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Long-wave infrared (LWIR, 8-12 $\mu m$ wavelengths) is a spectral band of vital importance to thermal imaging. Conventional LWIR optics made from single-crystalline Ge and chalcogenide glasses are bulky and fragile. The challenge is exacerbated for wide field-of-view (FOV) optics, which traditionally mandates multiple cascaded elements that severely add to complexity and cost. Here we designed and experimentally realized a LWIR metalens platform based on bulk Si wafers featuring 140$^\circ$ FOV. The metalenses, which have diameters exceeding 4 cm, were fabricated using a scalable wafer-level process involving photolithography and deep reactive ion etching. Using a metalens-integrated focal plane array, we further demonstrated wide-angle thermal imaging.

Active optical metasurfaces are rapidly emerging as a major frontier in photonics research, development, and commercialization. They promise compact, light-weight, and energy-efficient reconfigurable optical systems with unprecedented performance and functions that can be dynamically defined on-demand. Compared to their passive counterparts, the reconfiguration capacity of active metasurfaces also set additional challenges in scalable design, manufacturing, and control toward their practical deployment. This perspective aims to review the state-of-the-art of active metasurface technologies and their applications while highlighting key research advances essential to enabling their transition from laboratory curiosity to commercial reality.

Metasurfaces have provided a novel and promising platform for the realization of compact and large-scale optical devices. The conventional metasurface design approach assumes periodic boundary conditions for each element, which is inaccurate in most cases since the near-field coupling effects between elements will change when surrounded by non-identical structures. In this paper, we propose a deep learning approach to predict the actual electromagnetic (EM) responses of each target meta-atom placed in a large array with near-field coupling effects taken into account. The predicting neural network takes the physical specifications of the target meta-atom and its neighbors as input, and calculates its phase and amplitude in milliseconds. This approach can be applied to explain metasurfaces' performance deterioration caused by mutual coupling and further used to optimize their efficiencies once combined with optimization algorithms. To demonstrate the efficacy of this methodology, we obtain large improvements in efficiency for a beam deflector and a metalens over the conventional design approach. Moreover, we show the correlations between a metasurface's performance and its design errors caused by mutual coupling are not bound to certain specifications (materials, shapes, etc.). As such, we envision that this approach can be readily applied to explore the mutual coupling effects and improve the performance of various metasurface designs., Comment: 16 pages, 10 figures

Active metasurfaces promise reconfigurable optics with drastically improved compactness, ruggedness, manufacturability, and functionality compared to their traditional bulk counterparts. Optical phase change materials (O-PCMs) offer an appealing material solution for active metasurface devices with their large index contrast and nonvolatile switching characteristics. Here we report what we believe to be the first electrically reconfigurable nonvolatile metasurfaces based on O-PCMs. The O-PCM alloy used in the devices, Ge2Sb2Se4Te1 (GSST), uniquely combines giant non-volatile index modulation capability, broadband low optical loss, and a large reversible switching volume, enabling significantly enhanced light-matter interactions within the active O-PCM medium. Capitalizing on these favorable attributes, we demonstrated continuously tunable active metasurfaces with record half-octave spectral tuning range and large optical contrast of over 400%. We further prototyped a polarization-insensitive phase-gradient metasurface to realize dynamic optical beam steering., Comment: 12 pages, 5 figures

Metasurfaces have shown promising potentials in shaping optical wavefronts while remaining compact compared to bulky geometric optics devices. Design of meta-atoms, the fundamental building blocks of metasurfaces, relies on trial-and-error method to achieve target electromagnetic responses. This process includes the characterization of an enormous amount of different meta-atom designs with different physical and geometric parameters, which normally demands huge computational resources. In this paper, a deep learning-based metasurface/meta-atom modeling approach is introduced to significantly reduce the characterization time while maintaining accuracy. Based on a convolutional neural network (CNN) structure, the proposed deep learning network is able to model meta-atoms with free-form 2D patterns and different lattice sizes, material refractive indexes and thicknesses. Moreover, the presented approach features the capability to predict meta-atoms' wide spectrum responses in the timescale of milliseconds, which makes it attractive for applications such as fast meta-atom/metasurface on-demand designs and optimizations.

Active metasurfaces, whose optical properties can be modulated post-fabrication, have emerged as an intensively explored field in recent years. The efforts to date, however, still face major performance limitations in tuning range, optical quality, and efficiency especially for non mechanical actuation mechanisms. In this paper, we introduce an active metasurface platform combining phase tuning covering the full 2$\pi$ range and diffraction-limited performance using an all-dielectric, low-loss architecture based on optical phase change materials (O-PCMs). We present a generic design principle enabling switching of metasurfaces between two arbitrary phase profiles and propose a new figure-of-merit (FOM) tailored for active meta-optics. We implement the approach to realize a high-performance varifocal metalens operating at 5.2 $\mu$m wavelength. The metalens is constructed using Ge2Sb2Se4Te1 (GSST), an O-PCM with a large refractive index contrast ($\Delta$ n > 1) and unique broadband low-loss characteristics in both amorphous and crystalline states. The reconfigurable metalens features focusing efficiencies above 20% at both states for linearly polarized light and a record large switching contrast ratio of 29.5 dB. We further validated aberration-free imaging using the metalens at both optical states, which represents the first experimental demonstration of a non-mechanical active metalens with diffraction-limited performance.

Metasurfaces have become a promising means for manipulating optical wavefronts in flat and high-performance optical devices. Conventional metasurface device design relies on trial-and-error methods to obtain target electromagnetic (EM) response, an approach that demands significant efforts to investigate the enormous number of possible meta-atom structures. In this paper, a deep neural network approach is introduced that significantly improves on both speed and accuracy compared to techniques currently used to assemble metasurface-based devices. Our neural network approach overcomes three key challenges that have limited previous neural-network-based design schemes: input/output vector dimensional mismatch, accurate EM-wave phase prediction, as well as adaptation to 3-D dielectric structures, and can be generically applied to a wide variety of metasurface device designs across the entire electromagnetic spectrum. Using this new methodology, examples of neural networks capable of producing on-demand designs for meta-atoms, metasurface filters, and phase-change reconfigurable metasurfaces are demonstrated., Comment: 18 pages, 8 figures