In this paper, a deep learning-based algorithm is presented, 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 (such as geometrical design parameters) of the nanostructure in its response functionality. To demonstrate the practical capabilities of this DL-based technique, it is applied 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 mentioning that the demonstrated approach is general and can be used in a large range of problems as long as enough training data are provided.
Herein, a new approach for using the intelligence aspects of artificial intelligence for knowledge discovery rather than device optimization in electromagnetic (EM) nanostructures is presented. This approach uses training data obtained through full‐wave EM simulations of a series of nanostructures to train geometric deep learning algorithms to assess the range of feasible responses as well as the feasibility of a desired response from a class of EM nanostructures. To facilitate the knowledge discovery, this approach combines the dimensionality reduction technique with convex‐hull and one‐class support‐vector‐machine (SVM) algorithms to find the range of the feasible responses in the latent response space of the EM nanostructure. More importantly, the one‐class SVM algorithm can be trained to provide the degree of feasibility of a response from a given nanostructure. This important information can be used to modify the initial structure to an alternative one that can enable an initially unfeasible response. To show the applicability of this approach, it is applied to two important classes of binary metasurfaces (MSs), formed by an array of plasmonic nanostructures, and periodic MSs formed by an array of dielectric nanopillars. These theoretical and experimental results confirm the unique features of this approach for knowledge discovery in EM nanostructures.
In contrast to lossy plasmonic metasurfaces (MSs), wideband dielectric MSs comprising of subwavelength nanostructures supporting Mie resonances are of great interest in the visible wavelength range. Here, for the first time to our knowledge, we experimentally demonstrate a reflective MS consisting of a square-lattice array of Hafnia (HfO 2 ) nanopillars to generate a wide color gamut. To design and optimize these MSs, we use a deep-learning algorithm based on a dimensionality reduction technique. Good agreement is observed between simulation and experimental results in yielding vivid and high-quality colors. We envision that these structures not only empower the high-resolution digital displays and sensitive colorimetric biosensors but also can be applied to on-demand applications of beaming in a wide wavelength range down to deep ultraviolet.J o u r n a l N a me , [ y e a r ] , [ v o l . ] , 1-9 | 1 arXiv:1907.01595v2 [physics.app-ph]
Efficient hybrid plasmonic-photonic metasurfaces that simultaneously take advantage of the potential of both pure metallic and all-dielectric nanoantennas are identified as an emerging technology in flat optics. Nevertheless, postfabrication tunable hybrid metasurfaces are still elusive. Here, we present a reconfigurable hybrid metasurface platform by incorporating the phase-change material Ge2Sb2Te5 (GST) into metal-dielectric meta-atoms for active and nonvolatile tuning of properties of light. We systematically design a reduced-dimension meta-atom, which selectively controls the hybrid plasmonic-photonic resonances of the metasurface via the dynamic change of optical constants of GST without compromising the scattering efficiency. As a proof-of-concept, we experimentally demonstrate two tunable metasurfaces that control the amplitude (with relative modulation depth as high as ≈80%) or phase (with tunability >230°) of incident light promising for high-contrast optical switching and efficient anomalous to specular beam deflection, respectively. Our findings further substantiate dynamic hybrid metasurfaces as compelling candidates for next-generation reprogrammable meta-optics.
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