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.

Metasurfaces have become a promising means for manipulating optical wavefronts in flat and high-performance optical devices. Conventionally metasurface device design relies on trial-anderror methods to obtain target electromagnetic (EM) responses, which 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 metaatoms, metasurface filters, and phase-change reconfigurable metasurfaces are demonstrated.Here we propose an implicit way to construct and train the networks to predict the amplitude and phase responses of meta-structures. For a typical meta-structure, like the one shown in Fig. 1A,

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 in the full 2π 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 binary switching of metasurfaces between arbitrary phase profiles and propose a new figure-of-merit (FOM) tailored for reconfigurable meta-optics. We implement the approach to realize a high-performance varifocal metalens operating at 5.2 μm wavelength. The reconfigurable metalens features a record large switching contrast ratio of 29.5 dB. We further validate aberration-free and multi-depth imaging using the metalens, which represents a key experimental demonstration of a non-mechanical tunable metalens with diffraction-limited performance.

Metasurfaces have enabled precise electromagnetic (EM) wave manipulation with strong potential to obtain unprecedented functionalities and multifunctional behavior in flat optical devices. These advantages in precision and functionality come at the cost of tremendous difficulty in finding individual meta‐atom structures based on specific requirements (commonly formulated in terms of EM responses), which makes the design of multifunctional metasurfaces a key challenge in this field. In this paper, a generative adversarial network that can tackle this problem and generate meta‐atom/metasurface designs to meet multifunctional design goals is presented. Unlike conventional trial‐and‐error or iterative optimization design methods, this new methodology produces on‐demand free‐form structures involving only a single design iteration. More importantly, the network structure and the robust training process are independent of the complexity of design objectives, making this approach ideal for multifunctional device design. Additionally, the ability of the network to generate distinct classes of structures with similar EM responses but different physical features can provide added latitude to accommodate other considerations such as fabrication constraints and tolerances. The network's ability to produce a variety of multifunctional metasurface designs is demonstrated by presenting a bifocal metalens, a polarization‐multiplexed beam deflector, a polarization‐multiplexed metalens, and a polarization‐independent metalens.

Optical metasurfaces, planar subwavelength nanoantenna arrays with the singular ability to sculpt wavefront in almost arbitrary manners, are poised to become a powerful tool enabling compact and high-performance optics with novel functionalities. A particularly intriguing research direction within this field is active metasurfaces, whose optical response can be dynamically tuned postfabrication, thus allowing a plurality of applications unattainable with traditional bulk optics. Designing reconfigurable optics based on active metasurfaces is, however, presented with a unique challenge, since the optical quality of the devices must be optimized at multiple optical states. In this article, we provide a critical review on the active meta-optics design principles and algorithms that are applied across structural hierarchies ranging from single meta-atoms to full meta-optical devices. The discussed approaches are illustrated by specific examples of reconfigurable metasurfaces based on optical phase-change materials.