ConspectusUpconverting nanoparticles (UCNPs) transform low-energy light into higher-energy photons, enabling applications in subwavelength and subsurface imaging, nanoscale sensing, therapeutics, optogenetics, printing, and optical computing. However, the widespread adoption of UCNPs is hindered by their low brightness and limited spectral tunability. Predicting the ideal nanoparticle architectures to overcome these limitations is challenging because UCNP photophysics are governed by highly nonlinear, complex energy transfer networks that span the excited states of lanthanide dopants. Due to the large number of possible combinations of dopants, concentrations, host matrices, heterostructures, and reaction conditions, optimizing the compositional and synthetic parameters of UCNPs using conventional trial-and-error approaches is intractable.This Account explores how researchers can overcome these challenges and enhance the properties of UCNPs using artificial intelligence (AI) and machine learning (ML). We first review how the early foundations of AI-guided discovery were established with automated experimental workflows and physical modeling. Using robotic synthesis platforms and differential rate equation models, researchers have successfully navigated high-dimensional compositional spaces to reveal optical phenomena, such as energy looping and photon avalanching, in nanoparticles.Building on these data-driven approaches, ML has been integrated into UCNP research initially for processing raw characterization data, such as automating the analysis of TEM images and time-resolved luminescence curves. AI approaches have been extended to interpret signals in applications that utilize UCNPs, such as classifying the cytotoxicity of drugs based on upconversion luminescence microscopy data. Most significantly, ML is driving the design of new UCNP compositions and structures, including our recent development of closed-loop active learning of UCNP core-shell heterostructures. By coupling Bayesian optimization with kinetic Monte Carlo (kMC) simulations, we achieved 110-fold enhancement in UCNP emission over just 40 iterations. To bypass the steep computational cost of simulating UCNP heterostructures with up to 9 shells, we leveraged differentiable deep learning surrogate models based on heterogeneous graph neural networks to perform inverse design. Notably, these hetero-GNNs were able to extrapolate far outside of the model's training data and predict UCNP heterostructure compositions with 6.5-fold more intense emission than the brightest UCNP in the training set.In the future, we predict that AI/ML approaches will become integral to the UCNP research. UCNP experiments may soon be accelerated by autonomous self-driving laboratories in which robotic synthesis, in-line characterization, and ML agents operate in a closed feedback loop to intelligently investigate underexplored chemical spaces. Large language models (LLMs) could parse literature to develop overarching hypotheses and detailed recipes for these autonomous workflows, with generative models suggesting novel structures to test. Together with human creativity and critical analysis, these AI tools will accelerate the discovery of advanced upconverting nanomaterials, aiding fundamental understanding of their mechanisms and inspiring a broader array of photonic applications.

Sub-wavelength terahertz imaging using asynchronous optical sampling of on-chip near-field sensors

The imaging of electromagnetic interference sources in devices is an interesting tool for electromagnetic compatibility pre-compliance testing, but it poses three significant challenges: i) the sources size is often subwavelength, meaning that the diffraction limit hinders their imaging, and the ii) spatial and iii) temporal features of the sources are uncontrolled. Indeed, in space, their spurious propagation occurs in devices with arbitrary shapes and materials; and in time, they occur either because of unintended radiation from digital or analogue signals, or because of random pulsed events such as electrostatic discharges. To overcome these issues, we i) propose to use the time reversal technique in conjunction with a resonant metalens for subwavelength imaging of these interference sources. We report the first single-shot subwavelength image of an electrostatic discharge, enabling to distinguish radiation from two PCB traces spaced 8mm apart. ii) To achieve device (space) agnosticism, the imaging method does not require simulation of the device or its environment thanks to a combination of frequency-domain data obtained through a scanner and time-domain data radiated from the device. iii) To ensure flexibility in the source frequency band, we present a novel design for a metalens that operates in a desired frequency band within the gigahertz range, thanks to effective medium theory.

