CN116648773A - Control device, adjustment method, lithographic apparatus and article manufacturing method - Google Patents

Control device, adjustment method, lithographic apparatus and article manufacturing method Download PDF

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Publication number
CN116648773A
CN116648773A CN202180082084.6A CN202180082084A CN116648773A CN 116648773 A CN116648773 A CN 116648773A CN 202180082084 A CN202180082084 A CN 202180082084A CN 116648773 A CN116648773 A CN 116648773A
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China
Prior art keywords
control
signal
deviation
adjustment
target
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CN202180082084.6A
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Chinese (zh)
Inventor
猪股裕也
畑智康
森川宽
伊藤正裕
草柳博一
朝仓康伸
石井祐二
桥本拓海
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Canon Inc
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Canon Inc
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Publication of CN116648773A publication Critical patent/CN116648773A/en
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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B11/00—Automatic controllers
    • G05B11/01—Automatic controllers electric
    • G05B11/36—Automatic controllers electric with provision for obtaining particular characteristics, e.g. proportional, integral, differential
    • G—PHYSICS
    • G03—PHOTOGRAPHY; CINEMATOGRAPHY; ANALOGOUS TECHNIQUES USING WAVES OTHER THAN OPTICAL WAVES; ELECTROGRAPHY; HOLOGRAPHY
    • G03F—PHOTOMECHANICAL PRODUCTION OF TEXTURED OR PATTERNED SURFACES, e.g. FOR PRINTING, FOR PROCESSING OF SEMICONDUCTOR DEVICES; MATERIALS THEREFOR; ORIGINALS THEREFOR; APPARATUS SPECIALLY ADAPTED THEREFOR
    • G03F7/00—Photomechanical, e.g. photolithographic, production of textured or patterned surfaces, e.g. printing surfaces; Materials therefor, e.g. comprising photoresists; Apparatus specially adapted therefor
    • G03F7/20—Exposure; Apparatus therefor
    • G—PHYSICS
    • G03—PHOTOGRAPHY; CINEMATOGRAPHY; ANALOGOUS TECHNIQUES USING WAVES OTHER THAN OPTICAL WAVES; ELECTROGRAPHY; HOLOGRAPHY
    • G03F—PHOTOMECHANICAL PRODUCTION OF TEXTURED OR PATTERNED SURFACES, e.g. FOR PRINTING, FOR PROCESSING OF SEMICONDUCTOR DEVICES; MATERIALS THEREFOR; ORIGINALS THEREFOR; APPARATUS SPECIALLY ADAPTED THEREFOR
    • G03F7/00—Photomechanical, e.g. photolithographic, production of textured or patterned surfaces, e.g. printing surfaces; Materials therefor, e.g. comprising photoresists; Apparatus specially adapted therefor
    • G03F7/70—Microphotolithographic exposure; Apparatus therefor
    • G03F7/70691—Handling of masks or workpieces
    • G03F7/70775—Position control, e.g. interferometers or encoders for determining the stage position
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
    • G05B13/027—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks only
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01L—SEMICONDUCTOR DEVICES NOT COVERED BY CLASS H10
    • H01L21/00—Processes or apparatus adapted for the manufacture or treatment of semiconductor or solid state devices or of parts thereof
    • H01L21/02—Manufacture or treatment of semiconductor devices or of parts thereof
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01L—SEMICONDUCTOR DEVICES NOT COVERED BY CLASS H10
    • H01L21/00—Processes or apparatus adapted for the manufacture or treatment of semiconductor or solid state devices or of parts thereof
    • H01L21/02—Manufacture or treatment of semiconductor devices or of parts thereof
    • H01L21/027—Making masks on semiconductor bodies for further photolithographic processing not provided for in group H01L21/18 or H01L21/34
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01L—SEMICONDUCTOR DEVICES NOT COVERED BY CLASS H10
    • H01L21/00—Processes or apparatus adapted for the manufacture or treatment of semiconductor or solid state devices or of parts thereof
    • H01L21/67—Apparatus specially adapted for handling semiconductor or electric solid state devices during manufacture or treatment thereof; Apparatus specially adapted for handling wafers during manufacture or treatment of semiconductor or electric solid state devices or components ; Apparatus not specifically provided for elsewhere
    • H01L21/68—Apparatus specially adapted for handling semiconductor or electric solid state devices during manufacture or treatment thereof; Apparatus specially adapted for handling wafers during manufacture or treatment of semiconductor or electric solid state devices or components ; Apparatus not specifically provided for elsewhere for positioning, orientation or alignment

Abstract

A control apparatus configured to generate a control signal for controlling a control target, comprising: a first compensation unit configured to generate a first signal based on a control deviation of a control target; a correction device configured to correct the control deviation using one of a plurality of adjustment units, each adjustment unit configured to generate a correction signal by correcting the control deviation according to a calculation formula of which one or more coefficients can be adjusted; a second compensation unit configured to generate a second signal by using the neural network based on the correction signal; and a computing unit configured to generate a control signal based on the first signal and the second signal.

Description

Control device, adjustment method, lithographic apparatus and article manufacturing method
Technical Field
The invention relates to a control device, an adjustment method, a lithographic apparatus and an article manufacturing method.
Background
In a photolithography process for manufacturing equipment such as a semiconductor device, a Flat Panel Display (FPD), and the like, a pattern of a mask is transferred onto a substrate using an exposure apparatus. For example, in positioning a mask and a substrate, the exposure apparatus needs to perform high-precision position control and synchronization control for a mask stage on which the mask is held and a substrate stage on which the substrate is held.
As the requirements for high-precision equipment are becoming more demanding, the precision requirements for position control and synchronization control of a stage such as the stage described above are becoming more demanding, and conventional feedback control alone may not be sufficient to achieve the required precision. In view of the above, efforts are being made to provide a neural network control device (PTL 1) in parallel with a conventional control device. In addition, a method for switching a neural network control device according to a state of a control target and performing compensation suitable for the control target has been developed (PTL 2).
