Method and system for controlling variable slit of photoetching machine and photoetching machine

Abstract

The invention provides a control method and system for a variable slit of a photoetching machine and the photoetching machine.The method comprises the steps that a global controller obtains current state data of all motors in a driving device, the global controller determines at least one motor set according to the incidence relation between all the motors in the driving device, and the current state data of all the motors in the driving device are sent to the driving device; the global controller carries out collaborative prediction based on a behavior prediction model obtained by pre-training according to the current state data of each motor in the target motor set to obtain behavior prediction data of each motor in the target motor set in at least one time step in the future, and sends each behavior prediction data to a distributed collaborative control network; and the distributed cooperative control network controls the operation of each motor through each motor controller according to each behavior prediction data. According to the invention, cooperative control of multiple motors is realized, and the real-time performance of motor control and the anti-interference capability of the system are improved.

Description

Technical Field

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

[0002] In semiconductor lithography processes, a variable slit is a key component of the illumination system, mainly composed of blades, a driving device, and a control device. Usually, four independent linear motors drive four blades to form a rectangular light-transmitting hole with a controllable size and position. The position of the blades is adjusted by the motors to dynamically and precisely control the exposure area. As the integrated circuit manufacturing process continues to shrink, the requirements for the accuracy, speed, and synchronization of variable slit control are getting higher and higher.

[0003] In the prior art, the variable slit control method mainly controls the motors individually through independent controllers. This method has problems such as insufficient coordination, limited real-time performance, weak anti-disturbance ability, and difficulty in considering multiple performance indicators. Summary of the Invention

[0004] The purpose of the present application is to provide a control method, system, and lithography machine for a variable slit of a lithography machine to solve the problems of insufficient coordination, limited real-time performance, weak anti-disturbance ability, and difficulty in considering multiple performance indicators in the variable slit control process in the prior art.

[0005] To achieve the above object, the technical solutions adopted in the present application are as follows: 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 cooperative control network, and a driving device. The driving device includes: a plurality of motors and motor controllers respectively connected to the motors. The method includes: The global controller obtains the current state data of each motor in the driving device. The current state data includes at least one of the following: the speed, position, and external disturbance information of each motor; 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 a first motor in the same motor group is affected by a second motor; The global controller performs cooperative prediction based on the current state data of each motor in the target motor group using a behavior prediction model pre-trained to obtain behavior prediction data of each motor in the target motor group at at least one future time step, and sends each piece of behavior prediction data to the distributed cooperative control network. The behavior prediction data includes: the predicted speed and predicted position of each motor in the target motor group; The distributed collaborative control network controls the operation of each of the motors according to the respective behavior prediction data and via each of the motor controllers.

[0006] Optionally, the global controller performs collaborative prediction based on the current state data of each motor in the target motor group using a pre-trained behavior prediction model, to obtain behavior prediction data of each motor in the target motor group for 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 the initial prediction data of each motor in the target motor group for at least one future time step; The global controller performs an optimization process on the initial prediction data based on a multi-objective optimization function and constraint conditions, to obtain behavior prediction data of each motor in the target motor group for multiple future time steps. The objective functions of the multi-objective optimization function include at least one of the following: motor positioning accuracy, motor response speed, and motor energy consumption. The constraint conditions include: motor physical limit information and control boundary information.

[0007] Optionally, the method further includes: Obtaining the current working mode of the driving device, where the current working mode includes: high-precision mode, high-speed mode, and energy-saving mode; Adjusting the weight of the corresponding objective function in the multi-objective optimization function according to the current working mode.

[0008] Optionally, 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; The method further includes: The disturbance observation module obtains the external disturbance information of each of the motors, where the external disturbance information is used to characterize the difference between the actual movement information and the target movement information of the motor; The parameter identification module identifies the motor parameter information of each motor in the driving device, where 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.

