An all optical prototype neuron based on optical Kerr material

Bhattacharya, Subhajit; Patra, Sankar Narayan; Mukhopadhyay, Sourangshu

doi:10.1016/j.ijleo.2014.06.174

Cited by 5 publications

(2 citation statements)

References 24 publications

(25 reference statements)

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“…They include thermo-optical [ 15 ], electro-optical (e.g. Pockels and Kerr effects) [ 16 – 18 ], magneto-optical [ 19 ], ionic-optical [ 20 ], piezo-optical [ 21 ], mechano-optical (deformation in MEMS or NEMS) [ 22 ], and phase-change mechanisms [ 23 – 25 ]. In metastructures, it is possible to enhance the tuning effects thanks to the sub-wavelength structures [ 26 ].…”

Section: Tunable Metastructure Nanophotonicsmentioning

confidence: 99%

Review: tunable nanophotonic metastructures

Ling,

Yoo

2023

Nanophotonics

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Tunable nanophotonic metastructures offer new capabilities in computing, networking, and imaging by providing reconfigurability in computer interconnect topologies, new optical information processing capabilities, optical network switching, and image processing. Depending on the materials and the nanostructures employed in the nanophotonic metastructure devices, various tuning mechanisms can be employed. They include thermo-optical, electro-optical (e.g. Pockels and Kerr effects), magneto-optical, ionic-optical, piezo-optical, mechano-optical (deformation in MEMS or NEMS), and phase-change mechanisms. Such mechanisms can alter the real and/or imaginary parts of the optical susceptibility tensors, leading to tuning of the optical characteristics. In particular, tunable nanophotonic metastructures with relatively large tuning strengths (e.g. large changes in the refractive index) can lead to particularly useful device applications. This paper reviews various tunable nanophotonic metastructures’ tuning mechanisms, tuning characteristics, tuning speeds, and non-volatility. Among the reviewed tunable nanophotonic metastructures, some of the phase-change-mechanisms offer relatively large index change magnitude while offering non-volatility. In particular, Ge–Sb–Se–Te (GSST) and vanadium dioxide (VO2) materials are popular for this reason. Mechanically tunable nanophotonic metastructures offer relatively small changes in the optical losses while offering large index changes. Electro-optically tunable nanophotonic metastructures offer relatively fast tuning speeds while achieving relatively small index changes. Thermo-optically tunable nanophotonic metastructures offer nearly zero changes in optical losses while realizing modest changes in optical index at the expense of relatively large power consumption. Magneto-optically tunable nanophotonic metastructures offer non-reciprocal optical index changes that can be induced by changing the magnetic field strengths or directions. Tunable nanophotonic metastructures can find a very wide range of applications including imaging, computing, communications, and sensing. Practical commercial deployments of these technologies will require scalable, repeatable, and high-yield manufacturing. Most of these technology demonstrations required specialized nanofabrication tools such as e-beam lithography on relatively small fractional areas of semiconductor wafers, however, with advanced CMOS fabrication and heterogeneous integration techniques deployed for photonics, scalable and practical wafer-scale fabrication of tunable nanophotonic metastructures should be on the horizon, driven by strong interests from multiple application areas.

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Section: Tunable Metastructure Nanophotonicsmentioning

confidence: 99%

Review: tunable nanophotonic metastructures

Ling,

Yoo

2023

Nanophotonics

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“…Apart from hardware accelerators for designated linear computation tasks, exemplary optical NNs (ONNs) also including nonlinearities have been implemented. A basic nonlinear behavior can be realized by nonlinear optical effects (16,17), hybrid optical-electronic schemes (18), and functional materials (13). Going one step further, optical spiking neurons based on vertical-cavity surfaceemitting lasers have been demonstrated (19,20).…”

Section: Introductionmentioning

confidence: 99%

Event-driven adaptive optical neural network

Brückerhoff-Plückelmann,

Bente,

Becker

et al. 2023

Sci. Adv.

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We present an adaptive optical neural network based on a large-scale event-driven architecture. In addition to changing the synaptic weights (synaptic plasticity), the optical neural network’s structure can also be reconfigured enabling various functionalities (structural plasticity). Key building blocks are wavelength-addressable artificial neurons with embedded phase-change materials that implement nonlinear activation functions and nonvolatile memory. Using multimode focusing, the activation function features both excitatory and inhibitory responses and shows a reversible switching contrast of 3.2 decibels. We train the neural network to distinguish between English and German text samples via an evolutionary algorithm. We investigate both the synaptic and structural plasticity during the training process. On the basis of this concept, we realize a large-scale network consisting of 736 subnetworks with 16 phase-change material neurons each. Overall, 8398 neurons are functional, highlighting the scalability of the photonic architecture.

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