List of references
Patent literature
PTL 1: PCT Japanese translation patent publication No.7-503563
PTL 2: japanese patent laid-open No.7-277286
Disclosure of Invention
Technical problem
Providing a plurality of neural network control devices may improve accuracy, but as a result, control calculation time increases. The parameters of the neural network control device are adjusted by machine learning, but it takes a long time to learn the parameters of a plurality of neural networks. Furthermore, when the state of the control target changes or the interference environment changes, the predetermined parameters of the neural network may no longer be optimal, and it may be necessary to perform parameter readjustment for a long time.
In view of the above, an object of the present invention is to provide a control device using a neural network that facilitates appropriate adjustment of control characteristics in a short time.
In order to achieve the above object, an aspect of the present invention provides a control apparatus configured to generate a control signal for controlling a control target, the apparatus comprising: a first compensation unit configured to generate a first signal based on a control deviation of a control target; a correction device configured to correct the control deviation using one of a plurality of adjustment units, each adjustment unit configured to generate a correction signal by correcting the control deviation according to a calculation formula of which one or more coefficients can be adjusted; a second compensation unit configured to generate a second signal by using the neural network based on the correction signal; and a computing unit configured to generate a control signal based on the first signal and the second signal.
In view of the above, an object of the present invention is to provide a control device using a neural network that facilitates appropriate adjustment of control characteristics in a short time.
Drawings
Fig. 1 is a diagram illustrating a configuration example of a system according to a first embodiment.
Fig. 2 is a diagram illustrating a configuration example of a system according to the first embodiment.
Fig. 3 is a diagram illustrating a configuration example of a control device in the system according to the first embodiment.
Fig. 4 is a diagram illustrating a configuration example of the control device in example 6.
Fig. 5 is a diagram illustrating a configuration example of the control device in example 7.
Fig. 6 is a diagram illustrating a configuration example of a control device in example 8.
Fig. 7 is a diagram illustrating a configuration example of a system according to the first embodiment.
Fig. 8 is a flowchart illustrating an operation example when the system according to the first embodiment is applied to a production apparatus.
Fig. 9 is a diagram illustrating an example of measurement results of interference suppression characteristics.
Fig. 10 is a diagram illustrating a configuration example of a stage control device according to a second embodiment.
Fig. 11 is a diagram illustrating a configuration example of a control board in the system according to the second embodiment.
Fig. 12 is a flowchart illustrating adjustment of the correction device.
Fig. 13 is a diagram illustrating an example of a position control deviation.
Fig. 14 is a diagram illustrating an example of the frequency analysis result.
Fig. 15 is a diagram illustrating a configuration example of an exposure apparatus.
Detailed Description
Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. In the drawings, the same members are denoted by the same reference numerals, and redundant description thereof is omitted.
< first embodiment >
Fig. 1 illustrates a configuration of a system SS according to an embodiment. The system SS is applied to, for example, a manufacturing apparatus for manufacturing articles. The manufacturing apparatus includes, for example, a processing apparatus configured to process an article or a portion of an article. The processing means may be, for example, one of the following: a lithographic apparatus configured to transfer a pattern onto a material or a member; a film forming device configured to form a film on a material or a member; an etching device configured to etch a material or a component; and a heating device configured to heat the material or the component.
The system SS includes, for example, a sequence unit 101, a control device 100, and a control target 103. The control apparatus 100 comprises a control device 102. The control apparatus 100 or the control device 102 generates a control signal MV for controlling the control target 103. In the case where the system SS is applied to a production system, a production sequence is supplied to the sequence unit 101. The production sequence defines the production process. The sequence unit 101 generates a target value R for controlling the control target 103 based on the production sequence, and supplies the target value R to the control apparatus 100 or the control device 102.
The control apparatus 100 or the control device 102 performs feedback control on the control target 103. More specifically, the control apparatus 100 controls the control target 103 based on a control deviation that is a difference between the target value R provided by the sequence unit 101 and the control value CV provided by the control target 103 such that the control value CV of the control target 103 follows the target value R. The control target 103 may have a sensor that detects the control value CV, and the control value CV detected by the sensor may be supplied to the control device 102. The target value R, the control signal MV, and the control value CV may be time-series data whose values vary with time.
As illustrated by way of example in fig. 2, the system SS may comprise a learning unit 201. The learning unit 201 may be configured to control a part of the apparatus 100 or to control an apparatus external to the apparatus 100. In the case where the learning unit 201 is configured as a device external to the control device 100, the learning unit 201 may be disconnected from the control device 100 after learning is completed. The learning unit 201 is configured to transmit a learning sequence prepared in advance to the sequence unit 101. The sequence unit 101 generates a target value R based on the learning sequence, and supplies the resultant target value R to the control device 102.
The control device 102 generates the control signal MV based on a control deviation that is a difference between the target value R generated and supplied by the sequence unit 101 according to the learning sequence and the control value CV supplied by the control target 103. The control device 102 comprises a neural network and generates the control signal MV by using the neural network. The control signal MV generated by the control device 102 is supplied to the control target 103, which control target 103 operates in accordance with this control signal MV. The control value CV obtained as a result of this operation is supplied to the control device 102. The control device 102 supplies the learning unit 201 with an operation history indicating the history of the operation of the control device 102 based on the target value R. The learning unit 201 determines a parameter value of the neural network of the control device 102 based on the operation history, and sets the parameter value to the neural network. The parameter values are determined by machine learning, such as reinforcement learning.
Fig. 3 is a diagram illustrating a configuration example of the control device 102. The control device 102 comprises a first compensation unit 301 configured to generate a first signal S1 based on a control deviation E and a correction device 303 configured to generate a correction signal CS by calculating the control deviation E according to a calculation formula whose coefficients can be adjusted. The control device 102 further comprises a second compensation unit 302 configured to generate a second signal S2 based on the correction signal CS, via a neural network, and a calculation unit 306 configured to generate a control signal MV based on the first signal S1 and the second signal S2.
