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Luca Dal Negro

Publications and source records attributed to Luca Dal Negro.

At least 19 recordsLinked to original sources

Quantum Theory of Third-harmonic Generation in Epsilon-Near-Zero Materials

We present a theoretical framework, based on the Green's tensor quantization method, to describe third-harmonic generation in epsilon-near-zero (ENZ) materials and derive analytical, closed-form solutions for the generation efficiency. We validate our model against experimental measurements of wavelength- and angle-resolved third-harmonic generation efficiency from 30 nm-thin ITO nanolayers at low pump intensity, described under the undepleted pump approximation. Our results provide a local and scalar effective model for quantum nonlinear processes in dispersive and lossy ENZ media, and establishes a simple and reliable framework for investigating a variety of nonlinear optical phenomena with applications to quantum sensing, quantum information, and quantum nondemolition measurements.

physics.optics

Mid-Infrared Single-Photon Detection via Enhanced Cross-Phase Modulation in Topology-Optimized Epsilon-Near-Zero Dual-Wavelength Nanocavities

We use the Green's tensor quantization theory for open resonant nanostructures with absorption losses to study cross-phase modulation (XPM) at the single-photon level in nanoscale Kerr-type epsilon-near-zero (ENZ) materials with an effective nonlinear susceptibility integrated inside dual-wavelength nanocavities. We obtain analytical formulas for the XPM frequency shifts in hybrid nanocavities that simultaneously trap a classical probe beam at 1.5 $μ$m and single-photon pump at 3 $μ$m wavelengths. We present a comprehensive analysis of the fundamental limits for mid-infrared single-photon detection in the quantum nondemolition modality for nanostructured cadmium oxide (CdO) regions with ENZ-enhanced nonlinearity embedded in a silicon (Si) environment inversely designed by free-form topology optimization. We numerically implement our theoretical results using finite element simulations within the rigorous framework of quasi-normal modes, demonstrating a single-photon XPM frequency shift $Δf_s \approx 55.6 \text{ GHz}$ with fractional shift (i.e., frequency pulling) $Δf_s / f_s \approx 2.78 \times 10^{-4}$ and addressing the feasibility of detection in the hybrid Si-CdO dual-wavelength nanocavity, either with a classical probe beam or a squeezed probe state, including the contributions of traditional limitations from self-phase modulation noise, thermorefractive noise, shot noise, and free-carrier absorption effects. Finally, we present a comparative size scaling analysis of the XPM phase shift and phase noise contributions for dual-wavelength nanocavities based on CdO and indium tin oxide (ITO) nonlinear ENZ materials. This work establishes a robust benchmark for the engineering of mid-infrared single-photon nonlinear devices such as nondemolition quantum detectors, sensors, and all-optical gates on a solid state photonic platform.

physics.optics

Structural and physical properties of gyromorphs and disordered stealthy hyperuniform media

Disordered stealthy hyperuniform materials combine liquid-like statistical isotropy with crystal-like homogeneity, suppressed density fluctuations at large length scales, bounded holes, and an isotropic structure factor that vanishes for a finite range of wavevectors. This combination yields unusual physical properties, including optical transparency, effective delocalization, ultrafast spreadability, optimal conductivity, and complete isotropic photonic bandgaps. Gyromorphs, point patterns whose structure factor includes rings of Bragg-like peaks arranged with discrete $G$-fold rotational symmetry, were recently introduced as counterexamples: disordered media that can somehow achieve the same physical properties, in some cases with higher performance, without stealthiness or hyperuniformity. In this paper, we resolve the puzzle of how gyromorphs fit consistently with the stealthy hyperuniform studies. We first show that gyromorphs are actually hyperuniform and, in the large-$G$ limit where they become nearly isotropic, belong to the weakest form of hyperuniformity, known as Class III. Thus, gyromorphs should have comparatively degraded physical properties compared to stealthy hyperuniform media, which belong to the strongest form of hyperuniformity, known as Class I. We verify this expectation using the rigorous spectral Green's matrix method for the calculation of the density of states (DOS) and Purcell factors in large arrays of electric dipoles. We find that gyromorphs display size-dependent pseudogaps richly populated by localized states rather than smooth band gaps like those found for highly stealthy hyperuniform materials or in deterministic structures such as Vogel spiral and triangular lattices. Furthermore, we predict similar disorder-induced degradation relative to stealthy hyperuniformity with regard to transparency, spreadability and diffusion properties.