[0009] Optionally, the disturbance observation module obtains the external disturbance information of each of the motors, including: After each of the motor controllers executes the control instruction, the disturbance observation module acquires 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.

[0010] Optionally, the parameter identification module identifies the motor parameter information of each motor in the driving device, including: The parameter identification module collects the 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 squares method to obtain the motor parameter information of each motor.

[0011] Optionally, the distributed cooperative control network controls the movement of each motor according to each behavior prediction data and via each motor controller, including: The distributed cooperative control network acquires the behavior prediction data of each motor according to the identifier of each motor; The distributed cooperative control network generates control instructions for each motor according to the behavior prediction data of each motor; The distributed cooperative control network sends the control instructions of each motor to the corresponding motor.

[0012] In a second aspect, the present application provides a control system for a variable slit of a lithography machine. The system includes the global controller, distributed cooperative control network, driving device, and a plurality of blades correspondingly connected to the driving device described in the first aspect. The driving device includes: a plurality of motors and motor controllers correspondingly connected to each motor. Each motor is respectively connected to a blade and is used to drive the blade to move.

[0013] Optionally, the system further includes the adaptive controller, parameter identification module, and disturbance observation module described in the first aspect. 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.

[0014] In a third aspect, an embodiment of the present application further provides a lithography machine, including: the control system for a variable slit of a lithography machine described in the second aspect above.

[0015] The beneficial effects of the present application are as follows: By grouping motors with an associated relationship and predicting the behavior at future time steps based on the current state data of the same motor group, collaborative prediction and collaborative control of motors with an associated relationship are achieved. Moreover, by predicting the behavior data at future time steps, the ability to track rapidly changing trajectories can be improved subsequently, thereby enhancing 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 enhancing the anti-disturbance ability during the motor control process.

[0016] To make the above objects, features, and advantages of the present application more obvious and understandable, the following specific preferred embodiments are given, and in conjunction with the accompanying drawings, the detailed description is as follows. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] To more clearly illustrate the technical solutions of the embodiments of the present application, the accompanying drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application, and thus should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can also be obtained based on these drawings without creative efforts.

[0018] Figure 1 Shows a schematic diagram of a variable slit provided by an embodiment of the present application; Figure 2 Shows a schematic diagram of the architecture of a control system provided by an embodiment of the present application; Figure 3 Shows a flowchart of a control method for a variable slit of a lithography machine provided by an embodiment of the present application; Figure 4 Shows a flowchart of determining behavior prediction data provided by an embodiment of the present application; Figure 5 Shows a flowchart of adjusting the weight of the objective function provided by an embodiment of the present application; Figure 6 Shows another schematic diagram of the architecture of a control system provided by an embodiment of the present application; Figure 7 Shows a flowchart of performing feedback correction provided by an embodiment of the present application; Figure 8 Shows a flowchart of determining external disturbance information provided by an embodiment of the present application; Figure 9 Shows a flowchart of determining motor parameter information provided by an embodiment of the present application; Figure 10 Shows a flowchart of a distributed collaborative control network issuing control instructions provided by an embodiment of the present application; Figure 11 The figure shows a schematic structural diagram of a global controller provided by an embodiment of the present application. Detailed implementation manners

[0019] To make the objectives, 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 with reference to the accompanying drawings in the embodiments of the present application. Apparently, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments. Components of the embodiments of the present application usually described and illustrated in the accompanying 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 accompanying drawings is not intended to limit the scope of the claimed present application, but merely represents selected embodiments of the present application. All other embodiments obtained by those skilled in the art based on the embodiments of the present application without creative efforts fall within the protection scope of the present application.

[0020] It should be noted that the term "including" will be used in the embodiments of the present application to indicate the existence of the subsequently stated features, but does not exclude adding other features.

[0021] The variable slit of a lithography machine is a mechanical device in the illumination system of the lithography machine, generally composed of four movable blades, where two blades can move along the X direction and the other two can move along the Y direction. Its function is to limit the size and central position of the illumination field of view on the mask surface by adjusting the positions of the blades, and to avoid exposing the area outside the exposure area by the imaging beam.

