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Please use this identifier to cite or link to this item: http://arks.princeton.edu/ark:/88435/dsp01s4655k75g
Title: Photonic neural networks for ultrafast neural information processing
Authors: Peng, Hsuan-Tung
Advisors: Prucnal, Paul
Contributors: Electrical Engineering Department
Keywords: Neuromorphic photonics
Photonic integrated circuits
Subjects: Optics
Artificial intelligence
Issue Date: 2022
Publisher: Princeton, NJ : Princeton University
Abstract: Photonic neural networks (PNNs) represent an important class of optical computing with the goal of producing an accelerated processor that combines the information processing capacity of neuromorphic systems, and the speed and bandwidth of photonics. This thesis focuses on system design, experimental demonstration and AI applications of PNNs using integrated photonics. Two main thrusts of the PNNs development in this thesis are: studying bio-inspired spiking network on InP-based integrated photonic circuits, and building scalable continuous-time neural network using silicon photonics. Toward the first thrust, we study the temporal dynamics of an integrated excitable laser, and demonstrate its analogy to a biological spiking neuron and its compatibility for large-scale system integration. With a solid experimental demonstration, we further propose the model of such photonic spiking neural network, and show its applications including temporal XOR task, time series processing, and recommendation systems. For the second thrust, we investigate a silicon photonics-based system to achieve both precise weight control and programmable nonlinearity. We further explore its application to real-world problems in communication systems. The proposed compact model using silicon photonic recurrent neural network enables real-time specific emitter identification, and provides a promising platform for future edge AI systems.
URI: http://arks.princeton.edu/ark:/88435/dsp01s4655k75g
Alternate format: The Mudd Manuscript Library retains one bound copy of each dissertation. Search for these copies in the library's main catalog: catalog.princeton.edu
Type of Material: Academic dissertations (Ph.D.)
Language: en
Appears in Collections:Electrical Engineering

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