Particle swarm optimization of GaAs-AlGaAS nanowire photonic crystals as two-dimensional diffraction gratings for light trapping

Convergence and Performance Analysis of a Particle Swarm Optimization Algorithm for Optical Tuning of Gold Nanohole Arrays

Gold nanohole arrays, hybrid metal/dielectric metasurfaces composed of periodically arranged air holes in a thick gold film, exhibit versatile support for both localized and propagating surface plasmons. Leveraging their capabilities, particularly in surface plasmon resonance-oriented applications, demands precise optical tuning. In this study, a customized particle swarm optimization algorithm, implemented in Ansys Lumerical FDTD, was employed to optically tune gold nanohole arrays treated as bidimensional gratings following the Bragg condition. Both square and triangular array dispositions were considered. Convergence and evolution of the particle swarm optimization algorithm were studied, and a mathematical model was developed to interpret its outcomes.

Shape and Composition Evolution in an Alloy Core-Shell Nanowire Heterostructure Induced by Adatom Diffusion

A core-shell nanowire heterostructure is an important building block for nanowire-based optoelectronic devices. In this paper, the shape and composition evolution induced by adatom diffusion is investigated by constructing a growth model for alloy core-shell nanowire heterostructures, taking diffusion, adsorption, desorption and incorporation of adatoms into consideration. With moving boundaries accounting for sidewall growth, the transient diffusion equations are numerically solved by the finite element method. The adatom diffusions introduce the position-dependent and time-dependent adatom concentrations of components A and B. The newly grown alloy nanowire shell depends on the incorporation rates, resulting in both shape and composition evolution during growth. The results show that the morphology of nanowire shell strongly depends on the flux impingement angle. With the increase in this impingement angle, the position of the largest shell thickness on sidewall moves down to the bottom of nanowire and meanwhile, the contact angle between shell and substrate increases to an obtuse angle. Coupled with the shell shapes, the composition profiles are shown as non-uniform along both the nanowire and the shell growth directions, which can be attributed to the adatom diffusion of components A and B. The impacts of parameters on the shape and composition evolution are systematically investigated, including diffusion length, adatom lifetime and corresponding ratios between components. This kinetic model is expected to interpret the contribution of adatom diffusion in growing alloy group-IV and group III-V core-shell nanowire heterostructures.

Quasi-Freeform Metasurfaces for Wide-Angle Beam Deflecting and Splitting

Metasurfaces attracted extensive interests due to their outstanding ability to manipulate the wavefront at a subwavelength scale. In this study, we demonstrated quasi-freeform metasurfaces in which the radius, location, and height of the nanocylinder building blocks were set as optimized structure parameters, providing more degrees of freedom compared with traditional gradient metasurfaces. Given a desired wavefront shaping objective, these structure parameters can be collectively optimized utilizing a hybrid optimized algorithm. To demonstrate the versatility and feasibility of our method, we firstly proposed metasurfaces with deflecting efficiencies ranging from 86.2% to 94.8%, where the deflecting angles can vary in the range of 29°-75.6°. With further study, we applied our concept to realize a variety of high-efficiency, wide-angle, equal-power beam splitters. The total splitting efficiencies of all the proposed beam splitters exceeded 89.4%, where a highest efficiency of 97.6%, a maximum splitting angle of 75.6°, and a splitting uniformity of 0.33% were obtained. Considering that various deflecting angles, and various splitting channels with different splitting angles, can be realized by setting the optical response of metasurfaces as the optimization target, we believe that our method will provide an alternative approach for metasurfaces to realize desired wavefront shaping.

Asymmetrical Dimer Photonic Crystals Enabling Outstanding Optical Sensing Performance

The exploration of the propensity of engineered materials to bring forward innovations predicated on their periodic nanostructured tailoring rather than the features of their individual compounds is a continuous pursuit that has propelled optical sensors to the forefront of ultra-sensitive bio-identification. Herein, a numerical analysis based on the Finite Element Method (FEM) was used to investigate and optimize the optical properties of a unidirectional asymmetric dimer photonic crystal (PhC). The proposed device has many advantages from a nanofabrication standpoint compared to conventional PhCs sensors, where integrating defects within the periodic array is imperative. The eigenvalue and transmission analysis performed indicate the presence of a protected, confined mode within the structure, resulting in a Fano-like response in the prohibited states. The optical sensor demonstrated a promising prospect for monitoring the DNA hybridization process, with a quality factor (QF) of roughly 1.53×105 and a detection limit (DL) of 4.4×10-5 RIU. Moreover, this approach is easily scalable in size while keeping the same attributes, which may potentially enable gaze monitoring.

Evaluation and Design of Colored Silicon Nanoparticle Systems Using a Bidirectional Deep Neural Network

Silicon nanoparticles (SiNPs) with lowest-order Mie resonance produce non-iridescent and non-fading vivid structural colors in the visible range. However, the strong wavelength dependence of the radiation pattern and dielectric function makes it very difficult to design nanoparticle systems with the desired colors. Most existing studies focus on monodisperse nanoparticle systems, which are unsuitable for practical applications. This study combined the Lorentz-Mie theory, Monte Carlo, and deep neural networks to evaluate and design colored SiNP systems. The effects of the host medium and particle size distribution on the optical and color properties of the SiNP systems were investigated. A bidirectional deep neural network achieved accurate prediction and inverse design of structural colors. The results demonstrated that the particle size distribution flattened the Mie resonance peak and influenced the reflectance and brightness of the SiNP system. The SiNPs generated vivid colors in all three of the host media. Meanwhile, our proposed neural network model achieved a near-perfect prediction of colors with high accuracy of the designed geometric parameters. This work accurately and efficiently evaluates and designs the optical and color properties of SiNP systems, thus accelerating the design process and contributing to the practical production design of color inks, decoration, and printing.

Polarization Control in Integrated Silicon Waveguides Using Semiconductor Nanowires

In this work, we show the design of a silicon photonic-based polarization converting device based on the integration of semiconduction InP nanowires on the silicon photonic platform. We present a comprehensive numerical analysis showing that full polarization conversion (from quasi-TE modes to quasi-TM modes, and vice versa) can be achieved in devices exhibiting small footprints (total device lengths below 20 µm) with minimal power loss (<2 dB). The approach described in this work can pave the way to the realization of complex and re-configurable photonic processors based on the manipulation of the state of polarization of guided light beams.