TRAINING MODEL CREATING METHOD, LASER DEVICE, AND ELECTRONIC DEVICE MANUFACTURING METHOD

A training model creating method includes creating a database by breaking a component in a laser device on a Digital Twin constructed by modeling electrical hardware and software of the laser device, and accumulating data in which the broken component and a failure phenomenon output from the Digital Twin are associated with each other; and training a training model using the data in the database as training data for machine learning so that, upon receiving an input of information on a failure phenomenon, the training model outputs information on a malfunction location corresponding to the input.

BACKGROUND

1. Technical Field

The present disclosure relates to a training model creating method, a laser device, and an electronic device manufacturing method.

2. Related Art

Recently, in a semiconductor exposure apparatus, improvement in resolution has been desired for miniaturization and high integration of semiconductor integrated circuits. For this purpose, an exposure light source that outputs light having a shorter wavelength has been developed. For example, as a gas laser device for exposure, a KrF excimer laser device for outputting laser light having a wavelength of about 248 nm and an ArF excimer laser device for outputting laser light having a wavelength of about 193 nm are used.

The KrF excimer laser device and the ArF excimer laser device each have a large spectral line width of about 350 to 400 μm in natural oscillation light. Therefore, when a projection lens is formed of a material that transmits ultraviolet rays such as KrF laser light and ArF laser light, there is a case in which chromatic aberration occurs. As a result, the resolution may decrease. Then, a spectral line width of laser light output from the gas laser device needs to be narrowed to the extent that the chromatic aberration can be ignored. For this purpose, there is a case in which a line narrowing module (LNM) including a line narrowing element (etalon, grating, and the like) is provided in a laser resonator of the gas laser device to narrow a spectral line width. In the following, a gas laser device with a narrowed spectral line width is referred to as a line narrowing gas laser device.

LIST OF DOCUMENTS

Patent Documents

SUMMARY

A training model creating method according to an aspect of the present disclosure includes creating a database by breaking a component in a laser device on a Digital Twin constructed by modeling electrical hardware and software of the laser device, and accumulating data in which the broken component and a failure phenomenon output from the Digital Twin are associated with each other; and training a training model using the data in the database as training data for machine learning so that, upon receiving an input of information on a failure phenomenon, the training model outputs information on a malfunction location corresponding to the input.

A laser device according to another aspect of the present disclosure includes an electrical hardware including a processor, a monitoring target including a sensor, a state of which is to be monitored by the processor, and equipment including a wiring for connecting the processor and the monitoring target; and a training model trained by machine learning to output, upon receiving an input of information on a failure phenomenon, information on a malfunction location corresponding to the input. Here, the training model is a model trained using data, as training data, in a database created by breaking a component in the laser device on a Digital Twin constructed by modeling the electrical hardware and software of the laser device and accumulating the data in which the broken component and a failure phenomenon output from the Digital Twin are associated with each other.

An electronic device manufacturing method according to another aspect of the present disclosure includes generating laser light using a laser device, outputting the laser light to an exposure apparatus, and exposing a photosensitive substrate to the laser light in the exposure apparatus to manufacture an electronic device. Here, the laser device includes an electrical hardware including a processor, a monitoring target including a sensor, a state of which is to be monitored by the processor, and equipment including a wiring for connecting the processor and the monitoring target; and a training model trained by machine learning to output, upon receiving an input of information on a failure phenomenon, information on a malfunction location corresponding to the input. The training model is a model trained using data, as training data, in a database created by breaking a component in the laser device on a Digital Twin constructed by modeling the electrical hardware and software of the laser device and accumulating the data in which the broken component and a failure phenomenon output from the Digital Twin are associated with each other.