[0022] Take Figure 1 as an example. Assume that the four motors in the figure are respectively connected to a blade to form a variable slit. By driving the blades to move through the motors in the figure, the shape and size of the formed exposure area can be changed, thereby improving the lithography accuracy, efficiency and the utilization rate of the mask.

[0023] In the prior art, generally, an independent motor controller is used to control the motor. For example Figure 1 each motor in is respectively connected to a motor controller, and each motor controller independently drives the motor.

[0024] However, when the four motors are independently controlled, it is difficult to ensure the shape accuracy and position accuracy of the formed exposure area, and there is a problem of insufficient coordination. The traditional controller control method cannot effectively predict the future state of the system, and there are problems of insufficient tracking ability and limited real-time performance for fast-changing trajectories. The existing variable slit control method has poor adaptability to disturbance information such as friction force change and load fluctuation, which will also affect the positioning accuracy. Moreover, it is difficult for the existing method to simultaneously take into account multiple performance indicators such as positioning accuracy, response speed and energy consumption.

[0025] Based on this, the present application proposes a control method for the variable slit of a lithography machine. By grouping strongly correlated motors and co-predicting the motor behavior within a future period based on the current state data of each motor in the motor group, control is performed based on the cooperative relationship between the motors, thereby enhancing the cooperation of motor control. And by predicting the motor behavior within a future period, the ability to track the trajectory can also be improved, enhancing the real-time performance of motor control.

[0026] In addition, in the present application, by comparing the actual movement information of the motor with the predicted movement information and adjusting the parameters of the motor controller based on the comparison result, the anti-disturbance ability during the variable slit control process can also be effectively improved. And by performing multi-objective optimization on the predicted motor behavior, the consideration and optimization of multiple performance indicators are also achieved.

[0027] Figure 2 It is a schematic diagram of the architecture of the control system for the variable slit of the lithography machine given in the present application. Refer to Figure 2 , the system includes: a global controller, a distributed cooperative network, and a driving device. Among them, the global controller is connected to the host computer and the distributed cooperative control network, and is used to receive the process requirement data sent by the host computer and receive the motor data sent by the driving device through the distributed cooperative control network. The distributed cooperative network is connected to the driving device. The driving device includes a plurality of motors and motor controllers respectively connected to the motors. Each motor is respectively connected to a blade, and the motor controller can control the motor to drive the blade to move.

[0028] Among them, the global controller can be a global MPC (Model Predictive Controller) controller implemented based on FPGA (Field-Programmable Gate Array), using a Xilinx Zynq UltraScale+ MPSoC chip, integrating an ARM Cortex-A53 processor and a programmable logic unit. Exemplarily, the global controller operates at a frequency of 1 kHz and completes system state estimation, multi-step prediction, and optimization calculation within each control cycle.

[0029] The distributed cooperative control network communicates with the global controller through a high-speed EtherCAT bus, receiving the behavior prediction data sent by the global controller; at the same time, it interacts with the driving device through a dedicated real-time communication network, sending control instructions and receiving status feedback.

[0030] The driving device can be composed of four distributed and collaborative motor controllers, and each motor controller is responsible for the precise control of a linear motor. Exemplarily, the motor controller can be implemented based on a high-performance DSP chip and operate at a frequency of 10 kHz to ensure fast response and precise execution. The driving device controller not only receives instructions from the distributed collaborative control network but also can exchange status information with each other through a high-speed communication network with a ring topology to achieve collaborative control.

[0031] Next, in combination with Figure 3 , the control method for the variable slit of the lithography machine in this application will be described. This method can be applied to Figure 2 the control system of the variable slit of the lithography machine shown in Figure 3 as shown in S301. The global controller acquires the current state data of each motor in the driving device, and the current state data includes at least one of the following: the speed, position, and external disturbance information of each motor.

