Please use this identifier to cite or link to this item: http://arks.princeton.edu/ark:/88435/dsp014q77ft77k
 Title: Silicon Photonic Neural Networks Authors: Zhou, Ellen Advisors: Prucnal, Paul Contributors: Mittal, Prateek Department: Electrical Engineering Class Year: 2016 Abstract: With increased processing need for high bandwidth, ultra-fast, low cost, and efficient communications systems, photonics are becoming a progressively more attractive alternative to electronics. Compact size and existing infrastructure allow for easy integration of silicon photonics in VLSI systems. Due to similarities in dynamical behavior, photonics lends itself naturally to neuromorphic computing; this thesis explores using silicon photonics to create analog neural networks. We successfully demonstrate a two node recurrent photonic neural network with Hopf bifurcations induced by weight control via MRR filters. This first demonstration of such dynamics represents a giant leap towards network-based models of physical computing with integrated silicon photonics. Extent: 91 pages URI: http://arks.princeton.edu/ark:/88435/dsp014q77ft77k Type of Material: Princeton University Senior Theses Language: en_US Appears in Collections: Electrical Engineering, 1932-2017

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