The correction device 303 includes a plurality of correction devices that include a first adjustment unit 303a and a second adjustment unit 303b, and that are capable of selecting an adjustment unit to be used (an adjustment unit to be connected) according to a control state. The control signal MV is the sum of the first signal S1 and the second signal S2. The computing unit 306 may be configured by an adder. The control signal MV is a signal obtained as a result of correcting the first signal S1 based on the second signal S2. The control device 102 comprises a subtractor 305 configured to generate a control deviation E indicative of the difference between the target value R and the control value CV. The control value CV is obtained by measurement of a sensor or the like (not shown) included in the control target 103. The difference between the control value obtained as a result of controlling the control target 103 based on the control signal MV and the target value R is smaller than the difference between the control value obtained as a result of controlling the control target 103 based on the first signal S1 and the target value R.
The control device 102 further includes an operation history storage unit 304. The learning unit 201 shown in fig. 2 is configured to learn to determine the parameter values of the neural network of the second compensation unit 302 shown in fig. 3. For learning by the learning unit 201, the operation history storage unit 304 stores operation histories required for learning by the learning unit 201, and supplies the stored operation histories to the learning unit 201. The operation history is, for example, a record of the correction signal CS as data input to the second compensation unit 302 and the second signal S2 as data output from the second compensation unit 302, but the operation history may be a control deviation and the second signal S2 as data output from the second compensation unit 302 or other data. The first and second adjustment units 303a and 303b may be trained using any parameter as an initial value.
Examples 1 to 5 below describe configuration examples of the correction device 303. More specifically, in examples 1 to 5, examples of the calculation formula used by the correction device 303 to generate the correction signal CS based on the control deviation E are described. For example, the computational formula may be a single formula or a polynomial.
Example 1
In example 1, the first adjusting unit 303a and the second adjusting unit 303b have control characteristics represented in the following formula (1). In equation (1), x represents an input (E) of the correction device 303, y represents an output (CS) of the correction device 303, and Kp is an arbitrary coefficient (constant).
[ formula 1]
y＝Kpx
Example 2
In example 2, the first adjusting unit 303a and the second adjusting unit 303b have control characteristics represented in the following formula (2). In equation (2), x represents an input (E) of the correction device 303, y represents an output (CS) of the correction device 303, t represents time, and Ki is an arbitrary coefficient (constant). Note that integration may be performed a plurality of times. The integral may be a fixed integral over a certain time interval or an indefinite integral.
[ formula 2]
y＝K i ∫x dt...(2)
Example 3
In example 3, the first adjusting unit 303a and the second adjusting unit 303b have control characteristics represented in the following formula (3). In equation (3), x represents an input (E) of the correction device 303, y represents an output (CS) of the correction device 303, t represents time, and Kd is an arbitrary coefficient (constant). Note that differentiation may be performed a plurality of times.
[ formula 3]
Example 4
In example 4, the first adjusting unit 303a and the second adjusting unit 303b have control characteristics represented in the following formula (4). In equation (4), x represents an input (E) of the correction device 303, y represents an output (CS) of the correction device 303, and Kp, ki, and Kd are each arbitrary coefficients (constants). Note that integration and differentiation may be performed a plurality of times.
[ equation 4]
Example 5
In example 5, the first adjusting unit 303a and the second adjusting unit 303b have control characteristics represented by a calculation formula (5) shown below. In equation (5), x denotes an input (E) of the correction device 303, y denotes an output (CS) of the correction device 303, n denotes an order of multiple integration, and m denotes an order of differentiation, kp is an arbitrary coefficient (constant), ki_n is an arbitrary coefficient (constant) of n-th integration, and kd_m is an arbitrary constant of m-th order differentiation.
[ equation 5]
Each of examples 1 to 5 may be understood as an example as follows: wherein the calculation formula used by the correction device 303 for generating the correction signal CS comprises at least one of a term proportional to the control deviation E, a term performing integration and a term performing differentiation.
Coefficients (constants) Kp, ki, kd, ki _n and kd_m of the calculation formulas described in examples 1 to 5 are examples of adjustable parameters of the correction device 303. The first and second adjusting units 303a and 303b determine optimal parameters using one of examples 1 to 5 in advance depending on a change in the control state. The control states include, for example, synchronous control switching, control device switching, operation mode switching, and changes in environment and disturbances such as temperature, noise, floor vibration, and/or the like. By selecting the first adjusting unit 303a or the second adjusting unit 303b according to the control state, optimal control characteristics can be obtained. The adjustment time required in the configuration including the plurality of correction devices is shorter than that in the configuration including the plurality of neural networks, and therefore, the configuration including the plurality of correction units is advantageous in terms of time shortening.
When the state of the control target 103 or the interference environment changes during the operation of the system SS, the changes can be dealt with by adjusting the values (parameter values) of the calculation formulas (coefficients of formulas) illustrated by way of example in examples 1 to 5. The time required to adjust the value of (the coefficient of) the calculation formula of the correction device 303 is shorter than the time required to retrain the neural network. Therefore, high control accuracy can be maintained without reducing the productivity of the system SS. That is, the introduction of the correction device 303 improves the inclusion of changes to the state of the control target 103 and/or changes in the disturbance environment.
Example 6
Examples 6 to 8 describe the relationship between the change in the control state and the switching of the adjustment unit used in the correction device 303.
Fig. 4 is a diagram illustrating a configuration example of the control device 102 in example 6. In example 6, as shown in fig. 4, it may be switched whether control is performed individually or synchronously for a plurality of control targets including the control target 103a and the control target 103 b. In example 6, the control state is a state determined by whether the plurality of control targets are individually or synchronously controlled, and the adjustment unit is appropriately switched depending on whether the plurality of control targets are synchronously controlled. Example 6 is configured as follows: the correction device 303 is switched to select whether to use the first adjusting unit 303a or the second adjusting unit 303b according to the state of the synchronization control switching unit 402. In example 6, a synchronous control method called a master-slave method is explained in which the axis on which the control target 103a is controlled is called a master axis, and the axis on which the control target 103b is controlled is called a slave axis, and the slave axis follows the master axis.
The control apparatus 102 takes control values CVa and CVb for respective control targets 103a and 103b measured by sensors (not shown) provided on the control target 103, and calculates differences of the control values CVa and CVb from the respective target values Ra and Rb as control deviations Ea and Eb.
