All-Optical NOT Logic Gate Based on Photonic Crystals

A novel scheme for implementation of all-optical NOT logic gate based on photonic crystal ring resonator has been proposed. Photonic crystal is comprised of two-dimensional square lattice of dielectric rods in air substrate. Indium phosphide with a refractive index of 3.1 is adopted as the material of the rods. The finite different time domain (FDTD) and plane wave Expansion (PWE) methods are used to analyze the behavior of the structure. The simulation results show that the contrast ratio is 10.97dB for NOT gate. Moreover, the operational wavelength of the input ports is 1.55µm. Since the structure has a simple geometric shape with clear operating principle, it is potentially applicable for photonic integrated circuits.DOI:http://dx.doi.org/10.11591/ijece.v3i4.290

New Design of Channel Drop Filter by Triangular Photonic Crystal

We have designed a new type of optical channel drop filter (CDF) based on two dimensional triangular lattice photonic crystals. CDF operation is based on coupling to the photonic crystal waveguide. The proposed structure is optimized to work as a CDF. For obtaining the CDF characteristics and band structure of the filter, the finite difference time domain (FDTD) method and plane wave expansion (PWE) method are used respectively. Dropping efficiency at 1556nm and quality factor (Q) of our proposed structure are 100% and 260, respectively. The quantities of quality factor and transmission efficiency are suitable for optical applications. The overall size of the proposed add drop filter is 191.97µm2, which is smaller than the filters already reported and it is highly desirable for photonic integrated circuits (PICs).DOI:http://dx.doi.org/10.11591/ijece.v3i1.193

Energy recovery from the rotary kiln of white cement Neyriz

A huge part of the thermal energy consumed in energy industries is lost after exiting the process. Some of this waste heat can be recovered by various methods such as generating electrical energy, generating hot air for the production process or providing hot water. In Iran, the cement industry has always been far from the optimal use of resources, including energy, due to the existence of energy subsidies. In this research, the heat loss from the baking system of Nireez white cement plant as a model industry of the country to evaluate the simultaneous production of heat and electricity by using the organic Rankine cycle that generates electric power has been investigated in Span Plus V12 software. This evaluation is done to calculate the heat loss, including the mass and energy balance on the cooking system, as well as the energy balance on the pre-cooking system. The heat energy obtained enters the Rankine cycle and is converted into energy by the turbine. The results show that 635 kilowatts of electrical energy was produced from energy recycling, which leads to the elimination of 317.5 kg/hour of greenhouse gas

A deep learning method for empirical spectral prediction and inverse design of all-optical nonlinear plasmonic ring resonator switches

Abstract All-optical plasmonic switches (AOPSs) utilizing surface plasmon polaritons are well-suited for integration into photonic integrated circuits (PICs) and play a crucial role in advancing all-optical signal processing. The current AOPS design methods still rely on trial-and-error or empirical approaches. In contrast, recent deep learning (DL) advances have proven highly effective as computational tools, offering an alternative means to accelerate nanophotonics simulations. This paper proposes an innovative approach utilizing DL for spectrum prediction and inverse design of AOPS. The switches employ circular nonlinear plasmonic ring resonators (NPRRs) composed of interconnected metal–insulator–metal waveguides with a ring resonator. The NPRR switching performance is shown using the nonlinear Kerr effect. The forward model presented in this study demonstrates superior computational efficiency when compared to the finite-difference time-domain method. The model analyzes various structural parameters to predict transmission spectra with a distinctive dip. Inverse modeling enables the prediction of design parameters for desired transmission spectra. This model provides a rapid estimation of design parameters, offering a clear advantage over time-intensive conventional optimization approaches. The loss of prediction for both the forward and inverse models, when compared to simulations, is exceedingly low and on the order of 10−4. The results confirm the suitability of employing DL for forward and inverse design of AOPSs in PICs

DL-based_FBG.gif

This file is an animation abstract of "Deep_learning-enabled_inverse_design_of_chirped_apodized_fiber_Bragg_gratings." The manuscript is due for peer review in the prestigious scientific journal Optica in 2024.</p

SquareRR_AOPS.gif

This file is an animation abstract of "Inverse_design_of_plasmon-based_nonlinear_square_resonators_enabled_by_deep_neural_networks." The manuscript is due for peer review in the prestigious scientific journal PhotoniX in 2024.</p

SquareRR_AOPS4.gif

This file is a 20-second-animated abstract of "Inverse_design_of_plasmon-based_nonlinear_square_resonators_enabled_by_deep_neural_networks." The manuscript is due for peer review in the prestigious scientific journal PhotoniX in 2024.</p