2020
DOI: 10.1515/nanoph-2020-0069
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AI-assisted on-chip nanophotonic convolver based on silicon metasurface

Abstract: AbstractConvolution operation is of great significance in on-chip all-optical signal processing, especially in signal analysis and image processing. It is a basic and important mathematical operation in the realization of all-optical computing. Here, we propose and experimentally implement a dispersionless metalens for dual wavelengths, a 4f optical processing system, and then demonstrate the on-chip nanophotonic convolver based on silicon metasurface with the optimization assi… Show more

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Cited by 40 publications
(21 citation statements)
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“…In the future, the free-space optical components can be replaced by the on-chip lenses which have been achieved in the nanophotonic convolver [39], on-chip deep learning systems [40][41][42][43] and integrated tunable varifocal lenses performing like error backpropagation in neural networks [44]. Once combined with on-chip lenses, our nonlinear JTC will have high energy efficiency, compact volume and high speed, and therefore promotes the potential convolution-related applications in many aspects, including image classification with large and deep convolutional neural networks, speech recognition and translation with the combination of convolutional neural networks and deep recurrent neural networks, autonomous driving by mapping raw pixels with convolutional neural networks, inverse design (like nanophotonic / plasmonic structures, self-adaptive microwave cloak) problem solving based on convolutional neural networks, and robotic manipulation with deep reinforcement learning.…”
Section: Discussionmentioning
confidence: 99%
“…In the future, the free-space optical components can be replaced by the on-chip lenses which have been achieved in the nanophotonic convolver [39], on-chip deep learning systems [40][41][42][43] and integrated tunable varifocal lenses performing like error backpropagation in neural networks [44]. Once combined with on-chip lenses, our nonlinear JTC will have high energy efficiency, compact volume and high speed, and therefore promotes the potential convolution-related applications in many aspects, including image classification with large and deep convolutional neural networks, speech recognition and translation with the combination of convolutional neural networks and deep recurrent neural networks, autonomous driving by mapping raw pixels with convolutional neural networks, inverse design (like nanophotonic / plasmonic structures, self-adaptive microwave cloak) problem solving based on convolutional neural networks, and robotic manipulation with deep reinforcement learning.…”
Section: Discussionmentioning
confidence: 99%
“…The limited resolution of data sampling is constrained by minimum waveguide spacing between the output ports to avoid coupling between the waveguides. To further improve the resolution, the output waveguides can fan out, 32 as opposed to being parallel to each other (as shown in Figure 1d). However, this method will lead to a very large device area.…”
Section: ■ Discussionmentioning
confidence: 99%
“…At the same time, a desired phase profile can be obtained by etching air slots of different lengths in a selected substrate . The desired profile for a broad-band lens can be calculated by an analysis or by using the inverse design to determine the length of each slot. , The main difference is that in our design we set broad boundaries for parameter space using the objective-first algorithm and then transform the output to a hole-based structure. Basically, the main purpose of both methods is to approach the desired phase with the help of the selected method.…”
Section: Sampling Inhomogeneous Medium With Bruggeman Effective Mediu...mentioning
confidence: 99%