Nonlinear 2D materials such as 3R-phase molybdenum disulfide (3R-MoS2) offer strong second-order optical nonlinearities in an atomically thin platform, making them attractive for on-chip frequency conversion, quantum light generation, and integrated nonlinear nanophotonics. However, the second harmonic generation (SHG) efficiency of monolayer or few-layer 3R-MoS2 deposited on planar substrates remains fundamentally limited by weak light-matter interaction, poor phase matching, and small interaction volumes. Here, we introduce NanoPhotoNet-PINL, a physics informed AI-driven inverse design framework based on a hybrid one-dimensional convolutional neural network and deep neural network autoencoder, tailored for nonlinear MLMs metasurfaces. The model directly maps target dual-resonant reflection spectra at the fundamental and second-harmonic wavelengths to the required multi-layer geometries and material compositions that maximize the effective nonlinear overlap with an embedded 3R-MoS2 sheet. By integrating Maxwell-based nonlinear electrodynamics into the inverse design loop, we compute the second-harmonic conversion efficiency and modal overlap factors for each predicted MLMs design, enabling physics-guided training and evaluation. Our approach achieves an inverse-design prediction efficiency exceeding ~99.2 % along the linear spectral manifold, while the optimized dual-resonant MLMs yield more than three orders of magnitude enhancement in SHG intensity compared to a bare 3R-MoS2 flake on a planar substrate. NanoPhotoNet-PINL establishes a generalizable paradigm for intelligent inverse design of nonlinear multi-layer metasurfaces and phase-matched dual-resonant cavities for high-efficiency second-order processes.
Nonlinear 2D materials such as 3R-phase molybdenum disulfide (3R-MoS2) offer strong second-order optical nonlinearities in an atomically thin platform, making them attractive for on-chip frequency conversion, quantum light generation, and integrated nonlinear nanophotonics. However, the second harmonic generation (SHG) efficiency of monolayer or few-layer 3R-MoS2 deposited on planar substrates remains fundamentally limited by weak light-matter interaction, poor phase matching, and small interaction volumes. Here, we introduce NanoPhotoNet-PINL, a physics informed AI-driven inverse design framework based on a hybrid one-dimensional convolutional neural network and deep neural network autoencoder, tailored for nonlinear MLMs metasurfaces. The model directly maps target dual-resonant reflection spectra at the fundamental and second-harmonic wavelengths to the required multi-layer geometries and material compositions that maximize the effective nonlinear overlap with an embedded 3R-MoS2 sheet. By integrating Maxwell-based nonlinear electrodynamics into the inverse design loop, we compute the second-harmonic conversion efficiency and modal overlap factors for each predicted MLMs design, enabling physics-guided training and evaluation. Our approach achieves an inverse-design prediction efficiency exceeding ~99.2 % along the linear spectral manifold, while the optimized dual-resonant MLMs yield more than three orders of magnitude enhancement in SHG intensity compared to a bare 3R-MoS2 flake on a planar substrate. NanoPhotoNet-PINL establishes a generalizable paradigm for intelligent inverse design of nonlinear multi-layer metasurfaces and phase-matched dual-resonant cavities for high-efficiency second-order processes.
Single-photon emitters (SPEs) based on nitrogen-vacancy centers in nanodiamonds (neutral NV0 (wavelength 575 nm) and negative NV- (wavelength 637 nm)) represent promising platforms for quantum nanophotonics applications, yet their emission efficiencies remain constrained by weak light-matter interactions. Multi-layer metasurfaces (MLM) offer unprecedented degrees of freedom for efficient light manipulation beyond conventional single-material metasurfaces, enabling dual-resonance cavities that can simultaneously enhance pump excitation and SPE collection. However, traditional trial-and-error and forward optimization methods face significant challenges in designing these complex structures due to the vast parameter space and computational demands. Here, we present NanoPhotoNet-Inverse, an artificial intelligence-driven inverse design framework based on a hybrid deep neural network architecture. This model efficiently performs inverse design of two dual-resonance MLM cavities to amplify pump and SPE emissions of different vacancies in nanodiamond and improve SPE collection. Our approach achieves inverse design prediction efficiency exceeding 98.7%, demonstrating three orders of magnitude amplification in SPE count rate and 50 picosecond lifetime, significantly surpassing conventional cavity designs. These remarkable enhancements in emission rates and collection efficiency position our platform as a transformative technology for advancing quantum communication networks, quantum computing architectures, quantum sensing applications, and quantum cryptography systems. Therefore, it opens new pathways for intelligent photonic device engineering in quantum technologies.
