Abstract: 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.

Abstract: A machine learning method according to a viewpoint of the present disclosure is a machine learning method for creating a learning model configured to estimate the life of a consumable of a laser apparatus, the method including acquiring first life-related information containing data on a parameter relating to the life of the consumable, the data recorded in correspondence with different numbers of oscillation pulses during a period from the start of use of the consumable to replacement thereof, dividing the first life-related information into a plurality of levels each representing the degree of degradation of the consumable in accordance with the numbers of oscillation pulses to create training data, creating the learning model by performing machine learning using the created training data, and saving the created learning model.

Abstract: A training data creation method according to an aspect of the present disclosure is used for machine learning of a learning model for predicting lifetime of a consumable of a laser device. The method includes acquiring first lifetime-related information including data of at least one lifetime-related parameter of the consumable recorded in association with each of numbers of oscillation pulses during a period from start of use to replacement of the consumable, determining a first deterioration degree of the consumable based on the number of oscillation pulses, determining a second deterioration degree of the consumable based on the at least one lifetime-related parameter, determining a third deterioration degree of the consumable based on the first deterioration degree and the second deterioration degree, and creating training data in which the first lifetime-related information and the third deterioration degree are associated with each other.

Abstract: An information processing device includes a processor and a storage device. The processor is configured to acquire data for each parameter provided from each of a light source device which generates pulse light and an exposure apparatus which performs exposure on a wafer with the pulse light output from the light source device, and time data associated with the data; to perform classification, based on the acquired data and time data, for each record of the data associated with same time data for distinguishing whether being data during exposure in which the wafer is irradiated with the pulse light or being data during non-exposure; to associate attribute information indicating an attribute according to the classification with each of the records; to cause the storage device to store the data and the time data associated with the attribute information; and to generate a chart using data read from the storage device.

Abstract: A maintenance management method for a lithography system according to a viewpoint of the present disclosure includes organizing and saving operating information for each of lithography cells that are each an apparatus group formed of a set of apparatuses and form the lithography system, organizing and saving maintenance information on consumables for each of the lithography cells, calculating a standard maintenance timing for each of the consumables for each of the lithography cells based on the operating information and the maintenance information on the consumable for each of the lithography cells, creating a maintenance schedule plan for each of the lithography cells or for each of manufacturing lines based on the standard maintenance timing, information on a downtime, and information on a loss cost due to the downtime for each of the lithography cells or for each of the manufacturing lines, and outputting the result of the creation of the maintenance schedule plan.

Abstract: A machine learning method according to a viewpoint of the present disclosure is a machine learning method for creating a learning model configured to estimate the life of a consumable of a laser apparatus, the method including acquiring first life-related information containing data on a parameter relating to the life of the consumable, the data recorded in correspondence with different numbers of oscillation pulses during a period from the start of use of the consumable to replacement thereof, dividing the first life-related information into a plurality of levels each representing the degree of degradation of the consumable in accordance with the numbers of oscillation pulses to create training data, creating the learning model by performing machine learning using the created training data, and saving the created learning model.

Abstract: A maintenance management method for a lithography system according to a viewpoint of the present disclosure includes organizing and saving operating information for each of lithography cells that are each an apparatus group formed of a set of apparatuses and form the lithography system, organizing and saving maintenance information on consumables for each of the lithography cells, calculating a standard maintenance timing for each of the consumables for each of the lithography cells based on the operating information and the maintenance information on the consumable for each of the lithography cells, creating a maintenance schedule plan for each of the lithography cells or for each of manufacturing lines based on the standard maintenance timing, information on a downtime, and information on a loss cost due to the downtime for each of the lithography cells or for each of the manufacturing lines, and outputting the result of the creation of the maintenance schedule plan.

Abstract: A storage case includes a storage space accommodating a projector, a cover body configured to open or close an upper opening of the storage space, an elevating mechanism provided in the storage space and configured to move the projector up and down, and an adjusting mechanism configured to adjust a projection direction of the projector moved up by the elevating mechanism.

Abstract: A storage case includes a storage space accommodating a projector, a cover body configured to open or close an upper opening of the storage space, an elevating mechanism provided in the storage space and configured to move the projector up and down, and an adjusting mechanism configured to adjust a projection direction of the projector moved up by the elevating mechanism.