Sadia Noureen - Academia.edu (original) (raw)

Papers by Sadia Noureen

Nanoscale

Metasurfaces are composed of a two-dimensional array of carefully engineered subwavelength struct... more Metasurfaces are composed of a two-dimensional array of carefully engineered subwavelength structures.

2020 17th International Bhurban Conference on Applied Sciences and Technology (IBCAST)

Metasurfaces, the planar analog of bulk metamaterials, can be patterned at the interface of two m... more Metasurfaces, the planar analog of bulk metamaterials, can be patterned at the interface of two media to control the amplitude, polarization, and phase of the incident light. Metasurfaces have provided a unique platform for the on-chip realization of various interesting phenomena such as light-twisting, structuring, lensing, imaging, beam-splitting, etc. Since the performance of metasurfaces based on metallic scatterers is restricted by ohmic losses and associated optical absorption, lossless dielectric materials (in the spectrum of interest) provide a promising alternative to acquire higher transmission efficiency. In this article, we utilize hydrogenated amorphous silicon (a-Si: H) nano-scatterers (with a subwavelength thickness of 300 nm), which provides a cost-effective (compared to TiO2 and GaN) and low-loss (compared to metallic counterparts) alternative, to design an efficient metasurface in the visible domain. The proposed metasurface can generate the finite energy Airy beam by simultaneously engineering the cross-polarized transmission amplitude and phase of the transmitted electromagnetic wave at the visible wavelength of 633 nm. The polarization and phase are engineered by varying the orientation of nano-bars while cross-polarized transmission amplitude is optimized by tuning the geometric parameters of nano-bars. Higher cross-polarized transmission amplitude (73%) is achieved compared to previously reported work. The proposed design will help us in developing subwavelength-thick flat devices that have numerous applications in integrated optics such as optical manipulation, bio-sensing, beam shaping, and forming optical bullets.

Metasurfaces are subwavelength artificially engineered devices which serve as the best alternativ... more Metasurfaces are subwavelength artificially engineered devices which serve as the best alternative for conventional bulk optical components, owing to their unique capability to control and manipulate the intensity, phase, and polarization of the electromagnetic waves. They have emerged as an exceptional podium for the miniaturization and on-chip implementation of many diverse prodigies such as 3D imaging, lensing, beam shaping and light-twisting, etc. Regardless of the rapid development in this domain, metasurfaces exhibiting metallic scatterers are still subjected to poor transmission efficiency due to high ohmic losses and optical absorptivity. In this work, we demonstrate a transmission-based highly efficient alldielectric metasurface to generate finite energy accelerating 1D and 2D airy beams at the visible wavelength of 633 nm by concurrently manipulating the phase and the amplitude of the cross-polarized transmitted light. The proposed metasurface employs hydrogenated amorphou...

Optical Materials Express, 2021

A standardized hybrid deep-learning model based on a combination of a deep convolutional network ... more A standardized hybrid deep-learning model based on a combination of a deep convolutional network and a recurrent neural network is proposed to predict the optical response of metasurfaces considering their shape and all the important dimensional parameters (such as periodicity, height, width, and aspect ratio) simultaneously. It is further used to aid the design procedure of the key components of solar thermophotovoltaics (STPVs), i.e., metasurface based perfect solar absorbers and selective emitters. Although these planar meta-absorbers and meta-emitters offer an ideal platform to realize compact and efficient STPV systems, a conventional procedure to design these is time taking, laborious, and computationally exhaustive. The optimization of such planar devices needs hundreds of EM simulations, where each simulation requires multiple iterations to solve Maxwell's equations on a case-by-case basis. To overcome these challenges, we propose a unique deep learning-based model that generates the most likely optical response by taking images of the unit cells as input. The proposed model uses a deep residual convolutional network to extract the features from the images followed by a gated recurrent unit to infer the desired optical response. Two datasets having considerable variance are collected to train the proposed network by simulating randomly shaped nanostructures in CST microwave studio with periodic boundary conditions over the desired wavelength ranges. These simulations yield the optical absorption/emission response as the target labels. The proposed hybrid configuration and transfer learning provide a generalized model to infer the absorption/emission spectrum of solar absorbers/emitters within a fraction of seconds with high accuracy, regardless of its shape and dimensions. This accuracy is defined by the regression metric mean square error (MSE), where the minimum MSE achieved for absorbers and emitters test datasets are 7.3 × 10−04 and 6.2 × 10−04 respectively. The trained model can also be fine-tuned to predict the absorption response of different thin film refractory materials. To enhance the diversity of the model. Thus it aids metasurface design procedure by replacing the conventional time-consuming and computationally exhaustive numerical simulations and electromagnetic (EM) software. The comparison of the average simulation time (for 10 samples) and the average DL model prediction time shows that the DL model works about 98% faster than the conventional simulations. We believe that the proposed methodology will open new research directions towards more challenging optimization problems in the field of electromagnetic metasurfaces.