DESCRIPTION OF EMBODIMENTS

1. Overview of Laser Device1.1 Configuration1.2 Operation1.3 Example of notification of error information1.4 Problem2. First Embodiment2.1 Configuration2.2 Operation2.2.1 Generation of training data and training phase2.2.2 Inference phase2.3 Effect3. Second Embodiment3.1 Configuration3.2 Operation3.3 Effect4. Third Embodiment4.1 Configuration4.2 Operation4.3 Effect5. Other examples of laser device6. Functional role of information processing system7. Electronic device manufacturing method8. Others

Hereinafter, embodiments of the present disclosure will be described in detail with reference to the drawings. The embodiment described below show some examples of the present disclosure and do not limit the contents of the present disclosure. Also, all configurations and operation described in the embodiments are not necessarily essential as configurations and operation of the present disclosure. Here, the same components are denoted by the same reference numeral, and duplicate description thereof is omitted.

1. Overview of Laser Device

FIG.1schematically shows an exemplary configuration of a laser device10. The laser device10is a KrF excimer laser device, and includes a line narrowing module (LNM)12, a chamber14, an inverter16, a front mirror (output coupling mirror)18, a monitor module20, a charger22, a pulse power module (PPM)24, a processor26, a gas supply device28, a gas exhaust device30, and an outlet port shutter32.

The chamber14includes windows34,36, a cross flow fan (CFF)38, a motor40for rotating the CFF38, a pair of electrodes42a,42b, an electric insulator44, a pressure sensor46, and a heat exchanger (not shown).

The PPM24is connected to the electrode42avia a feedthrough in the electric insulator44of the chamber14. The PPM24includes a semiconductor switch48, a charging capacitor (not shown), a pulse transformer (not shown), and a pulse compression circuit (not shown).

The front mirror18is a partial reflection mirror and is arranged to configure an optical resonator together with the LNM12. The chamber14is arranged on the optical path of the optical resonator. The LNM12includes a beam expander configured of two prisms50,52, a rotation stage54, and a grating56. The prisms50,52are arranged to expand the beam of light output from the window34of the chamber14in a Y direction and to cause the light to be incident on the grating56.

The grating56is arranged in the Littrow arrangement so that the incident angle and the diffraction angle of the laser light coincide with each other. Further, the prism52is arranged on the rotation stage54such that the incident angle and the diffraction angle of the laser light on the grating56change when the rotation stage54rotates.

The monitor module20includes beam splitters60,62, a pulse energy detector64, and a spectrum detector66. The beam splitter60is arranged on the optical path of the laser light output from the front mirror18so as to reflect a part of the laser light incident thereon to be incident on the beam splitter62.

The pulse energy detector64is arranged so that the laser light transmitted through the beam splitter62is incident thereon. The pulse energy detector64may be, for example, a photodiode that measures the light intensity of ultraviolet rays. The beam splitter62is arranged so that a part of the laser light incident thereon is reflected to enter the spectrum detector66. The spectrum detector66is, for example, a monitor etalon measurement device that measures interference fringes generated by an etalon with an image sensor. The center wavelength and the spectral line width of the laser light are measured based on the generated interference fringes.

The gas supply device28is connected to an inert gas supply source70which is a supply source of an inert laser gas and a halogen gas supply source72which is a supply source of a laser gas containing halogen gas via pipes74,76, respectively. In the case of the KrF excimer laser device, the inert laser gas is a mixed gas of a Kr gas and an Ne gas, and the laser gas containing a halogen gas is a mixed gas of an F: gas, a Kr gas, and an Ne gas. The gas supply device28is connected to the chamber14via a pipe78. The gas supply device28includes an automatic valve (not shown) and a mass flow controller (not shown) for supplying a predetermined amount of each of the inert laser gas and the laser gas containing halogen to the chamber14.

The gas exhaust device30is connected to the chamber14via a pipe80, and includes a halogen filter (not shown) for removing halogen and an exhaust pump (not shown), and is configured to exhaust the laser gas from which halogen is removed to the outside.

The outlet port shutter32is arranged on the optical path of the laser light output from the laser device10to the outside.

