High-throughput spectrum imaging data analysis of synchrotron X-ray photoelectron microscopy using machine learning

Nagamura Naoka, Matsumura Tarojiro, Akaho Shotaro, Nagata Kenji, Ando Yasunobu

doi:10.14886/sssj2008.2018.0_126

Bibliographic Information

Other Title

放射光走査型光電子顕微分光におけるスペクトルイメージングデータ解析の機械学習による高速化

Abstract

Synchrotron X-ray scanning photoelectron microscopy (SPEM) output two-dimensional spectral imaging. When we perform depth profiling and device operando analysis, parameters increase and the data quantity becomes enormous. To analyze this spectral big-data efficiently and help interpretation, we have developed a high-throughput procedure for automatic peak separation with low calculation cost by using machine learning framework. In the presentation, we introduce the application to experimental spectral datasets of atomic layer field effect transistor devices taken by a SPEM system in SPring-8.

Journal

Abstract of annual meeting of the Surface Science of Japan

Abstract of annual meeting of the Surface Science of Japan 2018 (0), 126-, 2018

The Japan Society of Vacuum and Surface Science

Details 詳細情報について

CRID: 1390001288094291840

NII Article ID: 130007519059

DOI: 10.14886/sssj2008.2018.0_126

Text Lang: ja

Data Source

JaLC
CiNii Articles

Abstract License Flag: Disallowed

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