2021 International Bhurban Conference on Applied Sciences and Technologies (IBCAST)

State of the art research in nanophotonics focuses on the development of compact and efficient on... more State of the art research in nanophotonics focuses on the development of compact and efficient on-chip devices that are compatible with integrated photonics. Optical Metasurfaces consisting two-dimensional array of subwavelength featured nanostructures have come forth as a perfect candidate to realize such compact photonics chips. They exhibit a unique capability of controlling and manipulating the electromagnetic waves to achieve novel optical responses. Regardless of the vast capabilities and immense potential of nanoscale optical components, their design procedure still suffers from some major drawbacks such as extreme time consumption and high computational resources requirement. A lot of manual work, numerical solutions and multiple simulations are required to achieve metasurfaces having desired responses. Here we present a unique extremely time efficient methodology for finding the optical response of photonic nanostructures using a hybrid deep neural network model that combines convolutional neural networks(CNN), Sequence modeling, and transfer learning. This model incorporates deep residual CNN (ResNet) to extract the spatial information form the images and other geometrical parameters of nanostructures, followed by a gated recurrent unit (GRU) based Sequence Model to map the feature vector to the output and predict the optical absorption spectrum of these structures. Later we utilize transfer learning to extend the model to accommodate nanostructures made up of different materials having diverse physical properties. Our experiments indicate that the proposed methodology accurately predicts the optical response with in a fraction of seconds, which makes it a potential alternative for the conventional time consuming and computationally exhaustive numerical simulations and EM software's.

Chaos, Solitons & Fractals

IEICE Communications Express

An electrically small metamaterial antenna is presented for ultra wide band applications. The pro... more An electrically small metamaterial antenna is presented for ultra wide band applications. The proposed design provides a tunable frequency band with maximum efficiency and a bandwidth of approximately 7.7 GHz. Due to its compact size and simple structure, it can be employed in numerous wireless communication devices. The geometry of the antenna consists of a monopole ellipse radiator and a uniquely modified Electric LC (ELC) structure based on the concept of magnetic imaging at the boundary and provides dual frequency bands of 2.56∼3.68 GHz and 4.92∼6.82 GHz. The antenna is then fused with an Electromagnetic Band Gap (EBG) structure to increase impedance matching and provide an ultra wide band of 2.72 GHz to 10.46 GHz.

Nanoscale

Metasurfaces are composed of a two-dimensional array of carefully engineered subwavelength struct... more Metasurfaces are composed of a two-dimensional array of carefully engineered subwavelength structures.

2020 17th International Bhurban Conference on Applied Sciences and Technology (IBCAST)

Metasurfaces, the planar analog of bulk metamaterials, can be patterned at the interface of two m... more Metasurfaces, the planar analog of bulk metamaterials, can be patterned at the interface of two media to control the amplitude, polarization, and phase of the incident light. Metasurfaces have provided a unique platform for the on-chip realization of various interesting phenomena such as light-twisting, structuring, lensing, imaging, beam-splitting, etc. Since the performance of metasurfaces based on metallic scatterers is restricted by ohmic losses and associated optical absorption, lossless dielectric materials (in the spectrum of interest) provide a promising alternative to acquire higher transmission efficiency. In this article, we utilize hydrogenated amorphous silicon (a-Si: H) nano-scatterers (with a subwavelength thickness of 300 nm), which provides a cost-effective (compared to TiO2 and GaN) and low-loss (compared to metallic counterparts) alternative, to design an efficient metasurface in the visible domain. The proposed metasurface can generate the finite energy Airy beam by simultaneously engineering the cross-polarized transmission amplitude and phase of the transmitted electromagnetic wave at the visible wavelength of 633 nm. The polarization and phase are engineered by varying the orientation of nano-bars while cross-polarized transmission amplitude is optimized by tuning the geometric parameters of nano-bars. Higher cross-polarized transmission amplitude (73%) is achieved compared to previously reported work. The proposed design will help us in developing subwavelength-thick flat devices that have numerous applications in integrated optics such as optical manipulation, bio-sensing, beam shaping, and forming optical bullets.

Metasurfaces are subwavelength artificially engineered devices which serve as the best alternativ... more Metasurfaces are subwavelength artificially engineered devices which serve as the best alternative for conventional bulk optical components, owing to their unique capability to control and manipulate the intensity, phase, and polarization of the electromagnetic waves. They have emerged as an exceptional podium for the miniaturization and on-chip implementation of many diverse prodigies such as 3D imaging, lensing, beam shaping and light-twisting, etc. Regardless of the rapid development in this domain, metasurfaces exhibiting metallic scatterers are still subjected to poor transmission efficiency due to high ohmic losses and optical absorptivity. In this work, we demonstrate a transmission-based highly efficient alldielectric metasurface to generate finite energy accelerating 1D and 2D airy beams at the visible wavelength of 633 nm by concurrently manipulating the phase and the amplitude of the cross-polarized transmitted light. The proposed metasurface employs hydrogenated amorphou...