The inverter16is a power supply device for the motor40that drives the CFF38and is configured to receive, from the processor26, the frequency of the power supplied to the motor40. The processor26in the present disclosure is a processing device including a storage device in which a control program is stored and a central processing unit (CPU) that executes the control program. The processor26is specifically configured or programmed to perform various processes included in the present disclosure. The processor26is electrically connected to a plurality of configurations of the laser device10, and is configured to communicate with them and to control them. The configurations connected to the processor26include unillustrated configurations as well. The processor26is also connected to an exposure apparatus90.

After causing the gas exhaust device30to exhaust the gas in the chamber14, the processor26causes the gas supply device28to fill the chamber14with the laser gas so that the mixed gas of Kr and Ne and the mixed gas of F2, Kr, and Ne provide a desired gas composition and a desired total gas pressure.

The processor26causes, via the inverter16, the motor40to rotate at a predetermined number of revolution so that the CFF38is rotated. As a result, the laser gas flows between the electrodes42a,42b. The processor26receives a target pulse energy Et from an exposure control unit92of the exposure apparatus90, and transmits data of a charge voltage Vhv to the charger22so that the pulse energy becomes Et. The charger22charges the charging capacitor of the PPM24so that the charge voltage Vhv is acquired.

When a light emission trigger signal Tr1is output from the exposure apparatus90, a trigger signal Tr2is input from the processor26to the semiconductor switch48of the PPM24in synchronization with the light emission trigger signal Tr1. When the semiconductor switch48is operated, a current flows from the charging capacitor of the PPM24, pulse compression is performed by a magnetic compression circuit, and a high voltage is applied between the electrodes42a,42b. As a result, discharge occurs between the electrodes42a,42b, and the laser gas is excited in a discharge space.

Excimer light is generated when the excited laser gas in the discharge space reaches the ground state. The excimer light reciprocates between the front mirror18and the LNM12and is amplified, thereby causing laser oscillation. As a result, the pulse laser light having line-narrowed is output from the front mirror18. The pulse laser light output from the front mirror18enters the monitor module20.

In the monitor module20, a part of the pulse laser light is sampled by the beam splitter60to enter the pulse energy detector64or the spectrum detector66via the beam splitter62. The pulse energy detector64measures the pulse energy E of the pulse laser light and transmits data thereof to the processor26. The spectrum detector66measures a center wavelength λ and a spectral line width ΔΔ of the pulse laser light and transmits data thereof to the processor26.

The processor26receives the target pulse energy Et and a target wavelength λt from the exposure apparatus90. The processor26performs various types of control including control of the pulse energy and control of the wavelength. In the control of the pulse energy, the charge voltage Vhv is controlled such that a difference ΔE between the pulse energy E measured by the pulse energy detector64and the target pulse energy Et approaches 0. In the control of the wavelength, the rotation angle of the rotation stage54is controlled such that a difference δλ between the center wavelength λ measured by the spectrum detector66and the target wavelength λt approaches 0.

As described above, the processor26receives the target pulse energy Et and the target wavelength λt from the exposure apparatus90, and causes the pulse laser light to be output in synchronization with the light emission trigger signal Tr1each time the light emission trigger signal Tr1is input.

When the laser device10repeats discharge, the electrodes42a,42bare worn, the halogen gas in the laser gas is consumed, and an impurity gas is generated. The decrease in the halogen gas concentration and the increase of the impurity gas in the chamber14cause a decrease in the pulse energy E of the pulse laser light and the stability of the pulse energy is adversely affected. The processor26executes the following gas control ([1]˜[3]) in order to suppress these adverse effects.

[1] Halogen Injection Control

The halogen injection control is gas control in which, during laser oscillation, the halogen gas consumed mainly by discharge in the chamber14is replenished by injecting a gas containing a halogen gas having a higher concentration than the halogen gas in the chamber14.

