arXiv subjects

Aaswath P. Raman

Publications and source records attributed to Aaswath P. Raman.

At least 19 recordsLinked to original sources

arXiv2026

Magneto-optic perturbation theory for near-complete violation of Kirchhoff's law of thermal emission at low magnetic fields

Magneto-optic photonic systems can violate Kirchhoff's law of thermal emission by breaking Lorentz reciprocity. We develop a dispersive perturbation theory yielding an analytical expression for magneto-optical resonance frequency shifts in plasmonic semiconductors under applied magnetic fields. This expression shows the shift is governed by the overlap of the mode's optical spin density with the magneto-optical material. We use this expression to design a III-V metasurface that achieves nonreciprocal emissivity contrast of 0.8 at only 0.1 T, and demonstrate that the theory can explain order-of magnitude differences in magnetic field sensitivity between different photonic structures.

physics.optics↗

arXiv2026

Nonreciprocal Thermophotonic Cooling

Solid-state cooling via electroluminescent emission from light-emitting diodes is a promising alternative to thermoelectric and vapor-compression refrigeration, but practical performance remains limited by nonradiative losses and unfavorable tradeoffs between efficiency and cooling power. Thermophotonic (TPX) architectures partially address this by recycling PV-generated power back to the LED, improving the coefficient of performance (COP) but introducing a parasitic backward photon flux from the PV that reduces the cooling power density. Here we show that this tradeoff can be circumvented by inserting a nonreciprocal semi-transparent intermediate layer that violates Kirchhoff's law of thermal radiation. The layer permits unity transmission from the LED to the PV while fully absorbing the backward PV flux, functioning as a radiative heat shield that re-emits toward the LED at a lower intermediate temperature. In the idealized limit for $Δ$ T = 50 K between the hot and cold side, the nonreciprocal filter improves the cooling power density by nearly an order of magnitude over the unfiltered TPX case while preserving the COP benefit, while a reciprocal filter provides no improvement. Incorporating Shockley-Read-Hall and Auger recombination into GaAs and InP-based LED device models, we find enhancements of approximately 50% in both cooling power density and COP persisting across temperature differences from $Δ$ T = 50 K to 100 K. These results highlight the potential importance of electromagnetic nonreciprocity in improving the real-world performance of thermophotonic cooling devices.

physics.app-ph↗

arXiv2026

Quasi-bandgap behavior in non-Hermitian photonic crystals

We investigate non-Hermitian photonic crystals in which the lossy and lossless constituents share the same real permittivity and differ only in their imaginary part. We characterize the complex band structure and reflection response of both one-dimensional (1D) and two-dimensional (2D) systems, and show that introducing even a small amount of material loss opens a quasi bandgap at the Brillouin-zone boundary. This quasi bandgap, absent in the lossless limit of the same structure, gives rise to sharp reflectivity peaks whose origin we explain through second-order perturbation theory. As an application of this behavior, we demonstrate a selective reflector combining a conventional photonic-crystal waveguide with a non-Hermitian photonic crystal, achieving wavelength-selective reflection with broadband absorption.

physics.optics↗

arXiv2025

Loss-driven gain enhancements driven by topological singularities in non-Hermitian photonic crystals defects

We show that purely lossy defects in one- and two-dimensional non-Hermitian photonic crystals can induce transmission matrix singularities not accessible with lossless defects. These singularities in turn can enable dramatic enhancement in overall system gain not accessible through conventional means. We further show that the underlying mechanism behind the loss-induced gain enhancement is due to the resonances being located specifically at topological branch cut singularities in the reflection coefficient with nontrivial winding numbers. The resulting resonances can exhibit exceptionally high quality factors in excess of $\sim 10^4$. Our work highlights the counterintuitive role of loss in engineering singularities in the gain response in non-Hermitian systems and its connection to topological phenomena in photonic systems.

physics.optics↗

arXiv2025

Experimental demonstration of corrugated nanolaminate films as reflective light sails

