque and speed range of the motor, and the control boundary information includes the voltage limit and current limit of the motor.

[0096] In the embodiments of the present application, by predicting the current state function based on a behavior prediction model to obtain initial prediction data, it is possible to predict the future state of the system and improve the variable slit system's ability to track rapidly changing trajectories. By optimizing the initial prediction data based on a multi-objective function to obtain behavior prediction data, the behavior prediction data can be optimized to simultaneously account for multiple performance indicators such as positioning accuracy, response speed, and energy consumption, achieving multi-objective data optimization.

[0097] Optionally, the present application can also adjust the multi-objective optimization function based on different working modes to adapt to different optimization directions, such as Figure 5 As shown, the method of the present application also includes:

[0098] S501. Acquire the current working mode of the driving device. The current working mode includes: high-precision mode, high-speed mode, and energy-saving mode.

[0099] Among them, the high-precision mode means that the focus needs to be on the positioning accuracy of the motor, the high-speed mode means that the focus needs to be on the response speed of the motor, and the energy-saving mode means that the focus needs to be on the energy consumption of the motor.

[0100] S502: Adjust the weight of the corresponding objective function in the multi-objective optimization function according to the current working mode.

[0101] Optionally, if the current operating mode is high-precision mode, the weight of the objective function of the motor's positioning accuracy can be adjusted, for example, by increasing the weight of the matrix Q in the above formula (1). If the current operating mode is high-speed mode, the weight of the objective function of the motor's response speed can be adjusted, for example, by reducing the weight of the matrix R in the above formula (1). If the current operating mode is energy-saving mode, the weight of the objective function of the motor's energy consumption can be adjusted, for example, by increasing the weight of the matrix S in the above formula (1).

[0102] It is worth noting that the adjustment process of the objective function weight is dynamic. During the operation of the control system, if the working mode changes, the weight of the objective function can be dynamically adjusted to achieve performance optimization under different working modes.

[0103] Optionally, the system of the present application may also perform feedback correction based on the output behavior prediction data and the actual behavior data of the motor.

[0104] Reference Figure 6 The system also includes: an adaptive controller, a parameter identification module, and a disturbance observation module. The parameter identification module and the disturbance observation module are in communication connection with the adaptive controller, and the adaptive controller is in communication connection with the global controller.

[0105] Figure 7 This is a flow chart for feedback correction, refer to Figure 7 , the feedback correction process of this application includes:

[0106] S701 : A disturbance observation module obtains external disturbance information of each motor. The external disturbance information is used to represent the difference between the actual movement information and the target movement information of the motor.

[0107] The actual movement information can be the actual position and actual speed of the motor, and the target movement information can be the predicted position and predicted speed of the motor. External disturbance information includes: friction changes, load fluctuations, and electromagnetic interference.

[0108] Optionally, the disturbance observation module includes a sensor connected to the motor for detecting the actual position and actual speed of the motor. The disturbance observation module can estimate the external disturbance information based on the actual position, actual speed of the motor and the predicted position and predicted speed obtained in the above step S303.

[0109] S702: The parameter identification module identifies and obtains motor parameter information of each motor in the drive device. The motor parameter information includes at least one of the following: motor inductance, motor resistance, and friction coefficient.

[0110] Optionally, the parameter identification module can be connected to the motor to collect real-time data such as the real-time voltage and real-time current of the motor, and estimate the motor parameter information based on the real-time data of the motor.

[0111] S703 : The adaptive controller sends the external disturbance information and the motor parameter information to the global controller, so that the global controller updates the motor parameters of each motor stored in the global controller based on the motor parameter information.

[0112] Optionally, the adaptive controller can send external disturbance information and motor parameter information to the global controller, and the global controller executes the above S303 step according to the external disturbance information to make the next prediction, and the global controller updates the motor parameters in the mathematical model of the motor according to the motor parameter information to improve the accuracy of subsequent behavior predictions.