Integrated quantum photonics requires chip-scale biphoton sources, but phase matching, nonlinear coupling, loss, and spectral purity are coupled in high-index anisotropic waveguides. We present NanoPhotoNet-MPM, a physics-informed neural surrogate for modal spontaneous parametric down-conversion (SPDC) in monoclinic NbOCl2 waveguides (d_22≈100 pm V^(-1)). The framework couples a DeepONet transverse eigensolver, regularised by the full-vectorial Helmholtz equations, with a quantum coupled-wave-equation propagator trained on 70 audited finite-difference eigenmode geometries. The trained model predicts a degenerate phase-matched crossing at w=283.13 nm and h=500.00 nm with an effective-index MAE of (4.16±0.75)×〖10〗^(-3), outperforming Gaussian process regression and plain multi-layer perceptron baselines by one to two orders of magnitude while guaranteeing wave-equation consistency. Joint spectral amplitude analysis at the degenerate design yields a heralded purity of P=0.122 (K=8.20), limited by symmetry-enforced group-velocity mismatch. A multi-mode genetic-algorithm search across Type-0, Type-I, and Type-II SPDC configurations identifies a Type-II design (m_p=4, m_s=0, m_i=1) at w=592.0 nm and h=270.0 nm that reaches P≈0.350 at L=0.5 mm with 95.9% idler survival under realistic propagation loss. The resulting framework is a generalizable surrogate-assisted design and active-learning guidance method for anisotropic van der Waals quantum photonic platforms.
Entangled photons are essential for photonic quantum technologies. Their generation typically relies on spontaneous parametric down-conversion, but conventional nonlinear crystals are bulky and hard to integrate on chips. Rhombohedral-stacked MoS2 combines a high refractive index, large second-order nonlinearity, and flexibility for heterogeneous integration, making it a promising platform for integrated quantum photonics. However, the typical thin-film form of 3R-MoS2 restricts the effective nonlinear interaction length, limiting entanglement generation efficiency in practical devices. To overcome this, phase-matching strategies in integrated waveguides are required but have so far remained undeveloped. Here, we introduce a waveguide-integrated 3R-MoS2 platform with periodic grooves to achieve quasi-phase matching, enhancing down-conversion efficiency. Leveraging χ(2) tensor symmetries and orthogonal waveguide modes, the design efficiently generates entangled photons, providing a compact, scalable route toward 2D-material-based integrated quantum photonic circuits.
Abstract Integrated photon-pair sources are crucial for scalable photonic quantum systems. Thin-film lithium niobate is a promising platform for on-chip photon-pair generation through spontaneous parametric down-conversion (SPDC). However, the device implementation faces practical challenges. Periodically poled lithium niobate (PPLN), despite enabling flexible quasi-phase matching, suffers from poor fabrication reliability and device repeatability, while conventional modal phase matching (MPM) methods yield limited efficiencies due to inadequate mode overlaps. Here, we introduce a layer-poled lithium niobate (LPLN) nanophotonic waveguide for efficient photon-pair generation. It leverages layer-wise polarity inversion through electrical poling to break spatial symmetry and significantly enhance nonlinear interactions for MPM, achieving a notable normalized second-harmonic generation (SHG) conversion efficiency of 4615% W-1 cm-2. Through a cascaded SHG and SPDC process, we demonstrate photon-pair generation with a normalized brightness of 3.1×106 Hz nm-1 mW-2 in a 3.3 mm long LPLN waveguide, surpassing existing on-chip sources under similar operating configurations. Crucially, our LPLN waveguides offer enhanced fabrication reliability and reduced sensitivity to geometric variations and temperature fluctuations compared to PPLN devices. We expect LPLN to become a promising solution for on-chip nonlinear wavelength conversion and non-classical light generation, with immediate applications in quantum communication, networking, and on-chip photonic quantum information processing.