Achieving laser-driven, reflective, relativistic light sails would represent a tremendous breakthrough for humankind, allowing us to advance our understanding of the solar system and deep space far beyond what we know from space probes, telescopes, and objects passing near Earth. Numerous sail film designs have been proposed, but none have been demonstrated that satisfy all of the stringent optical, mechanical, and mass budget constraints. Here we overcome this challenge by experimentally demonstrating a novel class of optically-optimized nanolaminate sails with strong and flexible hexagonally-corrugated microstructures. Our prototypes, fabricated from alumina and molybdenum disulfide using scalable semiconductor processing techniques, feature ultra-low areal densities of <1 g/m^2 and achieve experimentally-measured reflectivities of >50% and absorptivities of <4% within the Doppler-shifted laser wavelength range corresponding to accelerating to a fifth the speed of light. Moreover, we analyze reflectivity, strength, and mass constraints to show that our sails have the potential to achieve greater maximum velocities than other sail designs in the literature. Broadly, our films mark a significant leap forward toward plausible relativistic interstellar propulsion for intragalactic exploration

physics.optics↗

arXiv2025

The Thermal Cost of Harvesting the Solar Infrared Tail in Space-Based Photovoltaics

Space applications require the highest performing photovoltaic devices. Typically these are multi-junction devices that are capable of converting a larger bandwidth of the solar spectrum. However, the conversion of longer wavelength photons in the solar infrared tail results in a disproportionate increase in generated heat per watt of electricity, and since devices have limited heat rejection in space, this excess heat raises the operating temperature, lowering the efficiency of the entire device. Here we show that space-based photovoltaics should avoid converting the solar infrared tail past an optimum terminal cutoff wavelength, and that this will lead to higher electrical output. In addition, a lower operating temperature may lead to higher end-of-life power outputs.

physics.app-ph↗

arXiv2025

Directional Thermal Emission Across Both Polarizations in Planar Photonic Architectures

Directional and spectral control of thermal emission is essential for applications in energy conversion, imaging, and sensing. Existing planar, lithography-free epsilon-near-zero (ENZ) films only support transverse-magnetic (TM) control of thermal emission via the Berreman mode and cannot address transverse-electric (TE) waves due to the absence of natural optical magnetism over optical and infrared wavelengths Here, we introduce a hyperbolic metamaterial comprising alternating layers of degenerately-doped and intrinsic InAs that exhibits an epsilon-and-mu-near-zero (EMNZ) response, enabling dual-polarized, directionally and spectrally selective thermal emission. We first theoretically demonstrate that a mu-near-zero (MNZ) film on a perfect magnetic conductor supports a magnetic Berreman mode, absorbing TE-polarized radiation in analogy to the conventional Berreman mode supported in TM polarization. Using genetic and gradient-descent optimization, we design a dual-polarized emitter with independently tunable spectral peaks and emission angles. Parameter retrieval via homogenization confirms simultaneous EMNZ points at the target wavelengths and angles. Finally, experimental measurement of a sample fabricated via molecular beam epitaxy exhibits high absorptivity peaks for both polarizations in close agreement with simulations. This work realizes lithography-free, dual-polarized, spectrally and directionally selective emitters, offering a versatile platform for advanced infrared thermal management and device integration.

cond-mat.mtrl-sci↗

arXiv2024

Thermo-optic tuning of directional infrared emissivity

Tuning the spatial extent of directional thermal emission across an arbitrary, and fixed spectral bandwidth is a fundamentally enabling capability for a range of emerging applications such as thermophotovoltaics, thermal imaging, and radiative cooling. However, previous experimental demonstrations were limited to narrow bandwidths, and the resonance frequency itself changed significantly as a function of the reconfigured directional response. Here, we demonstrate thermo-optic tuning of directional infrared emissivity using InAs-based gradient ENZ materials functioning as broadband directional thermal emitters whose angular selectivity can be modified via thermal free-carrier effects. We experimentally demonstrate two emitters achieving a 5° and 10° increase in the angular extent of their directional emissivity in the p-polarization across a prescribed, broad wavelength range of operation (12.5 to 15$μ$m), for moderate temperatures below 400 K. Temperature-driven control of directional emissivity offers a new mode of post-fabrication control of radiative heat transfer that may in turn enable novel device functionalities.

