Matrix Factorization for Automatic Chemical Mapping from Electron Microscopic Spectral Imaging Datasets

• Shiga Motoki

  Department of Electrical, Electronic and Computer Engineering, Gifu University

• Muto Shunsuke

  Advanced Measurement Technology Center, Institute of Materials and Systems for Sustainability, Nagoya University

• Tatsumi Kazuyoshi

  Advanced Measurement Technology Center, Institute of Materials and Systems for Sustainability, Nagoya University

• Tsuda Koji

  Graduate School of Frontier Sciences, University of Tokyo Center for Materials Research by Information Integration, National Institute for Materials Science Biotechnology Research Institute for Drug Discovery, National Institute of Advanced Industrial Science and Technology

抄録

Advances in scanning transmission electron microscopy (STEM) techniques have enabled us to automatically obtain electron energy-loss (EELS)/energy-dispersive X-ray (EDX) spectral datasets from a specified region of interest (ROI) at an arbitrary step width, called spectral imaging (SI). Instead of manually identifying the potential constituent chemical components from the ROI, it is more effective and efficient to use a statistical approach for the automatic identification of the underlying chemical components and their spectra. This problem of automatic decomposition of chemical components can be formalized as a matrix factorization, which is a common problem setting in statistical machine learning. This paper first reviews several matrix factorization methods and then introduces our extension of a non-negative matrix factorization (NMF). The present NMF solves two problems: i) resolving overlapped spectral profiles, avoiding unnatural crosstalk, and ii) optimizing the number of chemical components. These effectiveness and comparisons with other matrix factorization methods are demonstrated using a real STEM-EELS dataset.

収録刊行物

• Transactions of the Materials Research Society of Japan

  Transactions of the Materials Research Society of Japan 41 (4), 333-336, 2016

  一般社団法人 日本MRS

キーワード

• Spectral Imaging
• Machine Learning
• Non-negative Matrix Factorization
• STEM-EELS/EDX

詳細情報

• CRID: 1390001205513446656
• NII論文ID: 130005172417
• DOI: 10.14723/tmrsj.41.333
• ISSN: 21881650
• ISSN: 13823469
• Web Site: https://www.jstage.jst.go.jp/article/tmrsj/41/4/41_333/_pdf
• 本文言語コード: en
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