Metalenses exhibit significant potential in various fields due to their ability to access comprehensive, complex information. The ability to integrate multiple features into a single device, along with its compact and efficient design, allows for the creation of miniature microscopy systems that showcase remarkable performance. By applying unique design techniques, we have developed and implemented polarization-dependent metalens. This metalens makes smooth transitions between edge enhancement imaging and bright field imaging possible. Employing the principles of geometric phase, we design a dual mode metalens by using hydrogenated amorphous silicon to physically manifest the necessary phase profiles for operation in the visible spectrum. These profiles contain a conventional hyperbolic configuration intended for bright-field imaging, along with spiral metalens with a topological charge of +1, tailored for edge-enhanced imaging functions. When utilizing Left Circular Polarization (LCP), our designed lens enables bright field imaging. Conversely, Right Circular Polarization (RCP) facilitates image edge enhancement. We showcase through numerical demonstration the metalens capability to focus and generate vortices under various states of circular polarization and validate its potential for diverse applications.
In the emerging field of photonic nano-structures, the optimization of chiral meta-surfaces has emerged as a pivotal challenge, particularly for applications such as asymmetric transmission, circular dichroism (CD) spectroscopy, imaging, and spin-selective absorption. Traditional metasurface design methodologies have often been tied to laborious parameter tuning and iterative simulations, demanding both computational resources and domain expertise. This work introduces a faster approach by leveraging advanced deep-learning algorithms to streamline the optimization of chiral meta-nano surfaces. While using diatomic unit-element as the meta-surface’s building blocks, our proposed methodology harnesses the power of neural networks to predict and refine the geometrical layout of achiral nano-bars. The proposed framework is a Tandem Inverse Model (TIM) that incorporates a forward asymmetric transmission predicting neural network (ATNN) cascaded with an inverse neural network (INN). ATNN is trained in advance to enable swift and accurate prediction of the asymmetric optical behavior of meta-atoms with an MSE as low as 5.8 × 10-4. The complete TIM assembly is then trained together while updating the weights of INN only and keeping the pre-trained ATNN part frozen. This stacked arrangement of the forward and the inverse design models successfully addresses the fundamental non-uniqueness issue suffered in the inverse design problems. With an MSE of about 3, the trained TIM model can optimize the nano-bar’s geometrical characteristics very rapidly. The suggested model, therefore, greatly accelerates the process of designing intricate chiral meta-atoms by simultaneously optimizing eight geometrical parameters in a matter of seconds. With this model, the optimal geometrical parameters of the achiral nano-bars of the meta-atom exhibited an AT of approximately 70%. Realizing such a high AT offers several uses, including lasers, optical cloaking, and electromagnetic shielding.
KEYWORDS: Design, Education and training, Principal component analysis, Performance modeling, Data modeling, Random forests, Absorption, Machine learning, Tungsten, Solar energy
Metasurfaces have been emerging increasingly due to their realization of various technologies in meeting the design of multi-functional, compact, highly efficient, tunable, and low-cost designs owing to the fact that they can manipulate electromagnetic (EM) waves in a sub-wavelength thickness. In the optical regime, they have been successful in realizing transmission, reflection and absorption for a wide range of interesting applications. The metasurface absorbers have found place in energy harvesting applications. However, their design and analysis is carried out using EM solvers which in general are heavily time-consuming due to their iterative nature of solving a problem. To mitigate the problem of slackness and computational burdensome, the machine learning (ML) is becoming popular for tackling the data related problems and have been in use for making the design of metasurfaces faster. In this work, three ML algorithms namely, XGBoost, Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest (RF) have been applied both in forward and inverse topologies for a tungsten based square-ring meta-absorber. The inverse training has been carried out by employing “principal component analysis” (PCA). The operation of a meta-absorber is dependent on its geometry; thus, the training has been carried out by varying all the geometrical features of the unit element under study. The prediction performance of the presented regression models is reckoned to be accurate that the predicted values are in the near vicinity of ground truth values. The minimum MSE for the forward model attained for the case of RF is 5.08 ×10−3 and that of R2 is 0.9952, whereas for the inverse model, the minimum MSE of 2.05 and R2 score of 0.958 with 200 PCA components is achieved. The prediction time is minimum for the LASSO algorithm which is as low as one second. The lower computation time, reliable prediction, and model-free nature of ML techniques have made them useful against data imperfections and are proven to be an effective solution to time-consuming and computationally expensive tools for metasurface design.
KEYWORDS: Free space optics, Silicon, Telecommunications, Wireless communications, Design, Polarization, Near infrared, Visible radiation, Signal intensity, Refractive index
Optical wireless communication (OWC) or free space optical (FSO) communication systems transmit information through the atmosphere using light beams in the visible or near-infrared (NIR) spectrum. Since the radio frequency spectrum is overwhelmed and unable to meet the key performance indicators of FSO communication, high data rates can be provided by communicating over the visible or NIR spectrum. In this study, we have proposed a complementary metal-oxide semiconductor (CMOS) compatible all-silicon spin-encoded metalens that offers several key advantages for FSO communication applications. Firstly, it can be flawlessly merged with CMOS electronic devices, enabling the development of miniature-sized and cost-effective FSO communication systems. Secondly, due to silicon's high refractive index and all-silicon design, the proposed metalens offer effective light focusing and manipulation. Moreover, silicon is opted for because it is a transparent material for the visible and NIR spectrum, hence making it an appropriate choice for manufacturing a device for the application of efficient FSO communication. Furthermore, the all-silicon design of metalens makes it possible to be seamlessly integrated with other silicon-based photonic elements, including waveguides, modulators, and detectors. Our proposed CMOS, compatible with all silicon spin-encoded metalens, is an ultra-compact design that is capable of providing dual focal points simultaneously and transmitting different messages to different users at run time. We have designed and simulated a 60 µm × 60 µm all-silicon spin-encoded metalens and acquired two focal points by shining the linearly polarized light. Metalens inclusion within the FSO communication system opens new avenues for next-generation OWC, offering better focusing of light beams, enhancing the signal intensity, and escalating the overall communication range.
Optical vortex (OV) beams characterized by their twisted wave phase and the Orbital Angular Momentum (OAM) have garnered significant interest and found practical utility in diverse applications like optical communication, particle manipulation, and many more. The topological charge plays a crucial role in defining and recognizing the OV beams, which are controlled by the number of twisted waves and the radius of the intensity profile. The reliance of OV beams on the topological charge poses limitations in numerous applications that necessitate coupling and higher topological charge values. The perfect vortex beams integrated with the Metasurface are the nanoscale solution for the abovementioned issues. Previous studies have controlled the shape and size of the ideal vortex beam. This study demonstrates the broadband generation of polygonal perfect vortex beams through all-glass metasurfaces, aiming to manipulate the beam's shape while preserving its Orbital Angular Momentum (OAM) and radius. The presented metasurfaces carry an array of nanoantennas made of zinc sulfide material to generate a broadband perfect vortex beam within the visible spectrum, from 475 nm to 650 nm. For proof of concept, we have generated OV beams exhibiting multiple shapes like hexagons and octagons, and these shapes helped us make asymmetric intensity profiles. These irregular intensity distributions will help to create flexible optical traps for nanoparticles. The implications of our study include nano-optical trapping, optical manipulation, and optical communication.
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