Diffractive integrated photonics for analog computing and machine learning
Author(s) | Wang, Zi | |
Date Accessioned | 2023-01-24T14:15:20Z | |
Date Available | 2023-01-24T14:15:20Z | |
Publication Date | 2022 | |
SWORD Update | 2022-09-21T16:08:23Z | |
Abstract | Recently, 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. | |
Advisor | Gu, Tingyi | |
Degree | Ph.D. | |
Department | University of Delaware, Department of Electrical and Computer Engineering | |
DOI | https://doi.org/10.58088/k4q0-mv60 | |
Unique Identifier | 1365075077 | |
URL | https://udspace.udel.edu/handle/19716/32140 | |
Language | en | |
Publisher | University of Delaware | |
URI | https://login.udel.idm.oclc.org/login?url=https://www.proquest.com/dissertations-theses/diffractive-integrated-photonics-analog-computing/docview/2717673702/se-2 | |
Keywords | Integrated photonics | |
Keywords | Machine learning | |
Keywords | Metasurface | |
Keywords | Spatial differentiation | |
Keywords | Wavelength demultiplexing | |
Title | Diffractive integrated photonics for analog computing and machine learning | |
Type | Thesis |