Emre Bor

Bio: Emre Bor is an academic researcher from TOBB University of Economics and Technology. The author has contributed to research in topics: Photonic crystal & Photonics. The author has an hindex of 9, co-authored 30 publications receiving 208 citations. Previous affiliations of Emre Bor include Ankara University.

Mid-infrared T-shaped photonic crystal waveguide for optical refractive index sensing

Abstract: In this paper, we propose and design a new type of an integrated optical sensor that performs sensing in a wide wavelength range corresponding to mid-infrared (mid-IR) spectrum. By engineering the structural parameters of square-lattice photonic crystal (PC) slab incorporated with a T-shaped air-slot, strong light confinement and interaction with the analytes are assured. Numerical analyses in the time and frequency domain are conducted to determine the structural parameters of the design. The direct interaction between the slot waveguide mode and the analyte infiltrated into the slot gives rise to highly sensitive refractive index sensors. The highest sensitivity of the proposed T-slotted PC sensor is 1040 nm/RIU within the range of analytes’ refractive indices n = 1.05-1.10, and the overall sensitivity corresponding to the higher refractive index range of n = 1.10-1.30 is around 500 nm/RIU. Moreover, for a realistic PC slab structure, we determined an average refractive index sensitivity of 530 nm/RIU within the range of n = 1.10-1.25 and an average sensitivity of 390 nm/RIU within the range of n = 1.00-1.30. Furthermore, we speculate on the possible approach for the fabrication and the optical characterization of the device. The assets of the device include being compact, having a feasible measurement and fabrication technique, and possessing label-free sensing characteristic. We expect that the presented work may lead to the further development of the mid-IR label-free biochemical sensor devices for detection of various materials and gases in the near future.

Ultracompact Photonic Structure Design for Strong Light Confinement and Coupling Into Nanowaveguide

Abstract: Different optimization algorithms have recently been utilized to design and improve the performance of many nanophotonic structures. We present the design of a compact photonic structure by an approach based on machine learning. Three-dimensional finite-difference time-domain method is integrated with a machine learning algorithm in order to design a photonic structure. In particular, a subwavelength focusing lens structure that operates at telecom wavelengths is designed to have desired beam properties such as subwavelength full-width at half-maximum value of 0.155 λ and suppressed side-lobe levels at focal point, where λ denotes the wavelength of incident light and equals to 1550 nm. The designed compact lens structure has the footprint of ${\text{2}}\times {\text{1}}\,{\mu} {\text{m}}^{\text{2}}$ with a slab thickness of 280 nm, which is the smallest photonic lens for subwavelength focusing of light to date comparing to its conventional ones. The focusing mechanism of designed lens structure is explained with the help of applying discrete Fourier transform to the two-dimensional dielectric distribution of the structure. It is also shown that, due to its strong light confinement property, the designed lens structure can be used as a waveguide-to-waveguide optical coupling device with a beamwidth compression ratio of 10:1 by integrating a nanowaveguide with the width of 200 nm to the output surface of lens structure. Normalized transmission efficiency of the optical coupling device is calculated as high as 0.62 at the wavelength of 1550 nm. The outcomes of the presented study show that machine learning can be beneficial for designing efficient compact photonic structures.

Differential evolution algorithm based photonic structure design: numerical and experimental verification of subwavelength λ/5 focusing of light.

Abstract: Photonic structure designs based on optimization algorithms provide superior properties compared to those using intuition-based approaches. In the present study, we numerically and experimentally demonstrate subwavelength focusing of light using wavelength scale absorption-free dielectric scattering objects embedded in an air background. An optimization algorithm based on differential evolution integrated into the finite-difference time-domain method was applied to determine the locations of each circular dielectric object with a constant radius and refractive index. The multiobjective cost function defined inside the algorithm ensures strong focusing of light with low intensity side lobes. The temporal and spectral responses of the designed compact photonic structure provided a beam spot size in air with a full width at half maximum value of 0.19λ, where λ is the wavelength of light. The experiments were carried out in the microwave region to verify numerical findings, and very good agreement between the two approaches was found. The subwavelength light focusing is associated with a strong interference effect due to nonuniformly arranged scatterers and an irregular index gradient. Improving the focusing capability of optical elements by surpassing the diffraction limit of light is of paramount importance in optical imaging, lithography, data storage, and strong light-matter interaction.

