Polarization of light and topological phases

The present invention provides optical modulators which comprise aligned chiral smectic liquid crystal cells within an optical resonance cavity. The cavity configurations include symmetric and asymmetric Fabry-Perot etalons. The liquid crystal cells can be planar- or homeotropically-aligned and can be discrete state or analog cells. The device configurations of the present invention provide discrete or continuous optical modulation of the phase, intensity, and wavelength of elliptically polarized light, without requiring polarization analyzers. The modulators are optically or electronically addressable in single pixels or arrays of multiple pixels. Certain homeotropically-aligned cells are provided as an aspect of this invention, as are certain variable retarders comprised of planar-aligned cells in combination with birefringent elements.

Chiral smectic liquid crystal optical modulators having variable retardation

In the recent years, we observe a dramatic boost of research in photonics empowered by the concepts of machine learning and artificial intelligence. The corresponding photonic systems, to which this new methodology is applied, can range from traditional optical waveguides to nanoantennas and metasurfaces, and these novel approaches underpin the fundamental principles of light-matter interaction developed for a smart design of intelligent photonic devices. Concepts and approaches of artificial intelligence and machine learning penetrate rapidly into the fundamental physics of light, and they provide effective tools for the study of the field of metaphotonics driven by optically-induced electric and magnetic resonances. Here, we introduce this new field with its application to metaphotonics and also present a summary of the basic concepts of machine learning with some specific examples developed and demonstrated for metasystems and metasurfaces.

Intelligent metaphotonics empowered by machine learning

The ferroelectric liquid crystals, because of their fast electro-optical response, are one of the most important classes of liquid crystals. Here, in this review, we have summarized the different electro-optical modes for ferroelectric liquid crystals. Clark–Lagerwall effect (surface stabilized ferroelectric liquid crystal), deformed helix ferroelectric (DHF) effect, electrically suppressed helix (ESH) mode, DHF orientational Kerr effect, and ESH diffraction modes have been discussed. All of the crucial features, that is, optics, electro-optics, dynamics, and their dependence on material parameters, operational regime, and applications, have been reviewed.

Ferroelectric liquid crystals: Excellent tool for modern displays and photonics

Abstract With the growing interest in the field of artificial materials, more advanced and sophisticated functionalities are required from phononic crystals and acoustic metamaterials. This implies a high computational effort and cost, and still the efficiency of the designs may be not sufficient. With the help of third-wave artificial intelligence technologies, the design schemes of these materials are undergoing a new revolution. As an important branch of artificial intelligence, machine learning paves the way to new technological innovations by stimulating the exploration of structural design. Machine learning provides a powerful means of achieving an efficient and accurate design process by exploring nonlinear physical patterns in high-dimensional space, based on data sets of candidate structures. Many advanced machine learning algorithms, such as deep neural networks, unsupervised manifold clustering, reinforcement learning and so forth, have been widely and deeply investigated for structural design. In this review, we summarize the recent works on the combination of phononic metamaterials and machine learning. We provide an overview of machine learning on structural design. Then discuss machine learning driven on-demand design of phononic metamaterials for acoustic and elastic waves functions, topological phases and atomic-scale phonon properties. Finally, we summarize the current state of the art and provide a prospective of the future development directions.

Intelligent on-demand design of phononic metamaterials

A spatial light modulator design consisting of cascaded or sandwiched layers of ferroelectric liquid crystals (FLC's) is investigated. The interrelation between the FLC material, the polarization of the incident illumination, and the achievable modulation states is characterized. Magnitude modulation is accomplished by standard methods by addressing the FLC layer with linearly polarized light and following it with a properly oriented analyzer. When the FLC is addressed with circularly polarized light, lossless phase modulation results with the phase states separated by twice the angle of rotation of the optical axes. A continuum of elliptical polarization states ties together the lossless phase states achievable by using circular polarization with the more well-known 0 degrees -180 degrees phase states obtainable with linearly polarized light. Layers of various bistable FLC materials can be cascaded, possibly with polarization control layers between some of the layers, to yield a spatial light modulator that produces multip e quantized bits of complex-valued modulation and with independent control of magnitude and phase states. Four-state phase modulation, ternary amplitude-phase modulation, and four-state magnitude modulation are demonstrated experimentally by using two layers of FLC.

Quantized complex ferroelectric liquid crystal spatial light modulators.

Here, we present a new approach based on manifold learning for knowledge discovery and inverse design with minimal complexity in photonic nanostructures. Our approach builds on studying sub-manifolds of responses of a class of nanostructures with different design complexities in the latent space to obtain valuable insight about the physics of device operation to guide a more intelligent design. In contrast to the current methods for inverse design of photonic nanostructures, which are limited to pre-selected and usually over-complex structures, we show that our method allows evolution from an initial design towards the simplest structure while solving the inverse problem.

Manifold Learning for Knowledge Discovery and Intelligent Inverse Design of Photonic Nanostructures: Breaking the Geometric Complexity.

Structural colors generated due to light scattering from static all-dielectric metasurfaces have successfully enabled high-resolution, high-saturation and wide-gamut color printing applications. Despite recent advances, most demonstrations of these structure-dependent colors lack post-fabrication tunability that hinders their applicability for front-end dynamic display technologies. Phase-change materials (PCMs), with significant contrast of their optical properties between their amorphous and crystalline states, have demonstrated promising potentials in reconfigurable nanophotonics. Herein, we leverage a tunable all-dielectric reflective metasurface made of a newly emerged class of low-loss optical PCMs with superb characteristics, i.e., antimony trisulphide (Sb$_2$S$_3$), antimony triselenide (Sb$_2$Se$_3$), and binary germanium-doped selenide (GeSe$_3$), to realize switchable, high-saturation, high-efficiency and high-resolution structural colors. Having polarization sensitive building blocks, the presented metasurface can generate two different colors when illuminated by two orthogonally polarized incident beams. Such degrees of freedom (i.e., structural state and polarization) enable a single reconfigurable metasurface with fixed geometrical parameters to generate four distinct wide-gamut colors suitable for a wide range of applications, including tunable full-color printing and displays, information encryption, and anti-counterfeiting.

Advanced Phase-Change Materials for Enhanced Meta-Displays

The authors present a general methodology for achieving any amount of pure phase modulation between 0 and 180 degrees by controlling the polarization of light used to address a given ferro-electric liquid crystal (FLC) material. Using this technique, an eight-state phase modulator made by cascading three FLC layers is demonstrated. Motivated by the need for predictable behavior within spatial light modulators (SLMs), experimental data characterizing the point by point spatial uniformity of an FLC device used for 0/90 degree phase modulation also is presented.

Polarization dependence and uniformity of FLC layers for phase modulation

Herein, for the first time to our knowledge, we experimentally demonstrate Fano resonant all-dielectric metasurfaces comprising of high- and median-index nanopillars (HfO 2 , TiO 2 and ZrO 2 ) with zero loss in visible range for polarization-sensitive structural coloration.

Fano Resonant All-Dielectric Metasurfaces for Polarization-Sensitive Structural Coloration

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