arXiv subjects

Marin Soljačić

Publications and source records attributed to Marin Soljačić.

At least 109 records · Page 6Linked to original sources

arXiv2020

Computational inverse design for ultra-compact single-piece metalenses free of chromatic and angular aberration

We present full-Maxwell topology-optimization design of a single-piece multlayer metalens, about 10 wavelengths~$λ$ in thickness, that simultaneously focuses over a $60^\circ$ angular range and a 23\% spectral bandwidth without suffering chromatic or angular aberration, a "plan-achromat." At all angles and frequencies it achieves diffraction-limited focusing (Strehl ratio $> 0.8$) and absolute focusing efficiency $> 50$\%. Both 2D and 3D axi-symmetric designs are presented, optimized over $\sim 10^5$ degrees of freedom. We also demonstrate shortening the lens-to-sensor distance while producing the same image as for a longer "virtual" focal length and maintaining plan-achromaticity. These proof-of-concept designs demonstrate the ultra-compact multi-functionality that can be achieved by exploiting the full wave physics of subwavelength designs, and motivate future work on design and fabrication of multi-layer meta-optics.

physics.optics↗

arXiv2020

Fullwave Maxwell inverse design of axisymmetric, tunable, and multi-scale multi-wavelength metalenses

We demonstrate new axisymmetric inverse-design techniques that can solve problems radically different from traditional lenses, including \emph{reconfigurable} lenses (that shift a multi-frequency focal spot in response to refractive-index changes) and {\emph{widely separated}} multi-wavelength lenses ($λ= 1\,μ$m and $10\,μ$m). We also present experimental validation for an axisymmetric inverse-designed monochrome lens in the near-infrared fabricated via two-photon polymerization. Axisymmetry allows fullwave Maxwell solvers to be scaled up to structures hundreds or even thousands of wavelengths in diameter before requiring domain-decomposition approximations, while multilayer topology optimization with $\sim 10^5$ degrees of freedom can tackle challenging design problems even when restricted to axisymmetric structures.

physics.optics↗

arXiv2020

End-to-End Nanophotonic Inverse Design for Imaging and Polarimetry

By co-designing a meta-optical front end in conjunction with an image-processing back end, we demonstrate noise sensitivity and compactness substantially superior to either an optics-only or a computation-only approach, illustrated by two examples: subwavelength imaging and reconstruction of the full polarization coherence matrices of multiple light sources. Our end-to-end inverse designs couple the solution of the full Maxwell equations---exploiting all aspects of wave physics arising in subwavelength scatterers---with inverse-scattering algorithms in a single large-scale optimization involving $\gtrsim 10^4$ degrees of freedom. The resulting structures scatter light in a way that is radically different from either a conventional lens or a random microstructure, and suppress the noise sensitivity of the inverse-scattering computation by several orders of magnitude. Incorporating the full wave physics is especially crucial for detecting spectral and polarization information that is discarded by geometric optics and scalar diffraction theory.

physics.optics↗

arXiv2020

Extracting Interpretable Physical Parameters from Spatiotemporal Systems using Unsupervised Learning

Experimental data is often affected by uncontrolled variables that make analysis and interpretation difficult. For spatiotemporal systems, this problem is further exacerbated by their intricate dynamics. Modern machine learning methods are particularly well-suited for analyzing and modeling complex datasets, but to be effective in science, the result needs to be interpretable. We demonstrate an unsupervised learning technique for extracting interpretable physical parameters from noisy spatiotemporal data and for building a transferable model of the system. In particular, we implement a physics-informed architecture based on variational autoencoders that is designed for analyzing systems governed by partial differential equations (PDEs). The architecture is trained end-to-end and extracts latent parameters that parameterize the dynamics of a learned predictive model for the system. To test our method, we train our model on simulated data from a variety of PDEs with varying dynamical parameters that act as uncontrolled variables. Numerical experiments show that our method can accurately identify relevant parameters and extract them from raw and even noisy spatiotemporal data (tested with roughly 10% added noise). These extracted parameters correlate well (linearly with $R^2 > 0.95$) with the ground truth physical parameters used to generate the datasets. We then apply this method to nonlinear fiber propagation data, generated by an ab-initio simulation, to demonstrate its capabilities on a more realistic dataset. Our method for discovering interpretable latent parameters in spatiotemporal systems will allow us to better analyze and understand real-world phenomena and datasets, which often have unknown and uncontrolled variables that alter the system dynamics and cause varying behaviors that are difficult to disentangle.

