Intelligent systems based on photonics

Автор: Golovastikov Nikita Vladimirovich, Dorozhkin Pavel Sergeevich, Soifer Victor Alexandrovich

Журнал: Онтология проектирования @ontology-of-designing

Рубрика: Общие вопросы формализации проектирования: онтологические аспекты

Статья в выпуске: 4 (42) т.11, 2021 года.

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This paper discusses the prospects of photonics, shows the relevance and applicability of photonics research. The potential of photonics technologies to answer the socio-economic challenges of the digital transformation age is revealed. Opportunities that emerge with the introduction of photonic devices to various technical systems designed for environmental protection and quality of life improvement are demonstrated. Concrete photonics structures and devices for such key applications as spectroscopy, analog optical calculations, and optical neural networks are closely examined. Possible applications for photonic sensors and new type spectrometers are outlined, their competitive advantages explored. Various geometries of extra fine compact photonic spectrometers are presented: based on digital planar diagrams, integrated into the photonic waveguides, metasurfaces, diffraction gratings with varying parameters. The benefits of analog optical computations against conventional electronic devices are discussed. Various nanophotonic structures designed for differential and integral operators are studied, solutions for edge detection are proposed. The concept for artificial intelligence implementation on the photonics platform using optical neural networks is analyzed. Various solutions are examined: containing sequences of diffraction elements and based on Huygens-Fresnel principle, as well as planar structures comprised of waveguides that interact as Mach-Zehnder interferometer. SPIE estimation of the international photonics market proposes that the peak of interest for this field is yet to be achieved and photonics will claim its place in the future technological landscape.

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Photonics, intelligent systems, spectrometry, optical computing, optical neural networks

Короткий адрес: https://sciup.org/170191747

IDR: 170191747   |   DOI: 10.18287/2223-9537-2021-11-4-422-436

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