Nanophotonic media for artificial neural inference

Optical neural network can process information in parallel by using the technology based on free-space and integrated platform. Over the last half century, the development of integrated circuits has been limited by Moore's law. We know that neural network is based on the digital computer for successive calculation, most of which cannot be made into real-time processing system. Therefore, it is necessary to develop ONN for real-time processing and device miniaturization. In this paper, we review the progress of optical neural networks. Firstly, based on the principle of artificial neural networks, we elaborate the essence of optical matrix multiplier for linear operation. Then we introduce the optical neural network achieved by free-space optical interconnection and waveguide optical interconnection. Finally we talk about the nonlinearity in optical neural networks. With the gradual maturity of nanotechnology and the rapid advancement of silicon photonic integrated circuits, the progress of integrated photonic neural network has been promoted. Therefore, the construction of optical neural network on the future integrated photonic platform has potential application value. INDEX TERMS Optical neural networks, optical waveguides, free-space optical interconnection, optical non-linearities, optical elements, photonic integrated circuits.

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“…In addition, E.Khoram introduced a nanophotonic medium that can be used for neural inference [62], as shown in Fig. 4(d).…”

Section: B Onn: Based On Integrated Optical Waveguide Platformmentioning

confidence: 99%

A Review of Optical Neural Networks

et al. 2020

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“…Recently, inverse design approaches based on local and global optimization techniques have attracted significant attention in enabling nontrivial high-performance meta-optic configurations targeted to a wide range of applications. Among several inverse design approaches, global step-by-step searching algorithms (such as genetic or particle swarm), adjoint-based topology optimization implementations, and neural network-assisted optimization approaches have proven to be compelling candidates to push forward highperformance nanophotonic devices [100][101][102][103][104][105][106][107][108][109][110][111][112][113][114][115][116]. Coupled to recent advances in nanofabrication technologies, physical modeling, and computational power, such single and multiobjective optimization approaches can benefit nextgeneration computational metaprocessors.…”

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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“…In order to demonstrate the application of this approach, we utilized it to design two nanophotonic devices: a wavelength demultiplexer [4] and a nanophotonic structure for artificial neural computing [27].…”

Section: Formulationmentioning

confidence: 99%

Controlling the minimal feature sizes in adjoint optimization of nanophotonic devices using b-spline surfaces

Khoram¹,

Qian²,

Yuan³

et al. 2020

Opt. Express

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Adjoint optimization is an effective method in the inverse design of nanophotonic devices. In order to ensure the manufacturability, one would like to have control over the minimal feature sizes. Here we propose utilizing a level-set method based on b-spline surfaces in order to control the feature sizes. This approach is first used to design a wavelength demultiplexer. It is also used to implement a nanophotonic structure for artificial neural computing. In both cases, we show that the minimal feature sizes can be easily parameterized and controlled. *

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Nanophotonic media for artificial neural inference

Cited by 118 publications

References 21 publications

A Review of Optical Neural Networks

A Review of Optical Neural Networks

Meta-optics for spatial optical analog computing

Controlling the minimal feature sizes in adjoint optimization of nanophotonic devices using b-spline surfaces

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