[0113] Alternatively, as Figure 8 As shown, the process of the above-mentioned disturbance observation module obtaining the external disturbance information of each motor includes:

[0114] S801: After each motor controller executes a control instruction, the disturbance observation module obtains the actual position and actual speed of each motor.

[0115] Optionally, the disturbance observation module includes a sensor that can obtain the actual position and actual speed of the motor connected thereto. The actual position and actual speed can represent the actual motion state of the motor under the current control instruction.

[0116] S802: The disturbance observation module determines external disturbance information according to the actual position, actual speed, predicted position, and predicted speed of each motor.

[0117] Optionally, a motor position difference can be determined based on the actual position and the predicted position. If the position difference is greater than a preset threshold, it indicates that an external disturbance has affected the motor's motion, causing the motor to not move at the expected position. Similarly, if the actual speed and the predicted speed are greater than a preset threshold, it indicates that an external disturbance has affected the motor's motion, causing the motor to not move at the expected speed.

[0118] Optionally, the position difference is caused by external disturbances, such as friction changes, load fluctuations, and electromagnetic interference. Based on the characteristics and changing trends of the difference, the disturbance observation module can combine motor parameters and a pre-built observer algorithm to determine the specific parameter values ​​of friction changes, load fluctuations, and electromagnetic interference.

[0119] Exemplarily, the disturbance observation module may estimate external disturbance information based on the following formula (2).

[0120] (2)

[0121] in, is the estimated value at time k, It is at the moment The estimated value of is the input value at time k, that is, the position difference and speed difference at time k, is the estimated input value at time k, and L represents the observer gain.

[0122] The following is a further explanation of the motor parameter information of each motor in the drive device obtained by the above parameter identification module. Figure 9 As shown, the above step S702 includes:

[0123] S901. The parameter identification module collects real-time data of each motor, including input voltage, input current, output speed, and motor position.

[0124] After each motor controller executes the control instruction, the parameter identification module can collect real-time data of each motor and estimate the parameters of the motor based on the real-time data.

[0125] S902 , the parameter identification module performs parameter estimation on the real-time data of each motor based on the recursive least squares method to obtain motor parameter information of each motor.

[0126] Recursive least squares (RLS) is an adaptive filtering algorithm used to update motor parameter estimates in real time. It optimizes parameter estimates by minimizing the sum of squared prediction errors. RLS allows for dynamic adjustment of parameter estimates to accommodate changes in motor parameters.

[0127] Optionally, the parameter identification model can perform parameter estimation using the following formula (3) to obtain motor parameter information of each motor.

[0128] (3)

[0129] Where θ represents the parameter vector, represents the gain matrix. is the parameter estimate vector at time k, It is at the moment The parameter estimation vector, y(k) is the real-time data at time k, is the transpose of the regression vector at time k.

[0130] In an embodiment of the present application, by identifying the real-time data of the motor and the external disturbance information, and sending the identified motor parameter information and external disturbance information to the global controller, the global controller can improve its anti-interference ability when performing behavior prediction.

[0131] The following is a further description of the distributed cooperative control network controlling the movement of each motor through each motor controller according to the prediction data of each behavior. Figure 10 As shown, the above step S304 includes:

[0132] S1001. The distributed collaborative control network obtains behavior prediction data of each motor according to the identification of each motor.

[0133] The current state data includes the motor's identification. When making predictions, the behavior prediction model can also add the motor's identification to the generated behavior prediction data and send the behavior prediction data of each motor group to the distributed collaborative control network. The distributed collaborative control network can then determine the behavior prediction data of each motor from the behavior prediction data of the motor group based on the motor's identification.

[0134] S1002. The distributed collaborative control network generates control instructions for each motor based on the behavior prediction data of each motor.

[0135] Optionally, the distributed collaborative control network generates control instructions based on the behavior prediction data, combined with the current state of the motor and the control target. The control instructions may include information such as the torque to be applied. The control target may be information such as the predicted position and predicted speed indicated by the behavior prediction data.

[0136] S1003 , the distributed collaborative control network sends the control instructions of each motor to the corresponding motor.