physics.comp-ph↗

arXiv2020

Non-Abelian Generalizations of the Hofstadter model: Spin-orbit-coupled Butterfly Pairs

The Hofstadter model, well-known for its fractal butterfly spectrum, describes two-dimensional electrons under a perpendicular magnetic field, which gives rise to the integer quantum hall effect. Inspired by the real-space building blocks of non-Abelian gauge fields from a recent experiment [Science, 365, 1021 (2019)], we introduce and theoretically study two non-Abelian generalizations of the Hofstadter model. Each model describes two pairs of Hofstadter butterflies that are spin-orbit coupled. In contrast to the original Hofstadter model that can be equivalently studied in the Landau and symmetric gauges, the corresponding non-Abelian generalizations exhibit distinct spectra due to the non-commutativity of the gauge fields. We derive the genuine (necessary and sufficient) non-Abelian condition for the two models from the commutativity of their arbitrary loop operators. At zero energy, the models are gapless and host Weyl and Dirac points protected by internal and crystalline symmetries. Double (8-fold), triple (12-fold), and quadrupole (16-fold) Dirac points also emerge, especially under equal hopping phases of the non-Abelian potentials. At other fillings, the gapped phases of the models give rise to $\mathbb{Z}_2$ topological insulators. We conclude by discussing possible schemes for the experimental realizations of the models in photonic platforms.

cond-mat.mes-hall↗

arXiv2020

Integration of Neural Network-Based Symbolic Regression in Deep Learning for Scientific Discovery

Symbolic regression is a powerful technique that can discover analytical equations that describe data, which can lead to explainable models and generalizability outside of the training data set. In contrast, neural networks have achieved amazing levels of accuracy on image recognition and natural language processing tasks, but are often seen as black-box models that are difficult to interpret and typically extrapolate poorly. Here we use a neural network-based architecture for symbolic regression called the Equation Learner (EQL) network and integrate it with other deep learning architectures such that the whole system can be trained end-to-end through backpropagation. To demonstrate the power of such systems, we study their performance on several substantially different tasks. First, we show that the neural network can perform symbolic regression and learn the form of several functions. Next, we present an MNIST arithmetic task where a separate part of the neural network extracts the digits. Finally, we demonstrate prediction of dynamical systems where an unknown parameter is extracted through an encoder. We find that the EQL-based architecture can extrapolate quite well outside of the training data set compared to a standard neural network-based architecture, paving the way for deep learning to be applied in scientific exploration and discovery.

cs.LG↗

arXiv2020

Sub-micron single-particle perovskite plasmonic nanolasers at room temperature

Plasmonic nanolasers have received a substantial interest for their promising applications in integrated photonics, optical sensing, and biomedical imaging. To date, a room-temperature plasmonic nanolaser, submicron in all dimensions, remains elusive in the visible regime due to high metallic losses. Here, we demonstrate single-particle lasing around 2.3 eV with full-submicron, cesium lead bromide perovskite (CsPbBr3) crystals atop polymer-coated gold substrates at room temperature. With a large number (~100) of devices in total, we systematically study the lasing action of plasmonic test and photonic control groups. The achieved smallest plasmonic laser was 0.56 micrometer x 0.58 micrometer x 0.32 micrometer in size, ten-fold smaller than that of our smallest photonic laser. Key elements to efficient plasmonic lasing are identified as enhanced optical gain by the Purcell effect, long carrier diffusivity, a large spontaneous emission factor, and a high group index. Our results shed light on three-dimensional miniaturization of plasmonic lasers.

