All-optical data encoding provides a route to ultrafast, low-energy photonic memory and computation, yet its implementation is hindered by weak nonlinearities and low emission efficiencies in conventional materials. Upconversion luminescence offers strong nonlinear behavior, but typical hosts suffer from inefficient energy transfer, dilute and randomly distributed dopants, and complex excitation demands. Here, we report thermally crystallized BaYF5:Yb,Er glass-ceramics that overcome low upconversion efficiency and dopant inhomogeneity through controlled Er3+ enrichment within BaYF5 nanocrystals. Thermal crystallization reduces interionic distances, promoting cooperative Yb3+-Er3+ sensitization and Er3+-Er3+ energy migration. The combination of theoretical modeling and experiments shows efficient population of high-energy emitting states, yielding ultrabright upconversion emission with enhancement factors of 113-, 118-, and 110-fold for the blue, green, and red channels under 980 nm excitation. Optimized nanocrystals also prolong emission lifetimes, suppress nonradiative relaxation, and produce clear saturation behavior. Leveraging strongly nonlinear responses, power- and pulse-width-modulated excitation enables precise control of both spectral and temporal outputs, forming a highly reconfigurable platform. This allows multidimensional data encoding and implementation of fundamental optical logic gates with high fidelity and self-referenced decoding. The system offers robust, secure, and scalable functionality for advanced all-optical information processing applications.

The increasing demand for ultrafast data processing and high-density information storage necessitates photonic platforms capable of optical logic operations and multidimensional data encoding. However, most optical materials exhibit insufficient nonlinear absorption and limited tunability, preventing threshold-controlled switching for deterministic logic. Lanthanide-doped upconversion nanoparticles circumvent these limitations through discrete ladder-type manifolds, energy transfer, and strong nonlinear excitation pathways. Here, we report time-tuned photon avalanche upconversion in NaYF4:Yb3+/Pr3+(15/0.5%)@NaYF4 nanoparticles under 852 nm pulsed excitation as a reconfigurable platform for logic and encoding. By independently modulating the pulse width and frequency, we establish orthogonal control over avalanche kinetics and branching ratios. Long pulse widths promote excited-state population buildup, enhancing cross-relaxation-assisted feeding into higher-lying Pr3+ states and producing dominant blue emission. Conversely, high repetition frequencies reduce ground-state recovery, increase Yb3+ sensitizer recycling, and drive saturation-like regimes favoring red channels. Rate equation simulations corroborate the observed transitions in the emission color, temporal profile, and threshold behavior. Reversible color switching was demonstrated in both the photon avalanche and saturation regimes with tunable blue-to-red emission ratios. These dynamics enable optical logic gates and multidimensional encoding leveraging emission wavelength, lifetime, and temporal switching, establishing a framework for high-speed photonic computing and scalable high-capacity data encoding.

The direct optical transportation of images through multimode fibres (MMFs) is highly sought after in compact photonic systems for MMF-based optical information processing. However, MMFs are highly scattering media, thus degrading information transmitted through them. Existing approaches utilize artificial neural networks or spatial light modulators to reconstruct images scrambled after propagation through the fibre. Despite these advances, achieving direct optical image transportation through MMFs using integrated optical elements with micrometre-scale footprints remains challenging. Here we develop a miniaturized diffractive neural network (DN2s) integrated on the distal facet of a MMF for the direct all-optical image transportation through the fibre. The DN2s has a footprint of 150 mu m by 150 mu m and is fabricated on the facet of a 0.35-m-long MMF using three-dimensional two-photon nanolithography. The fibre-integrated DN2s enables single-shot optical transportation of images with flat phases in real time for a constant configuration of the MMF. The system achieves a minimum image reconstruction feature size of approximately 4.90 mu m over a field of view 65 mu m by 65 mu m when imaging handwritten digits. Transfer learning is also demonstrated by the direct optical transportation of HeLa cell images projected by spatial light modulators, which were not part of the training dataset. The concept and implementation pave the way to the integration of miniaturized DN2s with MMFs for compact photonic systems with unprecedented functionalities.

Three-dimensional two-photon nanolithography (3D TPN) enables the mask-free fabrication of arbitrary 3D microstructures, which makes it a powerful tool for creating complex micrometer-scale diffractive optical elements (DOEs) in micro-optics and nanophotonics. Among these, the development of multiwavelength achromatic diffractive microlenses has emerged as a critical challenge, as conventional methods often rely on computationally intensive full-wave Maxwell simulations (e.g., finite-difference time-domain, FDTD) or complex meta-atom fabrication processes. Here, we propose an optimization framework that integrates the optical path difference equation into the Rayleigh-Sommerfeld diffraction equation, enabling direct optimization of height distribution for multiwavelength diffractive microlenses with significantly reduced computational demands compared to FDTD-based approaches. As a demonstration, we designed and fabricated a dual-wavelength (green and near-infrared) height-optimized achromatic microlens using 3D TPN, achieving excellent agreement between experimental characterization and theoretical calculations. Our approach offers a computationally efficient strategy for advancing multiwavelength DOE design, with potential applications in integrated photonics and bioimaging.

