Jeol Ltd.

Fuminori Uematsu

Shigeyuki Morishita

Ryusuke Sagawa

Tomohiro Nakamichi

Keito Aibara

Takaya Satoh

Takanori Murano

Masahiko Takei

Motofumi Saito

Prior to execution of primary correction, a first centering process, an in-advance correction of a particular aberration, and a second centering process are executed stepwise. In the first centering process and the second centering process, a ronchigram center is identified based on a ronchigram variation image, and is matched with an imaging center. In the in-advance correction and the post correction of the particular aberration, a particular aberration value is estimated based on a ronchigram, and the particular aberration is corrected based on the particular aberration value.

Electron microscope and method of correcting aberration

An aberration value estimator has a learned estimation model for estimating an aberration value set based on a Ronchigram. In a machine learning sub-system, a simulation is repeatedly executed while changing a simulation condition, and calculated Ronchigrams are generated in a wide variety and in a large number. By machine learning using the calculated Ronchigrams, the learned estimation model is generated.

Estimation model generation method and electron microscope

In a preliminary measurement, spectrums obtained by detecting characteristic X-rays emitted from preliminary measurement points are transmitted to a spectrum processing unit via a noise filter unit. In a main measurement, a spectrum obtained by detecting characteristic X-rays emitted from a main measurement point is transmitted to the spectrum processing unit by bypassing the noise filter unit. The noise filter unit includes a machine learning type filter constituted of a CNN or the like. In a learning process, teacher data are generated using artificially-generated noise.

X-ray measurement apparatus and X-ray measurement method

A method of measuring an aberration in an electron microscope includes: acquiring an image for measuring the aberration in the electron microscope; and measuring the aberration by using the image. In measuring the aberration, a direction of defocusing is specified based on a residual aberration that is uniquely determined by a configuration of an optical system of the electron microscope and an optical condition of the optical system.

Method of measuring aberration and electron microscope

An image conversion unit includes a selector and a plurality of image converters. Each image converter is formed from an estimator of machine learning type, and estimates, based on an image acquired under a first observation condition and as a reference image, an image which is presumed to be acquired under a second observation condition. When a particular reference image is selected from among a plurality of reference images displayed on a display, a second observation condition corresponding to the selected reference image is set in an observation mechanism as a next observation condition.

Charged particle beam apparatus and machine learning method

A preprocessor extracts a plurality of spectra to be processed, from an overall mass spectrum. A simulated spectrum generator having a learned model generates a simulated spectrum having a peak discriminating action, from each mass spectrum. A postprocessor generates a combined simulated spectrum based on the plurality of simulated spectra. A peak filter executes peak discrimination on a peak list using the combined simulated spectrum.

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Tokyo, Japan

Fuminori Uematsu