1,836 research outputs found

    Green silicon photonics

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    Silicon photonics have provided low-cost communication components for Internet applications and are now aimed towards providing environmentally friendly and green optical solutions. The need for energy-efficient photonics is due to the excessive energy dissipated in advanced electronics and an increase in power density that has posed a challenge to the most advanced chip-cooling technologies. The two-photon absorption (TPA)-generated free carriers need to be actively removed from the waveguide cover to eliminate any event of light absorption and a subsequent conversion to heat. Researchers have proposed an electro-optic modulator that takes advantage of the two-photon photovoltaic (TPPV) effect to attain negative static power dissipation. Photovoltaic power converters (PPC) are used for remote power delivery and are optimized for a wavelength range from 1,200 to 1,550 nm.published_or_final_versio

    Large-Scale Optical Neural Networks based on Photoelectric Multiplication

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    Recent success in deep neural networks has generated strong interest in hardware accelerators to improve speed and energy consumption. This paper presents a new type of photonic accelerator based on coherent detection that is scalable to large (N106N \gtrsim 10^6) networks and can be operated at high (GHz) speeds and very low (sub-aJ) energies per multiply-and-accumulate (MAC), using the massive spatial multiplexing enabled by standard free-space optical components. In contrast to previous approaches, both weights and inputs are optically encoded so that the network can be reprogrammed and trained on the fly. Simulations of the network using models for digit- and image-classification reveal a "standard quantum limit" for optical neural networks, set by photodetector shot noise. This bound, which can be as low as 50 zJ/MAC, suggests performance below the thermodynamic (Landauer) limit for digital irreversible computation is theoretically possible in this device. The proposed accelerator can implement both fully-connected and convolutional networks. We also present a scheme for back-propagation and training that can be performed in the same hardware. This architecture will enable a new class of ultra-low-energy processors for deep learning.Comment: Text: 10 pages, 5 figures, 1 table. Supplementary: 8 pages, 5, figures, 2 table

    FirstLight: Pluggable Optical Interconnect Technologies for Polymeric Electro-Optical Printed Circuit Boards in Data Centers

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    The protocol data rate governing data storage devices will increase to over 12 Gb/s by 2013 thereby imposing unmanageable cost and performance burdens on future digital data storage systems. The resulting performance bottleneck can be substantially reduced by conveying high-speed data optically instead of electronically. A novel active pluggable 82.5 Gb/s aggregate bit rate optical connector technology, the design and fabrication of a compact electro-optical printed circuit board to meet exacting specifications, and a method for low cost, high precision, passive optical assembly are presented. A demonstration platform was constructed to assess the viability of embedded electro-optical midplane technology in such systems including the first ever demonstration of a pluggable active optical waveguide printed circuit board connector. High-speed optical data transfer at 10.3125 Gb/s was demonstrated through a complex polymer waveguide interconnect layer embedded into a 262 mm × 240 mm × 4.3 mm electro-optical midplane. Bit error rates of less than 10-12 and optical losses as low as 6 dB were demonstrated through nine multimode polymer wave guides with an aggregate data bandwidth of 92.8125 Gb/s
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