Abstract We present a detailed study of the second order nonlinearity of 2D (mono-atomic layer) dichalcogenide MoS2, both in the visible and in the IR regime, and test its potential for spontaneous parametric down-conversion (SPDC), the amplification of vacuum fluctuations mediated by optical nonlinearity. We develop a model of SPDC from a deeply subwavelength nonlinear medium, where phase matching conditions are completely relaxed, and make predictions about the rate of emitted photons, their momentum, polarisation and spectrum. We show that detection in the visible spectral region is hindered by the strong photoluminescence background. Moving to the IR regime we observe indications of SPDC by performing polarization, power dependence and lifetime measurements around 1560 nm. We show that the signal from a single monolayer is qualitatively different from that generated by multi-layer MoS2. Finally, we characterize the latter as a new kind of photo-luminescence emission which is enhanced at the edges of multi-layer MoS2.
The recent advent of optical metasurfaces, ultra-thin structures composed of nanoresonators facilitating strong light concentration [1–3], opens the path towards efficient \nfrequency conversion [4–7] in a material thousand times thinner than a human hair [8]. \nThere is a remarkable capacity to individually shape each nanoresonator of the metasurface to spatially and spectrally control the conversion process with unprecedented \nnanoscale resolution, facilitating an ultimate flexibility to selectively convert, focus, \nand image different colours with a single metasurface. The strong enhancement of the \nnonlinear processes in dielectric nanoresonators is largely due to the absence of material \nabsorption and the excitation of Mie-type bulk resonances [2]. The highest conversion \nefficiency to date has been achieved employing III-V semiconductor nanostructures, \nsuch as AlGaAs which is a non-centrosymmetric material with high quadratic nonlinear \nsusceptibility. In particular, second-harmonic generation efficiencies up to 10−4 have \nbeen recently demonstrated [8–12], six orders of magnitude higher than in plasmonics. \nSuch capabilities can make an immense fundamental and practical impact, including \nthe quantum state generation in nonlinear metasurfaces as we discuss in this chapter. \nThe quantum state of correlated photon-pairs is the essential building block for photon entanglement [13, 14], which underpins many quantum applications, including secure networks, enhanced measurement and lithography, and quantum information processing [15]. One of the most versatile techniques for the generation of correlated photons is the process of spontaneous parametric down-conversion (SPDC) [16, 17], see \nFig. 1. It allows for an arbitrary choice of energy and momentum correlations between \nthe generated photons, robust operation at room temperature, as well as for spatial and \ntemporal coherence between simultaneously pumped multiple SPDC sources. Alternative approaches based on atom-like single photon emitters, such as solid-state fluorescent atomic defects [18], quantum dots [19, 20], and 2D host materials [21, 22], have \nreached a high degree of frequency indistinguishability, purity and brightness [19, 20]. \nHowever, this comes with the expense of operation at cryogenic temperatures and lack \nof spatial coherence between multiple quantum emitters. These features might limit \npossible applications and reduce the potential for device scalability. Furthermore, the \nsmall size of the atomic sources often requires complex schemes aimed at coupling to \noptical nanoantennas and improving the photon extraction efficiency [21]. \nThe miniaturization of SPDC quantum-light sources to micro and nanoscale dimensions is a continuing quest, as it enables denser integration of functional quantum \ndevices. Traditionally, bulky cm-sized crystals were utilized for SPDC, entailing the \ndifficulty of aligning multiple optical elements after the SPDC crystal, while offering \nrelatively low photon-pair rates [17]. As a first step of miniaturization, SPDC was \nrealized in low-index-contrast waveguides, which allowed confining light down to several square micrometers transversely to the propagation direction, significantly enhancing the conversion efficiency [23]. However, this approach still requires centimetres \nof propagation length, which makes the on-chip integration with other elements challenging [24]. The introduction of high-index contrast waveguides and ring resonators \nallowed for shrinking the sizes necessary for SPDC to millimetres [25], and to tens \nof micrometers [26]. However, further miniaturisation down to the metasurfaces composed of nanoresonators requires conceptually different approaches.