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Open AccessJournal ArticleDOI

Deep learning with coherent nanophotonic circuits

TLDR
A new architecture for a fully optical neural network is demonstrated that enables a computational speed enhancement of at least two orders of magnitude and three order of magnitude in power efficiency over state-of-the-art electronics.
Abstract
Artificial Neural Networks have dramatically improved performance for many machine learning tasks. We demonstrate a new architecture for a fully optical neural network that enables a computational speed enhancement of at least two orders of magnitude and three orders of magnitude in power efficiency over state-of-the-art electronics.

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Citations
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Journal ArticleDOI

Machine learning and the physical sciences

TL;DR: This article reviews in a selective way the recent research on the interface between machine learning and the physical sciences, including conceptual developments in ML motivated by physical insights, applications of machine learning techniques to several domains in physics, and cross fertilization between the two fields.
Journal ArticleDOI

Integrated lithium niobate electro-optic modulators operating at CMOS-compatible voltages

TL;DR: Monolithically integrated lithium niobate electro-optic modulators that feature a CMOS-compatible drive voltage, support data rates up to 210 gigabits per second and show an on-chip optical loss of less than 0.5 decibels are demonstrated.
Journal ArticleDOI

All-optical machine learning using diffractive deep neural networks

TL;DR: 3D-printed D2NNs are created that implement classification of images of handwritten digits and fashion products, as well as the function of an imaging lens at a terahertz spectrum.
Journal ArticleDOI

All-optical spiking neurosynaptic networks with self-learning capabilities.

TL;DR: An optical version of a brain-inspired neurosynaptic system, using wavelength division multiplexing techniques, is presented that is capable of supervised and unsupervised learning.
Journal ArticleDOI

Training Deep Neural Networks for the Inverse Design of Nanophotonic Structures

TL;DR: A tandem neural network architecture is demonstrated that tolerates inconsistent training instances in inverse design of nanophotonic devices and provides a way to train large neural networks for the inverseDesign of complex photonic structures.
References
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Book

Semiconductor Optical Amplifiers

TL;DR: The purpose of this monograph is to clarify the basic principles of modelling and show how these principles can be applied to the real-time environment.
PatentDOI

Optimal bistable switching in non-linear photonic crystals

TL;DR: An optical bi-stable switch includes a photonic crystal cavity structure, which is used to characterize a bi-stable switch so that optimal control is provided over input and output of the switch as mentioned in this paper.
Journal ArticleDOI

Broadcast and Weight: An Integrated Network For Scalable Photonic Spike Processing

TL;DR: An on-chip optical architecture to support massive parallel communication among high-performance spiking laser neurons and a novel approach to photonic spike processing represents a promising opportunity in unconventional computing is suggested.
Journal ArticleDOI

Fast bistable all-optical switch and memory on a silicon photonic crystal on-chip.

TL;DR: Owing to the high quality factor and the small volume of the nanocavities, the photon density inside the cavity becomes extremely high, which leads to a large reduction in operation power, which opens the possibility of an integrated optical logic circuit on a single chip, based on photonic crystals.
Journal ArticleDOI

Self-configuring universal linear optical component

TL;DR: It is shown how to design an optical device that can perform any linear function or coupling between inputs and outputs, and that other linear operations, including frequency and time mappings, are possible in principle, even if very challenging in practice, thus proving there is at least one constructive design for any conceivable linear optical component.
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