Artificial neural network assisted spectral scatterometry for grating quality control Article Swipe
Aleksi Mattila
,
J. Nysten
,
Ville Heikkinen
,
Jorma Kilpi
,
Virpi Korpelainen
,
Poul‐Erik Hansen
,
Petri Karvinen
,
Markku Kuittinen
,
Antti Lassila
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1088/1361-6501/ad4e52
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1088/1361-6501/ad4e52
Spectral scatterometry is a technique that allows rapid measurements of diffraction efficiencies of diffractive optical elements (DOEs). The analysis of such diffraction efficiencies has traditionally been laborious and time consuming. However, machine learning can be employed to aid in the analysis of measured diffraction efficiencies. In this paper we describe a novel system for providing measurements of multiple measurands rapidly and concurrently using a spectral scatterometer and an artificial neural network (ANN) which is trained utilising transfer learning. The ANN provides values for the pitch, height, and line widths of the DOEs. In addition, an uncertainty evaluation was performed. In the majority of the studied cases, the discrepancies between the values obtained using a scanning electron microscope (SEM) and artificial neural network assisted spectral scatterometer (ANNASS) for the grating parameters were below 5 nm. Furthermore, independent reference samples were used to perform a metrological validation. An expanded uncertainty ( k = 2) of 5.3 nm was obtained from the uncertainty evaluation for the measurand height. The height value measurements performed employing ANNASS and SEM are demonstrated to be in agreement within this uncertainty.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/1361-6501/ad4e52
- https://iopscience.iop.org/article/10.1088/1361-6501/ad4e52/pdf
- OA Status
- hybrid
- Cited By
- 6
- References
- 21
- Related Works
- 10
- OpenAlex ID
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Artificial neural network assisted spectral scatterometry for grating quality controlWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-05-21Full publication date if available
- Authors
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Aleksi Mattila, J. Nysten, Ville Heikkinen, Jorma Kilpi, Virpi Korpelainen, Poul‐Erik Hansen, Petri Karvinen, Markku Kuittinen, Antti LassilaList of authors in order
- Landing page
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https://doi.org/10.1088/1361-6501/ad4e52Publisher landing page
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https://iopscience.iop.org/article/10.1088/1361-6501/ad4e52/pdfDirect link to full text PDF
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://iopscience.iop.org/article/10.1088/1361-6501/ad4e52/pdfDirect OA link when available
- Concepts
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Artificial neural network, Grating, Quality (philosophy), Materials science, Computer science, Artificial intelligence, Physics, Optoelectronics, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
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6Total citation count in OpenAlex
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2025: 6Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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