Dynamic metasurfaces (MSs) have already shown great potential for empowering ultracompact reconfigurable optics. However, existing tuning strategies, including those based on refractive-index-modulating materials or mechanical reconfiguration, face significant challenges in achieving arbitrary and continuous 2π-phase modulation for two independent states subjected to dynamic two-dimensional wavefront shaping. This restriction has hindered the realization of meta-devices capable of switching between two completely different phase profiles and thereby realizing distinct optical functions. Here, we overcome this challenge using a microelectromechanical systems (MEMS)-tunable metalens (ML) platform that integrates a bilayer MS (BMS) with a piezoelectric MEMS mirror, enabling full 360° × 360° phase coverage in two independently addressable states while maintaining uniformly high reflection amplitude (between 0.74 and 0.85) and thus ensuring efficient operation. We experimentally realize two MEMS-tunable MLs: a switchable off-axis focusing ML, capable of dynamically shifting its focal position, and a switchable vortex ML, capable of alternating between conventional and vortex focusing states. Both meta-devices exhibit high-efficiency operation (30-40%) at their optimal wavelength, persistent dynamic functionalities throughout the spectral range of 650-850 nm, and fast switching with rise/fall times of 0.58/0.4 ms. The developed MEMS-tunable ML platform enables truly arbitrary, dynamically controlled dual-phase-map switching and can straightforwardly be extended to feature other dynamic functionalities, thereby enriching the portfolio of already developed compact, advanced, and dynamic optical systems.

Precise manipulation of on-chip optical modes using subwavelength structures is critical for miniaturizing devices and scaling high-speed optical interconnects, especially in Mode Division Multiplexing (MDM) systems. Leveraging advances in nanofabrication, devices engineered with subwavelength features enable versatile control over on-chip optical fields. Here, we propose a compact mode sorter based on a subwavelength waveguide grating, designed using the eigenmode expansion method. We analyze the optical field distribution at the focal plane of the on-chip lens for various incident modes. Implemented on a silicon-on-insulator (SOI) platform, the device achieves efficient separation of TE 0 , TE 1 , and TE 3 modes, within a footprint of only 8.3 µm. When assembled into a complete (de)multiplexer, the device yields a 0.79 dB, 0.97 dB, and 0.91 dB insertion loss for the TE 0 , TE 1 , and TE 3 channels, and 114 nm (1493–1607 nm) bandwidth for all channels with insertion loss is under 1.5 dB while crosstalk below 15 dB. Our simulation of fabrication tolerances demonstrates that this approach offers superior robustness compared to traditional devices. This work presents a generalizable design framework for metamaterial-based on-chip lenses using eigenmode analysis, paving the way for compact mode splitting and reconstruction applications.

Tomography enables volumetric visualization of internal structures with high fidelity, which has transformed diverse fields of science and engineering. Ultrasound tomography, in particular, provides radiation-free, real-time, and low-cost imaging. Conventional linear and planar array designs typically impose inherent limits on aperture, coverage, and resolution, hindering accurate imaging of deep structures. Here, we present an active and programmable circular meta-array of 2048 channels integrated with cylindrical acoustic lenses, generating a wide-angle point-source radiation in plane and a thin flat sound-sheet beam out of plane. This configuration delivers omnidirectional homogeneous coverage and dynamic focusing, allowing comprehensive full-matrix echo acquisition. Based on the circular meta-array, subwavelength resolution ultrasonic images (∼0.8λ) are obtained with delay multiply and sum reconstruction. Extending to the volumetric tomography, the acoustic system reconstructs complex 3D objects and resolves human soft tissues and musculoskeletal structures. Our work provides a versatile platform for ultrasound tomography and opens an avenue for various advanced applications in biomedical imaging and therapeutic monitoring.