[2] Partial Gas Exchange Control

The partial gas exchange control is gas control in which, during laser oscillation, a part of the laser gas in the chamber14is exchanged with a new laser gas so as to suppress an increase in the concentration of the impurity gas in the chamber14.

[3] Gas Pressure Control

The gas pressure control is gas control in which the pulse energy E is controlled by injecting the laser gas into the chamber14to change the total pressure of the laser gas when it is difficult to overcome, within a control range of the charge voltage Vhv, a decrease in the pulse energy E of the pulse laser light output from the laser device10.

When the laser gas is exhausted from the chamber14, the processor26controls the gas exhaust device30. The halogen gas is removed from the laser gas exhausted from the chamber14by a halogen filter, and the laser gas is exhausted to the outside of the laser device10.

The processor26transmits data of parameters such as the number of oscillation pulses, the charge voltage Vhv, a gas pressure Pch in the chamber14, and the pulse energy E of the laser light to a laser device management system (not shown).

1.3 Example of Notification of Error Information

FIG.2schematically shows the configuration for notifying error information based on information obtained from the monitor module20. The laser device10may output information from a plurality of sensors to a display84or a computer network. For example, the pulse energy detector64, the spectrum detector66, and a temperature sensor68that measures the temperature in the monitor module20, of the monitor module20, are connected to the processor26. The processor26may monitor state quantities of these sensors, and when an abnormal value is detected, the processor26may output a corresponding error code or each state quantity.

Other modules and equipment of the laser device10are similarly connected to the processor26and their operation are monitored by the processor26.

Various sensors connected to the processor26to monitor the state quantity, signal lines, interfaces, and relay devices which electrically connect the various sensors to the processor26, and electrical and electronic elements included in the processor26are collectively referred to as electrical hardware.

The electrical hardware is configured of the processor26, monitoring targets in the laser device10including sensors, states of which are to be monitored by the processor26, and equipment including wirings for connecting the processor26and the monitoring targets. Accordingly, the electrical hardware also includes an actuator with an encoder, a proximity switch, an Ethernet repeater, a sequencer, an AD converter, a DA converter, a programmable controller, and the like.

When the processor26detects an abnormality in the output from a module, a sensor, or the like under monitoring, the processor26notifies a service engineer FSE of an error or a state quantity with the display84or the like. Depending on the location of a malfunction or a failure, a plurality of errors and state quantities are displayed, and there is a case in which causes thereof are not easy to be identified.

In such a case, the service engineer FSE compares the error and abnormal behavior of the laser device10with a failure list88that summarizes failure phenomena that have occurred in the past, examines and determines a countermeasure or a replacement component, and performs adjustment, repair, or the like (seeFIG.3). The failure list88is a list of causes of failures and/or replacement components corresponding to errors and state quantities that have occurred in the past. Here, replacement of a component is included in the concept of “repair.” “Replacement” of a component includes replacing a component with a new one, as well as maintaining and/or recovering the function of a component by performing cleaning or the like on the component and repositioning the same component.

FIG.3schematically shows how the service engineer FSE deals with a failure phenomenon of the laser device10. The service engineer FSE confirms the failure phenomenon such as an error code and abnormal behavior of the laser device10notified by the display84or the like, and when identification of the cause or the like is difficult, refers to a countermeasure or a replacement component in the failure list88, and performs adjustment or repair of equipment, a component, or the like.

Therefore, when a new failure that is not included in the failure list88occurs, a countermeasure may not be clear. In this case, partial operation or acquisition of specific data is forced for specifying a malfunction location, and a long working time is required. When a malfunction location can still not be identified, a member of a development department needs to investigate the actual machine, which takes time to solve the problem.

