Intelligent nanophotonics: merging photonics and artificial intelligence at the nanoscale
Nanophotonics has been an active research field over the past two decades, triggered by the rising interests in exploring new physics and technologies with light at the nanoscale. As the demands of performance and integration level keep increasing, the design and optimization of nanophotonic devices...
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2019-01-01
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doaj-5b03dc249ee147e1a6f2926045911f7b2021-05-02T05:13:07ZengDe GruyterNanophotonics2192-86142019-01-018333936610.1515/nanoph-2018-0183nanoph-2018-0183Intelligent nanophotonics: merging photonics and artificial intelligence at the nanoscaleYao Kan0Unni Rohit1Zheng Yuebing2Department of Mechanical Engineering, The University of Texas at Austin, Austin, TX 78712, USATexas Materials Institute, The University of Texas at Austin, Austin, TX 78712, USADepartment of Mechanical Engineering, The University of Texas at Austin, Austin, TX 78712, USANanophotonics has been an active research field over the past two decades, triggered by the rising interests in exploring new physics and technologies with light at the nanoscale. As the demands of performance and integration level keep increasing, the design and optimization of nanophotonic devices become computationally expensive and time-inefficient. Advanced computational methods and artificial intelligence, especially its subfield of machine learning, have led to revolutionary development in many applications, such as web searches, computer vision, and speech/image recognition. The complex models and algorithms help to exploit the enormous parameter space in a highly efficient way. In this review, we summarize the recent advances on the emerging field where nanophotonics and machine learning blend. We provide an overview of different computational methods, with the focus on deep learning, for the nanophotonic inverse design. The implementation of deep neural networks with photonic platforms is also discussed. This review aims at sketching an illustration of the nanophotonic design with machine learning and giving a perspective on the future tasks.http://www.degruyter.com/view/j/nanoph.2019.8.issue-3/nanoph-2018-0183/nanoph-2018-0183.xml?format=INTdeep learning(nano)photonic neural networksinverse designoptimization |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yao Kan Unni Rohit Zheng Yuebing |
spellingShingle |
Yao Kan Unni Rohit Zheng Yuebing Intelligent nanophotonics: merging photonics and artificial intelligence at the nanoscale Nanophotonics deep learning (nano)photonic neural networks inverse design optimization |
author_facet |
Yao Kan Unni Rohit Zheng Yuebing |
author_sort |
Yao Kan |
title |
Intelligent nanophotonics: merging photonics and artificial intelligence at the nanoscale |
title_short |
Intelligent nanophotonics: merging photonics and artificial intelligence at the nanoscale |
title_full |
Intelligent nanophotonics: merging photonics and artificial intelligence at the nanoscale |
title_fullStr |
Intelligent nanophotonics: merging photonics and artificial intelligence at the nanoscale |
title_full_unstemmed |
Intelligent nanophotonics: merging photonics and artificial intelligence at the nanoscale |
title_sort |
intelligent nanophotonics: merging photonics and artificial intelligence at the nanoscale |
publisher |
De Gruyter |
series |
Nanophotonics |
issn |
2192-8614 |
publishDate |
2019-01-01 |
description |
Nanophotonics has been an active research field over the past two decades, triggered by the rising interests in exploring new physics and technologies with light at the nanoscale. As the demands of performance and integration level keep increasing, the design and optimization of nanophotonic devices become computationally expensive and time-inefficient. Advanced computational methods and artificial intelligence, especially its subfield of machine learning, have led to revolutionary development in many applications, such as web searches, computer vision, and speech/image recognition. The complex models and algorithms help to exploit the enormous parameter space in a highly efficient way. In this review, we summarize the recent advances on the emerging field where nanophotonics and machine learning blend. We provide an overview of different computational methods, with the focus on deep learning, for the nanophotonic inverse design. The implementation of deep neural networks with photonic platforms is also discussed. This review aims at sketching an illustration of the nanophotonic design with machine learning and giving a perspective on the future tasks. |
topic |
deep learning (nano)photonic neural networks inverse design optimization |
url |
http://www.degruyter.com/view/j/nanoph.2019.8.issue-3/nanoph-2018-0183/nanoph-2018-0183.xml?format=INT |
work_keys_str_mv |
AT yaokan intelligentnanophotonicsmergingphotonicsandartificialintelligenceatthenanoscale AT unnirohit intelligentnanophotonicsmergingphotonicsandartificialintelligenceatthenanoscale AT zhengyuebing intelligentnanophotonicsmergingphotonicsandartificialintelligenceatthenanoscale |
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1721495117538263040 |