Metamaterials and plasmonics potentially offer an ultimate control of light to enable a rich number of non-conventional devices and a testbed for many novel physical phenomena. However, optical loss in metamaterials and plasmonics is a fundamental challenge rendering many conceived applications not viable in practical settings. Many approaches have been proposed so far to mitigate losses, including geometric tailoring, active gain media, nonlinear effects, metasurfaces, dielectrics, and 2D materials. Here, we review recent efforts on the less explored and unique territory of “virtual gain” as an alternative approach to combat optical losses. We define the virtual gain as the result of any extrinsic amplification mechanism in a medium. Our aim is to accentuate virtual gain not only as a promising candidate to address the material challenge, but also as a design concept with broader impacts.
- Award ID(s):
- 2138701
- NSF-PAR ID:
- 10395114
- Date Published:
- Journal Name:
- Sensors
- Volume:
- 22
- Issue:
- 18
- ISSN:
- 1424-8220
- Page Range / eLocation ID:
- 6896
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
more » « less
-
more » « less
Abstract The field of plasmonics aims to manipulate and control light through nanoscale structuring and choice of materials. Finding materials with low‐loss response to an applied optical field while exhibiting collective oscillations due to intraband transitions is an outstanding challenge. This is viewed as a materials selection problem that bridges the gap between the large number of candidate materials and the high computational cost to accurately compute their individual optical properties. To address this, online databases that compile computational data for numerous properties of tens to hundreds of thousands of materials are combined with first‐principles simulations and the Drude model. By means of density functional theory (DFT), a training set of geometry‐dependent plasmonic quality factors for ≈1000 materials is computed and subsequently random‐forest regressors are trained on these data. Descriptors are limited to symmetry, quantities obtained using the chemical formula, and the Mendeleev database, which allows to rapidly screen 7445 candidates on Materials Project. Using DFT to compute quality factors for the 233 most promising materials, AlCu3, ZnCu, and ZnGa3are identified as excellent potential new plasmonic metals. This finding is substantiated by analyzing their electronic structure and interband optical properties in detail.
-
more » « less
Abstract The group III–V semiconductor photonic system is attractive to photonics engineers because it provides a complete set of photonic components. A plasmonic material that can be epitaxially integrated with the group III–V photonic system will potentially lead to many applications leveraging plasmonics and metamaterials. In this work, the shortest plasma wavelength ever reported in a III–V‐based material is demonstrated by epitaxially embedding ErAs into GaAs. This composite material acts as a tunable plasmonic material across the technologically important 2.68–6 µm infrared window. The growth window of this material is demonstrated to be much wider than other current heavily doped III–V plasmonic materials. Additionally, it is shown that the scattering rate can be reduced by increasing the growth temperature. The wide growth temperature range, designer plasmonic response, and the ease of epitaxial integration with other III–V semiconductor devices demonstrate the potential of ErAs:GaAs nanocomposites for the creation of a new type of metamaterial and other novel optoelectronic and nanophotonic applications.
-
The finite-difference time-domain (FDTD) method is a widespread numerical tool for full-wave analysis of electromagnetic fields in complex media and for detailed geometries. Applications of the FDTD method cover a range of time and spatial scales, extending from subatomic to galactic lengths and from classical to quantum physics. Technology areas that benefit from the FDTD method include biomedicine — bioimaging, biophotonics, bioelectronics and biosensors; geophysics — remote sensing, communications, space weather hazards and geolocation; metamaterials — sub-wavelength focusing lenses, electromagnetic cloaks and continuously scanning leaky-wave antennas; optics — diffractive optical elements, photonic bandgap structures, photonic crystal waveguides and ring-resonator devices; plasmonics — plasmonic waveguides and antennas; and quantum applications — quantum devices and quantum radar. This Primer summarizes the main features of the FDTD method, along with key extensions that enable accurate solutions to be obtained for different research questions. Additionally, hardware considerations are discussed, plus examples of how to extract magnitude and phase data, Brillouin diagrams and scattering parameters from the output of an FDTD model. The Primer ends with a discussion of ongoing challenges and opportunities to further enhance the FDTD method for current and future applications.more » « less
-
more » « less
Optical metamaterials manipulate light through various confinement and scattering processes, offering unique advantages like high performance, small form factor and easy integration with semiconductor devices. However, designing metasurfaces with suitable optical responses for complex metamaterial systems remains challenging due to the exponentially growing computation cost and the ill‐posed nature of inverse problems. To expedite the computation for the inverse design of metasurfaces, a physics‐informed deep learning (DL) framework is used. A tandem DL architecture with physics‐based learning is used to select designs that are scientifically consistent, have low error in design prediction, and accurate reconstruction of optical responses. The authors focus on the inverse design of a representative plasmonic device and consider the prediction of design for the optical response of a single wavelength incident or a spectrum of wavelength in the visible light range. The physics‐based constraint is derived from solving the electromagnetic wave equations for a simplified homogenized model. The model converges with an accuracy up to 97% for inverse design prediction with the optical response for the visible light spectrum as input, and up to 96% for optical response of single wavelength of light as input, with optical response reconstruction accuracy of 99%.
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/s22186896