Abstract Ultrawide bandgap semiconductor gallium oxide (Ga 2 O 3 ), with a natural bandgap of approximately 4.9 eV, has been extensively utilized in constructing solar-blind deep ultraviolet (DUV) photodetectors. To address the persistent challenges of high dark current and low photoresponsivity, metal nanostructured surface plasmons have been introduced to generate localized electric fields, thereby enhancing photodetection performances. Incident photons excite hot electrons within the metallic structures, which are subsequently injected into the photoactive semiconductor layer. When the resonance peak of the plasmonic structure matches the absorption peak of Ga 2 O 3 layer, localized surface plasmon resonance (LSPR) significantly boosts photon absorption and responsivity. Concurrently, the localized interfacial barrier restricts carrier transport, effectively suppressing dark current. This enhancement stems from charge density oscillations within the metallic nanoparticles, facilitating strong plasmon-exciton coupling. In this review, we systematically discuss Ga 2 O 3 -based solar-blind DUV photodetectors decorated with metal nanostructures, covering photoconductive, array, and heterojunction architectures. Furthermore, advances in broadband detection mechanisms, complex plasmonic designs, and subwavelength optics are explored. Compared with conventional devices, plasmon-enhanced photodetectors typically exhibit responsivity improvements from ~0.1 A/W to over tens of A/W and reduced dark current by 1–2 orders of magnitude. Finally, current challenges and future perspectives are outlined. However, challenges such as poor controllability of nanoparticle distribution, stability issues, and the trade-off between enhanced responsivity and increased noise remain to be addressed.

Acoustic imaging, especially ultrasound, underpins a wide range of applications from non-destructive evaluation to medical and materials analysis, yet its performance is ultimately constrained by lateral resolution. This review systematically summarizes recent advances in overcoming diffraction-limited resolution, encompassing traditional focusing techniques, transducer optimization, physical metamaterial lenses, and methods based on algorithmic optimization and deep learning technologies. It comprehensively covers approaches for enhancing acoustic lateral resolution, compares the differences and respective advantages and disadvantages of various methods, and proposes clear directions and recommendations for future research. This work provides robust guidance for subsequent research trends and development opportunities in higher-resolution acoustic imaging.

Fast imaging models are the cornerstone of computational lithography technology. Addressing the limitation that existing fast imaging models for surface plasmonic lithography (SPL) struggle to handle complex illumination conditions, this paper proposes a general fast imaging model based on the decomposition machine learning method, which is applicable to arbitrary illumination systems. First, the model utilizes rigorous electromagnetic field (EMF) simulation to construct a complete training library containing 81 reference point sources. By training the imaging transfer matrix (ITM), it achieves fast mapping of mask features. Furthermore, an approximation method based on inverse spatial distance weighting is proposed to achieve high-precision prediction for arbitrary non-reference point sources and partially coherent illumination systems. Simulation experiments demonstrate that the model exhibits excellent robustness under both TE and TM polarization states. The predicted photoresist images (PRI) are highly consistent with the calculation results of rigorous EMF simulation, with the root mean square error (RMSE) consistently controlled within 0.075. In terms of computational efficiency, for reference point sources within the library and partially coherent illumination based on them, the calculation speed of the model is improved by 30 to 60 times compared to rigorous simulation; for arbitrary non-reference point sources requiring approximation calculation and partially coherent illumination based on them, the calculation speed is improved by 7 to 26 times. This work significantly reduces computational costs while guaranteeing sub-wavelength imaging accuracy, providing a key efficient simulation tool for source-mask optimization (SMO) and optical proximity correction (OPC) in plasmonic lithography.

