Browsing by Author "Rafi, Rakayet"

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Dispersion-Engineered Silicon Carbide Channel Waveguide for Mid-Infrared Region Supercontinuum Generation
(Scopus, 2024-12-25) Rafi, Rakayet; Zakir, Zahida; Nath;, Runi; Sabuz;, Md.; Karim;, M R; Rahman, B
Supercontinuum generation in mid-infrared (mid-IR) regime is critical for major applications. This paper explores this phenomenon in Silicon Carbide based channel waveguide with Silica cladding. We numerically investigated a 1-cm-long channel waveguide using silicon carbide (SiC) as core material and Silica (SiO2) for upper and lower claddings which demonstrated anomalous dispersion across a wide spectral range around the pump wavelength, resulting in supercontinuum generation in the mid-infrared regime. Using a pump source at 1550 nm for a pulse duration of 50 femtoseconds at 5KW input pulse power, a supercontinuum (SC) spanning from 0.91 to 4.55 μm above -40 db spectral power has been generated. This is to the authors' knowledge, first exploration of silicon carbide as a guiding material in channel waveguide.
Recurrent Neural Network Architecture to Predict Supercontinuum Generation in Chalcogenide-Silica Hybrid Photonic Crystal Fiber
(Scopus, 2024-12-19) Chowdhury, Faisal Ahmed; Rafi, Rakayet; Hasan, Md. Shahedul; Karim, M R; Rahman, B M A; Ghosh, Sampad
: In this paper, we have introduced a novel deep learning approach as an efficient alternative to conventional numerical simulations for predicting supercontinuum generation in a chalcogenide-silica hybrid Photonic Crystal Fiber. Traditional simulations performed in COMSOL Multiphysics and subsequent supercontinuum spectrum analysis in MATLAB are computationally intensive and time-consuming. Our proposed Recurrent Neural Network (RNN) model, trained from scratch, demonstrates impressive accuracy in forecasting supercontinuum spectral bandwidth, achieving a mean square error of 0.00078784. This accuracy enables us to effectively map the optical characteristics of our model. Our results position the RNN model as a fast and accurate alternative to conventional numerical calculations for predicting supercontinuum spectral bandwidth. By significantly reducing computational time, our model enables rapid predictions of supercontinuum spectra, benefiting early cancer cell detection, frequency metrology, optical coherence tomography, spectroscopy, and hazardous material sensing. This is, to the authors' knowledge, the first exploration of supercontinuum generation using an RNN model in PCF
Supercontinuum Generation in Dispersion Engineered Silicon Carbide Photonic Crystal Fiber
(IEEE, 2024-06-23) Rafi, Rakayet; Nath, Runi; Sabuz, Md.; Zakir, Zahida; Karim, M R; Rahman, B
This study presents the design of a hexagonal pattern photonic crystal fiber (PCF) using silicon carbide as the guiding material. This is achieved by modifying the PCF's geometrical characteristics, such as lattice pitch and air hole diameter. We observed that flatter dispersion curves and less confinement loss may be achieved by raising the pitch from 1.5µm to 1.9µm. We have further investigated the Supercontinuum Generation phenomenon using Generalized Non-linear Schrodinger Equation (GNLSE), of a 10mm length of PCF design having parameters as pitch 1.9µm, air filled fraction 0.9. It resulted in spectra spanning from 800 nm to 2800 nm at above −40 dB spectral power range which is in the Mid Infrared region. This is, to the authors knowledge, first exploration of SupercontinuumGeneration in a SiC based photonic crystal fiber (PCF).
Supercontinuum Generation in Dispersion Engineered Silicon Carbide Photonic Crystal Fiber
(Scopus, 2024-05-23) Rafi, Rakayet; Nath, Runi; Karim, M R; Rahman, B; Sabuz, Md.; Zakir, Zahida
This study presents the design of a hexagonal pattern photonic crystal fiber (PCF) using silicon carbide as the guiding material. This is achieved by modifying the PCF's geometrical characteristics, such as lattice pitch and air hole diameter. We observed that flatter dispersion curves and less confinement loss may be achieved by raising the pitch from 1.5µm to 1.9µm. We have further investigated the Supercontinuum Generation phenomenon using Generalized Non-linear Schrodinger Equation (GNLSE), of a 10mm length of PCF design having parameters as pitch 1.9µm, air filled fraction 0.9. It resulted in spectra spanning from 800 nm to 2800 nm at above −40 dB spectral power range which is in the Mid Infrared region. This is, to the authors knowledge, first exploration of SupercontinuumGeneration in a SiC based photonic crystal fiber (PCF).