[0032] Optionally, the global controller is used to coordinate and manage the operation of all motors in the control system and is the central control unit of the control system. The global controller can be connected to the host computer, receive the process requirement data sent by the host computer, and determine the behavior of each motor according to the process requirement data and the current state data of each motor. The process requirement data can include: exposure dose, spot shape requirements, and scanning speed, etc.

[0033] The global controller can periodically acquire the current state data of each motor in the driving device. The current state data can be discrete data. In each cycle, each motor can report the current state data to Figure 2 the distributed collaborative control network in

[0034] and the distributed collaborative control network sends the current state data of each motor to the global controller.

[0035] 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 cycle. After each motor receives the data acquisition instructions, it sends the current state data to the global controller via the distributed collaborative control network.

[0036] Among them, the driving device includes a combination of multiple motors and motor controllers. Each motor controller is respectively 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.

[0037] Optionally, the external disturbance information includes: friction force 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 ability during the variable slit control process.

[0038] The external disturbance information can be observed by a disturbance observer module. The disturbance observer module can be a sensor connected to the motor, which is used to detect disturbance information such as friction force change, load fluctuation, and electromagnetic interference during the operation of the motor. As a possible implementation, the 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 end of the previous adjacent cycle.

[0039] S302. 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 multiple motors, and the movement information of the first motor in the same motor group is affected by the second motor.

[0040] Among them, the association relationship between the motors can be preset. In the variable slit system of a lithography machine, usually multiple blades need to move simultaneously to adjust the size and shape of the slit. These blades are driven by different motors. In the embodiments of the present application, the motors that have physical or functional mutual influence can be divided into one motor group. For example, the motors connected to the blades that need to be synchronously controlled are divided into one motor group to ensure that the motors in the same motor group can share data and perform synchronous control.

[0041] Exemplarily, Figure 2 Motors 1 and 2 in need to ensure simultaneous movement during the movement process, and motors 3 and 4 need to ensure synchronous movement. Therefore, an association relationship can be established between motors 1 and 2, and an association relationship can be established between motors 3 and 4 to obtain two motor groups.

[0042] The association relationship between the motors can be pre-saved in the global controller, and the motors with the association relationship are divided into one motor group. After receiving the current state data of each motor, the current state 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.

[0043] In another implementation, the association relationship identifier can be saved in the current state data of the motor. The association relationship identifier is used to indicate all the motors in at least one same motor group that have an association relationship with the current motor. After the global controller receives the current state data, the current state data of the same motor group can be used as a group of data to be processed according to the association relationship identifier in the current state data.

[0044] S303. The global controller performs collaborative prediction based on the current state data of each motor in the target motor group using a pre-trained behavior prediction model, obtains the behavior prediction data of each motor in the target motor group for 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.

[0045] Optionally, the target motor group can be any one of the motor groups in the above S302 step. The target motor group includes multiple motors. The global controller can use the current state data of all motors in the target motor group as a set of data to be processed and input it into the behavior prediction model.

[0046] The behavior prediction model can predict the behavior prediction data of the motor for 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.

[0047] Optionally, all motors can be modeled as a system in advance to obtain the system model of the motor. The system model can provide the dynamic coupling relationship between motors for subsequent prediction steps. After inputting the current state data of the motor group into the behavior prediction model, the behavior prediction model can combine the current state data of the target motor group and the dynamic coupling relationship between the target motor groups in the system model to predict the behavior prediction data of the target motor group for multiple future time steps.

[0048] Exemplarily, the dynamic model of a single motor and the kinematic model of the blade can be established first, and then combined with the coupling relationship between the electrodes, the dynamic model of each motor and the kinematic model of the blade can be integrated together to form a system model describing the entire variable slit system.

[0049] Optionally, the behavior prediction data includes the predicted speed and predicted position of each motor in the target motor group for at least one future time step.