physics.optics

A Unified Multiscale Auxiliary PINN Framework for Generalized Phonon Transport

Nanoscale thermal transport is governed by the phonon Boltzmann transport equation (BTE). However, simulating the sub-continuum dynamics remains computationally prohibitive due to the high dimensionality of the phase space and the intrinsic nonlinearity of the scattering collision operator. Traditional numerical solvers and standard physics-informed neural networks (PINNs) inherently struggle with these integro-differential equations due to deterministic quadrature limitations, artificial thermalization introduced by the relaxation time approximation (RTA), and multiscale spectral bias. This work introduces a multiscale auxiliary physics-informed neural network (MTNet) to solve the generalized equation of phonon radiative transfer (GEPRT). By leveraging an auxiliary formulation, this mesh-free framework recasts the GEPRT into a fully differential system, enabling the analytical evaluation of scattering operators via automatic differentiation and facilitating scalable multi-GPU parallelization. To circumvent optimization stiffness, the architecture employs a decoupled, shallow neural network explicitly constrained by radiative equilibrium. MTNet is validated by simulating steady-state cross-plane transport in a silicon thin film, successfully capturing ballistic-diffusive regimes and characteristic boundary slips across extreme temperature gradients ($ΔT = 100$ K) beyond the standard linearization approach. Furthermore, we show that our framework successfully solves a geometric inverse problem in a slab geometry, retrieving the unknown slab thickness based only on interface temperature constraints in the mesoscopic regime. Ultimately, MTNet establishes a robust, fully differentiable foundation for predicting high-fidelity kinetic transport and extracting material properties in next-generation nanostructures.

cond-mat.mes-hall

DDNet: A Unified Physics-Informed Deep Learning Framework for Semiconductor Device Modeling

The accurate modeling of semiconductor devices plays a critical role in the development of new technology nodes and next-generation devices. Semiconductor device designers largely rely on advanced simulation software to solve the drift-diffusion equations, a coupled system of nonlinear partial differential equations that describe carrier transport in semiconductor devices. While these tools perform well for forward modeling, they are not suitable to address inverse problems, for example, determining doping profiles, material, and geometrical parameters given a desired device performance. Meanwhile, physics-informed neural networks (PINNs) have grown in popularity in recent years thanks to their ability to efficiently and accurately solve inverse problems at minimal computational cost compared to forward problems. In this study, we introduce the Drift-Diffusion Network (DDNet), a unified physics-informed deep learning solver for the forward and inverse mesh-free solutions of the drift-diffusion equations of semiconductor device modeling. Using prototypical device configurations in one- and two spatial dimensions, we show that DDNet achieves low absolute and relative error compared to traditional simulation software while additionally solving user-defined inverse problems with minimal computational overhead. We expect that DDNet will benefit semiconductor device modeling by facilitating exploration and discovery of novel device structures across comprehensive parameter sets in a fully automated way.

physics.comp-ph

Performance limit of on-chip speckle spectrometers

Disorder-driven, integrated speckle spectrometers offer exceptional spectral resolution within a compact design. They benefit from enhanced optical path lengths due to multiple light scattering events, however, often at the cost of low optical throughput. Here, we investigate the relationship between these two figures of merit by systematically varying the scattering strength of random-uniform disorder distributions. Furthermore, we also investigate the temperature stability of such spectrometers. Our study shows that the device resolution can be tuned from 2 nm to 20 pm, while the operating temperature ranges from 1 to more than 6 degrees and throughput can be varied by more than a factor of 10, paving the way for application-tailored design of microscale high-resolution spectrometers.

