Patent Description:
As control devices for controlling the physical variable of a target object, classic controllers such as those based on PID control are often used. In recent years, besides controllers based on classic control theories and controllers based on modern control theories, controllers configured using machine learning may also be used. In addition, controllers combining a controller not including machine learning and a controller based on machine learning may also be used. A positioning apparatus described in <CIT> calculates the correction amount of a control command value by performing machine learning, and corrects a motor control command of a feedback control unit. As further prior art there may be mentioned <CIT>, which discloses a neural network based control system that includes a nominal control system augmented by adaptive control such as a neuro-controller which generates additional compensating control signals based on differences between a model and actual system output, <CIT>, which discloses a method of summing the outputs from a conventional PID controller and a neural network controller, and <CIT>, which discloses a control system for a nanoimprint system.

There is a problem related to the reliability of a control device using a controller based on machine learning. The possibility that a controller generated by machine learning outputs an abnormal control command for a situation that is greatly different from a situation given at the time of learning, that is, a situation that is greatly different from a group of situations given as learning data, may not be denied. This particularly causes a significant problem because it is difficult to grasp how a controller that contains a large-scale deep neural network where parameters are adjusted by machine learning responds (outputs) to input.

A processing apparatus including a feedback control device for solving the above-described problems is a feedback control device that takes information regarding a control deviation between a measured value and a desired value of a controlled object as input, and outputs a manipulated variable for the controlled object, including: a first control unit that takes information regarding the control deviation as input, and outputs a manipulated variable for the controlled object; a second control unit that takes information regarding the control deviation as input, and that includes a learning control unit in which a parameter for outputting a manipulated variable for the controlled object is determined by machine learning; and an adder that adds a first manipulated variable output from the first control unit and a second manipulated variable output from the second control unit. A manipulated variable from the adder is output to the controlled object. The second control unit includes a limiter that limits the second manipulated variable output from the second control unit.

Hereinafter, preferred embodiments of the present invention will be described in detail on the basis of the accompanying drawings. Note that a controlled object to be described is not limited to each embodiment, and the type of controlled object is not limited as long as it is a physical variable usable in feedback control. Typical controlled objects are listed below. Exemplary controlled objects include the displacement, velocity, or acceleration in the straight and rotational directions of an object, and the flow rate, flow velocity, or pressure of gas or fluid. Other exemplary controlled objects include the liquid level of fluid, the temperature of an object, gas, or liquid, and the electric current, voltage, or charge of an electric circuit. Other exemplary controlled objects include a magnetic flux in a magnetic field, a magnetic flux density, or the sound pressure of a sound field. These physical variables are measured by sensing units using known sensors, and the measured value is input to a control device. A controlled variable drive unit is an active element that applies a change to the physical variable of the controlled object, and, when the controlled object is the position, velocity, or acceleration of an object, motors, piezo-elements, or the like are used. Pumps, valves, or the like are used for gas, fluid, or the like. A driver or the like that operates electric current or voltage is used for an electric system.

In the present embodiment, an imprint apparatus will be described as an example of a lithography apparatus that forms a pattern on a substrate. <FIG> is a schematic diagram of the imprint apparatus. The imprint apparatus is an apparatus that includes a forming unit that forms a pattern of a cured product to which an uneven pattern of a mold is transferred, by bringing an imprinting material supplied onto a substrate into contact with the mold and applying curing energy to the imprinting material. For example, the imprint apparatus supplies an imprinting material onto a substrate, and cures the imprinting material in a state in which a mold where an uneven pattern is formed is in contact with the imprinting material on the substrate. By widening the distance between the mold and the substrate to peel off (release) the mold from the cured imprinting material, the imprint apparatus is able to transfer the pattern of the mold to the imprinting material on the substrate. Such a series of processes is referred to as an imprinting process, and is performed for each of a plurality of shot areas of the substrate. In short, in the case of performing an imprinting process for each of a plurality of shot areas of one substrate, the imprinting process is repeatedly performed for the number of shot areas of the substrate.

An imprint apparatus <NUM> includes a mechanical structure and a control system <NUM>. A main body structure <NUM> of the imprint apparatus <NUM> is placed on the floor with a tripod or quadruped anti-vibration mechanism <NUM> using an air spring or the like. A wafer (substrate) <NUM> is held by a wafer stage (substrate stage) <NUM> with a wafer chuck (not illustrated). The wafer stage <NUM> moves the wafer <NUM> with sufficient X-directional and Y-directional strokes in order to perform an imprinting process on each shot area of the entire surface of the wafer <NUM>. In addition, the wafer stage <NUM> has sufficient X-directional and Y-directional strokes for moving the wafer <NUM> to a replacement position for loading and unloading the wafer <NUM> with a wafer replacement hand (not illustrated).

