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 shown promising potentials in shaping optical wavefronts while remaining compact compared to bulky geometric optics devices. The design of meta-atoms, the fundamental building blocks of metasurfaces, typically relies on trial and error to achieve target electromagnetic responses. This process includes the characterization of an enormous amount of meta-atom designs with varying physical and geometric parameters, which 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 nearly freeform 2D patterns and different lattice sizes, material refractive indices and thicknesses. Moreover, the presented approach features the capability of predicting a meta-atom’s wide spectrum response in the timescale of milliseconds, attractive for applications necessitating fast on-demand design and optimization of a meta-atom/metasurface.

On-chip single-photon sources with high repetition rates are a fundamental block for quantum photonics and can enable applications such as high-speed quantum communication or quantum information processing. Ideally, such single-photon sources require a large on-chip photon extraction decay rate, especially over a broad spectral range, but this goal has remained elusive so far. Current approaches implemented to enhance the spontaneous emission rate include photonic crystals, optical cavities, metallic nanowires, and metamaterials. These approaches either have a strong reliance on the frequency resonance mechanisms, which unavoidably suffer from the issue of narrow working bandwidth, or are limited by poor outcoupling efficiency. Here, we propose a feasible scheme to enhance the on-chip photon extraction decay rate of quantum emitters through the tilting of the optical axis of hyperbolic metamaterials with respect to the end-facet of nanofibers. The revealed scheme is applicable to arbitrarily orientated quantum emitters over a broad spectral range extending up to ∼80 nm for visible light. This finding relies on the emerging unique feature of hyperbolic metamaterials if their optical axis is judiciously tilted. Hence, their supported high-k (i.e., wavevector) hyperbolic eigenmodes, which are intrinsically confined inside them if their optical axis is un-tilted, can now become momentum-matched with the guided modes of nanofibers, and more importantly, they can efficiently couple into nanofibers almost without reflection.

In this Letter, we adapt the direct search method to metasurface optimization. We show that the direct search algorithm, when coupled with deep learning techniques for free-form meta-atom generation, offers a computationally efficient optimization approach for metasurface optics. As an example, we apply the approach to optimization of achromatic metalenses. Taking advantage of the diverse dispersion responses of free-form meta-atoms, metalenses designed using this approach exhibit superior broadband performances compared to their multilevel diffractive counterparts. We further demonstrate an achromatic and wide-field-of-view metalens design.