2022
DOI: 10.1088/1367-2630/ac90e2
|View full text |Cite
|
Sign up to set email alerts
|

Inverse design of broadband, strongly-coupled plexcitonic nonlinear metasurfaces

Abstract: Hybrid photonic structures of plasmonic metasurfaces coupled to atomically thin semiconductors have emerged as a versatile platform for strong light-matter interaction, supporting both strong coupling and parametric nonlinearities. However, designing optimized nonlinear hybrid metasurfaces is a complex task, as the multiple parameters' contribution to the nonlinear response is elusive. Here we present a simple yet powerful strategy for maximizing the nonlinear response of the hybrid structures based on evoluti… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1

Citation Types

0
2
0

Year Published

2023
2023
2024
2024

Publication Types

Select...
4
1

Relationship

1
4

Authors

Journals

citations
Cited by 5 publications
(3 citation statements)
references
References 47 publications
0
2
0
Order By: Relevance
“…For example, the strong coupling between plasmonic nanocavities and monolayer semiconductors was optimized by a genetic algorithm with a novel figure-of-merit, with the evaluation of each configuration being based on the finite-difference time-domain method. [ 102 ] Furthermore, genetic algorithms can be accelerated further by ML, as was shown for the discovery of stable, compositionally variant, geometrically similar NP alloys for catalysis, resulting in a 50-fold reduction in the number of required energy calculations compared to a traditional “brute force” genetic algorithm. [ 103 ]…”
Section: Machine Learning Approaches In Nanotechnologymentioning
confidence: 99%
“…For example, the strong coupling between plasmonic nanocavities and monolayer semiconductors was optimized by a genetic algorithm with a novel figure-of-merit, with the evaluation of each configuration being based on the finite-difference time-domain method. [ 102 ] Furthermore, genetic algorithms can be accelerated further by ML, as was shown for the discovery of stable, compositionally variant, geometrically similar NP alloys for catalysis, resulting in a 50-fold reduction in the number of required energy calculations compared to a traditional “brute force” genetic algorithm. [ 103 ]…”
Section: Machine Learning Approaches In Nanotechnologymentioning
confidence: 99%
“…Furthermore, our optimization function can be combined with additional plasmonic phenomena derived from the near-field response, such as directionality, incoming polarization selectivity, focusing, plasmonic laser, enhanced nonlinearity, and more, leading to new functionalities. We have recently shown that the structures obtained via the near-field inverse design method can be used to greatly enhance parametric nonlinear processes, allowing for broadband nonlinear control over the entire range of frequencies of the hybridized modes …”
Section: Introductionmentioning
confidence: 99%
“…We have recently shown that the structures obtained via the near-field inverse design method can be used to greatly enhance parametric nonlinear processes, allowing for broadband nonlinear control over the entire range of frequencies of the hybridized modes. 29…”
Section: ■ Introductionmentioning
confidence: 99%