The control deviation Ea is input to the control device 301a. The inputs of the control device 301b and the correction device 303 set at a stage preceding the neural network 302 configured in parallel with the control device 301b may be switched depending on whether the control target 103a and the control target 103b are synchronously controlled. The input of the correction device 303 may be selected by the synchronous control switching unit 402 that switches whether the control target 103a and the control target 103b are synchronously controlled such that the input is given by the control deviation Eb or the synchronous deviation Ec indicating the difference between the control deviation Eb and the control deviation Ea. The output of the correction device 303 may choose whether to use the first adjusting unit 303a or the second adjusting unit 303b to give the output depending on the state of the synchronous control switching unit 402.
In the case where example 6 is specifically applied to the exposure apparatus, different adjustment units may be selected depending on whether the board stage and the mask stage are synchronized or they are otherwise operated. In this case, the parameters of the first and second adjusting units 303a and 303b are optimized for the case where the board mounting table and the mask mounting table are synchronized and the case where they are otherwise operated, respectively. The output of the first adjusting unit 303a or the second adjusting unit 303b selected according to the state of the synchronization control switching unit 402 is input to the neural network 302 (second compensating unit). The output of the compensation unit 301a is referred to as a control signal MVa. The output of the compensation unit 301b and the output of the neural network 302 are added together and the result is used as a control signal MVb. The control device 102 outputs control signals MVa and MVb to the control targets 103a and 103b, respectively.
The first adjusting unit 303a and the second adjusting unit 303b each determine an optimum parameter using one of examples 1 to 5 depending on the state of the synchronous control switching unit 402. By selecting the first adjusting unit 303a or the second adjusting unit 303b according to the state of the synchronous control switching unit 402, optimal control characteristics can be obtained.
The increase in adjustment time due to the configuration of the correction device 303 for selecting the optimal adjustment unit from the plurality of adjustment units is smaller than the increase in adjustment time due to the configuration of the plurality of neural networks. When the state of the control target 103 or the interference environment changes during the operation using one of examples 1 to 5, the changes can be handled by adjusting the parameters of examples 1 to 5. The time required for adjusting the first adjusting unit 303a and the second adjusting unit 303b is shorter than the time required for retraining the neural network. In example 6, even in the case where a plurality of compensations are performed according to the state of the control target and the disturbance environment, an increase in the calculation time and an increase in the learning time can be suppressed, and even when the state of the control target or the disturbance environment changes, an appropriate control characteristic can be realized in a short time.
Example 7
Fig. 5 is a diagram illustrating a configuration example of the control device 102 in example 7. In example 7, the compensation unit 301 is switched according to the state and operation of the control target 103 so that whether to use the compensation unit 301a or the compensation unit 301b can be selected. The control state in example 7 is a state determined by which one of the plurality of compensation units is used. The adjustment unit is switched depending on whether the compensation unit 301a or the compensation unit 301b is used. Example 7 is configured as follows: the correction device 303 is switched to select whether to use the first adjusting unit 303a or the second adjusting unit 303b according to the state of the compensating unit 301.
In the case where example 7 is specifically applied to an exposure apparatus, in which the gain is switched so that the compensation unit 301a is used during a board mounting stage exposure operation and the compensation unit 301b is used during a board transfer operation. That is, an adjustment unit for correcting the control deviation E is selected from a plurality of adjustment units based on whether or not the gain of the control target 103 is switched. In this case, the first and second adjusting units 303a and 303b determine in advance optimal parameters using one of examples 1 to 5 for the compensating unit 301a and 301b, respectively. By selecting the first adjusting unit 303a or the second adjusting unit 303b according to the state of the compensating unit 301, optimal control characteristics can be obtained.
The increase in adjustment time due to the configuration of the correction device 303 for selecting the optimal adjustment unit from the plurality of adjustment units is smaller than the increase in adjustment time due to the configuration of the plurality of neural networks. When the state of the control target 103 or the interference environment changes during the operation using one of examples 1 to 5, the changes can be handled by adjusting the parameters of examples 1 to 5. The time required for adjusting the first adjusting unit 303a and the second adjusting unit 303b is shorter than the time required for retraining the neural network. In example 7, even in the case where a plurality of compensations are performed according to the state of the control target and the disturbance environment, an increase in the calculation time and an increase in the learning time can be suppressed, and even when the state of the control target or the disturbance environment changes, an appropriate control characteristic can be realized in a short time.
Example 8
Fig. 6 is a diagram illustrating a configuration example of the control device 102 in example 8. The control state in example 8 is a state determined by whether or not the operation mode 403 of the control target is changing. A specific example of the operation mode 403 will be described later. Example 8 is configured as follows: the adjusting units are switched to select whether to use the first adjusting unit 303a or the second adjusting unit 303b according to the state of the operation mode 403.
In the case where example 7 is specifically applied to a stage apparatus used in an exposure apparatus or the like, switching may be performed according to whether the stage is in a stage acceleration period or operation is in another operation mode. In this case, the first and second adjusting units 303a and 303b determine the optimum parameters using one of examples 1 to 5 in advance depending on the state of the operation mode 403 of the control target 103. By selecting the first adjusting unit 303a or the second adjusting unit 303b according to the state of the operation mode 403, optimal control characteristics can be obtained.
The increase in adjustment time due to the configuration of the correction device 303 for selecting the optimal adjustment unit from the plurality of adjustment units is smaller than the increase in adjustment time due to the configuration of the plurality of neural networks. When the state of the control target 103 or the interference environment changes during the operation using one of examples 1 to 5, the changes can be handled by adjusting the parameters of examples 1 to 5. The time required for adjusting the first adjusting unit 303a and the second adjusting unit 303b is shorter than the time required for retraining the neural network. In example 8, when a plurality of compensations are performed according to the state of the control target and the disturbance environment, an increase in the calculation time and an increase in the learning time can be suppressed, and even when the state of the control target or the disturbance environment changes, an appropriate control characteristic can be realized in a short time.
As illustrated by way of example in fig. 7, the control device 100 may comprise a setting unit 202 for selecting whether to use the first adjusting unit 303a or the second adjusting unit. The setting unit 202 may also have a function of setting the parameter value of the correction device 303.