2. First Embodiment

FIG.4is an explanatory diagram showing an outline of a system that implements a method of creating a training model106according to a first embodiment. A Digital Twin100is constructed by modeling, in a digital space DGS such as a server or a cloud, electrical hardware including various sensors of the laser device10and software. The Digital Twin100is a digital copy of the laser device10. The Digital Twin100may include a physical model of the electrical hardware, some components, and the like of the laser device10. The Digital Twin100is configured to output an error code or a state quantity as a failure phenomenon in a similar manner as the laser device10in the real space when information (hereinafter, referred to as breakage component information) indicating that a certain component has been broken (malfunctioned) is input.

Information obtained by sequentially breaking components in the laser device10one by one or in combination of a plurality of components is input to the Digital Twin100to output a failure phenomenon, and breakage components and failure phenomena (behavior of the laser device10such as an error code and a state quantity) as a result are associated with each other and accumulated in the database104. The concept of a “component” when the components in the laser device10are sequentially broken includes a wiring of a transmission system such as a signal line a power supply line. “Breaking” includes disconnection of a wiring of the transmission system. The expression “breaking” or “breakage” of a component includes the concept of “being malfunctioned” or “malfunction.”

Breakage component information input to the Digital Twin100may be automatically generated by a program. By breaking, on the Digital Twin100, components in the laser device10one by one or by combining a plurality of components and changing the combination to break, data of failure phenomena corresponding to breakage components are obtained from the Digital Twin100. Data accumulated in the database104may be, for example, as shown inFIG.5.

FIG.5is a table showing an example of data accumulated in the database104. The breakage component may be data associated with a module or an assembly to which the breakage component belongs. An assembly is a unit in which a plurality of components are combined. The item “belonging module” shown inFIG.5may be an assembly to which the breakage component belongs. The “belonging module” is a component assembly serving s a replacement unit for component replacement at the time of maintenance, and includes concepts such as an assembly, equipment, and a detector serving as a replacement unit.

Data accumulated in the database104is used as training data for machine learning. That is, the training model106is trained by performing machine learning using the data (data created by the Digital Twin100) accumulated in the database104, and the training model106is created so as to output a malfunction location upon receiving an input of a failure phenomenon.

The training model106may output a module, an assembly, or the like to which a malfunction location belongs together with the output of the malfunction location. The training model106may be configured of an artificial intelligence (AI) using an expert system, a Bayesian network, or the like, or may use a neural network.

The database104and the training model106are configured to be accessible on the digital space DGS from a network. The network may be a wide area network such as the Internet.

The Digital Twin100and the database104may be constructed for each laser model of the laser device10. A training model for each laser model may be created using the database104constructed for each laser model of the laser device10. The training model106may be trained by databases of a plurality of laser models as well.

The digital space DGS is realized using a computer system including one or more processors (not shown) and one or more storage devices (not shown). The storage device is a non-transitory tangible computer-readable medium, and includes, for example, a memory as a main storage device and a storage as an auxiliary storage device. A computer that realizes the functions of the Digital Twin100, a computer that stores and manages the database104, and a computer that realizes processing functions of machine learning for training the training model106may each be configured by separate hardware, or a computer that realizes some or all of these processing functions may be configured by common hardware.

2.2.1 Generation of Training Data and Training Phase

The Digital Twin100functions as a simulation model that can virtually reproduce, in the digital space DGS, behavior of the laser device10in the real space. By using the Digital Twin100, it is possible to artificially create data of a failure phenomenon in which it is difficult to actually collect data from the laser device10in the real space, a failure phenomenon in which it takes a lot of time to actually collect data, and an extremely rare or unexpected failure phenomenon that has not been recorded in the past.

Thus, by performing supervised learning using a data set generated using the Digital Twin100as training data, the training model106trained to output a malfunction location in response to an input of a failure phenomenon is obtained. The input to the training model106in a training phase may include an error code indicating a failure phenomenon. The input to the training model106may also include an output value of a sensor. The training model106performs a class classification process of estimating a malfunction location based on input failure information, and outputs an estimation result (classification result). The output from the training model106may be a classification score indicating a certainty factor of a malfunction location.

