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Plasmonics and optical metastructures represent cutting-edge frontiers in nanophotonics, enabling on-demand control of light at the subwavelength scale. This special topic of the Journal of Applied Physics highlights the recent advancements and synergy of the two fields, delving into the fundamental physics governing plasmonic phenomena and showcasing innovative metastructures that hold significant potential for diverse applications, including sensing, optical manipulation, wireless communication, optical computing, and beyond. 

Tailoring optical and radiative properties has attracted significant attention recently due to its importance in advanced energy systems, nanophotonics, electro-optics, and nanomanufacturing. Metamaterials with micro- and nanostructures exhibit exotic radiative properties with tunability across the spectrum, direction, and polarization. Structures made from anisotropic or nanostructured materials have shown polarization-selective absorption bands in the mid-infrared. Characterizing the optical and radiative properties of such materials is crucial for both fundamental research and the development of practical applications. Mueller matrix ellipsometry offers a nondestructive and noninvasive technique for characterizing radiative properties. Although such ellipsometers have long been used to measure optical properties, their operational bandwidth is usually limited to the visible to near-infrared range, leaving the mid-infrared largely unexplored. In this work, a broadband mid-infrared ellipsometer, operating from 2 to 15 μm, is designed and constructed to measure 12 elements of the Mueller matrix. The results may be used to determine the full Mueller matrix under specific conditions. The performance of the ellipsometer is evaluated using nanostructured materials, including a 1D grating and a chiral F-shaped metasurface. The measurement results compared well to those obtained from rigorous-coupled-wave analysis and finite-difference time-domain simulations, suggesting that this setup offers a useful tool in optical property retrieval and the assessment of nanostructured materials. 

Abstract Photonic device development (PDD) has achieved remarkable success in designing and implementing new devices for controlling light across various wavelengths, scales, and applications, including telecommunications, imaging, sensing, and quantum information processing. PDD is an iterative, five-step process that consists of: (i) deriving device behavior from design parameters, (ii) simulating device performance, (iii) finding the optimal candidate designs from simulations, (iv) fabricating the optimal device, and (v) measuring device performance. Classically, all these steps involve Bayesian optimization, material science, control theory, and direct physics-driven numerical methods. However, many of these techniques are computationally intractable, monetarily costly, or difficult to implement at scale. In addition, PDD suffers from large optimization landscapes, uncertainties in structural or optical characterization, and difficulties in implementing robust fabrication processes. However, the advent of machine learning over the past decade has provided novel, data-driven strategies for tackling these challenges, including surrogate estimators for speeding up computations, generative modeling for noisy measurement modeling and data augmentation, reinforcement learning for fabrication, and active learning for experimental physical discovery. In this review, we present a comprehensive perspective on these methods to enable machine-learning-assisted PDD (ML-PDD) for efficient design optimization with powerful generative models, fast simulation and characterization modeling under noisy measurements, and reinforcement learning for fabrication. This review will provide researchers from diverse backgrounds with valuable insights into this emerging topic, fostering interdisciplinary efforts to accelerate the development of complex photonic devices and systems. 

Physical processes involving hot electrons, including their generation, transport, injection, and relaxation, have been an extensive area of research. The most widely utilized method for actuating the creation of hot electrons involves the excitation of plasmonic modes followed by their non-radiative decay, channeling the energy into these energetic carriers. Since plasmonics has already evolved into a mature field of scientific exploration, active plasmonic devices serve as an ideal platform to study hot-electron physics. In this Perspective article, we will provide the reader with a comprehensive outline of the physics underlying hot-electron dynamics. Emphasis will be placed on the characteristic timescales involved with the lifecycle of hot electrons, the generation and decay mechanisms of surface plasmon-induced hot electrons, and the material platforms suitable for such a study. Then, we will move on to discuss different temperature models used to explain the evolution of hot electrons and the changes in the optical properties of the materials they are generated in or injected into. Finally, we will focus on some of the interesting optical phenomena occurring at ultrafast timescales mediated by hot-carrier dynamics. Such a discussion is expected to incorporate valuable insights into our understanding of the synergistic relationship between hot-electron dynamics and active plasmonics, thereby paving the way for novel applications involving optoelectronics and energy conversion. 

