公开(公告)号：US20220342311A1

公开(公告)日：2022-10-27

申请号：US17858674

申请日：2022-07-06

Applicant: Gigaphoton Inc.

Inventor： Yutaka IGARASHI ,  Satoru KIKUCHI ,  Kunihiko ABE ,  Yuji MINEGISHI

IPC: G03F7/20 ,  H01S5/00

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.

公开(公告)号：US20210333788A1

公开(公告)日：2021-10-28

申请号：US17366586

申请日：2021-07-02

Applicant: Gigaphoton Inc.

Inventor： Kunihiko ABE ,  Yuji MINEGISHI ,  Satoru KIKUCHI ,  Osamu WAKABAYASHI

IPC: G05B23/02 ,  G06N20/00 ,  G06N3/02

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.