A breakage component associated with a failure phenomenon in the training data is used as correct answer data of a malfunction location corresponding to a failure phenomenon used as the input. As the output from the training model106, an estimation result of a module or an assembly to which a malfunction location belongs may be output together with the malfunction location or in place of the malfunction location. In this case, a module to which a broken component associated with a failure phenomenon in the training data belongs is used as correct answer data of a module or an assembly to which a malfunction location corresponding to a failure phenomenon used in the input belongs.

By training the training model106using a large number of data accumulated in the database104, parameters of the training model106may be updated to appropriate values, and the training model106may obtain a target inference performance. The trained training model106thus created may be utilized as a tool in place of the failure list88described inFIG.3.

FIG.6is an explanatory diagram schematically showing an example of a method of using a trained model created by implementing a method of creating a training model120according to the first embodiment. The training model120shown inFIG.6is a trained model trained by the method described inFIG.4to obtain a reasonable inference performance.

The training model120is incorporated in a computer such as a server accessible via the network. When the laser device10outputs information on a failure phenomenon (hereinafter, referred to as failure information), the failure information is input to the training model120via the network. At this time, the laser device10may be connected to the network. Alternatively, the service engineer FSE may input the failure information to the training model120via the network using a terminal (not shown) connectable to the network. The terminal operated by the service engineer FSE may be a notebook personal computer, a tablet terminal, or the like.

The training model120provides an estimation list of a malfunction location from the failure information to the service engineer FSE at the site via the network. At this time, the estimation list may be displayed on the display84of the laser device10or may be displayed on a display of the terminal carried by the service engineer FSE. At this time, at least one malfunction location estimated by the training model120is displayed. In addition, a module to which the malfunction location belongs may be displayed.

FIG.7is a table showing an example of the estimation list of the malfunction location estimated using the training model120. The display order in the estimation list may prioritize modules including more malfunction locations (seeFIG.7). That is, the estimation list is configured such that a module including a larger number of malfunction locations can be displayed with a higher priority. The “estimation order” inFIG.7corresponds to the priority order of the display. The service engineer FSE can quickly perform component replacement or adjustment operation by referring to the estimation list. If the estimation list is provided before the service engineer FSE goes to the site, the service engineer FSE can go to the site with a replacement component prepared in advance.

According to the method of the first embodiment, since the training model120provides the estimation list, it is possible to greatly shorten operation required for identifying a malfunction location. Further, it is possible to grasp in advance failure phenomena when every assumable component is broken on the Digital Twin100. Therefore, it is possible to immediately take an appropriate countermeasure for an error that has never occurred in the past or a failure that occurs at a very low frequency in the actual laser device10.

The method (creating method) of creating the training models106,120described in the first embodiment can be understood as a method (manufacturing method) of producing the training models106,120and a method of manufacturing a computer-readable medium in which the training models106,120are recorded.

3. Second Embodiment

FIG.8is an explanatory diagram showing an outline of a method of creating the training model106according to a second embodiment. The database104shown inFIG.8accumulates artificial failure data created using the Digital Twin100as described inFIG.4. Here, the Digital Twin100is omitted inFIG.8.FIG.8will be described in terms of differences fromFIG.4.

In addition to training using data in the database104, the training model106may be trained using data (actual data based on actual examples) indicating a correspondence relationship between an actual failure phenomenon obtained from the actual laser device10and a corresponding actual malfunction location. It is preferable that information on the actual malfunction location includes information on a module (malfunction module) to which the malfunction location belongs. Such data based on a failure phenomenon actually confirmed in the laser device10may be provided to the training model106via the network.

Data in which an actual failure phenomenon and an actual malfunction location are associated with each other is not limited to data obtained from a failure phenomenon that occurred in the operation site of the laser device10, and may be data obtained from a failure phenomenon that occurred in experiment in a development department or the like, and may include data of a failure list accumulated as failure cases of a laser model of a same type. Other system configurations may be similar to those ofFIG.4.

