arXiv subjects

Aaswath P. Raman

Publications and source records attributed to Aaswath P. Raman.

22 records · Page 2Linked to original sources

arXiv2020

Multiplexed Supercell Metasurface Design and Optimization with Tandem Residual Networks

Complex nanophotonic structures hold the potential to deliver exquisitely tailored optical responses for a range of applications. Metal-insulator-metal (MIM) metasurfaces arranged in supercells, for instance, can be tailored by geometry and material choice to exhibit a variety of absorption properties and resonant wavelengths. With this flexibility, however, comes a vast space of design possibilities that classical design paradigms struggle to effectively navigate. To overcome this challenge, here we demonstrate a tandem residual network approach to efficiently generate multiplexed supercells through inverse design. By using a training dataset with several thousand full-wave electromagnetic simulations in a design space of over three trillion possible designs, the deep learning model can accurately generate a wide range of complex supercell designs given a spectral target. Beyond inverse design, the presented approach can also be used to explore the structure-property relationships of broadband absorption and emission in such supercell configurations. Thus, this study demonstrates the feasibility of high-dimensional supercell inverse design with deep neural networks that is applicable to complex nanophotonic structures composed of multiple subunit elements that may exhibit coupling.

physics.optics↗

arXiv2020

Broadband directional control of thermal emission

Controlling the directionality of emitted far-field thermal radiation is a fundamental challenge in contemporary photonics and materials research. While photonic strategies have enabled angular selectivity of thermal emission over narrow sets of bandwidths, thermal radiation is inherently a broadband phenomenon. We currently lack the ability to constrain emitted thermal radiation to arbitrary angular ranges over broad bandwidths. Here, we introduce and experimentally realize gradient epsilon-near-zero (ENZ) material structures that enable broad spectrum directional control of thermal emission by supporting leaky electromagnetic modes that couple to free space at fixed angles over a broad bandwidth. We demonstrate two emitter structures consisting of multiple semiconductor oxides in a photonic configuration that enable gradient ENZ behavior over long-wave infrared wavelengths. The structures exhibit high average emissivity (greater than 0.6 and 0.7) in the p polarization between 7.7 and 11.5 micron over an angular range of 70 deg - 85 deg, and between 10.0 to 14.3 micron over an angular range of 60 deg-75 deg, respectively. Outside these angular ranges, the emissivity dramatically drops to 0.4 at 50 deg and 40 deg. The structures broadband thermal beaming capability enables strong radiative heat transfer only at particular angles and is experimentally verified through direct measurements of thermal emission. By decoupling conventional limitations on angular and spectral response, our approach opens new possibilities for radiative heat transfer in applications such as thermal camouflaging, solar heating, radiative cooling and waste heat recovery.

physics.optics↗

arXiv2020

Modeling and optimization of radiative cooling based thermoelectric generators

The possibility of night-time power generation has recently stimulated interest in using the radiative sky cooling mechanism with thermoelectric generators (TEG). These passive, low-temperature difference devices have been shown to generate electricity at night with no active input of heat needed, instead using the ambient air itself as the heat source. Here, we optimize both the geometry and operating conditions of radiative cooling driven thermoelectric (RC-TE) generators. We determine the optimal operating conditions, including maximum power point and maximum efficiency point, by developing a combined thermal and electrical model. Our results show that the optimal operating condition results in larger power output than was previously expected. Moreover, we show that maximum power density occurs when the area ratio between cooler and P or N element reaches an optimal value. Finally, we perform a parametric study that takes account of environmental and structural parameters to improve the performance of the RC-TE device, including enhancing heat transfer between the hot surface and ambient air, suppressing the cooling loss of the radiative cooler, and optimizing the geometry of individual thermocouples. Our work identifies how to maximize the output of RC-TE devices and provides comprehensive guidance on making use of this new passive power generation method.

physics.app-ph↗

arXiv2020

Elucidating the Behavior of Nanophotonic Structures Through Explainable Machine Learning Algorithms

A central challenge in the development of nanophotonic structures is identifying the optimal design for a target functionality, and understanding the physical mechanisms that enable the optimized device's capabilities. Previously investigated design methods for nanophotonic structures, including both conventional optimization approaches as well as nascent machine learning (ML) strategies, have made progress, yet they remain 'black boxes' that lack explanations for their predictions. Here we demonstrate that convolutional neural networks (CNN) trained to predict the electromagnetic response of classes of metal-dielectric-metal metamaterials, including complex freeform designs, can be explained to reveal deeper insights into the underlying physics of nanophotonic structures. Using an explainable AI (XAI) approach, we show that we can identify the importance of specific spatial regions of a nanophotonic structure for the presence or lack of an absorption peak. Our results highlight that ML strategies can be used for physics discovery, as well as design optimization, in optics and photonics.

physics.optics↗