The manipulation of light at the micrometer scale has been the core technology for the recent development of diffractive optics, especially for optical displays and diffractive neural networks (NNs). However, the factors that influence the diffraction efficiency of the diffractive surfaces have not been well understood. Taking advantage of a simplified physics-driven neural network model and two-photon nanolithography (TPN) technology, we theoretically and experimentally investigated the factors that influence the diffraction efficiency of the diffractive surfaces, such as diffraction propagation distances and a variety of diffraction patterns with different geometrical features, the result of which enables the generation of two-dimensional images with high diffraction efficiencies through the diffractive surfaces with a thickness of less than 1 μm and a size of 100 μm by 100 μm. The demonstrated results are of great significance for optical light manipulation in optical display and computational imaging using free-space diffractive optical elements.

The manipulation of light at the micrometer scale has been the core technology for the recent development of diffractive optics, especially for optical displays and diffractive neural networks (NNs). However, the factors that influence the diffraction efficiency of the diffractive surfaces have not been well understood. Taking advantage of a simplified physics-driven neural network model and two-photon nanolithography (TPN) technology, we theoretically and experimentally investigated the factors that influence the diffraction efficiency of the diffractive surfaces, such as diffraction propagation distances and a variety of diffraction patterns with different geometrical features, the result of which enables the generation of two-dimensional images with high diffraction efficiencies through the diffractive surfaces with a thickness of less than 1 μm and a size of 100 μm by 100 μm. The demonstrated results are of great significance for optical light manipulation in optical display and computational imaging using free-space diffractive optical elements.

Lanthanide-doped upconversion nanoparticles enable upconversion stimulated emission depletion microscopy with high photostability and low-intensity near-infrared continuous-wave lasers. Controlling energy transfer dynamics in these nanoparticles is crucial for super-resolution microscopy with minimal laser intensities and high photon budgets. However, traditional methods neglect the spatial distribution of lanthanide ions and its effect on energy transfer dynamics. Here, we introduce topology-driven energy transfer networks in lanthanide-doped upconversion nanoparticles for upconversion stimulated emission depletion microscopy with reduced laser intensities, maintaining a high photon budget. Spatial separation of Yb3+ sensitizers and Tm3+ emitters in 50-nm core-shell nanoparticles enhance energy transfer dynamics for super-resolution microscopy. Topology-dependent energy migration produces strong 450-nm upconversion luminescence under low-power 980-nm excitation. Enhanced cross-relaxation improves optical switching efficiency, achieving a saturation intensity of 0.06 MW cm−2 under excitation at 980 nm and depletion at 808 nm. Super-resolution imaging with a 65-nm lateral resolution is achieved using intensities of 0.03 MW cm−2 for a Gaussian-shaped excitation laser at 980 nm and 1 MW cm−2 for a donut-shaped depletion laser at 808 nm, representing a 10-fold reduction in excitation intensity and a 3-fold reduction in depletion intensity compared to conventional methods. These findings demonstrate the potential of harnessing topology-dependent energy transfer dynamics in upconversion nanoparticles for advancing low-power super-resolution applications. Topology-engineered upconversion nanoparticles enable low-power STED microscopy by optimizing energy transfer and cross-relaxation, achieving sub-diffraction resolution with significantly reduced excitation and depletion intensities for efficient super-resolution imaging.

This study introduces a method for precise modulation of upconversion luminescence (UCL) in UCNP thin films via laser-induced melting, enabling high-resolution, sub-micrometer optical patterning. By employing a 460-nm femtosecond (fs) laser, localized melting disrupts the UCNP crystalline lattice, facilitating tunable UCL quenching with efficiencies exceeding 90%. This approach relies solely on the intrinsic properties of UCNPs, avoiding the need for composite materials or complex multi-stimulus systems. The technique provides precise control of UCL modulation, making it suitable for sub-micrometer-scale optical patterning.