Although the wafer stage <NUM> is simply illustrated as a box with wheels in <FIG>, the wafer stage <NUM> is actually guided to move freely in the X-direction using static pressure guidance, and is given a driving force in the X-direction by a linear motor (drive unit). In addition, a Y stage (not illustrated) is movable in the Y-direction on the wafer stage <NUM> by static pressure guidance and the linear motor. The motor is driven by a drive circuit such as an electric current driver. A moving unit for moving a wafer which serves as a to-be-moved object includes a stage, a drive unit, and a drive circuit. Note that the configuration of the wafer stage <NUM> is not limited to this configuration, and a highly precise positioning stage used for a wafer stage of an exposure apparatus may be used.

The position in the X-direction of the wafer stage <NUM> is measured by a position measuring unit <NUM>. The position measuring unit <NUM> includes a scale (not illustrated) configured on the main body structure <NUM>, a head on the wafer stage <NUM>, and a linear encoder of an arithmetic unit. Similarly, a Y-axis encoder (not illustrated) for measuring the Y-direction is also provided. To measure the position of the wafer stage <NUM>, a combination of an interferometer provided on the main body structure <NUM> and a reflection mirror provided on the wafer stage <NUM> may be used.

A photo-curable resin serving as an imprinting material is supplied by a dispenser <NUM> to the position of a shot area of the wafer <NUM>. At this time, the wafer stage <NUM> positions the resin coating position on the wafer directly below the dispenser <NUM>. Next, the wafer stage <NUM> positions the resin coating position on the wafer directly below a mold <NUM> where a fine pattern is formed. The mold <NUM> is held by an imprinting head <NUM>. The imprinting head <NUM> is structured to be capable of moving the mold <NUM> in the Z-direction. The mold <NUM> waits at a positron above the wafer <NUM> in the Z-direction until the position of the shot area of the wafer <NUM> moves to the mold <NUM>. In response to positioning of the shot position of the wafer <NUM> directly below the mold <NUM>, the mold <NUM> is lowered by the imprinting head <NUM> to press a pattern portion of the mold <NUM> against the resin. To manufacture a semiconductor device or the like using the imprint apparatus, alignment with the previous layer is important in transferring the pattern of the mold <NUM> to the resin on the wafer <NUM>. An alignment detector <NUM> optically detects alignment marks (not illustrated) provided on both of the wafer <NUM> and the mold <NUM> to perform image processing, and detects misalignment of the alignment marks in the X- and Y-directions. This misalignment information is sent to the control system <NUM>, which will be described later, and alignment is performed by correcting the X- and Y-positions of the wafer stage <NUM> or the imprinting head <NUM>. In response to completion of the alignment, a lighting system <NUM> irradiates the resin with exposure light to cure the resin. After the resin is cured, the imprinting head <NUM> or the mold <NUM> is raised to release the mold <NUM> from the resin on the wafer <NUM>. With this series of processes, a pattern corresponding to the pattern engraved in the mold <NUM> is transferred to the resin on the wafer <NUM>. Similarly, imprinting processes are sequentially performed while changing the shot area position, and, in response to completion of the imprinting processes for all the shot areas of the wafer, the wafer stage <NUM> moves to the wafer replacement position. Then, the imprinted wafer is collected by the wafer replacement hand (not illustrated), and the next new wafer is supplied.

<FIG> is a diagram illustrating the outline of the control system <NUM> (feedback control unit) according to the present embodiment. A portion inside a dotted line corresponds to the control system <NUM>, and a digital calculator is used to perform complicated arithmetic operations. The control system <NUM> includes arithmetic processing units such as a CPU and an FPGA, and a storage device such as memory. A device main control unit <NUM> is a controller that controls the entire imprint apparatus, and has the role of sending commands to a control unit <NUM> and other control units (not illustrated) on the basis of sequence management of a job performed by the imprint apparatus.

A position command unit <NUM> obtains desired coordinates of a stage position from the device main control unit <NUM>, stores the desired coordinates, and sends these values to the control unit <NUM>. Misalignment information of alignment, which is obtained by the previously-described alignment detector <NUM>, is also input to the position command unit <NUM> and is reflected in the desired coordinates of the wafer stage <NUM>. The position measuring unit <NUM> measures the stage position at a predetermined time interval Δt, and sends the measured stage position to the control unit <NUM>.