[0050] It should be noted that during the process of behavior prediction, the behavior prediction model can first generate initial prediction data, and then the global controller optimizes the initial prediction data to obtain the behavior prediction data. During the process of data optimization, optimization processing can be performed based on the particle swarm algorithm combined with a multi-objective optimization function to ensure that the obtained behavior prediction data can satisfy multiple indicators simultaneously.

[0051] S304. The distributed collaborative control network controls the operation of each motor according to each behavior prediction data through each motor controller.

[0052] Optionally, the distributed collaborative control network may 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, so as to drive the blade connected to the motor to move along the trajectory indicated by the control instructions, so as to change the size, shape, etc. of the exposure area.

[0053] As a possible implementation, the distributed collaborative control network may be a network connected to each motor controller. Each motor controller is connected to the distributed collaborative control network and uploads the current state data to the global controller through the distributed collaborative control network. In addition, the distributed collaborative control network may 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.

[0054] As another possible implementation, the distributed collaborative control network may also be a network composed of each motor controller. The global controller sends the behavior prediction data to each motor controller in the distributed collaborative control network. After receiving the behavior prediction data, each motor controller may filter out the behavior prediction data of the motors in the same motor group as itself from it, and make decisions in combination with the behavior prediction data of the same group, generate control instructions, and control the operation of the motor based on the control instructions.

[0055] In the embodiments of the present application, by grouping motors with an association relationship and predicting the behavior at future time steps based on the current state data of the same motor group, collaborative prediction and collaborative control of motors with an association relationship are realized. Moreover, by predicting the behavior data at future time steps, the tracking ability for rapidly changing trajectories can also 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 ability during the motor control process.

[0056] The following is a further description of the above-mentioned global controller for performing collaborative prediction based on the behavior prediction model pre-trained 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 at at least one future time step, as Figure 4 shown, the above step S303 includes: 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 the initial prediction data of each motor in the target motor group at at least one future time step.

[0057] Optionally, the behavior prediction model can combine the input current state data and a pre-established system model for prediction to obtain initial prediction data of each motor in the target motor group for at least one future time step.

[0058] Among them, the initial prediction data includes: the initial predicted positions and initial predicted speeds of each motor in the target motor group for at least one future time step.

[0059] S402. The global controller performs an optimization process on the initial prediction data based on a multi-objective optimization function and constraint conditions to obtain behavior prediction data of each motor in the target motor group for multiple future time steps. The objective functions of the multi-objective optimization function include at least one of the following: motor positioning accuracy, motor response speed, and motor energy consumption. The constraint conditions include: motor physical limit information and control boundary information.

[0060] As a possible implementation, 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 perform an optimization process on the initial prediction data based on the multi-objective optimization function and constraint conditions to obtain the behavior prediction data.

[0061] Optionally, the multi-objective optimization function is as shown in the following formula (1) and can be a weighted objective function composed of multiple objective functions. Among them, J represents the objective function, represents the predicted value of the system output at time k for the future time, is the reference input value at time k + i, is the control input increment at time ε is the error term, and Q, R, and S are weight matrices used to balance the importance of different terms. Q is the weight matrix of the motor positioning accuracy objective function, R is the weight matrix of the motor response speed objective function, and S is the weight matrix of the motor energy consumption objective function.

[0062] (1) The constraint conditions are used to constrain the physical limitations and control boundaries of the motor during the optimization process. The motor physical limit information includes the maximum torque and speed range of the motor, etc., and the control boundary information includes the voltage limit and current limit of the motor, etc.

[0063] In the embodiments of the present application, by predicting the current state function based on the behavior prediction model to obtain the initial prediction data, the prediction of the future state of the system can be realized, and the tracking ability of the variable slit system for rapidly changing trajectories can be improved. By performing an optimization process on the initial prediction data based on the multi-objective function to obtain the behavior prediction data, the behavior prediction data can take into account multiple performance indicators such as positioning accuracy, response speed, and energy consumption at the same time, realizing the multi-objective data optimization.