Acoustic metamaterials offer powerful solutions for manipulating sound at subwavelength scales. One important application is super-resolved acoustic imaging, which relies on access to evanescent waves beyond the diffraction limit. Near-field techniques using subwavelength probes can capture these waves, revealing fine object details. Here, we introduce an experimental platform that harnesses airborne extraordinary transmission to couple evanescent acoustic waves into a subwavelength, zero-mass sonic meta-atom probe. By mounting a circular membrane at the tip of an air-filled waveguide with a conical tip, we exploit a modification of the acoustic inertance—caused by an object’s proximity—via the sonic Drexhage effect, leading to a downshift of the resonant frequency in the kilohertz range. Experimental results, supported by numerical and theoretical models, demonstrate that extreme subwavelength imaging is enabled by measuring the waveguide’s acoustic reflection coefficient, with lateral and depth resolutions of approximately and , respectively (where λ is the acoustic wavelength). The platform’s capabilities for texture measurement and noncontact scanning are also demonstrated.

Abstract This paper proposes a fast design method for electromagnetic focusing lenses based on transfer learning and convex optimization. By constructing a three-layer symmetric structure model and conducting CST-MATLAB joint simulation, 40000 sets of data covering 360° phase are obtained. A ResNet34 network with transfer learning is used to classify the phase. The classification reaches an accuracy of 89.94%. The predicted phase and amplitude are used as input to a convex optimization model. This model calculates the target phase distribution for the entire metamaterial. Based on the calculated results, the method synthesizes the lens structure. Simulations and measurements show that the lens achieves effective far-field focusing at 2.6 GHz. The sidelobe level remains below –12 dB. Compared with traditional empirical design methods, this approach improves accuracy and reduces development time. It is suitable for applications such as electromagnetic imaging, radar detection, and wireless energy transmission.

Three dimensional wide-angle broadband acoustic metamaterial Luneburg lens for detecting weak signals

The original concept of left-handed material has inspired the possibility of optical antimatter, where the effect of light propagation through a medium can be completely canceled by its complementary medium. Despite recent progress in the development of negative-index metamaterials, losses continue to be a significant barrier to realizing optical antimatter. In this work, we show that passive, lossy materials can be used to realize optical antimatter when illuminated by light at a complex frequency. We further establish that one can engineer arbitrary complex-valued permittivity and permeability in such materials. Strikingly, we show that materials with a positive index at real frequencies can act as negative-index materials under complex frequency excitation. Using our approach, we numerically demonstrate the optical antimatter functionality, as well as double focusing by an ideal perfect lens and superscattering. Our work demonstrates the power of temporally structured light in unlocking the promising opportunities of complementary media, which have until now been inhibited by material loss.

Extended depth of field in subwavelength THz imaging by a Bézier curve structure

Acoustic lens focusing is a commonly used method in high-intensity focused ultrasound (HIFU). However, traditional acoustic lens focusing suffers from low focusing efficiency and excessive sidelobes, which affect the efficacy and safety of HIFU treatment. To address this issue, this paper designs a periodic trapezoidal‑groove acoustic metasurface lens by leveraging the extraordinary acoustic transmission effect. Subsequently, its focal sound‑pressure level is calculated through theoretical analysis and finite‑element simulation, and is further validated experimentally. Finally, the influence of structural parameters-such as the period, center width, depth, and taper angle of the trapezoidal groove, as well as the amplitude of the excitation source-on the focusing performance of the acoustic metasurface lens is systematically analyzed. The results demonstrate that the periodic trapezoidal‑groove acoustic metasurface lens can further enhance focusing and suppress sidelobes within a specific frequency range; the frequency corresponding to the maximum sound pressure is determined by the period of the trapezoidal groove; and the shift of Wood's anomaly frequency is primarily governed by the groove depth. This study provides insights for the development of high‑performance acoustic‑lens-focused ultrasound transducers.