In the control unit <NUM>, a deviation calculating unit <NUM> calculates the difference (a control deviation, hereinafter referred to as a stage deviation) between the stage position (measured values) sent from the position measuring unit <NUM> and desired values of the stage position sent from the position command unit <NUM>, and sends the stage deviation to a controller <NUM> and a controller <NUM>. The controller <NUM> (first control unit) uses a PID control system, and the controller <NUM> (learning control unit) includes a control system including a neural network. The controller <NUM> takes information regarding a stage deviation as input, and outputs a manipulated variable for the wafer stage <NUM>. As for the output of the controller <NUM>, the upper and lower limits of the output value are added by an output limiter <NUM> (controller). In short, the output limiter <NUM> limits the range of the manipulated variable that may be output from the second control unit. Note that the controller <NUM> may be configured to contain the output limiter <NUM>. The illustration of the output limiter <NUM> is omitted in <FIG>. A control unit including the controller <NUM> and the output limiter <NUM> serves as the second control unit. The second control unit takes information regarding a stage deviation as input, and a parameter for outputting the manipulated variable for the wafer stage <NUM> is determined by machine learning. An adder <NUM> outputs the sum (the result of addition) of an output value U1 (first manipulated variable) generated by the controller <NUM> (first control unit) and an output value U2 (second manipulated variable) which is generated by the controller <NUM> and limited by the output limiter <NUM>.

<FIG> illustrates the configuration of the controller <NUM>. The controller <NUM> includes a deviation memory <NUM>, which stores a stage deviation log, and a neural network <NUM>. The deviation memory <NUM> saves a predetermined number (N, N is a natural number) of stage deviations, which correspond to the most recent N steps. As for the neural network <NUM>, a parameter such as a network weight is adjusted such that, in response to inputting the stage deviations for the N steps, which are stored in the deviation memory <NUM>, to the neural network <NUM>, output layers output a value corresponding to a correction amount of a command value (output value) of the controller <NUM>.

The network parameter of the neural network <NUM> in the controller <NUM> needs to be adjusted in advance in some way. Although a network parameter adjusting method based on reinforcement learning may be used as the adjustment method, the network parameter may be adjusted using any method. In addition, the neural network may be a network (policy network) that outputs one directly corresponding to the dimension of a command value, or a network (action value network) that calculates the value of a command value. In the case of an action value network, a selecting unit that selects an action with the maximum value is added after the neural network <NUM> in the controller <NUM>, and a command value selected by the selecting unit serves as the output of the controller <NUM>. In addition, the network parameter may be one that has been machine-learned in a state where the range of the second manipulated variable that may be output from the second control unit is limited by the output limiter <NUM>.

A configuration using an output limiter may be, for example, one described below. That is, let the upper limit of the correction amount be Cmax, and the lower limit thereof be Cmin. Then, the number of output layers of the neural network <NUM> is set to D. After that, the k-th output value is set to output an action value of the correction value C = K*(Cmax-Cmin)/(D-<NUM>)+Cmin. In doing so, the (discrete) correction output value of the controller <NUM> may be limited to [Cmin, Cmax]. Furthermore, by setting the command range of the controller <NUM> to take a value that cancels out a correction value output from the controller <NUM>, even if the controller <NUM> outputs an abnormal output, the controller <NUM> is able to suppress that command. The output value limiting method described here is only one example, and the output range of the controller <NUM> may be limited by various methods including other methods such as adding a limiter for continuous value outputs.

With the output limiter <NUM>, the output range -U1 to U1 of the controller <NUM> and the output range -U2 to U2 of the controller <NUM> satisfy the relationship |U1| > |U2| (|U| indicates the absolute value of U). In short, the range of the second manipulated variable output from the second control unit is smaller than the range of the first manipulated variable output from the controller <NUM>. Even if unexpected disturbance is applied to the neural network <NUM> and the output of the controller <NUM> diverges, its effect is within the range -U2 to U2. Because the controller <NUM> outputs the control command -U1 to U1 exceeding -U2 to U2, the effect of divergence of the controller <NUM> may be suppressed.

The output of the adder <NUM> goes through a D/A converter (not illustrated) to become an analog signal, which is then sent and input to an electric current driver <NUM>. The electric current driver <NUM> applies control to allow the value of electric current flowing through a coil of a motor <NUM> to become the output of the adder <NUM>. The thrust of the motor <NUM> is proportional to electric current flowing through the coil; thus, a force in accordance with the sum of the output values of the controller <NUM> and the controller <NUM> is applied to the wafer stage <NUM>.

