Presentation information

Interactive Session

General Session » Interactive Session

[2Xin5] インタラクティブ1

Wed. Jun 9, 2021 5:20 PM - 7:00 PM Room X (Poster room 1)

[2Xin5-17] Aberration measurement of scanning transmission electron microscope using machine learning

〇Fuminori Uematsu1, Shigeyuki Morishita1, Tomohiro Nakamichi1, Keito Aibara1, Ryusuke Sagawa1 (1.JEOL Ltd.)

Keywords:scanning transmission electron microscope, aberration measurement, Ronchigram

For high-resolution observation using a scanning transmission electron microscope (STEM), it is necessary to correct the aberrations of the lens that causes blur in images. In order to correct the aberrations, it is important to measure the aberration values correctly, and we have developed an aberration measurement method using a Ronchigram as a simple measurement method. However, the conventional method using a Ronchigram requires prior adjustment of the measurement parameters because depending on the experimental conditions, the measurement accuracy is significantly degraded when coma aberration is large, which is problematic for automating the measurement and correction of aberrations. In this study, we developed a machine learning regression model to measure coma aberration from a Ronchigram in order to realize a highly accurate measurement without setting the parameters. In order to estimate the aberration using the position and shape of the stripes appearing in a Ronchigram, we used a convolutional neural network in the model structure. By incorporating this regression model into STEM, it became possible to automatically and accurately measure and correct coma aberration.

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