Recent development of artificial neural networks (ANNs) and inverse design methods have demonstrated their prospective significance for planar diffractive lens design, with a plethora of optical lenses designed for wavelengths ranging from visible to Thz wavelengths. However, previous research to design planner diffractive lenses only considers the maximum intensity in the focus area or its derivatives as the optimization function, leaving the intensity outside the focus area unconsidered. We proposed and investigated a two-dimensional (2D) physics-driven ANN method assisted by the negative Pearson correlation coefficient (NPCC) to design microlenses with varied focusing distances, which takes the entire 2D intensity distribution at the focus plane as an optimization function. Taking advantage of 3D two-photon nanolithographic technology, sub-micrometer thickness microlenses with varied focusing lengths are designed and fabricated, achieving an average focusing efficiency of around 35%, and an average focusing spot size of about 1 µm. Furthermore, a microlens array (19 by 19 microlenses with a total size of 4 mm2) with a curved focusing plane was fabricated and integrated into a CMOS sensor, achieving direct object imaging under incoherent white light illumination. Our results demonstrate that the NPCC is a very useful optimization function for designing planar diffractive lenses, and the use of NPCC in ANNs is of great potential for the future design of functional diffractive optical elements in optics and nanophotonics.

Nanophotonics techniques, driven by optical microscopy, enable high-capacity data recording and readout, yet ensuring data security demands versatile, multi-stimuli activatable media. Lanthanide ion-doped upconversion nanoparticles hold promise for these applications by fine-tuning upconversion luminescence emission. While chemical methods customize this emission with meticulous adjustments, optical methods face challenges such as high laser beam intensities, setup complexity, and wavelength limitations. Efficiently modulating upconversion luminescence emission requires multi-stimuli methods or integrating upconversion nanoparticles with responsive materials. Here, dual-stimulus thermo-optical activation and optical microscopy are used to achieve sub-micrometer ultrahigh upconversion luminescence emission tuning in newly developed hybrid organic-inorganic upconversion nanocomposites for high-capacity and secure data storage and anticounterfeiting. Thermal stimulus (activation I) facilitates a 102-fold increase in absorption, promoting the formation of an inorganic network and complex. Optical stimulus via focused 460-nm femtosecond laser beam irradiation (activation II) enhances absorption by fivefold through sub-micrometer photo-polymerization bit recording. The method achieves a 92% decrease of 450-nm upconversion luminescence emission intensity after activation I and over 90% quenching of this emission after activation II, enabling sub-micrometer bit readout under 980-nm continuous-wave excitation laser beam irradiation. This work demonstrates the potential for high-capacity and secure data storage, anticounterfeiting, high-security encryption, and high-resolution display.

Along with the rapid development of information technology such as cloud computing, Internet of Things and artificial intelligence etc., huge amount of data is growing at an explosive rate. Conventional optical data storage (ODS) is a promising candidate for massive data storage. It is a sustainable and green technology with the advantages of low energy consumption, high data capacity and long life-time, which once had a high expectation in the data storage market. However, limited storage capacity is one of the main factors which prevents ODS from becoming a favorable competitor against hard disk drive (HDD), flash memory or magnetic tape. Unfortunately, the storage capacity of ODS is constrained by the optical diffraction limit. Although previously reported photoresists allow nanoscale lithography(1), they can hardly be used in nanoscale optical storage due to a functional deficiency of superresolution readout of the luminescent signal of the written structure. Herein, we endow photopolymerization nanolithography with AIE fluorescence characteristics, which does not destroy the existing nano-writing mechanism(2). We demonstrate nanoscale optical memory based on a photoresist film. And our nanoscale optical memory has the potential to hold as much data as a large petabyte-level HDD library.

We develop a novel Fourier-domain optical convolutional neural networks (FOCNNs) with multi-stage framework to hierarchical learn the image features at the speed of light. The FOCNN consists of two optical convolutional layers integrated with multiple parallel kernels and one optical fully-connected layer to form an all-optical CNN-like physical network structure. The FOCNN convolute the whole Fourier spectrum of the objects rather than the local receptive field of the objects, so it could extract the global and non-local features of the objects. In addition, the vortex phase is introduced to the optical convolutional kernels to extract the edge features. We incorporate this Fourier optics-based, parallel, one-step FOCNN in the tasks of semantic segmentation for pixel-level classification, and the capability of video-rate segmentation for objects is also demonstrated based on the programmable spatial light modulators, which demonstrated the computational power of FOCNN located in the range of Peta operations per second (POPS). Therefore, the FOCNN is useful for the real-time dynamic inference tasks, such as robotic vision, autonomous driving, and so on.

The development of cloud computing and artificial intelligence technology has increased data storage demands, causing an urgency to progress nanophotonics-enabled optical data storage. Inspiration can be taken from the working principles of the brain's memory that has high storage capacity and parallelism, integration between data storage and processing with self-learning ability, and low-power consumption. The correlation between the emerging neuroscience concept of cell engrams as the basic units of the brain's memory and nanophotonics techniques and materials can aid the development of neuromorphic optical data storage enabled by nanophotonics toward higher storage capacity and throughput and lower energy consumption. In this perspective, we explore the feasibility of a nanophotonics counterpart to biology by emulating the brain's memory based on cell engrams toward nanophotonics-enabled neuromorphic optical data storage. We overview emerging nanophotonics techniques and materials, as well as the challenges and opportunities of such an implementation for the future of optical data storage enabled by nanophotonics.

