노준석 교수

Abstract Deep learning has become the dominant approach in artificial intelligence to solve complex data‐driven problems. Originally applied almost exclusively in computer‐science areas such as image analysis and nature language processing, deep learning has rapidly entered a wide variety of scientific fields including physics, chemistry and material science. Very recently, deep neural networks have been introduced in the field of nanophotonics as a powerful way of obtaining the nonlinear mappin

Over the past decade, topology has emerged as a major branch in broad areas of physics, from atomic lattices to condensed matter. In particular, topology has received significant attention in photonics because light waves can serve as a platform to investigate nontrivial bulk and edge physics with the aid of carefully engineered photonic crystals and metamaterials. Simultaneously, photonics provides enriched physics that arises from spin-1 vectorial electromagnetic fields. Here, we review recent

Vectorial holography has gained a lot of attention due to the promise of versatile polarization control of structured light for enhanced optical security and multi-channel optical communication. Here, we propose a bifunctional metasurface which combines both structural color printing and vectorial holography with eight polarization channels towards advanced encryption applications. The structural colour prints are observed under white light while the polarization encoded holograms are reconstruc

Abstract Optical metamaterials have presented an innovative method of manipulating light. Hyperbolic metamaterials have an extremely high anisotropy with a hyperbolic dispersion relation. They are able to support high- k modes and exhibit a high density of states which produce distinctive properties that have been exploited in various applications, such as super-resolution imaging, negative refraction, and enhanced emission control. Here, state-of-the-art hyperbolic metamaterials are reviewed, s

Abstract Data‐driven design approaches based on deep learning have been introduced in nanophotonics to reduce time‐consuming iterative simulations, which have been a major challenge. Here, we report the first use of conditional deep convolutional generative adversarial networks to design nanophotonic antennae that are not constrained to predefined shapes. For given input reflection spectra, the network generates desirable designs in the form of images; this allows suggestions of new structures t

The rapid detection of biological and chemical substances in real time is particularly important for public health and environmental monitoring and in the military sector. If the process of substance detection to visual reporting can be implemented into a single miniaturized sensor, there could be a profound impact on practical applications. Here, we propose a compact sensor platform that integrates liquid crystals (LCs) and holographic metasurfaces to autonomously sense the existence of a volat

Metalenses have shown a number of promising functionalities that are comparable with conventional refractive lenses. However, current metalenses are still far from commercialization due to the formidable fabrication costs. Here, we demonstrate a low-cost dielectric metalens that works in the visible spectrum. The material of the metalens consists of a matrix-inclusion composite in which a hierarchy satisfies two requirements for the single-step fabrication; a high refractive index and a pattern-

Recent introduction of data-driven approaches based on deep-learning technology has revolutionized the field of nanophotonics by allowing efficient inverse design methods. In this paper, a simultaneous inverse design of materials and structure parameters of core-shell nanoparticles is achieved for the first time using deep learning of a neural network. A neural network to learn the correlation between the extinction spectra of electric and magnetic dipoles and core-shell nanoparticle designs, wh

Metasurface-generated holography has emerged as a promising route for fully reproducing vivid scenes by manipulating the optical properties of light using ultra-compact devices. However, achieving multiple holographic images using a single metasurface is still difficult due to the capacity limit of a single meta-atom. In this work, an inverse design method based on gradient-descent optimization is presented to encode multiple pieces of holographic information into a single metasurface. The propo

Structured light (SL)-based depth-sensing technology illuminates the objects with an array of dots, and backscattered light is monitored to extract three-dimensional information. Conventionally, diffractive optical elements have been used to form laser dot array, however, the field-of-view (FOV) and diffraction efficiency are limited due to their micron-scale pixel size. Here, we propose a metasurface-enhanced SL-based depth-sensing platform that scatters high-density ~10 K dot array over the 18

Optically variable devices (OVDs) are in tremendous demand as optical indicators against the increasing threat of counterfeiting. Conventional OVDs are exposed to the danger of fraudulent replication with advances in printing technology and widespread copying methods of security features. Metasurfaces, two-dimensional arrays of subwavelength structures known as meta-atoms, have been nominated as a candidate for a new generation of OVDs as they exhibit exceptional behaviors that can provide a mor

Metasurfaces have been continuously garnering attention in both scientific and industrial fields, owing to their unprecedented wavefront manipulation capabilities using arranged subwavelength artificial structures. To date, research has mainly focused on the full control of electromagnetic characteristics, including polarization, phase, amplitude, and even frequencies. Consequently, versatile possibilities of electromagnetic wave control have been achieved, yielding practical optical components