physics.optics

Nonlinear Quantum Electrodynamics of Epsilon-Near-Zero Nanocavities

We investigate single-photon nonlinear refractive index change and frequency shift of Epsilon-Near-Zero (ENZ) sub-wavelength nanocavities. We apply the rigorous quantum Langevin-noise approach in the framework of Green's tensor quantization method to realistic ENZ materials with causal dispersion and derive closed-form analytical solutions for cavities with spherical geometry. This is achieved by employing a fully nonperturbative methodology for the analysis of open quantum systems with single-photon Kerr-type nonlinearity. The analytical results are validated numerically using the established quasi-normal mode expansion method and extended to nonspherical nanocavity geometries that can be experimentally fabricated using state-of-the-art electron lithography. Our findings establish a rigorous benchmark for understanding single-photon nonlinear optical effects in Kerr-type ENZ nanostructures with losses and are of importance to emerging quantum technology applications, including on-chip single-photon nondemolition detection, quantum sensing, and controlled quantum gates driven by enhanced photon blockade effects at the nanoscale.

physics.optics

Enhancement of the Third Harmonic Generation Efficiency of ITO nanolayers coupled to Tamm Plasmon Polaritons

We study the enhancement of third harmonic generation in indium tin oxide nanolayers coupled to Tamm plasmon polaritons (TPPs). The TPPs are excited at the interface between a thin gold mirror and a silicon dioxide/silicon nitride (SiO2/Si3N4) distributed Bragg reflector with a 30nm-thick indium tin oxide (ITO) nanolayer embedded inside the topmost dielectric layer under the metal mirror. This ITO nanolayer exhibits epsilon-near-zero (ENZ) behavior at near infrared wavelengths. By tuning the angle of incidence and the TPP resonance conditions, we achieve sub-wavelength confinement of the electromagnetic field, resulting in a 8x enhancement of the nonlinear optical response of the structure compared to the isolated ITO nanolayer at its optimal ENZ condition. We further investigate the dependence of the THG signal on the incident angle and sample orientation, confirming that the enhancement is driven by the excitation of the TPP mode with a characteristic asymmetric behavior. Numerical simulations of local field factors based on the transfer matrix method (TMM) fully support our findings. Our study demonstrates that the TPP-ENZ platform offers a versatile and highly efficient approach to enhancing nonlinear optical processes, with potential applications in frequency conversion, optical signal processing, and the development of more efficient nonlinear photonic devices.

physics.optics

Multiscale Physics-Informed Neural Networks for the Inverse Design of Hyperuniform Optical Materials

In this article, we employ multiscale physics-informed neural networks (MscalePINNs) for the inverse design of finite-size photonic materials with stealthy hyperuniform (SHU) disordered geometries. Specifically, we show that MscalePINNs can capture the fast spatial variations of complex fields scattered by arrays of dielectric nanocylinders arranged according to isotropic SHU point patterns, thus enabling a systematic methodology to inversely retrieve their effective dielectric profiles. Our approach extends the recently developed high-frequency homogenization theory of hyperuniform media and retrieves more general permittivity profiles for applications-relevant finite-size SHU systems, unveiling unique features related to their isotropic nature. In particular, we numerically corroborate the existence of a transparency region beyond the long-wavelength approximation, enabling effective and isotropic homogenization even without disorder-averaging, in contrast to the case of uncorrelated Poisson random patterns. The flexible multiscale network approach introduced here enables the efficient inverse design of more general effective media and finite-size optical metamaterials with isotropic electromagnetic responses beyond the limitations of traditional homogenization theories.

physics.optics

Localization landscape of optical waves in multifractal photonic membranes

In this paper, we investigate the localization properties of optical waves in disordered systems with multifractal scattering potentials. In particular, we apply the localization landscape theory to the classical Helmholtz operator and, without solving the associated eigenproblem, show accurate predictions of localized eigenmodes for one- and two-dimensional multifractal structures. Finally, we design and fabricate nanoperforated photonic membranes in silicon nitride (SiN) and image directly their multifractal modes using leaky-mode spectroscopy in the visible spectral range. The measured data demonstrate optical resonances with multiscale intensity fluctuations in good qualitative agreement with numerical simulations. The proposed approach provides a convenient strategy to design multifractal photonic membranes, enabling rapid exploration of extended scattering structures with tailored disorder for enhanced light-matter interactions.

physics.optics

Field theory description of the non-perturbative optical nonlinearity of epsilon-near-zero media