Unified Predictive Modeling of Supercontinuum Spectra
(Elsevier, 2024-08-15) Rafi, Rakayet; Karim, M.R.; Ghosh, Sampad; Rahman, B.M.A.
This paper explores the application of the Closed-Form Continuous-Time Neural Networks (CfC) model in predicting supercontinuum spectra in a unified dataset of various core materials in a planar waveguide. Through numerical simulations and dataset generation, the study constructs a robust model capable of accurately predicting spectral behavior under different conditions and material variation. Initially, the unified dataset comprises spectral data for three materials: Silicon Nitride (Si N ), Lithium Niobate (LiNbO ), and Silicon Carbide (SiC). The CfC model demonstrates remarkable accuracy in capturing spectral nuances and exhibits a minimal Mean Squared Error (MSE) loss value of . Subsequently, the dataset is expanded to include Tantalum Pentoxide (Ta O ), introducing a fourth material for evaluation. The inclusion of Ta O data further validates the model’s scalability and generalization capabilities with Mean Squared Error (MSE) loss value of . The advantage of the CfC model in precisely predicting supercontinuum spectra while retaining computational efficiency is demonstrated by comparisons with conventional methods. The CfC model is a viable tool for enhancing predictive modeling in photonics research because of its scalability and generalization characteristics. This will pave the way for future developments in deep learning techniques for optical device optimization.
Unified predictive modeling of supercontinuum spectra: Using multi-material data with Closed-Form Continuous Time Neural Networks
(Scopus, 2024-08-15) Rafi, Rakayet; Karim, M.R.; Ghosh, Sampad; Rahman, B.M.A.
This paper explores the application of the Closed-Form Continuous-Time Neural Networks (CfC) model in predicting supercontinuum spectra in a unified dataset of various core materials in a planar waveguide. Through numerical simulations and dataset generation, the study constructs a robust model capable of accurately predicting spectral behavior under different conditions and material variation. Initially, the unified dataset comprises spectral data for three materials: Silicon Nitride (SiN), Lithium Niobate (LiNbO), and Silicon Carbide (SiC). The CfC model demonstrates remarkable accuracy in capturing spectral nuances and exhibits a minimal Mean Squared Error (MSE) loss value of . Subsequently, the dataset is expanded to include Tantalum Pentoxide (TaO), introducing a fourth material for evaluation. The inclusion of TaO data further validates the model’s scalability and generalization capabilities with Mean Squared Error (MSE) loss value of . The advantage of the CfC model in precisely predicting supercontinuum spectra while retaining computational efficiency is demonstrated by comparisons with conventional methods. The CfC model is a viable tool for enhancing predictive modeling in photonics research because of its scalability and generalization characteristics. This will pave the way for future developments in deep learning techniques for optical device optimization.
Unified Predictive Modeling of Supercontinuum Spectra: Using Multi-Material Data With Closed-Form Continuous Time Neural Networks
(Elsevier, 2024-08-15) Rafi, Rakayet; Karim, M.R.; Ghosh, Sampad; Rahman, B.M.A.
This paper explores the application of the Closed-Form Continuous-Time Neural Networks (CfC) model in predicting supercontinuum spectra in a unified dataset of various core materials in a planar waveguide. Through numerical simulations and dataset generation, the study constructs a robust model capable of accurately predicting spectral behavior under different conditions and material variation. Initially, the unified dataset comprises spectral data for three materials: Silicon Nitride (Si N ), Lithium Niobate (LiNbO ), and Silicon Carbide (SiC). The CfC model demonstrates remarkable accuracy in capturing spectral nuances and exhibits a minimal Mean Squared Error (MSE) loss value of . Subsequently, the dataset is expanded to include Tantalum Pentoxide (Ta O ), introducing a fourth material for evaluation. The inclusion of Ta O data further validates the model’s scalability and generalization capabilities with Mean Squared Error (MSE) loss value of . The advantage of the CfC model in precisely predicting supercontinuum spectra while retaining computational efficiency is demonstrated by comparisons with conventional methods. The CfC model is a viable tool for enhancing predictive modeling in photonics research because of its scalability and generalization characteristics. This will pave the way for future developments in deep learning techniques for optical device optimization.