Abstract Lightweight, miniaturized optical imaging systems are vastly anticipated in these fields of aerospace exploration, industrial vision, consumer electronics, and medical imaging. However, conventional optical techniques are intricate to downscale as refractive lenses mostly rely on phase accumulation. Metalens, composed of subwavelength nanostructures that locally control light waves, offers a disruptive path for small-scale imaging systems. Recent advances in the design and nanofabrication of dielectric metalenses have led to some high-performance practical optical systems. This review outlines the exciting developments in the aforementioned area whilst highlighting the challenges of using dielectric metalenses to replace conventional optics in miniature optical systems. After a brief introduction to the fundamental physics of dielectric metalenses, the progress and challenges in terms of the typical performances are introduced. The supplementary discussion on the common challenges hindering further development is also presented, including the limitations of the conventional design methods, difficulties in scaling up, and device integration. Furthermore, the potential approaches to address the existing challenges are also deliberated. 

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Dielectric metalens for miniaturized imaging systems: progress and challenges

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Abstract Nanophotonic devices have enabled microscopic control of light with an unprecedented spatial resolution by employing subwavelength optical elements that can strongly interact with incident waves. However, to date, most nanophotonic devices have been designed based on fixed-shape optical elements, and a large portion of their design potential has remained unexplored. It is only recently that free-form design schemes have been spotlighted in nanophotonics, offering routes to make a break from conventional design constraints and utilize the full design potential. In this review, we systematically overview the nascent yet rapidly growing field of free-form nanophotonic device design. We attempt to define the term “free-form” in the context of photonic device design, and survey different strategies for free-form optimization of nanophotonic devices spanning from classical methods, adjoint-based methods, to contemporary machine-learning-based approaches.

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Free-form optimization of nanophotonic devices: from classical methods to deep learning

In this paper, a wideband InP-based hybrid plasmonic nano-antenna (HPNA) operating at telecommunication wavelengths has been proposed. Monolithically integrating InP-based lasers with hybrid plasmonic waveguide (HPW) as a feed line of the proposed HPNA on the same InGaAsP/InP wafer can increase the antenna efficiency. A new vertical director has been employed to have a highly directive horizontal radiation pattern. This enhancement is attributed to the efficient coupling between the radiation patterns of arm elements as well as reduced side lobes and back-lobes levels due to the achieved impedance matching. As a result, the directivity has been increased considerably, 3.6 dBi at 193.5 THz (1550 nm) and 1.1 dBi at 229 THz (1310 nm). The HPNA shows the high directivity, total efficiency and quality factor of 11.8, 97.49% and 94.57, respectively. Further, to verify the validity of confining the fundamental TM mode to a thin layer with the lower refractive index, both theoretical and numerical methods have been employed. Therefore, we have derived an analytical formula to investigate the HPW dispersion relation based on the transfer matrix theory and genetic algorithm. Moreover, due to the HPNA ability to receive an optical signal from free space and transmit it to the waveguide based on the reciprocity theorem, the HPNA performance as an optical wireless on-chip nano-link has been investigated analytically and numerically. Additionally, to obtain a high optical power signal and steering the beam angle, the antenna gain and directivity have been calculated with two different types of array structure by controlling the relative phase shift between the array elements and elements number. To validate the array design performance, a three dimensional full-wave numerical simulation and array factor theory have been exploited. The HPNA fabrication is compatible with generic foundry technology.

Analytic approach to study a hybrid plasmonic waveguide-fed and numerically design a nano-antenna based on the new director

We report on the use of hollow-core photonic crystal fibers to monitor the evolution of chemical reactions. The combination of tight confinement and long interaction length allows single-pass spect...

Online Monitoring of Microscale Liquid-Phase Catalysis Using in-Fiber Raman Spectroscopy

``Slow'' light is important for accessing various optical phenomena, due to the strong interaction with matter, but its realization in a free-form plasmonic waveguide requires consideration. The authors determine the optimized geometry for ultimate light trapping in a plasmonic cavity, with a quality factor near the theoretical limit, at an unusually short length. This saturation of quality factor enables the design of light-trapping devices of extremely small footprint. The ratio of quality factor to footprint is found to be comparable to that of state-of-the-art photonic resonators.

Ultimate Light Trapping in a Free-Form Plasmonic Waveguide

We present the use of in-situ Raman spectroscopy within optofluidic hollow-core photonic crystal fibers to monitor reactions involving photo-induced electron transfer processes, demonstrating their utility to better understand mechanisms of photochemical reactions. © 2020 The Authors

In-Situ Raman Spectroscopy of Reaction Products in Optofluidic Hollow-Core Fiber Microreactors

Metasurface-based color routers are emerging as next-generation optical components for image sensors, replacing classical color filters and microlens arrays. In this work, we report how the design parameters such as the device dimensions and refractive indices of the dielectrics affect the optical efficiency of the color routers. Also, we report how the design grid resolution parameters affect the optical efficiency and discover that the fabrication of a color router is possible even in legacy fabrication facilities with low structure resolutions.

Design parameters of free-form color routers for subwavelength pixelated CMOS image sensors

In the field of optics, precise control of light with arbitrary spatial resolution has long been a sought-after goal. Freeform nanophotonic devices are critical building blocks for achieving this goal, as they provide access to a design potential that could hardly be achieved by conventional fixed-shape devices. However, finding an optimal device structure in the vast combinatorial design space that scales exponentially with the number of freeform design parameters has been an enormous challenge. In this study, we propose physics-informed reinforcement learning (PIRL) as an optimization method for freeform nanophotonic devices, which combines the adjoint-based method with reinforcement learning to enhance the sample efficiency of the optimization algorithm and overcome the issue of local minima. To illustrate these advantages of PIRL over other conventional optimization algorithms, we design a family of one-dimensional metasurface beam deflectors using PIRL, obtaining more performant devices. We also explore the transfer learning capability of PIRL that further improves sample efficiency and demonstrate how the minimum feature size of the design can be enforced in PIRL through reward engineering. With its high sample efficiency, robustness, and ability to seamlessly incorporate practical device design constraints, our method offers a promising approach to highly combinatorial freeform device optimization in various physical domains.

Sanmun Kim

Papers

Free-form optimization of nanophotonic devices: from classical methods to deep learning

Ultimate Light Trapping in a Free-Form Plasmonic Waveguide

In-Situ Raman Spectroscopy of Reaction Products in Optofluidic Hollow-Core Fiber Microreactors

Design parameters of free-form color routers for subwavelength pixelated CMOS image sensors

Physics-informed reinforcement learning for sample-efficient optimization of freeform nanophotonic devices