physics.optics↗

arXiv2023

Resonant Anti-Reflection Metasurface for Infrared Transmission Optics

A fundamental capability for any transmissive optical component is anti-reflection, yet this capability is challenging to achieve in a cost-efficient manner over longer infrared wavelengths. We demonstrate that Mie resonant nanophotonic structures enhance transmission in Silicon, allowing it to function as an effective optical material over long-wave infrared wavelengths. This approach enables a window optic with up to 40\% greater transmission than equal thickness unpatterned Si. Imaging comparisons with unpatterned silicon and off-the-shelf Germanium optics are shown, as well as basic broadband slant edge MTF measurements. Overall, we demonstrate how Mie-resonant structures can be used to improve optical transmission through window optics of arbitrary lithographically patternable optical media, and highlight their possible use in imaging applications.

physics.optics↗

arXiv2022

Simultaneous control of spectral and directional emissivity with gradient epsilon-near-zero InAs photonic structures

Controlling both the spectral bandwidth and directional range of emitted thermal radiation is a fundamental challenge in modern photonics and materials research. Recent work has shown that materials with a spatial gradient in their epsilon near zero response can support broad spectrum directionality in their emissivity, enabling high radiance to specific angles of incidence. However, this capability has been limited spectrally and directionally by the availability of materials supporting phonon-polariton resonances over long-wave infrared wavelengths. Here, we design and experimentally demonstrate an approach using doped III-V semiconductors that can simultaneously tailor spectral peak, bandwidth and directionality of infrared emissivity. We epitaxially grow and characterize InAs-based gradient ENZ photonic structures that exhibit broadband directional emission with varying spectral bandwidths and peak directions as a function of their doping concentration profile and thickness. Due to its easy-to-fabricate geometry we believe this approach provides a versatile photonic platform to dynamically control broadband spectral and directional emissivity for a range of emerging applications.

physics.optics↗

arXiv2022

DeepAdjoint: An All-in-One Photonic Inverse Design Framework Integrating Data-Driven Machine Learning with Optimization Algorithms

In recent years, hybrid design strategies combining machine learning (ML) with electromagnetic optimization algorithms have emerged as a new paradigm for the inverse design of photonic structures and devices. While a trained, data-driven neural network can rapidly identify solutions near the global optimum with a given dataset's design space, an iterative optimization algorithm can further refine the solution and overcome dataset limitations. Furthermore, such hybrid ML-optimization methodologies can reduce computational costs and expedite the discovery of novel electromagnetic components. However, existing hybrid ML-optimization methods have yet to optimize across both materials and geometries in a single integrated and user-friendly environment. In addition, due to the challenge of acquiring large datasets for ML, as well as the exponential growth of isolated models being trained for photonics design, there is a need to standardize the ML-optimization workflow while making the pre-trained models easily accessible. Motivated by these challenges, here we introduce DeepAdjoint, a general-purpose, open-source, and multi-objective "all-in-one" global photonics inverse design application framework which integrates pre-trained deep generative networks with state-of-the-art electromagnetic optimization algorithms such as the adjoint variables method. DeepAdjoint allows a designer to specify an arbitrary optical design target, then obtain a photonic structure that is robust to fabrication tolerances and possesses the desired optical properties - all within a single user-guided application interface. Our framework thus paves a path towards the systematic unification of ML and optimization algorithms for photonic inverse design.

eess.SP↗

arXiv2022

Hybrid Supervised and Reinforcement Learning for the Design and Optimization of Nanophotonic Structures