physics.optics↗

arXiv2020

Contextualizing Enhances Gradient Based Meta Learning

Meta learning methods have found success when applied to few shot classification problems, in which they quickly adapt to a small number of labeled examples. Prototypical representations, each representing a particular class, have been of particular importance in this setting, as they provide a compact form to convey information learned from the labeled examples. However, these prototypes are just one method of representing this information, and they are narrow in their scope and ability to classify unseen examples. We propose the implementation of contextualizers, which are generalizable prototypes that adapt to given examples and play a larger role in classification for gradient-based models. We demonstrate how to equip meta learning methods with contextualizers and show that their use can significantly boost performance on a range of few shot learning datasets. We also present figures of merit demonstrating the potential benefits of contextualizers, along with analysis of how models make use of them. Our approach is particularly apt for low-data environments where it is difficult to update parameters without overfitting. Our implementation and instructions to reproduce the experiments are available at https://github.com/naveace/proto-context.

cs.LG↗

arXiv2019

A Recurrent Ising Machine in a Photonic Integrated Circuit

Conventional computing architectures have no known efficient algorithms for combinatorial optimization tasks, which are encountered in fundamental areas and real-world practical problems including logistics, social networks, and cryptography. Physical machines have recently been proposed and implemented as an alternative to conventional exact and heuristic solvers for the Ising problem, one such optimization task that requires finding the ground state spin configuration of an arbitrary Ising graph. However, these physical approaches usually suffer from decreased ground state convergence probability or universality for high edge-density graphs or arbitrary graph weights, respectively. We experimentally demonstrate a proof-of-principle integrated nanophotonic recurrent Ising sampler (INPRIS) capable of converging to the ground state of various 4-spin graphs with high probability. The INPRIS exploits experimental physical noise as a resource to speed up the ground state search. By injecting additional extrinsic noise during the algorithm iterations, the INPRIS explores larger regions of the phase space, thus allowing one to probe noise-dependent physical observables. Since the recurrent photonic transformation that our machine imparts is a fixed function of the graph problem, and could thus be implemented with optoelectronic architectures that enable GHz clock rates (such as passive or non-volatile photonic circuits that do not require reprogramming at each iteration), our work paves a way for orders-of-magnitude speedups in exploring the solution space of combinatorially hard problems.

physics.optics↗

arXiv2019

Observation of Unidirectional Bound States in the Continuum Enabled by Topological Defects

Unidirectional radiation is important for a variety of optoelectronic applications. Many unidirectional emitters exist, but they all rely on the use of materials or structures that forbid outgoing waves, i.e. mirrors. Here, we theoretically propose and experimentally demonstrate a class of resonances in photonic crystal slabs, which only radiate towards a single side with no mirror placed on the other side - we call them ``unidirectional bound states in the continuum". These resonances are found to emerge when a pair of half-integer topological charges in the polarization field bounce into each other in the momentum space. We experimentally demonstrate such resonances in the telecommunication regime, where we achieve single-sided quality factor as high as 1.6e5, equivalent to a radiation asymmetry ratio of 27.7 dB. Our work represents a vivid example of applying topological principles to improve optoelectronic devices. Possible applications of our work include grating couplers, photoniccrystal surface-emitting lasers, and antennas for light detection and ranging.

physics.optics↗

arXiv2019

Synthesis and Observation of Non-Abelian Gauge Fields in Real Space

Gauge fields, real or synthetic, are crucial for understanding and manipulation of physical systems. The associated geometric phases can be measured, for example, from the Aharonov--Bohm interference. So far, real-space realizations of gauge fields have been limited to Abelian (commutative) ones. Here we report an experimental synthesis of non-Abelian gauge fields in real space and the observation of the non-Abelian Aharonov--Bohm effect with classical waves and classical fluxes. Based on optical mode degeneracy, we break time-reversal symmetry in different manners---via temporal modulation and the Faraday effect---to synthesize tunable non-Abelian gauge fields. The Sagnac interference of two final states, obtained by reversely-ordered path integrals, demonstrates the non-commutativity of the gauge fields. Our work introduces real-space building blocks for non-Abelian gauge fields, relevant for classical and quantum exotic topological phenomena.

