表面と真空
Online ISSN : 2433-5843
Print ISSN : 2433-5835
特集「2018年日本表面真空学会学術講演会特集号III」
機械学習を用いた光電子収量分光(PYS)の閾値予測の測定データを用いた検証
柳生 進二郎吉武 道子知京 豊裕長田 貴弘
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2019 年 62 巻 8 号 p. 504-510

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In order to estimate the threshold of photoelectron yield spectroscopy (PYS), regression estimator was created by machine learning using calculation data based on Fowler's theory. We compared verification data with the prediction by regression estimators. The verification data measured by AC-3 instrument manufactured by Riken Keiki Co. are the number of 87 (Au : 31, Metals except for Au : 16, oxide · semiconductor : 15, organic : 25) with estimate value by analyst (label data). As a result, the prediction performance was 70% over in the range of difference between label data and estimation from ±0.2 eV. On the other hand, spectra in the difference of 0.3 eV were included in difficult labeling by analysts. The prediction by machine learning gives the guide to analysis.

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この記事はクリエイティブ・コモンズ [表示 - 非営利 4.0 国際]ライセンスの下に提供されています。
https://creativecommons.org/licenses/by-nc/4.0/deed.ja
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