Abstract This paper presents a novel square Maxwell fish-eye lens (SMFEL), designed using the Schwarz–Christoffel conformal transformation method, aimed at effectively controlling the propagation of flexural waves and achieving high-resolution imaging. Traditional Maxwell fish-eye lenses, due to their unique refractive index distribution, can focus waves from any point without aberrations to their antipodal point. However, in practical applications, they often encounter impedance mismatch issues at the boundary. To address this issue, the proposed SMFEL resolves the impedance mismatch induced by boundary effects in conventional lens designs by leveraging a thin plate structure with varying thickness—all while preserving outstanding imaging capabilities. To explore the lens’s imaging traits under different excitation positions and frequency conditions, researchers carried out numerical simulations and experimental verifications. The results show that the SMFEL can achieve subwavelength resolution imaging over a wide frequency range (10–30 kHz) and demonstrates good focusing effects at various frequencies. Further experimental verification reveals that the imaging performance of the SMFEL is highly consistent with the numerical simulation results, proving its potential applications in fields such as structural health monitoring, non-destructive testing, and high-resolution acoustic imaging. This study provides a new approach to flexural wave control and demonstrates the broad application prospects of square lens designs in flexural wave imaging.

Electromagnetic Simulation of Metamaterial Lens Antenna with Negative Refractive Index at 28GHz

Metamaterials with Fabry–Pérot (FP) resonance have proven effective for underwater ultrasound imaging. The propagation phenomenon can be understood as a spatial filter with linear dispersion over a finite bandwidth. However, conventional imaging techniques are constrained by the diffraction limit or rely on a strong impedance mismatch between the metamaterial and water. In this paper, we propose a columnar array metamaterial designed for underwater imaging based on FP resonances and validate the proposed design through numerical simulations. The acoustic pressure transmission coefficient, together with the normalized acoustic pressure distribution, is analyzed to quantitatively evaluate imaging quality and verify the physical effectiveness of the model. This novel structure enables deep subwavelength imaging underwater, maintaining excellent and stable imaging performance within a 0.4 kHz bandwidth centered around the operating frequency. We use air-filled metamaterials to create strong acoustic coupling and establish effective sound isolation. This approach significantly enhances imaging resolution, while optimizing energy loss at multiple interfaces, an issue in previous studies. Additionally, in contrast to resonance- or refraction-based approaches such as Helmholtz resonators or hyperlens designs, the proposed FP-resonant metamaterial offers an alternative mechanism for achieving near-field subwavelength imaging through controlled wave transmission and confinement. We also examine the influence of various parameters, such as imaging distance, incidence distance, and array periodicity, on imaging performance. The results demonstrate that the columnar array metamaterial holds great potential for underwater ultrasound imaging applications.

Ultra-wideband dual-polarized shared aperture antenna with gradient refractive index metamaterial lens

ABSTRACT Acoustic metamaterials have garnered significant research interest due to their unique wave-manipulation capabilities, with potential applications spanning noise control, subwavelength imaging, and non-destructive testing. However, conventional design paradigms rely on trial-and-error experimentation, biomimetic inspiration, and finite element analysis (FEA). These approaches suffer from three critical limitations, namely prohibitive experimental costs, serendipity-driven biological analogies, and computationally intensive simulation cycles. To address these challenges, this study proposes a surrogate model-driven framework for rapid optimal design and performance prediction of gradient acoustic metamaterials (GAM). Specifically, a bidirectional mapping between structural parameters and frequency response characteristics is established through three key innovations: 1) Systematic construction of parameter domains via rigorous screening of structural and frequency-response descriptors; 2) Implementation of Gaussian process regression to quantify nonlinear relationships between design variables and acoustic performance metrics while generating probabilistic predictions with error bounds; 3) Development of an uncertainty-aware prediction model that simultaneously outputs predicted values and confidence intervals, effectively bypassing computationally expensive FEA iterations. Experimental validation demonstrates exceptional accuracy and computational efficiency. Notably, the dual-output capability enhances engineering decision-making by quantifying design reliability, while maintaining physical interpretability through explicit parameter-performance correlations. This paradigm-shifting approach establishes a data-driven design framework for acoustic metamaterials, with methodological innovations readily generalisable to multifunctional metamaterial systems requiring performance predictability and design interpretability.