physics.optics↗

arXiv2019

Large-Scale Optical Neural Networks based on Photoelectric Multiplication

Recent success in deep neural networks has generated strong interest in hardware accelerators to improve speed and energy consumption. This paper presents a new type of photonic accelerator based on coherent detection that is scalable to large ($N \gtrsim 10^6$) networks and can be operated at high (GHz) speeds and very low (sub-aJ) energies per multiply-and-accumulate (MAC), using the massive spatial multiplexing enabled by standard free-space optical components. In contrast to previous approaches, both weights and inputs are optically encoded so that the network can be reprogrammed and trained on the fly. Simulations of the network using models for digit- and image-classification reveal a "standard quantum limit" for optical neural networks, set by photodetector shot noise. This bound, which can be as low as 50 zJ/MAC, suggests performance below the thermodynamic (Landauer) limit for digital irreversible computation is theoretically possible in this device. The proposed accelerator can implement both fully-connected and convolutional networks. We also present a scheme for back-propagation and training that can be performed in the same hardware. This architecture will enable a new class of ultra-low-energy processors for deep learning.

cs.ET↗

arXiv2019

Plasmon-Emitter Interactions at the Nanoscale

Plasmon-emitter interactions are of paramount importance in modern nanoplasmonics and are generally maximal at short emitter-surface separations. However, when the separation falls below 10-20 nm, the classical theory progressively deteriorates due to its neglect of quantum mechanical effects such as nonlocality, electronic spill-out, and Landau damping. Here, we show how this neglect can be remedied by presenting a unified theoretical treatment of mesoscopic electrodynamics grounded on the framework of Feibelman $d$-parameters. Crucially, our technique naturally incorporates nonclassical resonance shifts and surface-enabled Landau damping - a nonlocal damping effect - which have a dramatic impact on the amplitude and spectral distribution of plasmon-emitter interactions. We consider a broad array of plasmon-emitter interactions ranging from dipolar and multipolar spontaneous emission enhancement, to plasmon-assisted energy transfer and enhancement of two-photon transitions. The formalism presented here gives a complete account of both plasmons and plasmon-emitter interactions at the nanoscale, constituting a simple yet rigorous and general platform to incorporate nonclassical effects in plasmon-empowered nanophotonic phenomena.

cond-mat.mes-hall↗

arXiv2019

Maximal Spontaneous Photon Emission and Energy Loss from Free Electrons

Free electron radiation such as Cerenkov, Smith--Purcell, and transition radiation can be greatly affected by structured optical environments, as has been demonstrated in a variety of polaritonic, photonic-crystal, and metamaterial systems. However, the amount of radiation that can ultimately be extracted from free electrons near an arbitrary material structure has remained elusive. Here we derive a fundamental upper limit to the spontaneous photon emission and energy loss of free electrons, regardless of geometry, which illuminates the effects of material properties and electron velocities. We obtain experimental evidence for our theory with quantitative measurements of Smith--Purcell radiation. Our framework allows us to make two predictions. One is a new regime of radiation operation---at subwavelength separations, slower (nonrelativistic) electrons can achieve stronger radiation than fast (relativistic) electrons. The second is a divergence of the emission probability in the limit of lossless materials. We further reveal that such divergences can be approached by coupling free electrons to photonic bound states in the continuum (BICs). Our findings suggest that compact and efficient free-electron radiation sources from microwaves to the soft X-ray regime may be achievable without requiring ultrahigh accelerating voltages.

