A Review of Optical Neural Networks

Sui, Xiubao; Wu, Qiuhao; Liu, Jia; Chen, Qian; Gu, Guohua

doi:10.1109/access.2020.2987333

Cited by 124 publications

(79 citation statements)

References 81 publications

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“…A useful comparison assessment on the classification accuracy of Modified National Institute of Standards and Technology (MNIST) data as well as insightful discussions on the fundamental information capacity of the system are also provided. The introduced platform can be leveraged for practical implementation of hybrid photonic-electronic neural networks [130][131][132].…”

Section: Discussionmentioning

confidence: 99%

Meta-optics for spatial optical analog computing

2020

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Rapidly growing demands for high-performance computing, powerful data processing, and big data necessitate the advent of novel optical devices to perform demanding computing processes effectively. Due to its unprecedented growth in the past two decades, the field of meta-optics offers a viable solution for spatially, spectrally, and/or even temporally sculpting amplitude, phase, polarization, and/or dispersion of optical wavefronts. In this review, we discuss state-of-the-art developments, as well as emerging trends, in computational metastructures as disruptive platforms for spatial optical analog computation. Two fundamental approaches based on general concepts of spatial Fourier transformation and Green’s function (GF) are discussed in detail. Moreover, numerical investigations and experimental demonstrations of computational optical surfaces and metastructures for solving a diverse set of mathematical problems (e.g., integrodifferentiation and convolution equations) necessary for on-demand information processing (e.g., edge detection) are reviewed. Finally, we explore the current challenges and the potential resolutions in computational meta-optics followed by our perspective on future research directions and possible developments in this promising area.

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Section: Discussionmentioning

confidence: 99%

Meta-optics for spatial optical analog computing

2020

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“…As discussed, optical diffractive neural network could execute various ML tasks as well [28,126]. An exploratory milestone of diffractive neural networks is displayed in Fig.…”

Section: Optical Diffractive Neural Networkmentioning

confidence: 99%

Intelligent designs in nanophotonics: from optimization towards inverse creation

Wang

Yan

et al. 2021

PhotoniX

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Applying intelligence algorithms to conceive nanoscale meta-devices becomes a flourishing and extremely active scientific topic over the past few years. Inverse design of functional nanostructures is at the heart of this topic, in which artificial intelligence (AI) furnishes various optimization toolboxes to speed up prototyping of photonic layouts with enhanced performance. In this review, we offer a systemic view on recent advancements in nanophotonic components designed by intelligence algorithms, manifesting a development trend from performance optimizations towards inverse creations of novel designs. To illustrate interplays between two fields, AI and photonics, we take meta-atom spectral manipulation as a case study to introduce algorithm operational principles, and subsequently review their manifold usages among a set of popular meta-elements. As arranged from levels of individual optimized piece to practical system, we discuss algorithm-assisted nanophotonic designs to examine their mutual benefits. We further comment on a set of open questions including reasonable applications of advanced algorithms, expensive data issue, and algorithm benchmarking, etc. Overall, we envision mounting photonic-targeted methodologies to substantially push forward functional artificial meta-devices to profit both fields.

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“…Distribution of the computing power in the whole network, with each neuron performing simultaneously small parts of the whole computational activity. Nevertheless, a big obstacle of photonic circuits has been the great volume of optical devices and the absence of susceptibility in contrast to the traditional integrated electronic circuits [13], [18], [21]. The materializations mainly on silicon (Si) and additionally on Indium Phosphide (InP) constitute a great innovative breakthrough in the materialization of integrated photonics, which is a reality nowadays.…”

Section: Photonic Neuromorphic Processorsmentioning

confidence: 99%

“…However, the most important development in the application efforts and standardization of NSoCs is spotted in the expanded efforts for developing fully Optical Neural Networks (ONN) [18], [19] and Photonic Neural Networks (PNN) [15], [20], [21]. The previously mentioned systems are based on the evolutions of optical technology and the most recent researches concerning photonics.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Survey on Nanophotonics Neural Networks

Demertzis¹,

Παπαδόπουλος²,

Iliadis³

et al. 2021

Preprint

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: In the last years, materializations of neuromorphic circuits based on nanophotonic arrangements have been proposed, which contain complete optical circuits, laser, photodetectors, photonic crystals, optical fibers, flat waveguides, and other passive optical elements of nanostructured materials, which eliminate the time of simultaneous processing of big groups of data, taking advantage of the quantum perspective and thus highly increasing the potentials of contemporary intelligent computational systems. This article is an effort to record and study the research that has been conducted concerning the methods of development and materialization of neuromorphic circuits of Neural Networks of nanophotonic arrangements. In particular, an investigative study of the methods of developing nanophotonic neuromorphic processors, their originality in neuronic architectural structure, their training methods and their optimization has been realized along with the study of special issues such as optical activation functions and cost functions.

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A Review of Optical Neural Networks

Cited by 124 publications

References 81 publications

Meta-optics for spatial optical analog computing

Meta-optics for spatial optical analog computing

Intelligent designs in nanophotonics: from optimization towards inverse creation

A Comprehensive Survey on Nanophotonics Neural Networks

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