Ceren Babayigit

Bio: Ceren Babayigit is an academic researcher from TOBB University of Economics and Technology. The author has contributed to research in topics: Photonic crystal & Spatial filter. The author has an hindex of 4, co-authored 19 publications receiving 89 citations. Previous affiliations of Ceren Babayigit include Ankara University & University of California, Irvine.

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.

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.

Fano-like resonances in nanostructured thin films for spatial filtering

Abstract: Fano-like resonant coupling of electromagnetic radiation with planar waveguiding modes of nanostructured thin films is proposed and realized experimentally Different from conventional Fano coupling to compact resonators with the discrete spectrum, we report Fano-like coupling to infinitely extended planar waveguiding modes of the spatially unbound system We fabricated the films by the ion beam sputtering method on nano-modulated substrates The observed Fano-like process shows extremely strong sensitivity with respect to the wavelength and especially to the incidence angle of the radiation and can potentially be used for frequency and spatial filtering of light in transmission/reflection through/from such nanostructured thin films

Analytical, Numerical, and Experimental Investigation of a Luneburg Lens System for Directional Cloaking

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

Efficient spectrum prediction and inverse design for plasmonic waveguide systems based on artificial neural networks

Abstract: In this article, we propose a novel approach to achieve spectrum prediction, parameter fitting, inverse design and performance optimization for the plasmonic waveguide coupled with cavities structure (PWCCS) based on artificial neural networks (ANNs). The Fano resonance and plasmon induced transparency effect originated from the PWCCS have been selected as illustrations to verify the effectiveness of ANNs. We use the genetic algorithm to design the network architecture and select the hyper-parameters for ANNs. Once ANNs are trained by using a small sampling of the data generated by Monte Carlo method, the transmission spectrums predicted by the ANNs are quite approximate to the simulated results. The physical mechanisms behind the phenomena are discussed theoretically, and the uncertain parameters in the theoretical models are fitted by utilizing the trained ANNs. More importantly, our results demonstrate that this model-driven method not only realizes the inverse design of the PWCCS with high precision but also optimizes some critical performance metrics for transmission spectrum. Compared with previous works, we construct a novel model-driven analysis method for the PWCCS which are expected to have significant applications in the device design, performance optimization, variability analysis, defect detection, theoretical modeling, optical interconnects and so on.

Efficient spectrum prediction and inverse design for plasmonic waveguide systems based on artificial neural networks

Abstract: In this paper, we propose a novel approach to achieve spectrum prediction, parameter fitting, inverse design, and performance optimization for the plasmonic waveguide-coupled with cavities structure (PWCCS) based on artificial neural networks (ANNs). The Fano resonance and plasmon-induced transparency effect originated from the PWCCS have been selected as illustrations to verify the effectiveness of ANNs. We use the genetic algorithm to design the network architecture and select the hyperparameters for ANNs. Once ANNs are trained by using a small sampling of the data generated by the Monte Carlo method, the transmission spectra predicted by the ANNs are quite approximate to the simulated results. The physical mechanisms behind the phenomena are discussed theoretically, and the uncertain parameters in the theoretical models are fitted by utilizing the trained ANNs. More importantly, our results demonstrate that this model-driven method not only realizes the inverse design of the PWCCS with high precision but also optimizes some critical performance metrics for the transmission spectrum. Compared with previous works, we construct a novel model-driven analysis method for the PWCCS that is expected to have significant applications in the device design, performance optimization, variability analysis, defect detection, theoretical modeling, optical interconnects, and so on.

Tunable Mid-Infrared Nanoscale Graphene-Based Refractive Index Sensor

Abstract: A nanoscale refractive index sensor comprising of input–output graphene parallel plane waveguides coupled through a resonator is proposed. Operating as a wavelength selective structure in mid-infrared region, the sensor performance is based on the variation of the resonance wavelength of the structure. The simulation results obtained by numerical method of the finite difference time domain reveal linear dependency between the resonance wavelengths and the refractive indices of the material injected into the coupling resonator of the sensor, predictable by the resonance criterion of the resonator. The wavelength resolution of the detection system determines the overall sensor resolution. To utilize voltage dependency of graphene chemical potential, the wavelength range of the performance of the sensor can be tuned appropriately. The proposed sensor can be used as a platform for design of the sensors utilizing in various chemical and biomedical systems.

Two-Dimensional photonic crystal Biosensors: A review

Abstract: Photonic crystals are nanoscale structures that affect the motion of photons. The strong light limitation in photonic crystals and the adjustment of its structural parameters have led to the emergence of photonic crystal biosensors. Moreover, the use of holes as a feature of photonic crystals has resulted in sensors that are very sensitive to low refractive index changes with a small sensing area, which offers flexibility and integration on single-chip systems. Using emerging optofluidic technology, label-free biosensors are on the rise. In this review, we examine various types of photonic crystal sensors, such as waveguides, nanoresonators, LX resonators, holes, multi-channel resonators, nano RINGS resonators, and fibers. These sensors are based on the measurement of biomolecules and the refractive index properties that have been identified. Finally, a variety of challenges and guidelines for the construction of label-free diagnostic biosensors are examined.