From higher computational efficiency to enabling the discovery of novel and complex structures, deep learning has emerged as a powerful framework for the design and optimization of nanophotonic circuits and components. However, both data-driven and exploration-based machine learning strategies have limitations in their effectiveness for nanophotonic inverse design. Supervised machine learning approaches require large quantities of training data to produce high-performance models and have difficulty generalizing beyond training data given the complexity of the design space. Unsupervised and reinforcement learning-based approaches on the other hand can have very lengthy training or optimization times associated with them. Here we demonstrate a hybrid supervised learning and reinforcement learning approach to the inverse design of nanophotonic structures and show this approach can reduce training data dependence, improve the generalizability of model predictions, and shorten exploratory training times by orders of magnitude. The presented strategy thus addresses a number of contemporary deep learning-based challenges, while opening the door for new design methodologies that leverage multiple classes of machine learning algorithms to produce more effective and practical solutions for photonic design.

cs.LG↗

arXiv2022

Temporal coupled-mode theory for thermal emission from multiple arbitrarily coupled resonators

Controlling the spectral response of thermal emitters has become increasingly important for a range of energy and sensing applications. Conventional approaches to achieving arbitrary spectrum selectivity in photonic systems have entailed combining multiple resonantly emissive elements together to achieve a range of spectral profiles through numerical optimization, with a universal theoretical framework lacking. Here, we develop a temporal coupled mode theory for thermal emission from multiple, arbtirarily-coupled resonators. We validate our theory against numerical simulations of complex two- and three-dimensional nanophotonic thermal emitters, highlighting the anomalous thermal emission spectra that can emerge when multiple resonators with arbitrary properties couple to each other with varying strengths.

physics.optics↗

arXiv2022

Enhancing Adjoint Optimization-based Photonics Inverse Design with Explainable Machine Learning

A fundamental challenge in the design of photonic devices, and electromagnetic structures more generally, is the optimization of their overall architecture to achieve a desired response. To this end, topology or shape optimizers based on the adjoint variables method have been widely adopted due to their high computational efficiency and ability to create complex freeform geometries. However, the functional understanding of such freeform structures remains a black box. Moreover, unless a design space of high-performance devices is known in advance, such gradient-based optimizers can get trapped in local minima valleys or saddle points, which limits performance achievable through this inverse design process. To elucidate the relationships between device performance and nanoscale structuring while mitigating the effects of local minima trapping, we present an inverse design framework that combines adjoint optimization, automated machine learning (AutoML), and explainable artificial intelligence (XAI). Integrated with a numerical electromagnetics simulation method, our framework reveals structural contributions towards a figure-of-merit (FOM) of interest. Through an explanation-based reoptimization process, this information is then leveraged to minimize the FOM further than that obtained through adjoint optimization alone, thus overcoming the optimization's local minima. We demonstrate our framework in the context of waveguide design and achieve between 39% and 74% increases in device performance relative to state-of-the-art adjoint optimization-based inverse design across a range of telecom wavelengths. Results of this work therefore highlight machine learning strategies that can substantially extend and enhance the capabilities of a conventional, optimization-based inverse design algorithm while revealing deeper insights into the algorithm's designs.

physics.optics↗

arXiv2021

Multi-scale photonic emissivity engineering for relativistic lightsail thermal regulation

The Breakthrough Starshot Initiative aims to send a gram-scale probe to Proxima Centuri B using a laser-accelerated lightsail traveling at relativistic speeds. Thermal management is a key lightsail design objective because of the intense laser powers required but has generally been considered secondary to accelerative performance. Here, we demonstrate nanophotonic photonic crystal slab reflectors composed of 2H-phase molybdenum disulfide and crystalline silicon nitride, highlight the inverse relationship between the thermal band extinction coefficient and the lightsail's maximum temperature, and examine the trade-off between the acceleration distance and setting realistic sail thermal limits, ultimately realizing a thermally endurable acceleration minimum distance of 16.3~Gm. We additionally demonstrate multi-scale photonic structures featuring thermal-wavelength-scale Mie resonant geometries, and characterize their broadband Mie resonance-driven emissivity enhancement and acceleration distance reduction. Our results highlight new possibilities in simultaneously controlling optical and thermal response over broad wavelength ranges in ultralight nanophotonic structures.