In the configuration of the control unit <NUM>, the controller <NUM> mainly plays the role of a feedback control unit. The controller <NUM> using a neural network has the function of further suppressing a stage deviation that may not be compensated for by the controller <NUM>. As a result, as compared with a control system including only the conventional controller <NUM>, a stage deviation may be made very small, thereby improving the imprint apparatus's stage (substrate) alignment accuracy.

Like the control unit <NUM>, as a result of the parallel use of a plurality of control systems taking a deviation as input, the control systems are likely to become unstable because of two factors, that is, the feedback gain becoming excessive and the outputs of the control systems acting as disturbances to each other. Therefore, generally, the physical variable serving as a controlled object is changed, and the configuration of multiple feedback loops having an inner loop and an outer loop is used. However, the stability of the control unit <NUM> is ensured in the present embodiment by causing the controller <NUM> using the output limiter <NUM> to function in a state where the stability of the controller <NUM> is ensured.

In addition, a control system using a conventional neural network has a configuration that takes not only a deviation, but also a position command and a control output as input. In this case, the neural network involves a great amount of computation, which makes it difficult, even with a digital calculator with high-performance arithmetic capability, to perform arithmetic operations within a certain period of time. In the present embodiment, the neural network in the controller <NUM> takes only a deviation as input, thus reducing the amount of computation and facilitating arithmetic operations to be performed within a certain period of time.

According to the present embodiment, reduction of the reliability of position control may be suppressed even when a controller generated by learning is used.

Next, a second embodiment will be described using <FIG> is a block diagram of the control unit <NUM>. This control unit <NUM> is different from that of the first embodiment in the point that the output of the controller <NUM> is provided with an on/off switch <NUM>. By turning off the on/off switch <NUM>, the control unit <NUM> has the same configuration as a conventional control system; and, by turning on the on/off switch <NUM>, the output of the controller <NUM> using the neural network functions. In short, the on/off switch <NUM> switches on/off the input of the second manipulated variable output from the second control unit to the moving unit.

Switching of the on/off switch <NUM> may be performed using a stage deviation calculated by the deviation calculating unit <NUM>. In the case where the on/off switch <NUM> is on, the stage deviation should be smaller than that in the case where the on/off switch <NUM> is off; however, the case is conceivable in which the stage deviation becomes greater when, for example, unexpected disturbance is applied to the neural network in the controller <NUM>. In this case, it is preferable to turn off the on/off switch <NUM>, and then re-do the learning (machine learning) of the neural network.

Switching of the on/off switch <NUM> may be performed by defining a threshold and using software in a calculator. In addition, numerals and waveforms may be displayed on a display unit such as a display to enable the operator of the imprint apparatus to monitor the displayed numerals and waveforms, and the operator may manually switch on/off the switch with a selecting unit such as a user interface.

Switching of the on/off switch <NUM> may also be performed in accordance with the job sequence of the imprint apparatus. In the imprint apparatus, the magnitude and tolerance of a stage deviation vary according to the job sequence. A large stage deviation occurs when, for example, the wafer stage <NUM> is transferring a wafer or moves from immediately below the dispenser <NUM> to immediately below the mold <NUM>; however, the magnitude of the stage deviation does not matter. Thus, the on/off switch <NUM> is turned off. A stage position deviation is directly linked to misalignment (pattern formation deviation) when aligning the mold <NUM> and the wafer <NUM>. Thus, highly precise positioning is necessary, and the on/off switch <NUM> is turned on. As described here, the switch may be switched on/off according to the type of job performed on a wafer serving as a target.

For switching, a job sequence timing signal sent from the device main control unit <NUM> may be used. Switching may be performed by using the timing signal as it is, or switching may be performed with a certain amount of delay time after reception of the timing signal. In addition, switching may be performed on the basis of the timing signal and the magnitude of a stage deviation. For example, even if the timing signal indicates the end of driving, a large stage deviation may be left over immediately after the wafer stage <NUM> is positioned immediately below the mold <NUM>. In this case, the on/off switch <NUM> is turned on at a time point at which the stage deviation becomes within certain values. Therefore, the control system <NUM> has a determination unit that determines whether the stage deviation is within a tolerance. In the case where it is determined that the deviation is not within the tolerance, the switch is turned off; and, in the case where it is determined that the deviation is within the tolerance, the switch is turned on.

Turning on the function of the controller <NUM> only in such situations where the stage deviation is relatively small is useful in the following two points: shortening the learning time of the neural network, and reducing the width of the output U2 of the controller <NUM> to enhance the stability of the control system.