Directional invisibility by genetic optimization

Abstract: In this Letter, the design of a directional optical cloaking by a genetic algorithm is proposed and realized experimentally. A three-dimensional finite-difference time-domain method is combined with the genetic optimization approach to generate the cloaking structure to directionally cloak a cylindrical object made of a perfect electrical conductor by suppressing the undesired scattered fields around the object. The optimization algorithm designs the permittivity distribution of the dielectric polylactide material to achieve an optical cloaking effect. Experimental verifications of the designed cloaking structure are performed at microwave frequencies, where the proposed structure is fabricated by 3D printing. The imperfect conformal mapping from a large-scale permittivity distribution and the compensation of the remaining scattering by a small-scale permittivity distribution are the basic physical mechanisms of the proposed optical cloaking.

Asymmetric light transmission effect based on an evolutionary optimized semi-Dirac cone dispersion photonic structure

Abstract: Scientific and Technological Research Council of Turkey (TUBITAK) Turkish Academy of Sciences

Refractive index less than two: photonic nanojets yesterday, today and tomorrow [Invited]

Abstract: Materials with relatively small refractive indices (n<2), such as glass, quartz, polymers, some ceramics, etc., are the basic materials in most optical components (lenses, optical fibres, etc.). In this review, we present some of the phenomena and possible applications arising from the interaction of light with particles with a refractive index less than 2. The vast majority of the physics involved can be described with the help of the exact, analytical solution of Maxwell’s equations for spherical particles (so called Mie theory). We also discuss some other particle geometries (spheroidal, cubic, etc.) and different particle configurations (isolated or interacting) and draw an overview of the possible applications of such materials, in connection with field enhancement and super resolution nanoscopy.

Algorithmic design issues in adaptive differential evolution schemes: Review and taxonomy

Abstract: The performance of most metaheuristic algorithms depends on parameters whose settings essentially serve as a key function in determining the quality of the solution and the efficiency of the search. A trend that has emerged recently is to make the algorithm parameters automatically adapt to different problems during optimization, thereby liberating the user from the tedious and time-consuming task of manual setting. These fine-tuning techniques continue to be the object of ongoing research. Differential evolution (DE) is a simple yet powerful population-based metaheuristic. It has demonstrated good convergence, and its principles are easy to understand. DE is very sensitive to its parameter settings and mutation strategy; thus, this study aims to investigate these settings with the diverse versions of adaptive DE algorithms. This study has two main objectives: (1) to present an extension for the original taxonomy of evolutionary algorithms (EAs) parameter settings that has been overlooked by prior research and therefore minimize any confusion that might arise from the former taxonomy and (2) to investigate the various algorithmic design schemes that have been used in the different variants of adaptive DE and convey them in a new classification style. In other words, this study describes in depth the structural analysis and working principle that underlie the promising and recent work in this field, to analyze their advantages and disadvantages and to gain future insights that can further improve these algorithms. Finally, the interpretation of the literature and the comparative analysis of the algorithmic schemes offer several guidelines for designing and implementing adaptive DE algorithms. The proposed design framework provides readers with the main steps required to integrate any proposed meta-algorithm into parameter and/or strategy adaptation schemes.

A Bidirectional Deep Neural Network for Accurate Silicon Color Design

Abstract: Silicon nanostructure color has achieved unprecedented high printing resolution and larger color gamut than sRGB. The exact color is determined by localized magnetic and electric dipole resonance of nanostructures, which are sensitive to their geometric changes. Usually, the design of specific colors and iterative optimization of geometric parameters are computationally costly, and obtaining millions of different structural colors is challenging. Here, a deep neural network is trained, which can accurately predict the color generated by random silicon nanostructures in the forward modeling process and solve the nonuniqueness problem in the inverse design process that can accurately output the device geometries for at least one million different colors. The key results suggest deep learning is a powerful tool to minimize the computation cost and maximize the design efficiency for nanophotonics, which can guide silicon color manufacturing with high accuracy.