[0137] The distributed collaborative control network can send the generated control instructions to the corresponding motor controller through the communication interface. After receiving the instructions, the motor controller converts them into specific drive signals to control the operation of the motor.

[0138] Based on the same inventive concept, the embodiment of the present application also provides a control system for the variable slit of the lithography machine corresponding to the control method of the variable slit of the lithography machine. Since the principle of solving the problem by the system in the embodiment of the present application is similar to the control method of the variable slit of the lithography machine in the above-mentioned embodiment of the present application, the implementation of the system can refer to the implementation of the method, and the repeated parts will not be repeated.

[0139] Reference Figure 2 The system includes a global controller, a distributed collaborative control network, a driving device and multiple blades connected to the driving device. The driving device includes: multiple motors and motor controllers connected to each motor. Each motor is connected to a blade and is used to drive the blade to move.

[0140] Continue to refer to Figure 6 The system also includes an adaptive controller, a parameter identification module, and a disturbance observation module. The parameter identification module and the disturbance observation module are in communication connection with the adaptive controller, and the adaptive controller is in communication connection with the global controller.

[0141] Figure 11A structural schematic diagram of a global controller provided in an embodiment of the present application is shown, including: a processor 1101, a storage medium 1102 and a bus 1103. The storage medium 1102 stores machine-readable instructions executable by the processor 1101. When the global controller runs the control method for the variable slit of the lithography machine as in the embodiment, the processor 1101 communicates with the storage medium 1102 through the bus 1103, and the processor 1101 executes the machine-readable instructions and the preamble of the method item of the processor 1101 to execute the steps of the above-mentioned control method for the variable slit of the lithography machine.

[0142] An embodiment of the present application further provides a computer-readable storage medium having a computer program stored thereon. The computer program is executed when a processor is running, and the processor executes the steps of the above-mentioned method for controlling the variable slit of a lithography machine.

[0143] The present application also provides a photolithography machine, which includes Figure 2 The system shown in the figure controls the variable slit based on the above-mentioned control method of the variable slit of the lithography machine.

[0144] In the embodiment of the present application, the computer program can also execute other machine-readable instructions when run by the processor to execute other methods described in the embodiment. For the specific execution method steps and principles, please refer to the description of the embodiment and will not be repeated here.

[0145] In the embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interface, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0146] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0147] In addition, each functional unit in the embodiments provided in the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0148] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program code.

[0149] It should be noted that similar numbers and letters represent similar items in the following figures. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. In addition, the terms "first", "second", "third", etc. are only used to distinguish the description and are not to be understood as indicating or implying relative importance.

[0150] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present application, which are used to illustrate the technical solutions of the present application, rather than to limit them. The scope of protection of the present application is not limited thereto. Although the present application has been described in detail with reference to the above-described embodiments, those skilled in the art should understand that any person skilled in the art can modify or easily conceive of changes to the technical solutions described in the above-described embodiments within the technical scope disclosed in the present application, or make equivalent replacements for some of the technical features thereof. However, these modifications, changes, or replacements do not deviate from the spirit and scope of the technical solutions of the embodiments of the present application. They should all be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A method for controlling a variable slit of a lithography machine, characterized in that: Applied to a control system, the control system includes: a global controller, a distributed collaborative control network and a drive device, the drive device includes: multiple motors and motor controllers corresponding to each motor; the method includes: The global controller acquires current state data of each motor in the driving device, wherein the current state data includes at least one of the following: speed, position, and external disturbance information of each motor; The global controller determines at least one motor group based on an association relationship between the motors in the drive device, wherein each motor group includes a plurality of the motors, the motors connected to the blades to be synchronously controlled are in the same motor group, and movement information of a first motor in the same motor group is affected by a second motor; The global controller performs collaborative prediction based on the current state data of each motor in the target motor group and a pre-trained behavior prediction model to obtain behavior prediction data of each motor in the target motor group in at least one future time step, and sends each behavior prediction data to the distributed collaborative control network, wherein the behavior prediction data includes: predicted speed and predicted position of each motor in the target motor group; The distributed cooperative control network controls the operation of each motor according to each behavior prediction data via each motor controller.

