60 research outputs found
Analog Computing Using Graphene-based Metalines
We introduce the new concept of "metalines" for manipulating the amplitude
and phase profile of an incident wave locally and independently. Thanks to the
highly confined graphene plasmons, a transmit-array of graphene-based metalines
is used to realize analog computing on an ultra-compact, integrable and planar
platform. By employing the general concepts of spatial Fourier transformation,
a well-designed structure of such meta-transmit-array combined with graded
index lenses can perform two mathematical operations; i.e. differentiation and
integration, with high efficiency. The presented configuration is about 60
times shorter than the recent structure proposed by Silva et al.(Science, 2014,
343, 160-163); moreover, our simulated output responses are in more agreement
with the desired analytic results. These findings may lead to remarkable
achievements in light-based plasmonic signal processors at nanoscale instead of
their bulky conventional dielectric lens-based counterparts
Deep Learning Reveals Underlying Physics of Light-matter Interactions in Nanophotonic Devices
In this paper, we present a deep learning-based (DL-based) algorithm, as a
purely mathematical platform, for providing intuitive understanding of the
properties of electromagnetic (EM) wave-matter interaction in nanostructures.
This approach is based on using the dimensionality reduction (DR) technique to
significantly reduce the dimensionality of a generic EM wave-matter interaction
problem without imposing significant error. Such an approach implicitly
provides useful information about the role of different features (or design
parameters such as geometry) of the nanostructure in its response
functionality. To demonstrate the practical capabilities of this DL-based
technique, we apply it to a reconfigurable optical metadevice enabling
dual-band and triple-band optical absorption in the telecommunication window.
Combination of the proposed approach with existing commercialized full-wave
simulation tools offers a powerful toolkit to extract basic mechanisms of
wave-matter interaction in complex EM devices and facilitate the design and
optimization of nanostructures for a large range of applications including
imaging, spectroscopy, and signal processing. It is worth to mention that the
demonstrated approach is general and can be used in a large range of problems
as long as enough training data can be provided
Dynamic Nanophotonic Structures Leveraging Chalcogenide Phase-Change Materials
Chip-scale nanophotonic devices have the potential to enable next-generation imaging, computing, communication, and engineered quantum systems with very stringent performance requirements on size, power, integrability, stability, and bandwidth. The emergence of meta-optic devices with deep subwavelength features has enabled the formation of ultra-thin flat optical structures to replace bulky conventional counterparts in free-space applications. Nevertheless, progress in meta-optics has been slowed due to the passive nature of existing devices and the urgent need for a reliable, fast, low-power, and robust reconfiguration mechanism.
In this research, I devised a new material and device platform to resolve this challenge. Through detailed theoretical design, nanofabrication, and experimental demonstration, I demonstrated the unique features of my proposed platform as an essential building block of truly scalable adaptive flat optics for the active manipulation of optical wavefronts. One of the key attributes of this research is the integration of CMOS-compatible materials for the fabrication of passive devices with phase-change materials that provide the largest known modulation of the index of refraction upon stimulation with an optical or electrical signal. A unique selection of phase-change materials for operation in the near-infrared and visible wavelengths has been made, followed by developing the optimum deposition and fabrication processes for the realization of nanophotonics devices that integrate these functional materials with semiconductor and plasmonic materials. A major breakthrough in this process was the design and realization of integrated electrical stimulation circuitry with far better performance compared to existing solutions.
Using this platform, I experimentally demonstrated the first electrically tunable meta-optic structure for fast optical switching with a high contrast ratio and dynamic wavefront scanning with a large steering angle. This is a major achievement as it essentially allows the engineering of a desired optical wavefront with fast reconfigurability at low power consumption. In an independent work, I demonstrated, for the first time, a nonvolatile meta-optic structure for high-resolution, wide-gamut, and high-contrast microdisplays with added polarization controllability and the possibility of implementation on a flexible substrate. Further features of this metaphotonic display include: 1) full addressability at the microscale pixel via fast electrical pulses; 2) super-resolution pixels with controllable brightness and contrast; and 3) a wide range of colors with high saturation and purity. Lastly, for the first time, I realized a hybrid photonic-plasmonic meta-optic platform with active control over the spatial, spectral, and temporal properties of an optical wavefront. This is a major achievement as it essentially allows the engineering of a desired optical wavefront with fast reconfigurability at low power consumption. These demonstrations are now being pursued in different directions for novel systems for imaging, sensing, computing, and quantum applications, just to name a few.Ph.D
Deep learning approach based on dimensionality reduction for designing electromagnetic nanostructures
In this paper, we demonstrate a computationally ecient new approach based on deep learning (DL) techniques for analysis, design, and optimization of electromagnetic (EM) nanostructures. We use the strong correlation among features of a generic EM problem to considerably reduce the dimensionality of the problem and thus, the computational complexity, without imposing considerable errors. By employing the dimensionality reduction concept using the more recently demonstrated autoencoder technique, we redene the conventional many-to-one design problem in EM nanostruc-tures into a one-to-one problem plus a much simpler many-to-one problem, which can be simply solved using an analytic formulation. This approach reduces the computational complexity in solving both the forward problem (i.e., analysis) and the inverse problem (i.e., design) by orders of magnitude compared to conventional approaches. In addition, it provides analytic formulations that, despite their complexity, can be used to obtain intuitive understanding of the physics and dynamics of EM wave interaction with nanostructures with minimal computation requirements. As a proof-of-concept, 1 we applied such an ecacious method to design a new class of on-demand recongurable optical metasurfaces based on phase-change materials (PCM). The experimental results of the fabricated devices are in good agreement with those predicted by the proposed approach. We envision that the integration of such a DL-based technique with full-wave commercial software packages oers a powerful toolkit to facilitate the analysis, design, and optimization of the EM nanostructures as well as explaining, understanding , and predicting the observed responses in such structures. It will thus enable to solve complex design problems that could not be solved with existing techniques
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