[0064] Optionally, the present application can also adjust the multi-objective optimization function based on different working modes to adapt to different optimization directions. For example, Figure 5 as shown, the method of the present application further includes: S501. Obtain the current working mode of the driving device, where the current working mode includes: high-precision mode, high-speed mode, and energy-saving mode.

[0065] Among them, the high-precision mode means that the positioning accuracy of the motor needs to be focused on, the high-speed mode means that the response speed of the motor needs to be focused on, and the energy-saving mode means that the energy consumption of the motor needs to be focused on.

[0066] S502. Adjust the weight of the corresponding objective function in the multi-objective optimization function according to the current working mode.

[0067] Optionally, if the current working mode is the high-precision mode, the weight of the objective function of the positioning accuracy of the motor can be adjusted. For example, increase the weight of matrix Q in the above formula (1). If the current working mode is the high-speed mode, the weight of the objective function of the response speed of the motor can be adjusted. For example, decrease the weight of matrix R in the above formula (1). If the current working mode is the energy-saving mode, the weight of the objective function of the motor energy consumption can be adjusted. For example, increase the weight of matrix S in the above formula (1).

[0068] It should be noted that the adjustment process of the above objective function weights is dynamic. During the operation of the control system, if the working mode changes, the weights of the objective functions can be dynamically adjusted to achieve performance optimization under different working modes.

[0069] Optionally, the system of the present application can also perform feedback correction based on the output behavior prediction data and the actual behavior data of the motor.

[0070] Referring to Figure 6 , 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.

[0071] Figure 7 is a flowchart for performing feedback correction. Referring to Figure 7 , the process of performing feedback correction in the present application includes: S701. The disturbance observation module obtains the external disturbance information of each motor, and the external disturbance information is used to characterize the difference between the actual movement information and the target movement information of the motor.

[0072] Among them, the actual movement information may be the actual position and actual speed of the motor, and the target movement information may be the predicted position and predicted speed of the motor. The external disturbance information includes: changes in friction, load fluctuations, electromagnetic interference, etc.

[0073] 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 and actual speed of the motor and the predicted position and predicted speed obtained in the above S303 step.

[0074] S702. The parameter identification module identifies the 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.

[0075] 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.

[0076] 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 saved in the global controller based on the motor parameter information.

[0077] Optionally, the adaptive controller can send the external disturbance information and the motor parameter information to the global controller. The global controller executes the above S303 step according to the external disturbance information for 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 prediction.

[0078] Optionally, as Figure 8 shown, the process of the above disturbance observation module obtaining the external disturbance information of each motor includes: S801. After each motor controller executes the control instruction, the disturbance observation module obtains the actual position and actual speed of each motor.

[0079] Optionally, the disturbance observation module includes a sensor, and the sensor can obtain the actual position and actual speed of the motor connected to it. The actual position and actual speed can characterize the real motion state of the motor under the current control instruction.

[0080] S802. The disturbance observation module determines the external disturbance information according to the actual position, actual speed, predicted position, and predicted speed of each motor.

[0081] Optionally, the position difference of the motor 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 external disturbances have affected the movement of the motor, causing the motor not to move to the expected position. Similarly, if the actual speed and the predicted speed are greater than the preset threshold, it indicates that external disturbances have affected the movement of the motor, causing the motor not to move at the expected speed.

[0082] Optionally, the position difference is caused by external disturbances, such as changes in friction, load fluctuations, and electromagnetic interference. Based on the characteristics and trends of the differences, the disturbance observation module can determine the specific parameter values of changes in friction, load fluctuations, and electromagnetic interference by combining motor parameters and a pre-constructed observer algorithm.

[0083] Exemplarily, the disturbance observation module can estimate the external disturbance information based on the following formula (2).

[0084] (2) Wherein, is the estimated value at time k, is the estimated value at time , 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.