Expansion Microscopy (ExM) is a widely used super-resolution technique that enables imaging of structures beyond the diffraction limit of light. However, ExM suffers from weak labeling signals and expansion distortions, limiting its applicability. Here, we present an innovative approach called Tetrahedral DNA nanostructure Expansion Microscopy (TDN-ExM), addressing these limitations by using tetrahedral DNA nanostructures (TDNs) for fluorescence labeling. Our approach demonstrates a 3- to 10-fold signal amplification due to the multivertex nature of TDNs, allowing the modification of multiple dyes. Previous studies have confirmed minimal distortion on a large scale, and our strategy can reduce the distortion at the ultrastructural level in samples because it does not rely on anchoring agents and is not affected by digestion. This results in a brighter fluorescence, better uniformity, and compatibility with different labeling strategies and optical super-resolution technologies. We validated the utility of TDN-ExM by imaging various biological structures with improved resolutions and signal-to-noise ratios.

Energy-intensive technologies and high-precision research require energy-efficient techniques and materials. Lens-based optical microscopy technology is useful for low-energy applications in the life sciences and other fields of technology, but standard techniques cannot achieve applications at the nanoscale because of light diffraction. Far-field super-resolution techniques have broken beyond the light diffraction limit, enabling 3D applications down to the molecular scale and striving to reduce energy use. Typically targeted super-resolution techniques have achieved high resolution, but the high light intensity needed to outperform competing optical transitions in nanomaterials may result in photo-damage and high energy consumption. Great efforts have been made in the development of nanomaterials to improve the resolution and efficiency of these techniques toward low-energy super-resolution applications. Lanthanide ion-doped upconversion nanoparticles that exhibit multiple long-lived excited energy states and emit upconversion luminescence have enabled the development of targeted super-resolution techniques that need low-intensity light. The use of lanthanide ion-doped upconversion nanoparticles in these techniques for emerging low-energy super-resolution applications will have a significant impact on life sciences and other areas of technology. In this review, we describe the dynamics of lanthanide ion-doped upconversion nanoparticles for super-resolution under low-intensity light and their use in targeted super-resolution techniques. We highlight low-energy super-resolution applications of lanthanide ion-doped upconversion nanoparticles, as well as the related research directions and challenges. Our aim is to analyze targeted super-resolution techniques using lanthanide ion-doped upconversion nanoparticles, emphasizing fundamental mechanisms governing transitions in lanthanide ions to surpass the diffraction limit with low-intensity light, and exploring their implications for low-energy nanoscale applications.

Far-field super-resolution optical technology provides ways for high-capacity super-resolution optical data storage. Typical techniques necessitate high laser beam power and lead to photo-damage. Since they can convert near-infrared excitation to ultraviolet and visible emission, upconversion nanoparticles have potential for photo-activation. Furthermore, they have excited energy levels with long lifetime for low-power super-resolution optical microscopy. We demonstrate the application of upconversion nanoparticles with high-order luminescence emission for low-power super-resolution photo-activation for low-power super-resolution optical data storage. Upconversion nanoparticles were mixed with photo-active compounds. To stimulate photo-activation in the nanocomposite, super-resolution irradiation was used. Written features demonstrated super-resolution size upon low laser beam power.

Microlenses integrated with color filters continue to be of great interest for sensors, light-emitting diodes, three-dimensional (3D) imaging, and 3D display applications. Many techniques have been investigated to reduce the thickness and the size of the total devices, and microlenses with hybrid functions have been developed. However, traditional hybrid microlenses usually work under coherent illumination due to the single material used and the lack of effective diffraction elements for incoherent illumination. Here, we demonstrate a new flat optics device based on a dual-material 3D metastructure that achieves simultaneous color filtering and focusing under spatially incoherent white light illumination. Our device comprises a nanocone array fabricated by 3D direct laser writing at the interface with a Fresnel metalens based on a typical two-dimensional (2D) material: graphene oxide-reduced graphene oxide. The unique spatial arrangement of the nanocone array enables us to double the acceptable spatial coherence of the illumination compared with conventional nanopillar-based structural color. The chemical and optical stabilities of the metalens under the laser fabrication conditions allow for the successful integration with the nanocone array (similar to 200 nm diameter) and diffraction-limited focusing (within 50 mu m). The dual-material 3D metastructure enables simultaneous filtering and focusing for selected wavelengths within a distance of 20 mu m under incoherent white light illumination. Our results show promise for expanding the degrees of freedom of light manipulation in flat optics and the implementation of hybrid and multifunctional photonic devices in CCD and CMOS-compatible, high-resolution light field prints for 3D display.