Chirality is a geometric property describing the lack of mirror symmetry. This unique feature enables photonic spin-selectivity in light–matter interaction, which is of great significance in stereochemistry, drug development, quantum optics, and optical polarization control. The versatile control of optical geometry renders optical metamaterials as an effective platform for engineered chiral properties at prescribed spectral regimes. Unfortunately, geometry-imposed restrictions only allow one circular polarization state of photons to effectively interact with chiral meta-structures. This limitation motivates the idea of discovering alternative techniques for dynamically reconfiguring the chiroptical responses of metamaterials in a fast and facile manner. Here, we demonstrate an approach that enables optical, sub-picosecond conversion of achiral meta-structures to transient chiral media in the visible regime with desired handedness upon the inhomogeneous generation of plasmonic hot electrons. As a proof of concept, we utilize linearly polarized laser pulse to demonstrate near-complete conversion of spin sensitivity in an achiral meta-platform—a functionality yet achieved in a non-mechanical fashion. Owing to the generation, diffusion, and relaxation dynamics of hot electrons, the demonstrated technique for all-optical creation of chirality is inherently fast, opening new avenues for ultrafast spectro-temporal construction of chiral platforms with on-demand spin-selectivity. 

Abstract Polarized thermal emission finds extensive applications in remote sensing, landmine detection, and target detection. In applications such as ellipsometry and biomedical analysis, the generation of emission with controllable polarization is preferred. It is desired to manipulate the polarization state over the full Stokes parameters. While numerous studies have demonstrated either linear or circular polarization control using metamaterials, full-Stokes thermal emission has not been explored. Here, a microstructure based on two layers of silicon carbide gratings is proposed to tailor the polarization state of thermal emission, covering the full-Stokes parameter range. The bilayer twisted-gratings structure breaks mirror symmetry. Wave interference at the interfaces and diffraction by the gratings enhance the emission dichroism, resulting in almost completely polarized emission. By adjusting the twist angle between the gratings, the polarization state can be continuously tuned from linear to circular, nearly covering the entire surface of Poincaré sphere. This study provides a design for tailoring full-Stokes emission with notable advantages over other plasmonic metasurfaces. 

Abstract Recent remarkable progress in artificial intelligence (AI) has garnered tremendous attention from researchers, industry leaders, and the general public, who are increasingly aware of AI's growing impact on everyday life. The advancements of AI and deep learning have also significantly influenced the field of nanophotonics. On the one hand, deep learning facilitates data‐driven strategies for optimizing and solving forward and inverse problems of nanophotonic devices. On the other hand, photonic devices offer promising optical platforms for implementing deep neural networks. This review explores both AI for photonic design and photonic implementation of AI. Various deep learning models and their roles in the design of photonic devices are introduced, analyzing the strengths and challenges of these data‐driven methodologies from the perspective of computational cost. Additionally, the potential of optical hardware accelerators for neural networks is discussed by presenting a variety of photonic devices capable of performing linear and nonlinear operations, essential building blocks of neural networks. It is believed that the bidirectional interactions between nanophotonics and AI will drive the coevolution of these two research fields. 

Understanding the ultrafast excitation and transport dynamics of plasmon-driven hot carriers is critical to the development of optoelectronics, photochemistry, and solar-energy harvesting. However, the ultrashort time and length scales associated with the behavior of these highly out-of-equilibrium carriers have impaired experimental verification of ab initio quantum theories. Here, we present an approach to studying plasmonic hot-carrier dynamics that analyzes the temporal waveform of coherent terahertz bursts radiated by photo-ejected hot carriers from designer nano-antennas with a broken symmetry. For ballistic carriers ejected from gold antennas, we find an ~11-femtosecond timescale composed of the plasmon lifetime and ballistic transport time. Polarization- and phase-sensitive detection of terahertz fields further grant direct access to their ballistic transport trajectory. Our approach opens explorations of ultrafast carrier dynamics in optically excited nanostructures.