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Paul R. Prucnal

Researcher at Princeton University

Publications -  635
Citations -  15792

Paul R. Prucnal is an academic researcher from Princeton University. The author has contributed to research in topics: Photonics & Optical switch. The author has an hindex of 53, co-authored 607 publications receiving 12740 citations. Previous affiliations of Paul R. Prucnal include The Chinese University of Hong Kong & Tsinghua University.

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Spread spectrum fiber-optic local area network using optical processing

Abstract: Spread spectrum code division multiple access (CDMA) allows asynchronous multiple access to a local area network (LAN) with no waiting. The additional bandwidth required by spread spectrum can be accommodated by using a fiber-optic channel and incoherent optical signal processing. New CDMA sequences are designed specifically for optical processing. It is shown that increasing the number of chips per bit, by using optical processing, allows an increase in capacity of a CDMA LAN. An experiment is performed demonstrating the performance of an optical CDMA LAN, operating at 100 Mbd with three users.
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A terahertz optical asymmetric demultiplexer (TOAD)

TL;DR: In this article, an optical nonlinear element asymmetrically placed in a short fiber loop is used for demultiplexing Tb/s pulse trains that requires less than 1 pJ of switching energy and can be integrated on a chip.
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Neuromorphic photonic networks using silicon photonic weight banks.

TL;DR: First observations of a recurrent silicon photonic neural network, in which connections are configured by microring weight banks are reported, and a mathematical isomorphism between the silicon photonics circuit and a continuous neural network model is demonstrated through dynamical bifurcation analysis.
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Photonics for artificial intelligence and neuromorphic computing

TL;DR: In this paper, the authors review recent advances in integrated photonic neuromorphic systems, discuss current and future challenges, and outline the advances in science and technology needed to meet those challenges.
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Photonics for artificial intelligence and neuromorphic computing

TL;DR: Recent advances in integrated photonic neuromorphic neuromorphic systems are reviewed, current and future challenges are discussed, and the advances in science and technology needed to meet those challenges are outlined.