[0085] The following is a further description of the motor parameter information of each motor in the drive device identified by the above parameter identification module. As Figure 9 shown, the above step S702 includes: S901. The parameter identification module collects the real-time data of each motor. The real-time data includes: input voltage, input current, output speed, and motor position.

[0086] After each motor controller executes the control instruction, the parameter identification module can collect the real-time data of each motor and perform parameter estimation of the motor based on the real-time data.

[0087] 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 the motor parameter information of each motor.

[0088] Among them, the recursive least squares method is an adaptive filtering algorithm used to update the estimated value of the motor parameters in real time. Specifically, it optimizes the parameter estimation by minimizing the sum of the squares of the prediction errors. Based on the recursive least squares method, the estimated value of the parameters can be dynamically adjusted to adapt to the changes in the motor parameters.

[0089] Optionally, the parameter identification model can perform parameter estimation through the following formula (3) to obtain the motor parameter information of each motor.

[0090] (3) where θ represents the parameter vector, represents the gain matrix. is the parameter estimation vector at time k, is at time of the parameter estimation vector, y(k) is the real-time data at time k, is the transpose of the regression vector at time k.

[0091] In the embodiments 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 the anti-interference ability when performing behavior prediction.

[0092] The following is a further description of the above-mentioned distributed cooperative control network for controlling the movement of each motor according to each behavior prediction data and via each motor controller, as Figure 10 shown, the above step S304 includes: S1001. The distributed cooperative control network obtains the behavior prediction data of each motor according to the identifiers of each motor.

[0093] The current state data includes the identifiers of the motors. When the behavior prediction model makes a prediction, it can also add the identifiers of the motors to the generated behavior prediction data, and send the behavior prediction data of each motor group to the distributed cooperative control network. The distributed cooperative control network can then determine the behavior prediction data of each motor from the behavior prediction data of the motor group based on the identifiers of the motors.

[0094] S1002. The distributed cooperative control network generates control instructions for each motor according to the behavior prediction data of each motor.

[0095] Optionally, the distributed cooperative control network generates control instructions according to the behavior prediction data, in combination with the current state and control target of the motor. The control instructions may include information such as the torque to be applied. Among them, the control target may be information such as the predicted position and predicted speed indicated by the behavior prediction data.

[0096] S1003. The distributed cooperative control network sends the control instructions of each motor to the corresponding motor.

[0097] The distributed cooperative 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.

[0098] Based on the same inventive concept, an embodiment of the present application further provides a control system for a variable slit of a lithography machine corresponding to the control method of the variable slit of the lithography machine. Since the principle of solving problems in the system in the embodiment of the present application is similar to that of the control method of the variable slit of the lithography machine in the above 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 elaborated here.

[0099] Referring to Figure 2 , the system includes a global controller, a distributed cooperative control network, a driving device, and a plurality of blades correspondingly connected to the driving device. The driving device includes: a plurality of motors and motor controllers correspondingly connected to each motor. Each motor is respectively connected to a blade and is used to drive the blade to move.

[0100] Continuing to refer to Figure 6 , 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.

[0101] Figure 11 FIG. shows a schematic structural diagram of a global controller provided in an embodiment of the present application, 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 of the variable slit of the lithography machine as in the embodiment, communication between the processor 1101 and the storage medium 1102 is through the bus 1103. The processor 1101 executes the machine-readable instructions, and the preamble part of the method item of the processor 1101 to execute the steps of the above control method of the variable slit of the lithography machine.

[0102] An embodiment of the present application further provides a computer-readable storage medium. A computer program is stored on the computer-readable storage medium. When the computer program is run by a processor, the processor executes the steps of the above control method of the variable slit of the lithography machine.

[0103] The present application also provides a lithography machine, which includes Figure 2 the system shown in FIG., and performs variable slit control based on the above control method of the variable slit of the lithography machine.

