2023
DOI: 10.1007/s11468-023-01795-z
|View full text |Cite
|
Sign up to set email alerts
|

Miniaturized Design of a 1 × 2 Plasmonic Demultiplexer Based on Metal–Insulator-Metal Waveguide for Telecommunication Wavelengths

Abstract: In this work, a numerical analysis of a compact 1 × 2 plasmonic demultiplexer based on a metal–insulator-metal (MIM) waveguide is presented. Two hollow circular cavities are side coupled to the bus waveguide on both sides. The cavities are designed in such a way that they resonate at the working wavelength of 1310 nm and 1550 nm. The mechanism of light coupling to an MIM waveguide has not been considered in previous studies. Therefore, a silicon tapered mode converter is integrated with a plasmonic demultiplex… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
5

Citation Types

0
8
0

Year Published

2023
2023
2024
2024

Publication Types

Select...
10

Relationship

0
10

Authors

Journals

citations
Cited by 24 publications
(8 citation statements)
references
References 52 publications
0
8
0
Order By: Relevance
“…DL learns rules for inputs and outputs from large amounts of data, enabling the construction of non-linear models for various applications. Deep learning optimization methods can be applied in the fields of metasurface device design, including sensor [22][23][24][25], demultiplexer [26,27], coupler [28,29], inferometer [30], etc, to improve their design efficiency.The use of neural networks to implement data-driven models provides a new approach for the design of electromagnetic structures [31][32][33][34][35][36], such as EIT [37], broadband absorption [38] and perfect absorption [39]. Deep learning uses neural networks to learn patterns in data, and after training and optimizing on a dataset of metasurfaces, neural networks can effectively predict the best metasurface design.…”
Section: Introductionmentioning
confidence: 99%
“…DL learns rules for inputs and outputs from large amounts of data, enabling the construction of non-linear models for various applications. Deep learning optimization methods can be applied in the fields of metasurface device design, including sensor [22][23][24][25], demultiplexer [26,27], coupler [28,29], inferometer [30], etc, to improve their design efficiency.The use of neural networks to implement data-driven models provides a new approach for the design of electromagnetic structures [31][32][33][34][35][36], such as EIT [37], broadband absorption [38] and perfect absorption [39]. Deep learning uses neural networks to learn patterns in data, and after training and optimizing on a dataset of metasurfaces, neural networks can effectively predict the best metasurface design.…”
Section: Introductionmentioning
confidence: 99%
“…Plasmonic nanoantennas have been employed for enhancing the electromagnetic fields for various applications such as biochemical sensing, photovoltaics, nonlinear optics, and photo-thermal therapy. Several kinds of plasmonic devices have been used in the last few years such as demultiplexers [15,16], sensors [17,18], interferometers [19] and couplers [20,21]. Although extensive research work has been carried out in the last few years on employing nanoantennas such as dipole nanoantennas, gold nanoring nanoantennas [22], nanoaperture antennas [23,24], and bowtie nanoantennas [25] for sensing applications, the values of E-field enhancements and SERS EM enhancement factors reported were not very large-with the E-field enhancement values less than 200 and SERS EM enhancement values less than 10 9 .…”
Section: Introductionmentioning
confidence: 99%
“…The fundamental principle of SPR is based on the interaction of p-polarized light with a silica-metal film interface. During total internal reflection at this interface, an evanescent wave penetrates the metal film, interacting with the free electrons and generating a surface plasmon [4,5]. Resonance occurs when the frequency of the evanescent wave matches that of the surface plasmon wave, disrupting the total internal reflection conditions.…”
Section: Introductionmentioning
confidence: 99%