The Analysis of Polymer Sample TOF-SIMS Data Using Autoencoder

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  • Ito Masaru
    Faculty of Science and Technology, Seikei University
  • Matsuda Kazuhiro
    Faculty of Science and Technology, Seikei University Surface Science Laboratories, Toray Research Center, Inc
  • Aoyagi Satoka
    Faculty of Science and Technology, Seikei University

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  • 自己符号化器（autoencoder）を用いた 高分子試料のTOF-SIMSデータ解析
  • ジコ フゴウカキ(autoencoder)オ モチイタ コウブンシ シリョウ ノ TOF-SIMS データ カイセキ

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Description

Time-of-flight secondary ion mass spectrometry （TOF-SIMS） data are generally so complex that multivariate analysis such as principal component analysis （PCA） and multivariate curve resolution （MCR） are often necessary to interpret TOF-SIMS data. Interpreting more complex TOF-SIMS data requires further data analysis methods using machine learning and deep learning. In this study, the application of autoencoder which is one of the unsupervised methods based on artificial neural networks into TOF-SIMS data of three polymers was evaluated.

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