Measuring complex refractive index through deep-learning-enabled optical reflectometry Article Swipe
Ziyang Wang
,
Yuxuan Lin
,
Kunyan Zhang
,
Wenjing Wu
,
Shengxi Huang
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1088/2053-1583/acc59b
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1088/2053-1583/acc59b
Optical spectroscopy is indispensable for research and development in nanoscience and nanotechnology, microelectronics, energy, and advanced manufacturing. Advanced optical spectroscopy tools often require both specifically designed high-end instrumentation and intricate data analysis techniques. Beyond the common analytical tools, deep learning methods are well suited for interpreting high-dimensional and complicated spectroscopy data. They offer great opportunities to extract subtle and deep information about optical properties of materials with simpler optical setups, which would otherwise require sophisticated instrumentation. In this work, we propose a computational approach based on a conventional tabletop optical microscope and a deep learning model called ReflectoNet . Without any prior knowledge about the multilayer substrates, ReflectoNet can predict the complex refractive indices of thin films and 2D materials on top of these nontrivial substrates from experimentally measured optical reflectance spectra with high accuracies. This task was not feasible previously with traditional reflectometry or ellipsometry methods. Fundamental physical principles, such as the Kramers–Kronig relations, are spontaneously learned by the model without any further training. This approach enables in-operando optical characterization of functional materials and 2D materials within complex photonic structures or optoelectronic devices.
Related Topics
Concepts
Reflectometry
Characterization (materials science)
Microelectronics
Photonics
Computer science
Instrumentation (computer programming)
Spectroscopy
Materials science
Refractive index
Optoelectronics
Ellipsometry
Optics
Nanotechnology
Thin film
Physics
Computer vision
Operating system
Time domain
Quantum mechanics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/2053-1583/acc59b
- OA Status
- hybrid
- Cited By
- 13
- References
- 38
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4327906756
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4327906756Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1088/2053-1583/acc59bDigital Object Identifier
- Title
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Measuring complex refractive index through deep-learning-enabled optical reflectometryWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-03-20Full publication date if available
- Authors
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Ziyang Wang, Yuxuan Lin, Kunyan Zhang, Wenjing Wu, Shengxi HuangList of authors in order
- Landing page
-
https://doi.org/10.1088/2053-1583/acc59bPublisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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hybridOpen access status per OpenAlex
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https://doi.org/10.1088/2053-1583/acc59bDirect OA link when available
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Reflectometry, Characterization (materials science), Microelectronics, Photonics, Computer science, Instrumentation (computer programming), Spectroscopy, Materials science, Refractive index, Optoelectronics, Ellipsometry, Optics, Nanotechnology, Thin film, Physics, Computer vision, Operating system, Time domain, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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13Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3, 2024: 7, 2023: 3Per-year citation counts (last 5 years)
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38Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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