Analog optical computing based on a dielectric meta-reflect array

Chizari, Ata; Abdollahramezani, Sajjad; Jamali, Mohammad Vahid; Salehi, Jawad A.

doi:10.1364/ol.41.003451

Cited by 145 publications

(63 citation statements)

References 37 publications

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“…In recent years, optical analog computing of spatial differentiation has attracted great attention, which enables an entire image processes on a single shot [12][13][14][15][16][17][18][19][20][21][22]. Various optical analog computing devices were designed for edge detection [2,[23][24][25][26][27][28][29][31][32][33][34].…”

Section: Introductionmentioning

confidence: 99%

Optical phase mining by adjustable spatial differentiator

2020

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Phase is a fundamental resource for optical imaging but cannot be directly observed with intensity measurements. The existing methods to quantify a phase distribution rely on complex devices and structures. Here we experimentally demonstrate a phase mining method based on so-called adjustable spatial differentiation, just generally by analyzing the polarization in light reflection on a single planar dielectric interface. With introducing an adjustable bias, we create a virtual light source to render the measured images with a shadow-cast effect. We further successfully recover the phase distribution of a transparent object from the virtual shadowed images. Without any dependence on resonance or material dispersion, this method directly stems from the intrinsic properties of light and can be generally extended to a board frequency range. arXiv:1911.00861v1 [physics.optics]

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Section: Introductionmentioning

confidence: 99%

Optical phase mining by adjustable spatial differentiator

2020

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“…[1][2][3][4][5] The development of reliable fabrication techniques to realize such nanostructures has opened up new opportunities for forming reliable flat optical components to replace the existing bulky optical elements. Numerous interesting functionalities have been demonstrated so far including planar lenses, [6] calculus metasurfaces, [7][8][9] meta-holograms, [10] nonlinear meta-modulators, [11,12] and computational imager. [13] However, DOI: 10.1002/adts.201900088 systematic realization of mature optical functionalities using complex nanostructures requires significant knowledge about the influence of nanostructure features on the interaction of EM waves, which currently can only be found using cumbersome numerical calculations.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Reveals Underlying Physics of Light–Matter Interactions in Nanophotonic Devices

Kiarashinejad

Abdollahramezani

Zandehshahvar

et al. 2019

Advcd Theory and Sims

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In this paper, a deep learning-based algorithm is presented, as a purely mathematical platform, for providing intuitive understanding of the properties of electromagnetic (EM) wave-matter interaction in nanostructures. This approach is based on using the dimensionality reduction (DR) technique to significantly reduce the dimensionality of a generic EM wave-matter interaction problem without imposing significant error. Such an approach implicitly provides useful information about the role of different features (such as geometrical design parameters) of the nanostructure in its response functionality. To demonstrate the practical capabilities of this DL-based technique, it is applied to a reconfigurable optical metadevice enabling dual-band and triple-band optical absorption in the telecommunication window. Combination of the proposed approach with existing commercialized full-wave simulation tools offers a powerful toolkit to extract basic mechanisms of wave-matter interaction in complex EM devices and facilitate the design and optimization of nanostructures for a large range of applications including imaging, spectroscopy, and signal processing. It is worth mentioning that the demonstrated approach is general and can be used in a large range of problems as long as enough training data are provided.

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“…Traditionally, optical analog computing in the spatial domain uses a bulky system of lenses and filters18. Recently, there are significant efforts seeking to miniaturize such computing elements down to a single wavelength or even subwavelength scale1920212223242526272829. In particular, Silva et al 19.…”

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confidence: 99%

Plasmonic computing of spatial differentiation

et al. 2017

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Optical analog computing offers high-throughput low-power-consumption operation for specialized computational tasks. Traditionally, optical analog computing in the spatial domain uses a bulky system of lenses and filters. Recent developments in metamaterials enable the miniaturization of such computing elements down to a subwavelength scale. However, the required metamaterial consists of a complex array of meta-atoms, and direct demonstration of image processing is challenging. Here, we show that the interference effects associated with surface plasmon excitations at a single metal–dielectric interface can perform spatial differentiation. And we experimentally demonstrate edge detection of an image without any Fourier lens. This work points to a simple yet powerful mechanism for optical analog computing at the nanoscale.

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Analog optical computing based on a dielectric meta-reflect array

Cited by 145 publications

References 37 publications

Optical phase mining by adjustable spatial differentiator

Optical phase mining by adjustable spatial differentiator

Deep Learning Reveals Underlying Physics of Light–Matter Interactions in Nanophotonic Devices

Plasmonic computing of spatial differentiation

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