The setting unit 202 may perform an adjustment process for switching the adjustment unit and adjusting the parameter value of the adjustment unit so that switching of the adjustment unit and determination and setting of the parameter value may be performed in the adjustment process, or the setting unit 202 may switch the adjustment unit and set the parameter value based on an instruction given by a user. In the former case, the setting unit 202 may send a confirmation sequence to the sequence unit 101 to confirm the operation of the control device 102, and may cause the sequence unit 101 to generate the target value R based on this confirmation sequence. Then, the setting unit 202 may obtain an operation history (for example, a history of control deviation) from the control device 102 operating based on this target value R, and may determine whether to switch the correction device 303 and/or may determine a parameter value based on the operation history. The setting unit 202 having such a function can be understood as an adjusting unit that switches the correction device 303 and adjusts the parameter value.
The setting unit 202 may obtain an operation history (for example, a history of control deviation) from the control device 102 during production of the target value R generated by the sequence unit 101 based on the production sequence, and may determine whether to perform adjustment of the parameter value of the correction device 303 based on the obtained operation history. Alternatively, the determination unit may be provided separately from the setting unit 202, so that in the production in which the sequence unit 101 generates the target value R based on the production sequence, the determination unit determines whether to adjust the parameter value of the correction device 303 set by the setting unit 202.
Next, a production example of the system according to the present embodiment is described below. Fig. 8 illustrates an operation example of the system SS according to the present embodiment when the system SS is applied to a production apparatus.
In step S501, the sequence unit 101 generates a target value R based on a given production sequence, and supplies it to the control apparatus 100 or the control device 102. The control apparatus 100 or the control device 102 controls the control target 103 based on the supplied target value R.
In step S502, the setting unit 202 obtains an operation history (for example, a history of control deviation) of the operation of the control device 102 in step S501.
In step S503, the setting unit 202 may determine whether to perform adjustment of the correction device 303 in terms of switching the adjustment unit or adjusting (or readjusting) the parameter value based on the operation history obtained in step S502. For example, when the operation history satisfies a predetermined condition, the setting unit 202 may determine that switching of the adjusting unit or adjustment (or readjustment) of the parameter value of the adjusting unit is to be performed. The predetermined condition is a condition that causes production to stop. For example, when the control deviation obtained as the operation history exceeds a specified value, it is determined that the correction device 303 needs to be adjusted. In the case where the adjustment of the correction device 303 is to be performed by the setting unit 202, the process proceeds to step S504, otherwise the process proceeds to step S505.
In step S504, the setting unit 202 performs adjustment of the correction device 303. This adjustment is performed in a state where the parameter value of the second compensation unit 302 is maintained to the previous value, and causes the parameter value (coefficient) of the correction device 303 to be reset.
In step S505, the sequence unit 101 determines whether to end production according to the production sequence. In the case where it is determined not to end the production, the process returns to step S501, and in the case where it is determined to end the production, the production is ended. According to the above-described processing, in the case of a state in which production is required to be stopped, it is possible to quickly adjust the parameter value of the correction device 303 and resume production with minimal interruption.
In step S504, the setting unit 202 transmits a confirmation sequence to the sequence unit 101 to cause the sequence unit 101 to execute the confirmation sequence, and the operation history (for example, the history of control deviation) in the confirmation sequence may be obtained from the control device 102. Then, the setting unit 202 performs frequency analysis on the operation history, determines a frequency to be improved based on the result of the frequency analysis, and determines a parameter value of the correction device 303 so that the control deviation at this frequency falls within a specified value. Further specific examples of step S504 will be described in the second embodiment.
Fig. 9 illustrates an example of measurement results of interference suppression characteristics. The interference suppression characteristic refers to a measurement result of the frequency response obtained as an output of the control deviation when the sine wave is input as the control signal MV in fig. 2. In fig. 9, the horizontal axis represents frequency and the vertical axis represents gain of the interference suppression characteristic. The interference suppression characteristic indicates the frequency response of the control deviation E when adding interference to the control signal MV, so a high gain indicates a low ability to suppress interference. On the other hand, a low gain indicates a high interference suppression capability of the system. In fig. 9, a broken line indicates an interference suppression characteristic obtained before the adjustment is performed, and a solid line indicates an interference suppression characteristic obtained after the adjustment is performed.
If the frequency indicated by the chain line in fig. 9 is determined as the frequency to improve the interference suppression characteristic and then step S504 is performed, the result is, for example, obtaining the interference suppression characteristic as shown by the solid line. The results show that the gain of the interference suppression characteristic becomes smaller at the frequency to be improved, i.e., the interference suppression characteristic is improved. In examples 1 to 8, when the parameter of the correction device 303 set at a stage preceding the neural network is adjusted, the interference suppression characteristic shown in fig. 9 may be used as an index of parameter adjustment.
< second embodiment >
In the second embodiment, an example is described in which the control system SS according to the first embodiment is applied to the stage control device 800. Matters not described in the second embodiment are identical to those of the first embodiment. Fig. 10 illustrates a hardware configuration of the stage control device 800 when the control system SS illustrated in fig. 1 is applied to the stage control device 800.
The stage control device 800 is configured to control the stage 804 in a state where an object such as a substrate is held on the stage 804, to control the position of the object. The stage control device 800 includes a control board 801, a current driver 802, a motor 803, a stage 804, and a sensor 805. The control board 801 corresponds to the control apparatus 100 or the control device 102 in the system SS according to the first embodiment. The current driver 802, the motor 803, the stage 804, and the sensor 805 correspond to the control target 103 in the system SS according to the first embodiment. However, the current driver 802 may be incorporated into the control board 801. Although not shown in fig. 10, the stage control apparatus 800 may include a sequence unit 101, a learning unit 201, and a setting unit 202.
The control board 801 may be supplied with a position target value as a target value from the sequence unit 101. The control board 801 may generate a current command as a control signal based on the position target value supplied from the sequence unit 101 and the position information supplied from the sensor 805, and may supply the current command to the current driver 802. The control board 801 may also supply an operation history to the sequence unit 101.
The current driver 802 may supply current to the motor 803 according to a current command. The motor 803 may be an actuator configured to convert a current supplied from the current driver 802 into a driving force and drive the mounting table 804 using the driving force. The stage 804 may hold an object such as a plate or mask. The sensor 805 can detect the position of the mounting table 804 and provide the obtained positional information to the control board 801.