physics.optics↗

arXiv2019

A General Theoretical and Experimental Framework for Nanoscale Electromagnetism

Local, bulk response functions, e.g permittivity, and the macroscopic Maxwell equations completely specify the classical electromagnetic problem, which features only wavelength $λ$ and geometric scales. The above neglect of intrinsic electronic length scales $L_{\text{e}}$ leads to an eventual breakdown in the nanoscopic limit. Here, we present a general theoretical and experimental framework for treating nanoscale electromagnetic phenomena. The framework features surface-response functions---known as the Feibelman $d$-parameters---which reintroduce the missing electronic length scales. As a part of our framework, we establish an experimental procedure to measure these complex, dispersive surface response functions, enabled by quasi-normal-mode perturbation theory and observations of pronounced nonclassical effects---spectral shifts in excess of 30% and the breakdown of Kreibig-like broadening---in a quintessential multiscale architecture: film-coupled nanoresonators, with feature-sizes comparable to both $L_{\text{e}}$ and $λ$.

physics.optics↗

arXiv2018

Topologically Enabled Ultra-high-Q Guided Resonances Robust to Out-of-plane Scattering

Due to their ability to confine light, optical resonators are of great importance to science and technology, yet their performances are often limited by out-of-plane scattering losses from inevitable fabrication imperfections. Here, we theoretically propose and experimentally demonstrate a class of guided resonances in photonic crystal slabs, where out-of-plane scattering losses are strongly suppressed due to their topological nature. Specifically, these resonances arise when multiple bound states in the continuum - each carrying a topological charge - merge in the momentum space and enhance the quality factors of all resonances nearby. We experimentally achieve quality factors as high as $4.9\times 10^5$ based on these resonances in the telecommunication regime, which is 12-times higher than ordinary designs. We further show this enhancement is robust across the samples we fabricated.Our work paves the way for future explorations of topological photonics in systems with open boundary condition and their applications in improving optoelectronic devices in photonic integrated circuits.

physics.optics↗

arXiv2018

Bound states in the continuum in fiber Bragg gratings

Optical fibers typically confine light through total internal reflection or through photonic bandgaps. Here we show that light can be perfectly guided in optical fibers through a different mechanism based on bound states in the continuum (BICs). In fibers with periodic Bragg gratings, we predict bona fide BICs in pure-polarization modes, as well as quasi-BICs in hybrid-polarization modes. These guided modes exist robustly without the need for fine structural tuning, and they persist even with the very small grating index contrasts that are available in conventional fiber Bragg gratings. The suppression of radiation loss arises from the coupling between a weakly-radiating mode and a strongly-radiating one. This finding opens the possibility of guiding light with BICs in optical fibers and their applications in distributed fiber sensors, in-line fiber filters, and high-power fiber lasers.

physics.optics↗

arXiv2018

Plasmonics in Argentene

Two-dimensional materials exhibit a fascinating range of electronic and photonic properties vital for nanophotonics, quantum optics and emerging quantum information technologies. Merging concepts from the fields of ab initio materials science and nanophotonics, there is now an opportunity to engineer new photonic materials whose optical, transport, and scattering properties are tailored to attain thermodynamic and quantum limits. Here, we present first-principles calculations predicting that Argentene, a single-crystalline hexagonal close-packed monolayer of Ag, can dramatically surpass the optical properties and electrical conductivity of conventional plasmonic materials. In the low-frequency limit, we show that the scattering rate and resistivity reduce by a factor of three compared to the bulk three-dimensional metal. Most importantly, the low scattering rate extends to optical frequencies in sharp contrast to e.g. graphene, whose scattering rate increase drastically in the near-infrared range due to optical-phonon scattering. Combined with an intrinsically high carrier density, this facilitates highly-confined surface plasmons extending to visible frequencies. We evaluate Argentene across three distinct figures of merit, spanning the spectrum of typical plasmonic applications; in each, Argentene outperforms the state-of-the-art. This unique combination of properties will make Argentene a valuable addition to the two-dimensional heterostructure toolkit for quantum electronic and photonic technologies.

physics.app-ph↗