Machine learning-enhanced surface plasmon resonance based photonic crystal fiber sensor.

This study introduces what we believe to be a novel photonic crystal fiber sensor utilizing surface plasmon resonance (SPR), incorporating four gold nanowires to enhance sensing capabilities. The research employs machine learning, specifically artificial neural networks (ANN), to predict confinement loss and sensitivity, achieving high accuracy without needing the imaginary part of the effective refractive index. The machine learning technique is applied in three different scenarios, resulting in mean squared errors of 0.084, 0.002, and 0.003, highlighting the reliability of the ANN models in predicting sensor outputs. Additionally, the sensor demonstrates impressive wavelength sensitivities of 2000-18000 nm/RIU (nanometers per refractive index unit) for refractive indices of 1.31-1.4 within the 720-1280 nm wavelength range, and a notable maximum amplitude sensitivity of 889.89 RIU-1. This integration of SPR, photonic crystal fiber, and machine learning not only optimizes sensor performance but also offers an efficient methodology for prediction, showcasing the potential of machine learning in advancing optical sensor design.

Optimized high-speed all-optical 2-bit ADCbased on two-dimensional photonic crystal nanoresonators.

In the present paper, a new 2-bit analog-to-digital converter (ADC) was designed and simulated by using 2D photonic crystal (PC) structures to create a relatively faster and smaller structure. For this purpose, a PC structure with a square lattice and silicon rods in the air bed was used. In the proposed structure, a combination of an optical filter with a linear waveguide, optical nanoresonators, and interference effects was used to create a 2-bit ADC. To create a structure in optimal conditions with maximum output optical power, the size of nanoresonators was scanned to reach the best size. The proposed structure operated at the operating wavelength of 1550 nm with a response time of about 1.63 ps, a sampling rate of about 613 GS/s, and a resolution sampling rate product (RSRP) value of about 2453 ks. Additionally, the size of the structure was about 194µm2, which is small compared with other structures proposed in this field; it also enjoys high simplicity and flexibility like structures with other functions, including 4-bit converters. The amount of power used to create different logic states was at the rate of mW/µm2, which is much lower than the amount used in similar structures and is achieved using nonlinear effects and materials. Therefore, due to the excellent results obtained, this structure is recommended to be used in optical integrated circuits. The plane wave expansion method was used to extract the photonic bandgap, and the finite-difference time-domain method was used to obtain the results related to the output spectrum of the designed structures.

Designing an ultracompact all-optical 4-to-2 encoder and investigating its optical power consumption.

In this paper, an ultracompact all-optical encoder based on a photonic crystal nanoresonator was designed. The proposed structure consists of several waveguides and two nanoresonators. The nanoresonators were designed by reducing the radius of the dielectric rods. To analyze the all-optical encoder, plane-wave expansion and finite-difference time-domain methods were, respectively, applied to calculate the bandgap diagram and to obtain the transmission and propagation of optical field. The contrast ratio, delay time, data transfer speed, and total footprint of the logic gate equaled 9.51 dB, 0.24 ps, 4.16 Tb/s, and ${148}\;\unicode{x00B5} {{\rm m}^2}$148µm2, respectively. In addition to these parameters, two new parameters were investigated: the range of optical power required, and the frequency range for better logic gate efficiency. Due to the ultracompacted size, low power consumption, low delay time, and simplicity of structure, this all-optical encoder is suitable for use in low-power optical integrated circuits.

Inductive Loop in the Impedance Response of Perovskite Solar Cells Explained by Surface Polarization Model.

The analysis of perovskite solar cells by impedance spectroscopy has provided a rich variety of behaviors that demand adequate interpretation. Two main features have been reported: First, different impedance spectral arcs vary in combination; second, inductive loops and negative capacitance characteristics appear as an intrinsic property of the current configuration of perovskite solar cells. Here we adopt a previously developed surface polarization model based on the assumption of large electric and ionic charge accumulation at the external contact interface. Just from the equations of the model, the impedance spectroscopy response is calculated and explains the mentioned general features. The inductance element in the equivalent circuit is the result of the delay of the surface voltage and depends on the kinetic relaxation time. The model is therefore able to quantitatively describe exotic features of the perovskite solar cell and provides insight into the operation mechanisms of the device.

Computational study of a label-free biosensor based on a photonic crystal nanocavity resonator.

In this paper, we demonstrate and theoretically investigate a compact two-dimensional (2D) photonic crystal biosensor implemented by a waveguide and cavity. Biomaterials such as DNA molecules and proteins trapped inside a hole cause resonant wavelength shifting at the output terminal. The quality factor and sensitivity were obtained at about 4000 and 1.63 nm/fg, respectively. Also, we investigated this structure as a bulk refractive index sensor with a sensitivity of about 165.45 nm/RIU (refractive index units). Then, we modified the structure as a multichannel biosensor. This biosensor has the capability of highly parallel operation because of special architecture that was obtained by lattice shifting of a single hole around the cavity. Each channel had a different resonant cavity wavelength and the filling of analyte in selected holes caused resonant wavelength shifting, independently. Plane wave expansion (PWE) and finite difference time domain (FDTD) methods were used to analyze and compute the sensor characteristics.