國立虎尾科技大學 |

Design and Optimization of Large-Area Meta-Optics.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Design and Optimization of Large-Area Meta-Optics./
Author:	Zhelyeznyakov, Maksym.
Description:	1 online resource (126 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 84-10, Section: B.
Contained By:	Dissertations Abstracts International84-10B.
Subject:	Optics. -
Online resource:	click for full text (PQDT)
ISBN:	9798379410391

Design and Optimization of Large-Area Meta-Optics.
Zhelyeznyakov, Maksym.

Design and Optimization of Large-Area Meta-Optics. - 1 online resource (126 pages)

Source: Dissertations Abstracts International, Volume: 84-10, Section: B.

Thesis (Ph.D.)--University of Washington, 2023.

Includes bibliographical references

In recent years, quasi-periodic arrays of dielectric scatterers, also known as metasurfaces have been shown to manipulate electromagnetic scattering with sub-wavelength spatial resolution. These devices have proven to be incredibly useful for reducing the form factor of conventional optical devices, as well as creating novel optical devices that are not possible to realize with conventional refractive optics. The design of these devices is however non- trivial. Conventional forward design methods are useful for generating only a small subset of possible designs. More complicated designs as well as optimization of existing forward designs requires solving the full-wave Maxwell's equations many times in order to converge to an optimal device design. This thesis outlines three main advancements in computational electromagnetics for the design of metasurfaces. Firstly, we extend the Generalized Multi-sphere Mie Scattering (GMMT) method to ellipsoidal scatterers. This method solves Maxwell's equations and computes gradients with respect to geometric design parameters of metasurfaces composed of ellipsoidal, dielectric scatterers analytically. We use this method to fully inverse design a metasurface lens, a meta-grating that focuses light at different focal planes for different polarizations, and improve the efficiency of a forward designed lens from 25.59% to 32.00%. The second method is a data-driven method that predicts electromagnetic fields from wavelength-scale cylindrical pillars, obtaining a low-dimensional representation of the data via the singular value decomposition. This method fits a differentiable neural-network model of the input geometries and configurations of the metasurface scatterers to the low-dimensional representations of the output field. This framework was used to design a device that produces an annular focal spot for the λ = 400nm wave- length, and a focal spot for the λ = 633nm wavelength. Finally, we present a data-free machine learning framework for predicting electromagnetic field responses from metasurfaces by using physics informed neural networks. This method predicts full-fields from 1D meta-gratings by a neural network that is trained by minimizing the residual norm of the Maxwell operator. We use this technique to design a set of large-area (1mm) meta-lenses exhibiting higher efficiency than the ones designed under the local phase approximation. We validate our designs experimentally, as see a maximum intensity improvement of up to 53%.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798379410391Subjects--Topical Terms:

595336
Optics.
Subjects--Index Terms:

Computational physicsIndex Terms--Genre/Form:

554714
Electronic books.

Design and Optimization of Large-Area Meta-Optics.
LDR:03872ntm a22004097 4500 001 1143729
005 20240517104937.5
006 m o d
007 cr mn ---uuuuu
008 250605s2023 xx obm 000 0 eng d
020 $a 9798379410391
035 $a (MiAaPQ)AAI30310818
035 $a AAI30310818
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Zhelyeznyakov, Maksym. $3 1468510
245 1 0 $a Design and Optimization of Large-Area Meta-Optics.
264 0 $c 2023
300 $a 1 online resource (126 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 84-10, Section: B.
500 $a Advisor: Majumdar, Arka.
502 $a Thesis (Ph.D.)--University of Washington, 2023.
504 $a Includes bibliographical references
520 $a In recent years, quasi-periodic arrays of dielectric scatterers, also known as metasurfaces have been shown to manipulate electromagnetic scattering with sub-wavelength spatial resolution. These devices have proven to be incredibly useful for reducing the form factor of conventional optical devices, as well as creating novel optical devices that are not possible to realize with conventional refractive optics. The design of these devices is however non- trivial. Conventional forward design methods are useful for generating only a small subset of possible designs. More complicated designs as well as optimization of existing forward designs requires solving the full-wave Maxwell's equations many times in order to converge to an optimal device design. This thesis outlines three main advancements in computational electromagnetics for the design of metasurfaces. Firstly, we extend the Generalized Multi-sphere Mie Scattering (GMMT) method to ellipsoidal scatterers. This method solves Maxwell's equations and computes gradients with respect to geometric design parameters of metasurfaces composed of ellipsoidal, dielectric scatterers analytically. We use this method to fully inverse design a metasurface lens, a meta-grating that focuses light at different focal planes for different polarizations, and improve the efficiency of a forward designed lens from 25.59% to 32.00%. The second method is a data-driven method that predicts electromagnetic fields from wavelength-scale cylindrical pillars, obtaining a low-dimensional representation of the data via the singular value decomposition. This method fits a differentiable neural-network model of the input geometries and configurations of the metasurface scatterers to the low-dimensional representations of the output field. This framework was used to design a device that produces an annular focal spot for the λ = 400nm wave- length, and a focal spot for the λ = 633nm wavelength. Finally, we present a data-free machine learning framework for predicting electromagnetic field responses from metasurfaces by using physics informed neural networks. This method predicts full-fields from 1D meta-gratings by a neural network that is trained by minimizing the residual norm of the Maxwell operator. We use this technique to design a set of large-area (1mm) meta-lenses exhibiting higher efficiency than the ones designed under the local phase approximation. We validate our designs experimentally, as see a maximum intensity improvement of up to 53%.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Optics. $3 595336
650 4 $a Applied mathematics. $3 1069907
650 4 $a Computer engineering. $3 569006
650 4 $a Electrical engineering. $3 596380
653 $a Computational physics
653 $a Metasurfaces
653 $a Nanophotonics
653 $a Neural networks
653 $a Optics
655 7 $a Electronic books. $2 local $3 554714
690 $a 0752
690 $a 0364
690 $a 0544
690 $a 0464
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of Washington. $b Electrical and Computer Engineering. $3 1465511
773 0 $t Dissertations Abstracts International $g 84-10B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30310818 $z click for full text (PQDT)