Fig. 11 is a block diagram illustrating a configuration example of the control board 801. The control board 801 may include: the first compensation unit 301 configured to generate the first signal S1 based on the position control deviation E of the stage 804 as a control target, and the correction device 303 configured to generate the correction signal CS by correcting the control deviation E according to a calculation formula whose coefficient can be adjusted. The control board 801 may further include: a second compensation unit 302 configured to generate a second signal S2 based on the correction signal CS through a neural network, and a calculation unit 306 configured to generate a current command as a control signal based on the first signal S1 and the second signal S2. The control board 801 may further include: a subtractor 305 configured to generate a control deviation E as a difference between the position target value PR and the position information.
The stage control device 100 according to the second embodiment may further include a learning unit 201 in the first embodiment as described above with reference to fig. 7. The learning unit 201 may be configured to learn to determine parameter values of the neural network of the second compensation unit 302. For learning by the learning unit 201, the operation history storage unit 304 may store an operation history required for learning by the learning unit 201, and may supply the stored operation history to the learning unit 201. The operation history may be, for example, a record of the correction signal CS as data input to the second compensation unit 302 and the second signal S2 as data output from the second compensation unit 302, but the operation history may be other data.
The stage control device 100 according to the second embodiment may include a setting unit 202. The setting unit 202 may perform an adjustment process for adjusting the parameter value of the correction device 303, and by this adjustment process, the parameter value of the correction device 303 is determined and set, or the setting unit 202 may set the parameter value of the correction device 303 based on a command given by the user.
Using fig. 8, the operation of the stage apparatus 800 when the stage control apparatus 800 according to the second embodiment is applied to a production apparatus is illustrated by way of example. In step S501, the sequence unit 101 may generate a position target value PR based on a given production sequence and may provide it to the stage control device 800. The stage control device 800 controls the position of the stage 804 based on the position target value PR.
In step S502, the setting unit 202 obtains the operation history (for example, the history of control deviation) of the control board 801 in step S501.
In step S503, the setting unit 202 may determine whether to perform adjustment of the correction device 303 in terms of switching the adjustment unit or adjusting (or readjusting) the parameter value based on the operation history obtained in step S502. For example, the setting unit 202 may determine that adjustment of the correction device 303 is to be performed when the operation history satisfies a predetermined condition. The predetermined condition is a condition indicating that production is to be stopped. For example, when the maximum value of the position control deviation during constant-rate driving of the mounting table 804 exceeds a predetermined specified value, it is determined that the correction device 303 needs to be adjusted. In the case where the adjustment of the correction device 303 is to be performed by the setting unit 202, the process proceeds to step S504, otherwise the process proceeds to step S505.
In step 504, the setting unit 202 may perform adjustment of the correction device 303. In step S505, the sequence unit 101 determines whether to end production according to the production sequence. In the case where it is determined not to end the production, the process returns to step S501, and in the case where it is determined to end the production, the production is ended.
Fig. 12 illustrates a specific example of the processing of the adjustment parameter value (or readjustment parameter value) in the adjustment of the correction device 303 in step S504. In step S601, the setting unit 202 may transmit a confirmation sequence to the sequence unit 101 to confirm the operation of the stage control apparatus 800, and cause the sequence unit 101 to generate the position target value PR based on the confirmation sequence. In step S602, the setting unit 202 may obtain the position control deviation E as the operation history from the control device 102 that operates based on the position target value PR.
With reference to fig. 13, the following describes the variation of the position control deviation E before and after the parameter adjustment. Fig. 13 illustrates an example of position control deviation before and after parameter adjustment. In fig. 13, the horizontal axis represents time and the vertical axis represents the position control deviation E. In fig. 13, a broken line curve represents the position control deviation E before the parameter value of the correction device 303 indicating that the position control accuracy has deteriorated is adjusted. By adjusting the parameter values, fluctuations in the position control deviation E can be reduced.
In step S603, the setting unit 202 may perform frequency analysis on the position control deviation E obtained in step S602. With reference to fig. 14, the frequency analysis results before and after parameter adjustment are described. Fig. 14 illustrates an example of frequency analysis results obtained before and after parameter adjustment. In fig. 14, the horizontal axis represents frequency and the vertical axis represents power spectrum. The dashed line indicates the frequency at which the maximum spectrum occurs prior to adjustment. In step S604, the setting unit 202 may determine, as the frequency to be improved, a frequency at which the maximum spectrum appears in the power spectrum, for example.
The processing in steps S605 to S610 is a specific example of adjustment processing for adjusting the parameter value of the correction device 303. In this specific example, the steepest descent method is adopted as a method of adjusting the parameter value, but other methods may be used. In step S605, the setting unit 202 initializes n to 1. For example, the calculation formula at the correction device 303 includes the following three terms-first order integral term; a proportional term; and in the case of the first-order derivative term, the parameters whose values are to be adjusted are the following three parameters: ki; kp; and Kd. The parameter value pn obtained after the nth adjustment is represented by the following formula (6).
[ formula 6]
In step S606, the setting unit 202 may set any initial value to the parameter value p1 in the first adjustment of the parameter value pn. In the nth adjustment, the parameter value pn may be set according to the following expression (8).
The objective function J (pn) for adjusting the parameter value pn may be, for example, a gain of the interference suppression characteristic at the frequency determined in step S604. In step S607, the setting unit 202 may measure the gradient vector grad J (pn) of the objective function J (pn). The gradient vector grad J (pn) can be given by the following expression (7). The gradient vector grad J (pn) can be measured by varying each of the elements Ki-n, kp-n and Kd-n of the parameter value pn by a small amount.
[ formula 7]
In step S608, the setting unit 202 may determine whether the value of each element of the gradient vector grad J (pn) is less than or equal to a specified value as the convergence determination in the steepest descent method. In the case where the value of each element of the gradient vector grad J (pn) is smaller than the specified value, the setting unit 202 may end adjusting the parameter value of the correction device 303. On the other hand, if the value of each element of the gradient vector grad J (pn) is greater than the specified value, the setting unit 202 may calculate a parameter value pn+1 in step S609. In this step S609, the parameter value pn+1 may be calculated according to the equation (8) shown below using, for example, any constant α greater than 0. In step S610, the setting unit 202 adds 1 to the value of n and returns to step S606.