In this paper we introduce a fully non-perturbative approach for the description of the optical nonlinearity of epsilon-near-zero (ENZ) media. In particular, based on the rigorous Feynman path integral method, we develop a dressed Lagrangian field theory for light-matter interactions and discuss its application to dispersive Kerr-like media with order-of-unity light-induced refractive index variations. Specifically, considering the relevant case of Indium Tin Oxide (ITO) nonlinearities, we address the novel regime of non-perturbative refractive index variations in ENZ media and establish that it follows naturally from a scalar field theory with a Born-Infeld (BI) Lagrangian. Moreover, we developed a predctive model that includes the intrinsic saturation effects originating from the light-induced modification of the Drude terms in the linear dispersion of ITO materials. Our results extend the Huttner-Barnett-Bechler electrodynamics model to the case of non-perturbative optical Kerr-like media providing an intrinsically nonlinear, field-theoretic framework for understanding the exceptional nonlinearity of ITO materials beyond traditional perturbation theory.

physics.optics

High-throughput speckle spectrometers based on multifractal scattering media

We present compact integrated speckle spectrometers based on monofractal and multifractal scattering media in a silicon-on-insulator platform. Through both numerical and experimental studies we demonstrate enhanced optical throughput, and hence signal-to-noise ratio, for a number of random structures with tailored multifractal geometries without affecting the spectral decay of the speckle correlation functions. Moreover, we show that the developed multifractal media outperform traditional scattering spectrometers based on uniform random distributions of scattering centers. Our findings establish the potential of low-density random media with multifractal correlations for integrated on-chip applications beyond what is possible with uncorrelated random disorder.

physics.optics

Enhanced Nonlinearity of Epsilon-Near-Zero Indium Tin Oxide Nanolayers with Tamm Plasmon-Polariton States

Recently, materials with vanishingly small permittivity, known as epsilon-near-zero (ENZ) media, emerged as promising candidates to achieve nonlinear optical effects of unprecedented magnitude on a solid-state platform. In particular, the ENZ behavior of Indium Tin Oxide (ITO) thin films resulted in Kerr-type nonlinearity with non-perturbative refractive index variations that are key to developing more efficient Si-compatible devices with sub-wavelength dimensions such as all-optical switchers, modulators, and novel photon detectors. In this contribution, we propose and demonstrate enhancement of the nonlinear index variation of 30 nm-thick ITO nanolayers by silicon dioxide/silicon nitride (SiO2/SiN) Tamm plasmon-polariton structures fabricated by radio-frequency magnetron sputtering on transparent substrates under different annealing conditions. In particular, we investigate the linear and nonlinear optical properties of ITO thin films and resonant photonic structures using broadband spectroscopic ellipsometry and intensity dependent Z-scan nonlinear characterization demonstrating enhancement of optical nonlinearity with refractive index variations as large as in the non-perturbative regime. Our study reveals that the efficient excitation of strongly confined plasmon-polariton Tamm states substantially boost the nonlinear optical response of ITO nanolayers providing a stepping stone for the engineering of more efficient infrared devices and nanostructures for a broad range of applications including all-optical data processing, nonlinear spectroscopy, sensing, and novel photodetection modalities.

physics.optics

Auxiliary Physics-Informed Neural Networks for Forward, Inverse, and Coupled Radiative Transfer Problems

In this paper, we develop and employ auxiliary physics-informed neural networks (APINNs) to solve forward, inverse, and coupled integro-differential problems of radiative transfer theory (RTE). Specifically, by focusing on the relevant slab geometry and scattering media described by different types of phase functions, we show how the proposed APINN framework enables the efficient solution of Boltzmann-type transport equations through multi-output neural networks with multiple auxiliary variables associated to the Legendre expansion terms of the considered phase functions. Furthermore, we demonstrate the successful application of APINN to the coupled radiation-conduction problem of a participating medium and find distinctive temperature profiles beyond the Fourier thermal conduction limit. Finally, we solve the inverse problem for the Schwarzschild-Milne integral equation and retrieve the single scattering albedo based solely on the knowledge of boundary data, similar to what is often available in experimental settings. The present work significantly expands the current capabilities of physics-informed neural networks for radiative transfer problems that are relevant to the design and understanding of complex scattering media and photonic structures with applications to metamaterials, biomedical imaging, thermal transport, and semiconductor device modeling.

cond-mat.dis-nn

Inverse design of functional photonic patches by adjoint optimization coupled to the generalized Mie theory