astro-ph.IM↗

arXiv2021

Accurately Quantifying Radiative Cooling Potentials: A Temperature-correction to the Transmittance-based approximation

Theoretical calculations of the cooling potential of radiative cooling materials are crucial for determining their cooling capability under different meteorological conditions and evaluating their performance. To enable these calculations, accurate models of long-wave infrared downwelling atmospheric irradiance are needed, However, the transmittance-based cosine approximation, which is widely used to determine radiative cooling potentials, does not account for the cooling potential arising from heat loss to the colder reaches of the atmosphere itself. Here, we show that use of the approximation can lead to > 10% underestimation of the cooling potential relative to MODTRAN 6 outputs. We propose a temperature correction to the transmittance-based approximation which accounts for heat loss to the cold upper atmosphere, and significantly reduces this underestimation, while retaining the advantages of the original model. In light of the widespread and continued use of the transmittance-based model, our results highlight an important source of potential errors and a means to correct for them.

physics.ao-ph↗

arXiv2021

Global Inverse Design Across Multiple Photonic Structure Classes Using Generative Deep Learning

Understanding how nano- or micro-scale structures and material properties can be optimally configured to attain specific functionalities remains a fundamental challenge. Photonic metasurfaces, for instance, can be spectrally tuned through material choice and structural geometry to achieve unique optical responses. However, existing numerical design methods require prior identification of specific material-structure combinations, or device classes, as the starting point for optimization. As such, a unified solution that simultaneously optimizes across materials and geometries has yet to be realized. To overcome these challenges, we present a global deep learning-based inverse design framework, where a conditional deep convolutional generative adversarial network is trained on colored images encoded with a range of material and structural parameters, including refractive index, plasma frequency, and geometric design. We demonstrate that, in response to target absorption spectra, the network can identify an effective metasurface in terms of its class, materials properties, and overall shape. Furthermore, the model can arrive at multiple design variants with distinct materials and structures that present nearly identical absorption spectra. Our proposed framework is thus an important step towards global photonics and materials design strategies that can identify combinations of device categories, material properties, and geometric parameters which algorithmically deliver a sought functionality.

physics.optics↗

arXiv2021

Nanostructured Plasmonic Metal Surfaces as Optical Components for Infrared Imaging and Sensing

Thermal imaging and sensing technologies offer critical information about our thermally radiant world, and in recent years, have seen dramatic increases in usage for a range of applications. However, the cost and technical finesse of manufacturing infrared optical components remain a major barrier towards the democratization of these technologies. In this report, we present a solution processed plasmonic reflective filter or PRF as a scalable and inexpensive thermal infrared optic. The PRF selectively absorbs sunlight and specularly reflects thermal infrared TIR wavelengths with performance comparable to state-of-the-art TIR optics made of materials like Germanium. Unlike traditional infrared optical components, however, the PRF can be conveniently fabricated using inexpensive materials and a dip and dry chemical synthesis technique, and crucially, has manufacturing costs that are orders of magnitude lower. We experimentally demonstrate the core optical functionality of the PRF, as well as its integration into infrared imaging and sensing systems without compromising their thermographic or radiometric capabilities. From a practical standpoint, the inexpensive and convenient fabricability of the PRF represent a significant advance towards making the benefits of thermal imaging and sensing systems more affordable and accessible. Scientifically, our work demonstrates a previously unexplored optical functionality and a new direction for versatile chemical synthesis in designing optical components.

physics.optics↗