Diffractive integrated photonics for analog computing and machine learning

Author(s)Wang, Zi
Date Accessioned2023-01-24T14:15:20Z
Date Available2023-01-24T14:15:20Z
Publication Date2022
SWORD Update2022-09-21T16:08:23Z
AbstractRecently, optical analog signal processing draws a lot of attentions due to the rising of deep learning, and integrated photonics is a promising platform. In this thesis, the author propose a design method combining the metasurface with the integrated photonics, and demonstrate the analog computing and machine learning meta-systems. The author demonstrate both forward and inversely design methods for different applications: spatial differentiation, pattern classification and wavelength demultiplexing. Compared with free-space optical systems, the proposed on-chip meta-system allows more compact footprint and better mechanical robustness.
AdvisorGu, Tingyi
DegreePh.D.
DepartmentUniversity of Delaware, Department of Electrical and Computer Engineering
DOIhttps://doi.org/10.58088/k4q0-mv60
Unique Identifier1365075077
URLhttps://udspace.udel.edu/handle/19716/32140
Languageen
PublisherUniversity of Delaware
URIhttps://login.udel.idm.oclc.org/login?url=https://www.proquest.com/dissertations-theses/diffractive-integrated-photonics-analog-computing/docview/2717673702/se-2
KeywordsIntegrated photonics
KeywordsMachine learning
KeywordsMetasurface
KeywordsSpatial differentiation
KeywordsWavelength demultiplexing
TitleDiffractive integrated photonics for analog computing and machine learning
TypeThesis
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