[0104] In the embodiment of the present application, when the computer program is run by a processor, it can also execute other machine-readable instructions to execute other methods described in the embodiment. For the specific method steps and principles of execution, refer to the description of the embodiment, and details will not be elaborated here.

[0105] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For another 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 displayed or discussed coupling or direct coupling or communication connection between each other can be through some communication interfaces. The indirect coupling or communication connection of the devices or units can be in electrical, mechanical or other forms.

[0106] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place, or they can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0107] In addition, each functional unit in the embodiments provided in the present application can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit.

[0108] If the functions are implemented in the form of software function 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, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks or optical discs that can store program codes.

[0109] It should be noted that similar reference numerals and letters indicate similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. In addition, the terms "first", "second", "third", etc. are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance.

[0110] Finally, it should be noted that the above-described embodiments are only specific embodiments of the present application, which are used to illustrate the technical solutions of the present application, rather than limiting it. The protection scope of the present application is not limited thereto. Although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that any person skilled in the art within the technical scope disclosed by the present application can still modify the technical solutions recorded in the foregoing embodiments, or can easily think of changes, or perform equivalent replacements on some of the technical features; and these modifications, changes or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application. All should be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims described.

Claims

1. A control method for a variable slit of a lithography machine, characterized in that, A control system applied to a variable slit of a lithography machine. The system includes: a global controller, a distributed collaborative control network, and a driving device. The driving device includes: a plurality of motors and motor controllers respectively connected to the motors. The method includes: The global controller acquires the current state data of each motor in the driving device. The current state data includes at least one of the following: the speed, position, and external disturbance information of each motor. 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 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 using a pre-trained behavior prediction model, obtains the behavior prediction data of each motor in the target motor group at at least one future time step, and sends each piece of the 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. The distributed collaborative control network controls the operation of each motor according to each piece of the behavior prediction data and via each motor controller.

2. The method according to claim 1, wherein The global controller performs collaborative prediction based on the current state data of each motor in the target motor group using a pre-trained behavior prediction model, obtains the behavior prediction data of each motor in the target motor group at 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 the initial prediction data of each motor in the target motor group at at least one future time step. The global controller performs an optimization process on the initial prediction data based on a multi-objective optimization function and constraint conditions, and obtains the behavior prediction data of each motor in the target motor group at 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 constraint conditions include: motor physical limit information and control boundary information.

3. The method according to claim 2, wherein The method further includes: Acquiring the current working mode of the driving device. The current working mode includes: high-precision mode, high-speed mode, and energy-saving mode. Adjusting the weight of the corresponding objective function in the multi-objective optimization function according to the current working mode.

4. The method according to claim 1, wherein 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. The method further includes: The disturbance observation module acquires the external disturbance information of each motor. The external disturbance information is used to characterize the difference between the actual movement information and the target movement information of the motor. The parameter identification module identifies the motor parameter information of each motor in the driving device. 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 according to claim 4, characterized in that, The disturbance observation module obtains the external disturbance information of each motor, including: After each motor controller 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 according to claim 4, wherein The parameter identification module identifies the motor parameter information of each motor in the driving device, including: The parameter identification module collects the real-time data of each motor, and the real-time data includes: input voltage, input current, output speed, motor position; The parameter identification module performs parameter estimation on the real-time data of each motor based on the recursive least squares method to obtain the motor parameter information of each motor.

7. The method according to claim 1, wherein The distributed cooperative control network controls the movement of each motor according to each behavior prediction data and via each motor controller, including: The distributed cooperative control network obtains the behavior prediction data of each motor according to the identifier of each motor; The distributed cooperative control network generates control instructions for each motor according to the behavior prediction data of each motor; The distributed cooperative control network sends the control instructions of each motor to the corresponding motor.

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

9. The system according to claim 8, wherein 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 lithography machine, characterized in that, Including the control system of the variable slit of the lithography machine as claimed in claim 8 or claim 9.

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