Nanophotonics-enabled optical data storage in the age of machine learning

The growing data availability has accelerated the rise of data-driven and data-intensive technologies, such as machine learning, a subclass of artificial intelligence technology. Because the volume of data is expanding rapidly, new and improved data storage methods are necessary. Advances in nanopho...

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Autores principales:	Simone Lamon, Qiming Zhang, Min Gu
Formato:	article
Lenguaje:	EN
Publicado:	AIP Publishing LLC 2021
Materias:	Applied optics. Photonics TA1501-1820
Acceso en línea:	https://doaj.org/article/289577e6a1704303ba429c2e568e4a21
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spelling	oai:doaj.org-article:289577e6a1704303ba429c2e568e4a212021-12-01T18:51:31ZNanophotonics-enabled optical data storage in the age of machine learning2378-096710.1063/5.0065634https://doaj.org/article/289577e6a1704303ba429c2e568e4a212021-11-01T00:00:00Zhttp://dx.doi.org/10.1063/5.0065634https://doaj.org/toc/2378-0967The growing data availability has accelerated the rise of data-driven and data-intensive technologies, such as machine learning, a subclass of artificial intelligence technology. Because the volume of data is expanding rapidly, new and improved data storage methods are necessary. Advances in nanophotonics have enabled the creation of disruptive optical data storage techniques and media capable of storing petabytes of data on a single optical disk. However, the needs for high-capacity, long-term, robust, and reliable optical data storage necessitate breakthrough advances in existing optical devices to enable future developments of artificial intelligence technology. Machine learning, which employs computer algorithms capable of self-improvement via experience and data usage, has proven an unrivaled tool to detect and forecast data patterns and decode and extract information from images. Furthermore, machine learning has been combined with physical and chemical sciences to build new fundamental principles and media. The integration of nanophotonics-enabled optical data storage with emerging machine learning technologies promises new methods for high-resolution, accurate, fast, and robust optical data writing and reading, as well as the discovery, design, and optimization of nanomaterials and nanostructures with new functionalities for next-generation nanophotonics-enabled optical data storage. In this Perspective, we review advances in nanophotonics-enabled optical data storage and discuss the role of machine learning in next-generation nanophotonics-enabled optical data storage.Simone LamonQiming ZhangMin GuAIP Publishing LLCarticleApplied optics. PhotonicsTA1501-1820ENAPL Photonics, Vol 6, Iss 11, Pp 110902-110902-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Applied optics. Photonics TA1501-1820
spellingShingle	Applied optics. Photonics TA1501-1820 Simone Lamon Qiming Zhang Min Gu Nanophotonics-enabled optical data storage in the age of machine learning
description	The growing data availability has accelerated the rise of data-driven and data-intensive technologies, such as machine learning, a subclass of artificial intelligence technology. Because the volume of data is expanding rapidly, new and improved data storage methods are necessary. Advances in nanophotonics have enabled the creation of disruptive optical data storage techniques and media capable of storing petabytes of data on a single optical disk. However, the needs for high-capacity, long-term, robust, and reliable optical data storage necessitate breakthrough advances in existing optical devices to enable future developments of artificial intelligence technology. Machine learning, which employs computer algorithms capable of self-improvement via experience and data usage, has proven an unrivaled tool to detect and forecast data patterns and decode and extract information from images. Furthermore, machine learning has been combined with physical and chemical sciences to build new fundamental principles and media. The integration of nanophotonics-enabled optical data storage with emerging machine learning technologies promises new methods for high-resolution, accurate, fast, and robust optical data writing and reading, as well as the discovery, design, and optimization of nanomaterials and nanostructures with new functionalities for next-generation nanophotonics-enabled optical data storage. In this Perspective, we review advances in nanophotonics-enabled optical data storage and discuss the role of machine learning in next-generation nanophotonics-enabled optical data storage.
format	article
author	Simone Lamon Qiming Zhang Min Gu
author_facet	Simone Lamon Qiming Zhang Min Gu
author_sort	Simone Lamon
title	Nanophotonics-enabled optical data storage in the age of machine learning
title_short	Nanophotonics-enabled optical data storage in the age of machine learning
title_full	Nanophotonics-enabled optical data storage in the age of machine learning
title_fullStr	Nanophotonics-enabled optical data storage in the age of machine learning
title_full_unstemmed	Nanophotonics-enabled optical data storage in the age of machine learning
title_sort	nanophotonics-enabled optical data storage in the age of machine learning
publisher	AIP Publishing LLC
publishDate	2021
url	https://doaj.org/article/289577e6a1704303ba429c2e568e4a21
work_keys_str_mv	AT simonelamon nanophotonicsenabledopticaldatastorageintheageofmachinelearning AT qimingzhang nanophotonicsenabledopticaldatastorageintheageofmachinelearning AT mingu nanophotonicsenabledopticaldatastorageintheageofmachinelearning
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Nanophotonics-enabled optical data storage in the age of machine learning

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