Transient perturbative nonlinear responses of plasmonic materials

Recent investigations on optical nonlinearities of plasmonic materials suggest their responses may be even beyond the usual perturbative description. To better understand these surprisingly strong responses, we develop here a simple but general approach to describe the nonlinear optical response of plasmonic materials up to $n$th perturbation order. We apply the approach to understand spectral broadening occurring in resonant metasurfaces and investigate the enhancement of high-harmonic generation from multiply-resonant metasurfaces, predicting an over million-fold enhancement of higher harmonics.Comment: 6 pages, 2 figure

Demonstration of Optical Nonlinearity in InGaAsP/InP Passive Waveguides

We report on the study of the third-order nonlinear optical interactions in In$_{x}$Ga$_{1-x}$As$_{y}$P$_{1-y}$/InP strip-loaded waveguides. The material composition and waveguide structures were optimized for enhanced nonlinear optical interactions. We performed self-phase modulation, four-wave mixing and nonlinear absorption measurements at the pump wavelength 1568 nm in our waveguides. The nonlinear phase shift of up to $2.5\pi$ has been observed in self-phase modulation experiments. The measured value of the two-photon absorption coefficient $\alpha_2$ was 15 cm/GW. The four-wave mixing conversion range, representing the wavelength difference between maximally separated signal and idler spectral components, was observed to be 45 nm. Our results indicate that InGaAsP has a high potential as a material platform for nonlinear photonic devices, provided that the operation wavelength range outside the two-photon absorption window is selected

DeepTx: Deep Learning Beamforming with Channel Prediction

Machine learning algorithms have recently been considered for many tasks in the field of wireless communications. Previously, we have proposed the use of a deep fully convolutional neural network (CNN) for receiver processing and shown it to provide considerable performance gains. In this study, we focus on machine learning algorithms for the transmitter. In particular, we consider beamforming and propose a CNN which, for a given uplink channel estimate as input, outputs downlink channel information to be used for beamforming. The CNN is trained in a supervised manner considering both uplink and downlink transmissions with a loss function that is based on UE receiver performance. The main task of the neural network is to predict the channel evolution between uplink and downlink slots, but it can also learn to handle inefficiencies and errors in the whole chain, including the actual beamforming phase. The provided numerical experiments demonstrate the improved beamforming performance.Comment: 27 pages, this work has been submitted to the IEEE for possible publication; v2: Fixed typo in author name, v3: a revisio

Using surface lattice resonances to engineer nonlinear optical processes in metal nanoparticle arrays

Collective responses of localized surface plasmon resonances, known as surface lattice resonances (SLRs) in metal nanoparticle arrays, can lead to high quality factors (~100), large local-field enhancements and strong light-matter interactions. SLRs have found many applications in linear optics, but little work of the influence of SLRs on nonlinear optics has been reported. Here we show how SLRs could be utilized to enhance nonlinear optical interactions. We devote special attention to the sum-frequency, difference-frequency, and third-harmonic generation processes because of their potential for the realization of novel sources of light. We also demonstrate how such arrays could be engineered to enhance higher-order nonlinear optical interactions through cascaded nonlinear processes. In particular, we demonstrate how the efficiency of third-harmonic generation could be engineered via cascaded second-order responses

HybridDeepRx: Deep Learning Receiver for High-EVM Signals

In this paper, we propose a machine learning (ML) based physical layer receiver solution for demodulating OFDM signals that are subject to a high level of nonlinear distortion. Specifically, a novel deep learning based convolutional neural network receiver is devised, containing layers in both time- and frequency domains, allowing to demodulate and decode the transmitted bits reliably despite the high error vector magnitude (EVM) in the transmit signal. Extensive set of numerical results is provided, in the context of 5G NR uplink incorporating also measured terminal power amplifier characteristics. The obtained results show that the proposed receiver system is able to clearly outperform classical linear receivers as well as existing ML receiver approaches, especially when the EVM is high in comparison with modulation order. The proposed ML receiver can thus facilitate pushing the terminal power amplifier (PA) systems deeper into saturation, and thereon improve the terminal power-efficiency, radiated power and network coverage.Comment: To be presented in the 2021 IEEE International Symposium on Personal, Indoor and Mobile Radio Communication

Thermal Control of Plasmonic Surface Lattice Resonances

Plasmonic metasurfaces exhibiting collective responses known as surface lattice resonances (SLRs) show potential for realizing tunable and flat photonic components for wavelength-selective processes, including lasing and optical nonlinearities. However, post-fabrication tuning of SLRs remains challenging, limiting the applicability of SLR-based components. Here, we demonstrate how the properties of high quality factor SLRs are easily modified by breaking the symmetry of the nanoparticle surroundings. We break the symmetry by changing the refractive index of the overlying immersion oil simply by controlling the ambient temperature of the device. We show that already modest temperature changes of 10{\deg}C can increase the quality factor of the investigated SLR from 400 to 750. Our results demonstrate accurate and reversible modification of the properties of the SLRs, paving the way towards tunable SLR-based photonic devices. On a more general level, our results demonstrate how symmetry breaking of the surrounding dielectric environment can be utilized for efficient and potentially ultrafast modification of the SLR properties

Phase-Matched Second-Harmonic Generation from Metasurfaces Inside Multipass Cells

We demonstrate a simple and scalable approach to increase conversion efficiencies of nonlinear metasurfaces by incorporating them into multipass cells and by letting the pump beam to interact with the metasurfaces multiple times. We experimentally show that by metasurface design, the associated phase-matching criteria can be fulfilled. As a proof of principle, we achieve phase matching of second-harmonic generation (SHG) using a metasurface consisting of aluminium nanoparticles deposited on a glass substrate. The phase-matching condition is verified to be achieved by measuring superlinear dependence of the detected SHG as a function of number of passes. We measure an order of magnitude enhancement in the SHG signal when the incident pump traverses the metasurface up to 9 passes. Results are found to agree well with a simple model developed to estimate the generated SHG signals. We also discuss strategies to further scale-up the nonlinear signal generation. Our approach provides a clear pathway to enhance nonlinear optical responses of metasurface-based devices. The generic nature of our approach holds promise for diverse applications in nonlinear optics and photonics

Polarized THG Microscopy Identifies Compositionally Different Lipid Droplets in Mammalian Cells

Peer reviewe

Nonlinear nonlocal metasurfaces

Optical metasurfaces have recently emerged as the game changer in light manipulation and opened up new perspectives in many subfields of optics and photonics. Recent developments in nonlocal metasurfaces, in which the nanoscale building blocks respond to the incoming light collectively rather than as individual objects, are especially promising for enhancing and controlling the nonlinear optical phenomena. In this article, we provide a brief overview of the basic principles of nonlocal metasurfaces in the context of their nonlinear optical functionalities. We discuss the origin and the regimes of the nonlocal response, covering the aspects of multiple scattering, radiation damping, quality factor, local-field enhancement, and temporal dynamics. Some important aspects are illustrated by computational examples. We also give our personal viewpoint on the selected ideas and research directions in nonlocal and nonlinear metasurfaces, including the role of spatial symmetry in nonlocal interactions, the effects of phase and momentum matching in frequency conversion, as well as the possibilities offered by new material platforms and novel concepts, such as bound states in the continuum, parity-time symmetry, and time-variant metasurfaces.publishedVersionPeer reviewe