[ formula 8]
p n+1 ＝p n -αgrad J(p n )...(8)
In step S611, the setting unit 202 may transmit a confirmation sequence to the sequence unit 101 to confirm the operation of the stage control apparatus 800, and cause the sequence unit 101 to generate the position target value PR based on the confirmation sequence. In step S612, the setting unit 202 may obtain the position control deviation E as the operation history from the control device 102 that operates based on the position target value PR.
In step S613, the setting unit 202 determines whether the position control deviation E obtained in step S612 is less than or equal to a specified value. In the case where the position control deviation E is greater than the specified value, the process returns to step S601 to perform the adjustment again. In the case where the position control deviation E is less than or equal to the specified value, the adjustment may be completed.
According to the present embodiment, in the case where a state or disturbance of a control target including the mounting table 804 changes, the change can be dealt with by adjusting the parameter value of the correction device 303. For example, in the example shown in fig. 13, the position control deviation indicated by the broken line is reduced to the position control deviation indicated by the solid line, which brings about improvement in control accuracy.
In the example of equation (6), the number of parameters of the correction device 303 is only three, which is far less than the number of parameters in a typical neural network. For example, in the case of using a deep neural network, if the dimension of the input layer is 5, the dimension of the hidden layer is 32 in each of the two stages, and the dimension of the output layer is 8, the number of parameters is 1545. The adjustment of the parameter values of the correction device 303 takes less time than determining the values of these 1545 parameters by performing relearning. Therefore, high control accuracy can be maintained without reducing productivity of the stage control device 800.
< third embodiment >
In the third embodiment, an example in which the control system SS according to the first embodiment is applied to the exposure apparatus EXP is described. Matters not described in the third embodiment are identical to those of the first embodiment. Fig. 15 schematically illustrates a configuration example of the exposure apparatus EXP according to the present embodiment. The exposure device EXP may be configured to scan the exposure device.
The exposure apparatus EXP may include, for example, an illumination light source 1000, an illumination optical system 1001, a mask stage 1003, a projection optical system 1004, and a board stage 1006. The illumination source 1000 may include, but is not limited to, a mercury vapor lamp, an excimer laser source, or an EUV light source. The exposure light 1010 from the illumination light source 1000 is shaped into the shape of the illumination area of the projection optical system 1004 by the illumination optical system 1001 with uniform illuminance. In one example, the exposure light 1010 may be shaped as a rectangle that is long in the X direction, which is an axis perpendicular to a plane formed by the Y axis and the Z axis. The exposure light 1010 may be shaped into a circular arc shape depending on the type of the projection optical system 1004. The pattern of the mask (original plate) 1002 is irradiated with the shaped exposure light 1010. The exposure light 1010 having passed through the pattern of the mask 1002 forms an image of the pattern of the mask 1002 on the surface of a plate 1005 (substrate) by a projection optical system 1004.
The mask 1002 is held by a mask stage 1003 by vacuum suction or the like. The board 1005 is held by suction cup 1007 of board mounting table 1006 by vacuum suction or the like. The positions of the mask stage 1003 and the board stage 1006 may be controlled by a multi-axis position control device equipped with a position sensor 1030 such as a laser interferometer or a laser scale, a drive system 1031 such as a linear motor, and a control apparatus 1032. The position measurement value output from the position sensor 1030 may be provided to the control device 1032. The control apparatus 1032 generates a control signal based on the position control deviation, which is the difference between the position target value and the measured position value, and supplies it to the driving system 1031, thereby driving the mask stage 1003 and the board stage 1006. By scanning and exposing the plate 1005 while driving the mask stage 1003 and the plate stage 1006 in synchronization in the Y direction, the pattern of the mask 1002 is transferred to the plate 1005 (photosensitive material on the plate).
Next, an example in which the second embodiment is applied to the control board stage 1006 will be described. The control board 801 in fig. 11 corresponds to the control device 1032, the current driver 802 and the motor 803 correspond to the drive system 1031, the mounting table 804 corresponds to the board mounting table 1006, and the sensor 805 corresponds to the position sensor 1030. By controlling the board stage 1006 using a control device with a neural network, positional control deviation of the board stage 1006 can be reduced. This can improve the overlay accuracy and the like. The parameter values of the neural network may be determined by a predetermined learning sequence. However, when the state of the control target or the disturbance environment changes compared to the state at the time of learning, the control accuracy of the board mount 1006 deteriorates. Even in this case, by adjusting the parameter values of the correction device, the adjustment can be completed in a shorter time than in the case of retraining the neural network. Therefore, high control accuracy can be maintained without reducing productivity of the exposure apparatus.
An example in which the second embodiment is applied to control of the mask stage 1003 is described. The control board 801 in fig. 11 corresponds to the control device 1032, the current driver 802 and the motor 803 correspond to the driving system 1031, the mounting table 804 corresponds to the mask mounting table 1003, and the sensor 805 corresponds to the position sensor 1030.
Also in the case where the second embodiment is applied to the control of the mask stage 1003, the positional control deviation of the mask stage 1003 can be reduced. This can improve the overlay accuracy and the like. The parameter values of the neural network may be determined by a predetermined learning sequence. However, when the state of the control target or the disturbance environment changes compared to the state at the time of learning, the control accuracy of the mask stage 1003 deteriorates. Even in this case, by adjusting the parameter values of the correction device, the adjustment can be completed in a shorter time than in the case of retraining the neural network. Therefore, high control accuracy can be maintained without reducing productivity of the exposure apparatus.
The second embodiment can be applied not only to control of a stage in an exposure apparatus but also to control of a stage in other lithographic apparatus such as an imprint apparatus, an electron beam lithography apparatus, and the like. The first embodiment or the second embodiment can also be applied to control of a movable member such as a hand holding an article in a conveying mechanism that conveys the article, for example.