Next, a third embodiment will be described using <FIG> is a block diagram of the control unit <NUM>. This control unit <NUM> is different from that of the first embodiment in the point that the stage deviation, which is the input of the controller <NUM>, is subjected to a bandpass filter <NUM>, which attenuates (stops) a certain band. A signal from the bandpass filter <NUM> is input to the learning control unit. A high-pass filter, a low-pass filter, a band-pass filter, or a notch filter may be used for the bandpass filter <NUM>. Each filter may have a different stopband. For example, when the performance of the controller <NUM> drops as a result of a stage deviation at high frequencies, high frequencies of a band where the controller <NUM> operates may be attenuated using a low-pass filter. Similarly, a notch filter may be used when the control performance of the controller <NUM> drops as a result of a specific frequency range. If the performance of the controller <NUM> at low frequencies is sufficient, the controller <NUM> is responsible for low frequencies; thus, a high-pass filter may be used for the bandpass filter <NUM>. A combination of these filters may be used. In addition, the bandpass filter <NUM> to be used may be changed (switched) according to the job of the imprint apparatus. In this case, because learning of the neural network is performed in accordance with the type of bandpass filter <NUM>, control is performed by switching to parameters of the controller <NUM> corresponding to the bandpass filter <NUM>. In short, the parameters of the controller <NUM> include a first parameter determined by machine learning using a deviation in a first band as input in the case where a first filter is used. In addition, the parameters also include a second parameter determined by machine learning using a deviation in a second band different from the first band as input in the case where a second filter is used.

Although a mold with a pattern portion is used for the imprint apparatus in the above-described embodiments, the above-described position control device is also applicable to a planarizing apparatus (molding apparatus) that molds resin on a substrate to planarize the resin using a mold without a pattern portion. For example, this is applicable to stage position control of a mold or a substrate.

In addition, the above-described position control device is applicable to an exposure apparatus that includes a forming unit that forms a pattern on a substrate by illuminating a mask and transferring the mask's pattern to the substrate using a projection optical system. For example, this is applicable to position control of a substrate stage or a mask stage.

Furthermore, the present technology is applicable to a measurement apparatus or a processing apparatus other than the imprint apparatus. The measurement apparatus includes the above-described position control device in order to control the position of a target object, and a measurement unit that measures the object whose position is controlled by the position control device. Examples of the measurement unit include a contact-type probe and a contactless interferometer. In addition, the processing apparatus includes the above-described position control device in order to control the position of a target object, and a processing unit that processes the object whose position is controlled by the position control device. Examples of the processing unit include a bite (cutting tool) and a laser.

An article manufacturing method is suitable for manufacturing an article such as a micro-device including a semiconductor device, or an element with a micro-structure. The article manufacturing method of the present embodiment includes a step of forming a pattern using the above-mentioned imprint apparatus (imprinting method) on an imprinting material supplied (applied) to a substrate; and a step of processing the substrate on which the pattern is formed in the former step. Furthermore, the manufacturing method includes other conventional steps (such as oxidation, film formation, deposition, doping, planarization, etching, resist stripping, dicing, bonding, and packaging). The article manufacturing method of the present embodiment is advantageous in at least one of the performance, quality, productivity, and production cost of the article, compared with conventional methods.

Next, a method of manufacturing an article (such as a semiconductor IC element, a liquid crystal display element, a color filter, or a MEMS) using the above-described exposure apparatus will be described. The article is manufactured by: a step of exposing, using the above-described exposure apparatus, a substrate (such as a wafer or a glass substrate) coated with a photosensitive agent; a step of developing the substrate (photosensitive agent); and processing the developed substrate in other conventional processing steps. The other conventional steps include etching, resist stripping, dicing, bonding, and packaging. According to the present manufacturing method, an article whose quality is higher than conventional ones may be manufactured.

Claim 1:
A processing apparatus (<NUM>) comprising:
a feedback control device (<NUM>); and
a processing unit configured to process an object controlled by the feedback control device,
characterized in that
the feedback control device is configured to take information regarding a control deviation between a measured value and a desired value of a controlled object as input, and output a manipulated variable for the controlled object, and comprises:
a first control unit (<NUM>) configured to take information regarding the control deviation as input, and output a manipulated variable for the controlled object;
a second control unit (<NUM>) configured to take information regarding the control deviation as input, and that includes a learning control unit in which a parameter for outputting a manipulated variable for the controlled object is determined by machine learning; and
an adder (<NUM>) configured to add a first manipulated variable output from the first control unit and a second manipulated variable output from the second control unit, wherein:
a manipulated variable from the adder is output to the controlled object, and
the second control unit includes a limiter (<NUM>) configured to limit the second manipulated variable output from the second control unit.