Deep learning with coherent nanophotonic circuits
Yichen Shen,Nicholas C. Harris,Scott Skirlo,Dirk Englund,Marin Soljacic +4 more
- Vol. 11, Iss: 7, pp 441-446
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.read more
Citations
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Journal ArticleDOI
A submicrometre silicon-on-insulator resonator for ultrasound detection
Rami Shnaiderman,Georg Wissmeyer,Okan Ülgen,Qutaiba Mustafa,Andriy Chmyrov,Vasilis Ntziachristos +5 more
TL;DR: The detector enables ultra-miniaturization of ultrasound readings, enabling ultrasound imaging at a resolution comparable to that achieved with optical microscopy, and potentially enabling the development of very dense ultrasound arrays on a silicon chip.
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Topological optical differentiator
TL;DR: In this article, the authors established a connection between two-dimensional optical spatial differentiation and a nontrivial topological charge in the optical transfer function, and experimentally demonstrated an isotropic 2D spatial differentiation with a broad spectral bandwidth, by using the simplest photonic device.
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Progress of infrared guided-wave nanophotonic sensors and devices
TL;DR: An overview of the recent progress of research on infrared guided-wave nanophotonic sensors is provided, showing the advantages of high sensitivity, low limit of detection, low crosstalk, strong detection multiplexing capability, immunity to electromagnetic interference, small footprint and low cost.
Journal ArticleDOI
Hardware error correction for programmable photonics
TL;DR: In this paper, the authors present a deterministic approach to correcting circuit errors by locally correcting hardware errors within individual optical gates, and apply their approach to simulations of large scale optical neural networks and infinite impulse response filters implemented in programmable photonics, finding that they remain resilient to component error well beyond modern day process tolerances.
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Modeling Electrical Switching of Nonvolatile Phase-Change Integrated Nanophotonic Structures with Graphene Heaters
TL;DR: This work model electrical switching of GST-clad integrated nanophotonic structures with graphene heaters based on the programmable GST-on-silicon platform and facilitates the analysis and understanding of the thermal-conduction heating-enabled phase transitions on PICs and supports the development of the future large-scale PCM-based electronic-photonic systems.
References
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