We propose a rigorous approach for the inverse design of functional photonic structures by coupling the adjoint optimization method and the two-dimensional generalized Mie theory (2D-GMT) for the multiple scattering problem of finite-size arrays of dielectric nanocylinders optimized to display desired functions. We refer to these functional scattering structures as "photonic patches". We briefly introduce the formalism of 2D-GMT and the critical steps necessary to implement the adjoint optimization algorithm to photonic patches with designed radiation properties. In particular, we showcase several examples of periodic and aperiodic photonic patches with optimal nanocylinder radii and arrangements for radiation shaping, wavefront focusing in the Fresnel zone, and for the enhancement of the local density of states (LDOS) at multiple wavelengths over micron-size areas. Moreover, we systematically compare the performances of periodic and aperiodic patches with different sizes and find that optimized aperiodic Vogel spiral geometries feature significant advantages in achromatic focusing compared to their periodic counterparts. Our results show that adjoint optimization coupled to 2D-GMT is a robust methodology for the inverse design of compact photonic devices that operate in the multiple scattering regime with optimal desired functionalities. Without the need of spatial meshing, our approach provides efficient solutions at strongly reduced computational burden compared to standard numerical optimization techniques and suggests compact device geometries for on-chip photonics and metamaterials technologies.

physics.optics

Wave localization in number-theoretic landscapes

We investigate the localization of waves in aperiodic structures that manifest the characteristic multiscale complexity of certain arithmetic functions with a central role in number theory. In particular, we study the eigenspectra and wave localization properties of tight-binding Schrödinger equation models with on-site potentials distributed according to the Liouville function $λ(n)$, the Möbius function $μ(n)$, and the Legendre sequence of quadratic residues modulo a prime (QRs). We employ Multifractal Detrended Fluctuation Analysis (MDFA) and establish the multifractal scaling properties of the energy spectra in these systems. Moreover, by systematically analyzing the spatial eigenmodes and their level spacing distributions, we show the absence of level repulsion with broadband localization across the entire energy spectra. Our study introduces deterministic aperiodic systems whose eigenmodes are all strongly localized in realistic finite one-dimensional systems and provides opportunities for novel quantum and classical devices of particular importance to cold-atom experiments in engineered speckle potentials and enhanced light-matter interactions.

cond-mat.dis-nn

Design of ultracompact broadband focusing spectrometers based on deep diffractive neural networks

We propose the inverse design of ultracompact, broadband focusing spectrometers based on adaptive deep diffractive neural networks (a-D$^2$NNs). Specifically, we introduce and characterize two-layer diffractive devices with engineered angular dispersion that focus and steer broadband incident radiation along predefined focal trajectories with desired bandwidth and $5$ nm spectral resolution. Moreover, we systematically study the focusing efficiency of two-layer devices with side length $L=100~μ\mathrm{m}$ and focal length $f=300~\,μ\mathrm{m}$ across the visible spectrum and we demonstrate accurate reconstruction of the emission spectrum from a commercial superluminescent diode. The proposed a-D$^2$NNs design method extends the capabilities of efficient multi-focal diffractive optical devices to include single-shot focusing spectrometers with customized focal trajectories for applications to ultracompact multispectral imaging and lensless microscopy.

physics.optics

Enhanced wave localization in multifractal scattering media

In this paper we study the structural, scattering, and wave localization properties of multifractal arrays of electric point dipoles generated from multiplicative random fields with different degrees of multiscale correlations. Specifically, using the rigorous Green's matrix method, we investigate the scattering resonances and wave localization behavior of systems with $N=10^{4}$ dipoles and demonstrate an enhanced localization behavior in highly inhomogeneous multifractal structures compared to homogeneous fractals, or monofractals. We show distinctive spectral properties, such as the absence of level repulsion in the strong multiple scattering regime and power-law statistics of level spacings, which indicate a clear localization transition enhanced in non-homogeneous multifractals. Our findings unveil the importance of multifractal structural correlations in the multiple scattering regime of electric dipole arrays and provide an efficient model for the design of multiscale nanophotonic systems with enhanced light-matter coupling and localization phenomena beyond what is possible with traditional fractal systems.

physics.optics