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Highly accurate classification of material types from spectroscopic ellipsometry heatmap measurements using deep learning.

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Title: Highly accurate classification of material types from spectroscopic ellipsometry heatmap measurements using deep learning.
Authors: Hayashi, Masahiro1, Hilfiker, James N.2, Maekawa, Takuji3, Tampo, Hitoshi4, Koida, Takashi4, Fujiwara, Hiroyuki1, fujiwara.hiroyuki.u7@f.gifu-u.ac.jp
Source: Applied Physics Letters; 11/3/2025, Vol. 127 Issue 18, p1-6, 6p
Database: Applied Science & Technology Source
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Header DbId: aci
DbLabel: Applied Science & Technology Source
An: 189179099
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Highly accurate classification of material types from spectroscopic ellipsometry heatmap measurements using deep learning.
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  Data: <searchLink fieldCode="JN" term="%22Applied+Physics+Letters%22">Applied Physics Letters</searchLink>; 11/3/2025, Vol. 127 Issue 18, p1-6, 6p
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=189179099
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      – Type: doi
        Value: 10.1063/5.0303070
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      – Code: eng
        Text: English
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        PageCount: 6
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      – TitleFull: Highly accurate classification of material types from spectroscopic ellipsometry heatmap measurements using deep learning.
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            NameFull: Hayashi, Masahiro
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            NameFull: Hilfiker, James N.
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            NameFull: Maekawa, Takuji
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            NameFull: Tampo, Hitoshi
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            NameFull: Koida, Takashi
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            – D: 03
              M: 11
              Text: 11/3/2025
              Type: published
              Y: 2025
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