Pushing the Limits of Metasurface Cloak Using Global Inverse Design

Abstract

formation optics render an object invisible by guiding the flow of light around the hidden object without disturbance to the internal region. [2][3][4][5][6] The underlying physics is attributed to the form invariance of Maxwell's equations: a coordinate transformation can squeeze normal free space from a volume into a shell, only with volumetric constitutive parameters and electromagnetic (EM) fields. Not only in electromagnetics, but transformation optics have also thus far been evolved into a fashionable tool in the realm of sound, heat flow, water waves, and so on. In theory, this method is perfect; however, in an experiment, it is hampered by the bulky material compositions with both anisotropy and inhomogeneity. Substantial efforts have been devoted to mitigating the requirements, such as bilinear transformation optics without singularities and quasi-conformal transformations for ground-plane cloak. [7][8][9][10][11][12][13] Yet, these tradeoffs also impair the cloaking performance to some extend and make the application scenario become more specific.Metasurface cloak is another kind of cloaking methodology that develops contemporaneously. [14][15][16] By adding a layer of deliberately-designed metasurfaces over a hidden region or object, the scattered field can be reconstructed to be similar to that of the pure background, as if there were no region or object. [17][18][19][20][21] Compared with a bulky metamaterial cloak, a metasurface cloak has the distinct advantages of negligible thickness, easy fabrication, and low loss, ushering it popularity in both academia and industry. [22][23][24][25][26] The last several years have seen enormous progress to demonstrate metasurface cloak across different spectra and generalize it from reflection to transmission geometries. [27] Moreover, by incorporating active components and intelligent algorithms, it is promising to transform conventional static cloaking modality to dynamic cloaking that can self-direct to ever-changing external stimuli and environment. [28,29] These advancements are highly demanded for a multitude of practical applications involving moving objects and dynamic environments.Although the current arsenal of design techniques provides enormous capability, metasurface cloak, as well as other cloaking strategies, suffers from some inherent limitations that need to be lifted or pushed, [15] as schematically illustrated in Figure 1a. First, we notice that almost all previous metasurface cloaks have been demonstrated in a convex shape. [19,22,30] The breakthroughs of transformation optics and metamaterials have kickstarted the study of modern invisibility cloak since the beginning of this century. Many cloaking methodologies have been progressively proposed for specific application scenarios, among which metasurface cloak is largely welcomed owing to its salient features of negligible thickness, easy fabrication, and low loss. Similar to other cloaking methodologies, however, metasurface cloak suffers from inherent limits that impair it to a convex shap...

Autonomous aeroamphibious invisibility cloak with stochastic-evolution learning

“…Recently, deep learning is poised to expedite on-demand photonic design and mitigate the imperfections in conventional methods 17 – 22 Its unique advantages lie in the data-driven nature to allow a computational model to discover useful information from given data and thus carry out tasks without explicit programmed and procedural instructions. The past decade has witnessed a proliferation of deep-learning-enabled forward/inverse design, spectral correlation, intelligent metadevices, and latent physics discovery with different network architectures.…”

“…For a customer-defined cloaking effect, brute-force search in tandem with lengthy case-by-case full-wave simulations is suboptimal because it inevitably degrades the working efficiency of the invisibility cloak 17 – 22 So far, although deep learning has been substantially applied for the inverse design of subwavelength meta-atoms, the generalization to the entire large-scale metadevices is not readily followed due to the dimensional curse and intractable nonuniqueness issue. Third, a fully self-driving intelligent cloak necessitates the buildup of a highly complex perception–decision–execution system, including full-context awareness of incoming waves and environment, and the attitude recognition of cloak 1 .…”

Spatial multiplexing encryption with cascaded metasurfaces

“…In our spatial multiplexing optical encryption framework, the multiplexing of the holographic imaging space under circular polarized incidence is crucial. To address this, various methods have been proposed, such as genetic algorithms and deep learning techniques [25][26][27][28][29][30]. In this work, we highlight the application of diffractive neural networks as an example.…”

Realization of Index Modulation with Intelligent Spatiotemporal Metasurfaces

“…To further intellectualize the IM, we adopt deep learning algorithms to bridge the propagation route of harmonic waves with the time-varying sequences required by spatiotemporal metasurfaces. To mitigate the nonuniqueness issue, [46][47][48] we adopt tandem neural networks where the forward model is attached to realize the fast convergence of the inverse model. In addition, we discuss how to generalize the input of neural networks for the vague target.…”