2. The method for controlling a variable slit of a lithography machine according to claim 1, wherein: The global controller performs collaborative prediction based on the pre-trained behavior prediction model according to the current state data of each motor in the target motor group to obtain behavior prediction data of each motor in the target motor group in at least one future time step, including: The global controller inputs the current state data of each motor in the target motor group into the behavior prediction model, and the behavior prediction model predicts and obtains the initial prediction data of each motor in the target motor group for at least one future time step; The global controller optimizes the initial prediction data based on a multi-objective optimization function and constraints to obtain behavior prediction data of each motor in the target motor group in multiple future time steps. The objective function of the multi-objective optimization function includes at least one of the following: motor positioning accuracy, motor response speed and motor energy consumption. The constraints include: motor physical limitation information and control boundary information.

3. The method for controlling a variable slit of a lithography machine according to claim 2, wherein: The method further comprises: Acquire a current working mode of the driving device, where the current working mode includes: high-precision mode, high-speed mode, and energy-saving mode; The weights of corresponding objective functions in the multi-objective optimization function are adjusted according to the current working mode.

4. The method for controlling a variable slit of a lithography machine according to claim 1, wherein: The control system further includes: an adaptive controller, a parameter identification module, and a disturbance observation module, wherein the parameter identification module and the disturbance observation module are in communication with the adaptive controller, and the adaptive controller is in communication with the global controller; The method further comprises: The disturbance observation module obtains external disturbance information of each of the motors, where the external disturbance information is used to represent the difference between the actual movement information and the target movement information of the motor; The parameter identification module identifies and obtains motor parameter information of each motor in the drive device, wherein the motor parameter information includes at least one of the following: motor inductance, motor resistance, and friction coefficient; The adaptive controller sends the external disturbance information and the motor parameter information to the global controller, so that the global controller updates the motor parameters of each motor stored in the global controller based on the motor parameter information.

5. The method for controlling a variable slit of a lithography machine according to claim 4, wherein: The disturbance observation module obtains external disturbance information of each motor, including: After each of the motor controllers executes the control instruction, the disturbance observation module obtains the actual position and actual speed of each motor; The disturbance observation module determines the external disturbance information according to the actual position, actual speed, predicted position and predicted speed of each motor.

6. The method for controlling a variable slit of a lithography machine according to claim 4, wherein: The parameter identification module identifies and obtains motor parameter information of each motor in the drive device, including: The parameter identification module collects real-time data of each motor, and the real-time data includes: input voltage, input current, output speed, and motor position; The parameter identification module performs parameter estimation on the real-time data of each motor based on the recursive least square method to obtain motor parameter information of each motor.

7. The method for controlling a variable slit of a lithography machine according to claim 1, wherein: The distributed collaborative control network controls the movement of each motor via each motor controller according to each behavior prediction data, including: The distributed collaborative control network obtains behavior prediction data of each motor according to the identification of each motor; The distributed collaborative control network generates control instructions for each motor based on the behavior prediction data of each motor; The distributed collaborative control network sends control instructions of each motor to the corresponding motor.

8. A control system for implementing the control method of a variable slit of a lithography machine according to any one of claims 1 to 7, characterized in that: The control system includes: a global controller, a distributed collaborative control network, a drive device and multiple blades connected to the drive device. The drive device includes: multiple motors and motor controllers connected to each motor. Each motor is connected to a blade and is used to drive the blade to move.

9. The control system according to claim 8, characterized in that: The system further includes: an adaptive controller, a parameter identification module, and a disturbance observation module. The parameter identification module and the disturbance observation module are communicatively connected to the adaptive controller, and the adaptive controller is communicatively connected to the global controller.

10. A photolithography machine, characterized in that: Comprising a control system as claimed in claim 8 or claim 9.

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