Optical Materials Express, 2021

A standardized hybrid deep-learning model based on a combination of a deep convolutional network ... more A standardized hybrid deep-learning model based on a combination of a deep convolutional network and a recurrent neural network is proposed to predict the optical response of metasurfaces considering their shape and all the important dimensional parameters (such as periodicity, height, width, and aspect ratio) simultaneously. It is further used to aid the design procedure of the key components of solar thermophotovoltaics (STPVs), i.e., metasurface based perfect solar absorbers and selective emitters. Although these planar meta-absorbers and meta-emitters offer an ideal platform to realize compact and efficient STPV systems, a conventional procedure to design these is time taking, laborious, and computationally exhaustive. The optimization of such planar devices needs hundreds of EM simulations, where each simulation requires multiple iterations to solve Maxwell's equations on a case-by-case basis. To overcome these challenges, we propose a unique deep learning-based model that generates the most likely optical response by taking images of the unit cells as input. The proposed model uses a deep residual convolutional network to extract the features from the images followed by a gated recurrent unit to infer the desired optical response. Two datasets having considerable variance are collected to train the proposed network by simulating randomly shaped nanostructures in CST microwave studio with periodic boundary conditions over the desired wavelength ranges. These simulations yield the optical absorption/emission response as the target labels. The proposed hybrid configuration and transfer learning provide a generalized model to infer the absorption/emission spectrum of solar absorbers/emitters within a fraction of seconds with high accuracy, regardless of its shape and dimensions. This accuracy is defined by the regression metric mean square error (MSE), where the minimum MSE achieved for absorbers and emitters test datasets are 7.3 × 10−04 and 6.2 × 10−04 respectively. The trained model can also be fine-tuned to predict the absorption response of different thin film refractory materials. To enhance the diversity of the model. Thus it aids metasurface design procedure by replacing the conventional time-consuming and computationally exhaustive numerical simulations and electromagnetic (EM) software. The comparison of the average simulation time (for 10 samples) and the average DL model prediction time shows that the DL model works about 98% faster than the conventional simulations. We believe that the proposed methodology will open new research directions towards more challenging optimization problems in the field of electromagnetic metasurfaces.

2021 International Bhurban Conference on Applied Sciences and Technologies (IBCAST)

State of the art research in nanophotonics focuses on the development of compact and efficient on... more State of the art research in nanophotonics focuses on the development of compact and efficient on-chip devices that are compatible with integrated photonics. Optical Metasurfaces consisting two-dimensional array of subwavelength featured nanostructures have come forth as a perfect candidate to realize such compact photonics chips. They exhibit a unique capability of controlling and manipulating the electromagnetic waves to achieve novel optical responses. Regardless of the vast capabilities and immense potential of nanoscale optical components, their design procedure still suffers from some major drawbacks such as extreme time consumption and high computational resources requirement. A lot of manual work, numerical solutions and multiple simulations are required to achieve metasurfaces having desired responses. Here we present a unique extremely time efficient methodology for finding the optical response of photonic nanostructures using a hybrid deep neural network model that combines convolutional neural networks(CNN), Sequence modeling, and transfer learning. This model incorporates deep residual CNN (ResNet) to extract the spatial information form the images and other geometrical parameters of nanostructures, followed by a gated recurrent unit (GRU) based Sequence Model to map the feature vector to the output and predict the optical absorption spectrum of these structures. Later we utilize transfer learning to extend the model to accommodate nanostructures made up of different materials having diverse physical properties. Our experiments indicate that the proposed methodology accurately predicts the optical response with in a fraction of seconds, which makes it a potential alternative for the conventional time consuming and computationally exhaustive numerical simulations and EM software's.

Chaos, Solitons & Fractals

IEICE Communications Express

An electrically small metamaterial antenna is presented for ultra wide band applications. The pro... more An electrically small metamaterial antenna is presented for ultra wide band applications. The proposed design provides a tunable frequency band with maximum efficiency and a bandwidth of approximately 7.7 GHz. Due to its compact size and simple structure, it can be employed in numerous wireless communication devices. The geometry of the antenna consists of a monopole ellipse radiator and a uniquely modified Electric LC (ELC) structure based on the concept of magnetic imaging at the boundary and provides dual frequency bands of 2.56∼3.68 GHz and 4.92∼6.82 GHz. The antenna is then fused with an Electromagnetic Band Gap (EBG) structure to increase impedance matching and provide an ultra wide band of 2.72 GHz to 10.46 GHz.