As a method of utilizing actual data as training data, for example, there is a method of training the training model106using data in the database104to create the training model120in which inference performance of an allowable practical level is obtained, and then further performing additional training on the training model120using actual data as additional training data to enhance the inference performance. In this case, while the training model120already having an inference performance of a practical level is utilized at the service site, performance of the training model120can be further improved based on actual data obtained from the laser device10at the site, and the training model120can be updated.

Another method is to train the training model106using actual data as training data together with data in the database104in the course of training in achieving the inference performance of a target practical level.

According to the method of the second embodiment, inference of the training model120can be gradually improved by sequentially inputting actual data based on actual examples into the training model120.

Further, according to the method of the second embodiment, it is possible to improve the inference accuracy of the training models106,120by training the training models106,120using actual data based on actual examples in addition to data artificially created using the Digital Twin100.

FIG.9is an explanatory diagram showing laser devices10,10B each including the training model120according to a third embodiment and an outline of an application example thereof. In the configuration shown inFIG.9, components common to those inFIGS.4and8are denoted by the same reference numerals. The training model120trained using the data in the database104may be mounted on the laser device10. The training model120may be mounted on the laser device10B other than the laser device10. When the training model120is created for each laser model, the laser devices10,10B are the same laser model. When the training model120is created as a universal model that can perform inference for a plurality of laser models, the laser devices10,10B may be different laser models. The laser device10B includes a display84B for displaying various types of information.

The laser devices10,10B may be shipped with the training model120mounted thereon, or the training model120may be downloaded into the devices via the network after shipment and installation. The training model120may be integrated into software in the laser devices10,10B.

The training model120may be trained in the digital space DGS such as a server or a cloud as in the first or second embodiment.

The training model120in the laser device10may be accessibly connected to a control unit, software, a memory, or the like of the laser device10, and may receive data related to a failure phenomenon from the processor26.

When the training model120in the laser device10receives data related to a failure phenomenon, the training model120presents an estimation list (seeFIG.5) including the estimation result of a failure cause and a malfunction location to the service engineer FSE at the site in a manner such as displaying the estimation list on the display84, similarly to the first embodiment. Alternatively, the laser device10may transmit an estimation list of a failure cause and a malfunction location to a terminal of a laser device manufacturer or the service engineer FSE via the network, or may output the estimation list to a fault detection and classification (FDC) system. The same applies to operation of the training model120in the other laser device10B.

The training model120may be updated via the network as appropriate. For example, data of a malfunction location corresponding to a failure phenomenon that has actually occurred may be collected from the laser devices10,10B at any time, and the training model120may be updated by the training model106that is trained on the digital space DGS at any time using the collected actual data as additional training data. The model may be updated periodically, or an operator may specify the update timing.

According to the configuration of the third embodiment, since the service engineer FSE can quickly obtain a solution at the site or in advance, it is possible to further shorten the time required for troubleshooting. Since it is possible to estimate a malfunction location even in a single laser device, an appropriate countermeasure can be immediately presented even when, for example, a failure occurs in a communication function or the condition of a communication line is poor. Further, by performing additional training on the training model106using actual data, it is possible to further improve the inference accuracy of the training model120, and the training model120can be kept in the latest state via the network.

5. Other Examples of Laser Device

FIG.1shows an example of a line narrowing KrF excimer laser device, but the present invention is not limited to this example, and may be a line narrowing ArF excimer laser device. AlthoughFIG.1shows an example of the single chamber laser device10, the present invention is not limited to this example, and may be a laser device including a master oscillator that outputs line-narrowed pulse laser light and an amplifier that amplifies the pulse laser light output from the master oscillator by a chamber including an excimer laser gas.