< example of article manufacturing method >
The article manufacturing method according to the embodiment of the present invention is suitable for, for example, manufacturing a Flat Panel Display (FPD). The article manufacturing method according to the present embodiment includes a process of forming a latent image pattern on a photosensitive agent coated on a substrate using the above-described exposure apparatus (substrate exposure process) and a process of developing a substrate on which the latent image pattern is formed in the previous process. The fabrication process also includes other well known processes (oxidation, deposition, evaporation, doping, planarization, etching, resist stripping, dicing, bonding, packaging, etc.). The article manufacturing method according to the present embodiment is advantageous in at least one of performance, quality, productivity, and production cost of an article, as compared with the conventional method.
The present application has been described above with reference to the embodiments. Note that the present application is not limited to these embodiments, and various modifications and variations are possible within the scope of the present application.
The present application is not limited to the above-described embodiments, but various changes and modifications may be made without departing from the spirit and scope of the present application. Accordingly, the appended claims are intended to disclose the scope of the present application.
The present application claims the benefit of japanese patent application No.2020-205546 filed on 11/12/2020, the entire contents of which are incorporated herein by reference.

Claims (23)

1. A control apparatus configured to generate a control signal for controlling a control target, comprising:
a first compensation unit configured to generate a first signal based on a control deviation of a control target;
a correction device configured to correct the control deviation using one of a plurality of adjustment units, each adjustment unit configured to generate a correction signal by correcting the control deviation according to a calculation formula of which one or more coefficients can be adjusted;
a second compensation unit configured to generate a second signal by using the neural network based on the correction signal; and
And a computing unit configured to generate a control signal based on the first signal and the second signal.
2. The control apparatus according to claim 1, wherein in the correction device, an adjustment unit for correcting the control deviation is selected from the plurality of adjustment units based on a control state of the control target.
3. The control device according to claim 1 or 2, wherein an adjustment unit for correcting a control deviation is selected from the plurality of adjustment units based on whether or not a control target and another different control target are to be synchronously controlled.
4. The control device according to claim 1 or 2, wherein an adjustment unit for correcting the control deviation is selected from the plurality of adjustment units based on whether or not switching of the gain of the control target occurs.
5. The control device according to claim 1 or 2, wherein an adjustment unit for correcting a control deviation is selected from the plurality of adjustment units based on a state of the first compensation unit.
6. The control device according to claim 1 or 2, wherein an adjustment unit for correcting a control deviation is selected from the plurality of adjustment units based on whether a change in an operation mode of a control target has occurred.
7. The control device according to one of claims 1 to 6, wherein the calculation formula includes a term proportional to the control deviation.
8. The control device according to one of claims 1 to 7, wherein the calculation formula includes a term that performs integration on the control deviation.
9. The control device according to one of claims 1 to 8, wherein the calculation formula includes a term that performs differentiation on the control deviation.
10. The control device according to one of claims 1 to 6, wherein the calculation formula includes at least one of a term proportional to the control deviation, a term that performs integration on the control deviation, and a term that performs differentiation on the control deviation.
11. The control device according to one of claims 1 to 10, further comprising: and a setting unit configured to adjust the correction apparatus, wherein the setting unit selects an adjustment unit for correcting the control deviation from the plurality of adjustment units.
12. The control apparatus according to claim 11, wherein the setting unit sets the calculation formula.
13. The control device according to claim 11 or 12, wherein the setting unit resets the one or more coefficients of the calculation formula when the control deviation satisfies a predetermined condition.
14. The control device according to claim 13, wherein the predetermined condition includes a control deviation exceeding a specified value.
15. The control device according to one of claims 1 to 14, further comprising: a learning unit configured to determine parameter values of the neural network by machine learning.
16. The control device according to one of claims 1 to 15, wherein,
the control signal is a signal obtained by correcting the first signal based on the second signal, and
the difference between the control value obtained as a result of controlling the control target based on the control signal and the target value for controlling the control target is smaller than the difference between the control value obtained as a result of controlling the control target based on the first signal and the target value for controlling the control target.
17. A control apparatus configured to generate a control signal for controlling a control target, comprising:
a first compensation unit configured to generate a first signal based on a control deviation of a control target;
a correction device configured to correct the control deviation using one of a plurality of adjustment units, each adjustment unit configured to generate a correction signal by correcting the control deviation according to a calculation formula;
A second compensation unit configured to generate a second signal by using the neural network based on the correction signal; and
and a computing unit configured to generate a control signal based on the first signal and the second signal.
18. The control apparatus according to claim 17, wherein in the correction device, an adjustment unit for correcting the control deviation is selected from the plurality of adjustment units based on a control state of the control target.
19. The control apparatus according to claim 17 or 18, wherein the calculation formula includes at least one of a term proportional to the control deviation, a term that performs integration on the control deviation, and a term that performs differentiation on the control deviation.
20. A stage control device configured to control a stage holding an object to control a position of the object, the stage control device comprising the control device according to one of claims 1 to 19.
21. A lithographic apparatus configured to transfer a pattern of a document plate onto a substrate, comprising: the control device according to one of claims 1 to 19, configured to control a position of a substrate or an original plate.
22. A method of manufacturing an article, comprising:
transferring a pattern of a document plate onto a substrate using the lithographic apparatus of claim 21; and
The substrate obtained through the transfer is processed,
wherein an article is obtained from the substrate obtained through the treatment.
23. A method of adjusting a control apparatus including a first compensation unit configured to generate a first signal based on a control deviation of a control target, a correction device configured to correct the control deviation using one of a plurality of adjustment units, each adjustment unit configured to generate a correction signal by correcting the control deviation, a second compensation unit configured to generate a second signal based on the correction signal by using a neural network, and a calculation unit configured to generate the control signal based on the first signal and the second signal, the method comprising:
in the correction device, performing the adjustment includes: an adjusting unit for correcting the control deviation is selected from the plurality of adjusting units based on the control state of the control target.
CN202180082084.6A 2020-12-11 2021-12-07 Control device, adjustment method, lithographic apparatus and article manufacturing method Pending CN116648773A (en)

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US5396415A (en) 1992-01-31 1995-03-07 Honeywell Inc. Neruo-pid controller
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