The University of Tokyo

Gigaphoton Inc.

Shuntaro Tani

Yohei Kobayashi

Kouji Kakizaki

Hironori Igarashi

Hiroharu Tamaru

Yutsuki Aoyagi

Deep learning is performed by using a material of a processing object, a laser beam parameter showing a property of laser beam which the processing object is irradiated with, and pre-processed part data and post-processed part data that respectively reflect laser processing-involved three-dimensional shapes of a processed part before and after irradiation of the processing object with the laser beam. A first relationship of input data that are the material of the processing object, the pre-processed part data, and the laser beam parameter to output data that is the post-processed part data after irradiation with the laser beam in relation to the input data is accordingly obtained as one learning result.

Machine learning method used for laser processing system, simulation apparatus, laser processing system and program

A configuration of the present disclosure specifies a signal point of a specific component and a background point, based on time series two-dimensional spectroscopic image data of a gas; calculates a correlation coefficient between a time series detection signal at the signal point and a time series detection signal at the background point; and generates a time series detection signal of the specific component, based on a corrected time series detection signal obtained by subtracting a product of the correlation coefficient and the time series detection signal at the background point from the time series detection signal at the signal point.

Specific component detection method, determination method, and apparatuses using these methods

A laser processing system is equipped with a processing laser beam irradiation device configured to irradiate a processing object with processing laser beam and perform ablation processing. The laser processing system is configured to obtain an ablation image of a processed portion of the processing object based on scattered light from the processed portion during processing of the processing object with the processing laser beam and to estimate an ablation volume by applying a learning result obtained by deep learning of a relationship between the ablation image and the ablation volume to the obtained ablation image.

Laser processing system

A laser system includes A. a laser apparatus configured to output pulsed laser light; B. a rare gas chamber; C. a light focusing optical system configured to focus the pulsed laser light in the rare gas chamber to excite the rare gas; D. a filter chamber configured to selectively transmit EUV light contained in harmonic light produced in the rare gas chamber; E. an exhauster connected to the filter chamber; F. at least one through hole disposed in the optical path between the rare gas chamber and the filter chamber; G. a rare gas supplier; H. a flow rate control valve configured to control the flow rate of the rare gas flowing from the rare gas supplier into the rare gas chamber; I. a first pressure sensor configured to detect the pressure of the rare gas in the rare gas chamber; J. a first controller configured to control the flow rate control valve in such a way that the pressure detected with the first pressure sensor falls within a reference range; and K. a second controller configured to control the pulse energy of the pulsed laser light outputted from the laser apparatus based at least on the pressure detected with the first pressure sensor.

Growing community of inventors

Tokyo, Japan

Shuntaro Tani