2D Super‐Resolution Metrology Based on Superoscillatory Light
Abstract: Progress in the semiconductor industry relies on the development of increasingly compact devices consisting of complex geometries made from diverse materials. Precise, label‐free, and real‐time metrology is needed for the characterization and quality control of such structures in both scientific research and industry. However, optical metrology of 2D sub‐wavelength structures with nanometer resolution remains a major challenge. Here, a single‐shot and label‐free optical metrology approach that determines 2D features of nanostructures, is introduced. Accurate experimental measurements with a random statistical error of 18 nm (λ/27) are demonstrated, while simulations suggest that 6 nm (λ/81) may be possible. This is far beyond the diffraction limit that affects conventional metrology. This metrology employs neural network processing of images of the 2D nano‐objects interacting with a phase singularity of the incident topologically structured superoscillatory light. A comparison between conventional and topologically structured illuminations shows that the presence of a singularity with a giant phase gradient substantially improves the retrieval of object information in such an optical metrology. This non‐invasive nano‐metrology opens a range of application opportunities for smart manufacturing processes, quality control, and advanced materials characterization.
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Erschienen in
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2D Super‐Resolution Metrology Based on Superoscillatory Light ; day:05 ; month:08 ; year:2024 ; extent:6
Advanced science ; (05.08.2024) (gesamt 6)
- Urheber
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Wang, Yu
Chan, Eng Aik
Rendón‐Barraza, Carolina
Shen, Yijie
Plum, Eric
Ou, Jun‐Yu
- DOI
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10.1002/advs.202404607
- URN
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urn:nbn:de:101:1-2408051423454.329999233926
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
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14.08.2025, 11:02 MESZ
Datenpartner
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Wang, Yu
- Chan, Eng Aik
- Rendón‐Barraza, Carolina
- Shen, Yijie
- Plum, Eric
- Ou, Jun‐Yu
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