Further, in the laser device including the master oscillator and the amplifier, the master oscillator may be a solid-state laser device in which a solid-state laser and a nonlinear crystal are combined and which outputs laser light line-narrowed in an amplifiable wavelength range of an ArF laser or a KrF laser.

6. Functional Role of Information Processing System

As described with reference toFIG.4, the information processing system that artificially generates data related to various failure phenomena using the Digital Twin100can function as a data generation device that automatically generates failure data. In addition, the information processing system can function as a database creation device in that the automatically generated data can be converted into a database to create the database104. The database104created using the Digital Twin100may be a failure data group covering all failure phenomena that can occur in the laser device10. Even if all of the failure phenomena cannot be strictly covered, it is preferable that the failure data group covers almost all of the expected failure phenomena.

In addition to the role of providing training data to the training model106, the database104may also be used, for example, to accept input of a search key and return a search result of data in the database104.

As described with reference toFIGS.4and8, a machine learning system (machine learning device) that performs machine learning using data in the database104as training data and trains the training model106functions as a training model creation device. The information processing system that estimates a malfunction location from a failure phenomenon using the trained training model120functions as a malfunction location estimation device. Further, the information processing system that estimates, from a failure phenomenon, a module that is a replacement component to which a malfunction location belongs using the training model120functions as a replacement component estimation device or a failure countermeasure support device. The training model120is not limited to being incorporated in the laser devices10,10B, a terminal carried by a service engineer FSE, and the like, and may be applied as a Software as a Service (Saas) that is deployed in a cloud server or the like, accepts an input of information on a failure phenomenon via the network, and returns an estimation result of a malfunction location.

Further, it is also possible to distribute a program that causes a computer to realize a part or all of the processing functions as the data generation device, the machine learning device, the replacement component estimation device, and the failure countermeasure support device by recording the program in a non-transitory tangible computer-readable medium.

7. Electronic Device Manufacturing Method

FIG.10schematically shows a configuration example of the exposure apparatus90. The exposure apparatus90includes an illumination optical system804and a projection optical system806. As described with reference toFIG.8, the laser device10may be configured to include the training model120. The laser device10generates pulse laser light and outputs the pulse laser light to the exposure apparatus90. The illumination optical system804illuminates a reticle pattern of a reticle (not shown) arranged on a reticle stage RT with laser light incident from the laser device10. The projection optical system806causes the laser light transmitted through the reticle to be imaged as being reduced and projected on a workpiece (not shown) arranged on a workpiece table WT. The workpiece is a photosensitive substrate such as a semiconductor wafer on which photoresist is applied.

The exposure apparatus90synchronously translates the reticle stage RT and the workpiece table WT to expose the workpiece to the laser light reflecting the reticle pattern. After the reticle pattern is transferred onto the semiconductor wafer by the exposure process described above, a semiconductor device can be manufactured through a plurality of processes. The semiconductor device is an example of the “electronic device” in the present disclosure. In place of the laser device10, the other laser device10B shown inFIG.9, a line narrowing ArF excimer laser device, or a laser device including a master oscillator and an amplifier may be used.

The description above is intended to be illustrative and the present disclosure is not limited thereto. Therefore, it would be obvious to those skilled in the art that various modifications to the embodiments of the present disclosure would be possible without departing from the spirit and the scope of the appended claims. Further, it would be also obvious to those skilled in the art that the embodiments of the present disclosure would be appropriately combined.

The terms used throughout the present specification and the appended claims should be interpreted as non-limiting terms unless clearly described. For example, terms such as “comprise”, “include”, “have”, and “contain” should not be interpreted to be exclusive of other structural elements. Further, indefinite articles “a/an” described in the present specification and the appended claims should be interpreted to mean “at least one” or “one or more.” Further, “at least one of A, B, and C” should be interpreted to mean any of A, B, C, A+B, A+C, B+C, and A+B+C as well as to include combinations of any thereof and any other than A, B, and C.