Souvik Ghosh

Optical metasurface is a combination of manufactured periodic patterns of many artificial nanostructured unit cells, which can provide unique and attractive optical and electrical properties. Additionally, the function of the metasurface can be altered by adjusting the metasurface's size and configuration to satisfy a particular required property. However, once it is fabricated, such specific property is fixed and cannot be changed. Here, phase change material (PCM) can play an important role due to its two distinct states during the phase transition, referred to as amorphous and crystalline states, which exhibit significantly different refractive indices, particularly in the infrared wavelength. Therefore, a combination of metasurface with a phase change material may be attractive for achieving agile and tunable functions. In this paper, we numerically investigate an array of silicon cylinders with a thin PCM layer at their centers. The GST and GSST are the most well-known PCMs and were chosen for this study due to their non-volatile properties. This structure produces two resonant modes, magnetic dipole and electric dipole, at two different resonating wavelengths. We have numerically simulated the effect of cylinder's height and diameter on the reflecting profile, including the effect of thickness of the phase change material. Additionally, it is shown here that a superior performance can be achieved towards reduced insertion loss, enhanced extinction ratio, and increased figure of merit when a GST layer is replaced by a GSST layer. (C) 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

The increasing use of nanomaterials and scalable, high-yield nanofabrication process are revolutionizing the development of novel biosensors. Over the past decades, researches on nanotechnology-mediated biosensing have been on the forefront due to their potential application in healthcare, pharmaceutical, cell diagnosis, drug delivery, and water and air quality monitoring. The advancement of nanoscale science relies on a better understanding of theory, manufacturing and fabrication practices, and the application specific methods. The topology and tunable properties of nanoparticles, a part of nanoscale science, can be changed by different manufacturing processes, which separate them from their bulk counterparts. In the recent past, different nanostructures, such as nanosphere, nanorods, nanofiber, core–shell nanoparticles, nanotubes, and thin films, have been exploited to enhance the detectability of labelled or label-free biological molecules with a high accuracy. Furthermore, these engineered-materials-associated transducing devices, e.g., optical waveguides and metasurface-based scattering media, widened the horizon of biosensors over a broad wavelength range from deep-ultraviolet to far-infrared. This review provides a comprehensive overview of the major scientific achievements in nano-biosensors based on optical fiber, nanomaterials and terahertz-domain metasurface-based refractometric, labelled and label-free nano-biosensors.

The Artificial Neural Network (ANN) has become an attractive approach in Machine Learning (ML) to analyze a complex data-driven problem. Due to its time efficient findings, it has became popular in many scientific fields such as physics, optics, and material science. This paper presents a new approach to design and optimize the electromagnetic plasmonic nanostructures using a computationally efficient method based on the ANN. In this work, the nanostructures have been simulated by using a Finite Element Method (FEM), then Artificial Intelligence (AI) is used for making predictions of associated sensitivity (S), Full Width Half Maximum (FWHM), Figure of Merit (FOM), and Plasmonic Wavelength (PW) for different paired nanostructures. At first, the computational model is developed by using a Finite Element Method (FEM) to prepare the dataset. The input parameters were considered as the Major axis, a, the Minor axis, b, and the separation gap, g, which have been used to calculate the corresponding sensitivity (nm/RIU), FWHM (nm), FOM, and plasmonic wavelength (nm) to prepare the dataset. Secondly, the neural network has been designed where the number of hidden layers and neurons were optimized as part of a comprehensive analysis to improve the efficiency of ML model. After successfully optimizing the neural network, this model is used to make predictions for specific inputs and its corresponding outputs. This article also compares the error between the predicted and simulated results. This approach outperforms the direct numerical simulation methods for predicting output for various input device parameters.

Gold nanoantennas have been used in a variety of biomedical applications due to their attractive electronic and optical properties, which are shape- and size-dependent. Here, a periodic paired gold nanostructure exploiting surface plasmon resonance is proposed, which shows promising results for Refractive Index (RI) detection due to its high electric field confinement and diffraction limit. Here, single and paired gold nanostructured sensors were designed for real-time RI detection. The Full-Width at Half-Maximum (FWHM) and Figure-Of-Merit (FOM) were also calculated, which relate the sensitivity to the sharpness of the peak. The effect of different possible structural shapes and dimensions were studied to optimise the sensitivity response of nanosensing structures and identify an optimised elliptical nanoantenna with the major axis a, minor axis b, gap between the pair g, and heights h being 100 nm, 10 nm, 10 nm, and 40 nm, respectively. In this work, we investigated the bulk sensitivity, which is the spectral shift per refractive index unit due to the change in the surrounding material, and this value was calculated as 526–530 nm/RIU, while the FWHM was calculated around 110 nm with a FOM of 8.1. On the other hand, the surface sensing was related to the spectral shift due to the refractive index variation of the surface layer near the paired nanoantenna surface, and this value for the same antenna pair was calculated as 250 nm/RIU for a surface layer thickness of 4.5 nm.

An ultrasensitive hybrid fiber grating sensor has been designed and functionalized with a novel nanocomposite material for selective detection of lead ions in water. Sensor performance is enhanced using an artificial neural network-based calibration process.

Photonic crystal fibers (PCFs) are the specialized optical waveguides that led to many interesting applications ranging from nonlinear optical signal processing to high-power fiber amplifiers. In this paper, machine learning techniques are used to compute various optical properties including effective index, effective mode area, dispersion and confinement loss for a solid-core PCF. These machine learning algorithms based on artificial neural networks are able to make accurate predictions of above-mentioned optical properties for usual parameter space of wavelength ranging from 0.5-1.8 µm, pitch from 0.8-2.0 µm, diameter by pitch from 0.6-0.9 and number of rings as 4 or 5 in a silica solid-core PCF. We demonstrate the use of simple and fast-training feed-forward artificial neural networks that predicts the output for unknown device parameters faster than conventional numerical simulation techniques. Computation runtimes required with neural networks (for training and testing) and Lumerical MODE solutions are also compared.

In this work, a highly sensitive surface plasmon resonance (SPR) sensor based on a single mode fiber (SMF) incorporating a large microfluidic channel (MFC) for refractive index (RI) sensing is designed and optimized using a full-vectorial finite element method (FEM). The fluidic channel size can be varied according to the requirement due to the availability of the large cladding diameter of SMF, which makes it simple and easy to fabricate. The proposed novel sensor is favourable to both analytes and metallic strips. The D-shaped hollow section above the core is filled with the measurand analytes and a gold (Au) strip is deposited on the base of the MFC, as it is known as the most attractive metal for SPR. Our numerical simulations illustrate that the confinement loss of the designed sensor is highly influenced by the distance of the MFC from the core along with the width and thickness of the Au strip. The designed sensor shows an average sensitivity of 1350 nm/RIU and maximum sensitivity of 8250 nm/RIU in the sensing range of 1.33-1.35 and 1.41-1.43, respectively. However, for a small variation of n_a at a step of 0.005, within ranges like 1.415, 1.420, and 1.425, we have achieved a maximum sensitivity of 7000 nm/RIU, 9000 nm/RIU and 11000 nm/RIU, respectively. This novel SPR sensor with MFC can open up a new opportunity in the application of chemical and biological sensing.